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Proceedings of the 10th International Conference of AARSE, October 2014 417

GEOLOGIC MAPPING OF PARTS OF THE BENUE TROUGH, NIGERIA, USING REMOTELY SENSED SATELLITE DATA

Omoleomo Omo-Irabor1, Jude Ogala2, Jerry Osokpor 1, Anthonia Asadu 1, Brume Ovarare 1, Difference Ogagarue1

1. Department of Earth Sciences, Federal University of Petroleum Resources, Effurun, P.M.B. 1221,

Nigeria [email protected] 2. Department of Geology, Delta State University, Abraka, P.M.B. 1, Nigeria

KEYWORDS: Landsat ETM+, SRTM, GIS, Lithological units, Benue Trough

ABSTRACT

Remote sensing data and Geographic Information Systems (GIS) techniques provide vital tools for geologic mapping especially in complex and inaccessible terrain. Using an integrated image processing approach such as colour composite, image classification, principal component analysis (PCA), band ratio (BR) and geomorphological factors (e.g. slope, soil/rock texture, drainage pattern and density) a detailed mapping of geologic units in parts of the Benue Trough was achieved. The shaded relief image created from Shuttle Radar Topographic Mission (SRTM) provided digital topographic information corresponding to distinct geological boundaries. The results of this study demonstrated that the utility of Landsat ETM+ coupled with the SRTM in delineating lithological units.

INTRODUCTION

The importance use of remote sensing data and Geographic Information Systems (GIS) techniques in mapping the spatial distribution of geologic units is ever increasing due to improved and rapid technological advancements. Constraints posed by conventional geological mapping procedures include their inability to provide a synoptic view and challenges in mapping inaccessible regions. The Benue Trough Basin is one of the five sedimentary basins sub-divided into the lower, middle and upper sections. The area comprises of a lower Paleozoic basement complex overlain by Cretaceous/Tertiary sedimentary rocks. Available records indicate existing discrepancies in delineated lithological boundaries in the Benue Trough. Geologic mapping of parts of Nigeria using Landsat satellite data has been attempted by a number of studies (Ananaba et al., 1987; Ayodele et al., 2010; Kogbe, 1983; Koopmans, 1986; Odeyemi, 1993). The first geological map of Nigeria was produced by the Geological Surveys Nigeria with the aid of aerial photographs taken in 1964. This map was updated in 2004 and 2006 to include more information on the basement complex. Subsequent geological maps produced by several authors (Agagu et al., 1985; Akande et al., 1998; Akande et al., 2007; Edet, 1992; Mode, 2004; Omosuyi et al., 2002), for the lower and middle Benue Trough, were based on the generalized geological map, which had mostly inferred boundaries.

To improve the accuracy of geological mapping several studies have applied remote sensing techniques such as colour composite (CC), optimum index function (OIF), band ratio (BR) and principal component analysis (Rigol et al., 1998). Spectral data represent real physical properties, and useful environmental covariates can be derived for vegetation, soil, and parent material.


This paper presents the use of a combination of remote sensing data (Landsat ETM+ and

SRTM) and satellite image processing techniques such as principal component analysis (PCA), band ratio (BR) and colour composite to establish the geological boundaries of parts of the Benue Trough.

STUDY AREA AND GEOLOGICAL SETTING

The study area covering 12,000 km2 is located in the south-western end of the Benue Trough of Nigeria (Figure 1). The basin is bounded in the east by the Abakaliki Anticlinorium, in the west by the Benin hinge line; it extends northward to the lower Benue River and also forms a boundary with the Tertiary Niger Delta to the south.

Figure 1. Geologic map of study area

The evolution of the Benue Trough began during the Early Cretaceous with the formation of

the Benue – Abakaliki Trough as a failed arm of the rift triple junction associated with the separation of the African and South American continents and subsequent opening of the South Atlantic (Burke, 1996; Murat, 1972). The platform areas bordering the Benue Trough to the west (Anambra Platform) and to the east (Afikpo Platform) became downwarped due to the Santonian tectonism to form the Anambra Basin and Afikpo Syncline respectively (Benkhelil, 1989; Murat, 1972; Petters, 1978).

The lower Benue Trough also known as the Anambra Basin contains about 6 km thick Cretaceous/Tertiary sediments and is the structural link between the Cretaceous Benue Trough and the Tertiary Niger Delta (Mohammed, 2005). These sediments are characterized by three depositional cycles (Short et al., 1967) (Figure 2). These three cycles include (a) the Abakaliki-Benue Phase (Aptian-Santonian), (b) the Anambra-Benin phase (Campanian-Mid Eocene), and (c) the Niger


Delta phase (late Eocene-Pliocene) (Oboh-Ikuenobe et al., 2005). The oldest sedimentary unit in the area of study is the Asu River Group, which was deposited during the Albian times. It consists of about 2000 m of shale, siltstone, limestone, and lava flows, dykes and sills (Reyment, 1965). It is overlain in the region of interest by Eze-Aku Formation, comprising of black calcareous shales, shelly limestones, siltstone and sandstones (Akande & Erdtmann, 1998). The Eze-Aku formation is in turn overlain by Agwu Shale composed of shales with sandstones and minor limestones (Hoque, 1977). The second sedimentary cycle is responsible for the deposition of the Nkporo Group, Mamu Formation, Ajali Sandstone, Nsukka Formation, Imo Formation and Ameki Group. The third sedimentary cycle began Late Eocene as a result of a major earth movement that structurally inverted the Abakaliki region and displaced the depositional axis further to the south of the Anambra Basin. This led to the formation of the Niger Delta Basin.

Figure 2. Stratigraphy of south-eastern Nigeria

METHODS USED

A flowchart of the methods used in this study is presented in Figure3. One scene of orthorectified

Landsat ETM+ (path189/row55) and the corresponding SRTM data set of 17 December 2000 and

February 2000 respectively were acquired from the Global Land Cover Facility (GLCF) website

(http:/www.glfc.umd.edu). The Landsat ETM+ image acquired in the year 2002 provided spectral

information in six bands (1-5 and 7) with a spatial resolution of 30 m. SRTM data with 90 m spatial

resolution provided morphologic and topographic details. The SRTM was an international project

organised by the NASA, Italian Space Agency (ASI) and German Aerospace Center (DLR) flown in 2000

and covers 80 % of the world’s surface area (Reuter, et al., 2007). Geologic map of Enugu (SHELL-BP,

1957)(1:250,000) was scanned and served as a base map for this study.

Bende/Ameki Formation

Nsukka Formation

Enugu/Nkporo /Owelli Formation

Eocene

Maastrichian - Palaeocene Maastrichian

Campanian - Maastrichian

Imo Shale Group Palaeocene

Mamu/Ajali Formation

TERTIARY

UPPER CRETACEOUS

PERIOD/AGE FORMATION

Major Unconformity

Basement Complex

Awgu Formation

Odukpani Formation

Ogwashi – Asaba Formation

Coniacian - Santonian

Cenomenian

Albian

Oligocene - Miocene

QUATERNARY

Eze-Aku Shale Turonian

Asu River Group

PALEOZOIC

LOWER CRETACEOUS

Alluvium

Recent

Firs

t

Cyc

le

Seco

nd

Cyc

le

Thir

d

Cyc

le


Figure 3. Flowchart of methods used

Digital image processing techniques were carried out using Integrated Land and Water

Information System (ILWIS 3.31 Academic version). Image processing and classification, principal

component analysis (PCA), band ratioing were executed to discriminate the lithologic units in a GIS

environment. The Optimum Index Function (OIF) was used to select the best possible combination of

three bands from the Landsat ETM+ image. OIF is a statistical procedure for obtaining the highest

ranking among the three band combinations (Table 1). Band combination 1-4-7 provided the

maximum variance and produced the highest index ranking of 50.56%. This was therefore used for

the derivation of the True Colour Composite


Table 1. Optimum Index factors (OIF) for Landsat ETM+ image

Band Combination 1

Band Combination 2

Band Combination 3

Index Ranking (%)

1 4 7 50.56

1 4 5 36.71

3 4 7 36.01

1 3 4 33.81

4 5 7 31.37

2 4 7 30.94

The Maximum Likelihood supervised classification method was applied resulting in four distinct

land covers. PCA allowed for the extraction of new information as it reduced data dimensionality.

The first PCA band (PCA 1) contained the highest percentage variance of data highlighting the

spectral variation of data. Although optical sensor such as Landsat ETM+ have limitations in densely

vegetated areas such as the study area, SRTM was used in supplementing traditional 2-D thematic

mapping with information on the third elevation dimension in the form of a separate digital elevation

model (DEM) (Rabus, et al., 2003). The SRTM image was therefore georeferenced to the Landsat

ETM+ image.

Field visits were conducted during the dry season across geologic formation to test the accuracy

of the classified units, establish rock types and detect where possible, geologic contact boundaries.

Due to the large time lapse between the period the images were captured and the field visit,

landcover units could not be verified to a large extent. The geologic units were still consistent,

therefore, 58 sampling points were randomly chosen within the accessible region of the study area

for the validation of geologic units.

RESULTS AND DISCUSSION

The results obtained from the interpretation of remote sensing images were compared with

existing geological maps of the study area. Landsat ETM+ bands 1, 4 and 7 combination gave the

highest OIF of 50.56 % and was used for the supervised image classification (Figure 4a). The classified

image was superimposed on the SRTM data to improve the image elemental characteristics such as

texture, topography and drainage (Table 2). The PCA 1 of the Landsat ETM+ data contained 83.14 %

variance and showed a well-defined thin continuous sandstone unit of the Mamu Formation and

EzeAku Shale Group (Agala Sandstone). The sandstones were characterised with higher spectral

signature compared to the surrounding background (Figure 4b).


Figure 4. Land cover classes (a) and PCA 1 for Landsat ETM+ highlighting the Mamu and Eze-

Aku Shale Group lithologic units (b)

Table 2. Interpretation scheme for lithologic units

Formation Rock Type Topography Drainage Texture

Nsukka Sandstone, Clayey Shale,

Coal, Limestone

Undulating Moderate density dendritic

Moderate

Ajali Sandstone Flat Low density dendritic

Fine

Mamu Sandstone, Coal Steep slope High density dendritic

Coarse

Enugu Shale, Sandstone, Ironstone, Siltstone

Undulating High density parallel Moderate

Agwu Sandstone, Limestone, Coal, Siltstone

Flat Moderate density dendritic

Coarse

Eze-Aku (Agala) Shale, Limestone, Sandstone

Moderate Low density dendritic

Fine


Band ratio images 5/7, 5/1 and 4/1 displayed in RGB composite gave the best separation of

geologic units, although the interference of vegetation subdued the features. The final geologic map

is displayed in Figure 5.

Figure54. Geological map of study area

CONCLUSION

Parts of the lower and middle Benue Trough were mapped using Landsat ETM+, SRTM data

and field observations. The lithologic boundaries in the study area were mapped using the

combination of image enhancement techniques, SRTM data and field evidence. The use of image

enhancement techniques (i.e. PCA, band ratioing) aided the discrimination of lithologic units

especially those of sandstone rocks which had a higher spectral reflectance than the surrounding

features. PCA was particularly used to delineate the Mamu and Eze-Aku (Agala) units. SRTM data

provided information on the textural characteristics of the geologic units. The results obtained from

this study clearly demonstrated that these techniques provide time and cost effective methods for

lithological mapping.


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Oboh-Ikuenobe, F. E., Obi, C. G., & Jaramillo, C. A. (2005). Lithofacies, palynofacies, and sequence stratigraphy of Palaeogene strata in Southeastern Nigeria. Journal of African Earth Sciences, 41, 79-101.

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Omosuyi, G. O., Enikanselu, P. A., & Tolami, J. B. (2002). Lithological characteristics, clay mineral species and hydrocarbon potentials of the Campanian-Paleocene sediments from Umuna-1 well, Anambra Basin, Nigeria. Journal of Mining and Geology, 38(2), 103-110.

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THE LANDSCAPE OF POST MINING COMMUNITIES IN IJESA LAND, OSUN STATE, NIGERIA

Nathaniel Olugbade ADEOYE1 and Timothy Babatunde OMOTAJO2 1. Department of Geography, Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria

E-mail: [email protected]; [email protected]; Mobile: +2348036769270 2. Customary Court of Appeal, Osogbo, Osun State, Nigeria

E-mail: [email protected]; Mobile: +2348032616295 KEYWORDS: mining activities, land degradation, biodiversity loss, Ijesa land, Nigeria ABSTRACT Mining as part of human activities on land is an expanding industry that can provide sustainable economic, environmental and social benefits to communities and regions where it is taking place. Nonetheless, the extraction process often times have adverse effects on their immediate physical environment. This paper identified mining sites in the selected mining communities of Ijesa land, Nigeria; examined the forms, levels and extent of land degradation and finally analyzed spatial pattern of land degradation. The study utilized Global Positioning System (GPS) receiver to obtain the geographic coordinates of the mining sites. This was superimposed on Landsat ETM+ 2013 for analysis of spatial pattern of mining sites. The forms of land degradation were captured through field observations and photographs while the levels and extent of the degraded lands were measured with measuring tape and the values were determined using mathematical formula for calculating area of a circle. The reason for this is that the spatial resolution of the satellite data used for the study was too low to capture the degraded areas. Besides, vegetation had covered the surrounding and surface of some of them. Stone was tied on a rope to measure depth of abandoned mining pits while measuring tape was used to ascertain the values in metres. The study discovered thirty-seven (37) mining sites and three hundred and fifty four (354) mining pits as major form of land degradation, which ranges in sizes and depths. Other forms of degradation include loss of farmlands, vegetal covers and pollution of drinking water. The average depth of the mining pits was 3.4 metres and an estimate of 25.8 hectares of lands was badly damaged in each sampled mining site. In conclusion, the study advanced the frontier of our knowledge on the state of mining communities.

INTRODUCTION Mining as part of human activities on land is an expanding industry that can provide

sustainable economic, environmental and social benefits to communities and regions where it is taking place. The general importance of mining sector has been documented to include foreign exchange, employment and economic development (Obaje and Abba, 1996, Obaje, et al., 2005; Nwajiuba, 2000). Increasing realization of the potentials of the solid minerals mining sector in recent times has made the Federal Government of Nigeria to undertake a number of reforms in the sector in order to make it earn more non-oil foreign revenue for the country (Essaghah, et al., 2013).

In line with the Federal government decision to attract direct foreign investment to the solid mineral sector and to further diversify the Nigerian economy, Adebimpe and Akande, (2011) used the discounted cash flow micro-economic assessment to evaluate large scale iron ore production in Nigeria. The iron ore project has an initial investment cost of US$ 73.934 m, annual expenditure and benefit of US$ 48.128 m and US$ 270 m respectively .The net present value (NPV), internal rate of return (IRR) and payback period of financial analysis at 100% capacity utilizations are US$ 833.10 m, 444.36% and 6 years respectively. The economic assessment shows a positive NPV at both 75% and

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60% capacity utilizations. The study further shows that the iron ore project has good economic potential and will also guide potential investor(s) in making decision on whether or not to commit resources to the project.

In spite of the benefits associated with mining activities, the three stages of mineral development, viz; exploration, mining and processing, have always been responsible for different types of adverse effects on the physical environment, which include ecological disturbance, destruction of natural flora and fauna, pollution of air, land and water, instability of soil and rock masses, landscape degradation and radiation hazards (Abegunde, et al., 2007; Olanipekun, 2002; Aigbedion and Iyayi, 2007).

Studies have reported that the extraction and transportation of crude oil to the refineries, often lead to occurrence of oil spillage and emission of hot gaseous substances into agricultural land and water bodies in Niger Delta (Aghalimo, (2000), which consequently led to rural exodus in mineral producing area (Omuta, 1984). Other empirical studies revealed that underground mining often induced considerable environmental problems. For instance, mining may result in subsidence, which may be related to the extraction of rock material and water. This has been a major problem in the US where the Bureau of Mines has estimated that over 32,000 km has been affected and may well rise by another 10,000 km by the end of the century. Of course, subsidence can be fatal for those who actually engage in the mining operation. British history is littered with many infamous mining disasters, to the extent that they form a major part of prose, poetry and music (Adegbola, et al., 1983).

Studies have also established that large-scale mining of tin and associated minerals in the Jos Plateau resulted in a high degree of degradation of arable land, vegetation and landscape, as well as other environmental problems. Other localities affected by large-scale environmental damage are the Niger Delta as a result of oil and gas exploration and exploitation; Sagamu, Okpilla, Ewekoro, Ashaka and Gboko owing to quarrying of limestone and the establishment of cement manufacturing companies, and in Enugu as a result of coal mining (Dayo, 1999; Aigbedion and Iyayi, 2007).

The adverse effects of mining on their immediate physical environment have therefore, become an issue of concern, which became popular during the 1960s (Ronald, 1979). The waves of concern have translated into a number of researches that are solution based. Among the academics, scholarly writings have tried to explain the dimension and severity of mining activities on the environment. For instance, Gyang, et al., (2010) enumerated the problems associated with mineral development in Nigeria. It was discovered that in Zamfara, North Western Nigeria where active mining of gold, lead and other minerals was active, the indiscriminate manner in which the activity was carried out led to the death of about 300 people as a result of lead poisoning of shallow water sources and soils. Similarly, on the Jos Plateau, North Central Nigeria where mining of cassiterite and columbite took place, a total of 2,015 disturbances were recorded in the form of abandoned mine ponds, mine dumps and lotto’s, just as over 3000 oil spillage were recorded in the Niger Delta within the period of 4 years, resulting in the destruction of over 6,000 fish farms.

Adeyinka, et al., (2011) examined residents’ perception of the effects of mining activities on their environment using questionnaire survey in Ijero Local Government Area of Ekiti State, Nigeria. The study revealed that the Resident Tolerance Index (RTI) was found to be between “not tolerable and not at all tolerable”. The study further noted that only three variables such as high influx of people, increase in sales and services, and improved economic condition with RTI values above 3.0 (just tolerable) were the accrued benefits to the residents while the remaining 17 variables with RTI values of less than 3.0 were considered to have adverse effects on the environment. The study recommended that government should ensure that mining activities are controlled by enforcing appropriate legislations on the miners and at the same time provide adequate infrastructural facilities like potable water and electricity to enhance economic development in the area and ensure a sustainable community development.


Essaghah, et al., (2013) investigated environmental and socio-economic effects of Lead and Zinc Ores mining in Ishaiagu Community in Ivo Local Government Area of Ebonyi State, Nigeria. Results obtained from physico-chemical analysis of collected soil and water samples, as well as, related in-situ air parameter measurements revealed serious environmental pollution and degradation, threatening farming activities in the area. The study recommended that Governments at all levels should enforce all related environmental legislations as a matter of urgency in mining areas if another “Niger Delta Debacle” is to be avoided in various mining communities of the country.

Study also evaluated environmental hazards associated with artisanal gold mining in Luku,

Minna, North Central Nigeria. Results revealed that mining activity resulted in lot of physical

environmental impact such as land degradation, destruction of vegetation, erosion of soils and

degrading water quality. The laboratory analyses of the hazards showed that soils were

contaminated with elements such as, Pb (85.73 ppm), As (9.27 ppm), Cu (56.46 ppm), Zn (31 ppm),

Ni (85.55 ppm), Mn (283.73 ppm), Cd (1.68 ppm), Co (10.91 ppm), Mo (0.91 ppm), Hg (0.27 ppm), Ag

(0.73 ppm), and Zr (143.27 ppm). It was further discovered that these elements in the soil get

accumulated in plants and animals, and are passed on to human through the food chain, which

consequently can induced slow growth rate in plants and respiratory problem, liver and kidney

damage in man (Ako, et al., 2014). From the foregoing, it is obvious most researches on mining activities focused on the

benefits and the effects of mining on the physical environment such as, water resources, vegetal cover and biodiversity as well as agricultural activities. However, research is still sparse on the spatial pattern of land degradation in mining community, which perhaps informs the gap in our knowledge. The dimension of the land degradation in terms of the depths and areal extent are still poorly documented or undocumented. Based on these scenarios, the study therefore, identified mining sites in ten (10) mining communities of Ijesa land; examined the forms, levels and extent of land degradation and finally analyzed spatial pattern of land degradation. Mining in the study area has been done by illegal, inexperience, money conscious and unconcerned miners. While environments are being polluted, the illegal miners smile to the bank. The activities of the miners constituted environmental degradation to the local environment in the study area, which was the reason for the selection of the study area.

THE STUDY AREA

The study area consists of ten (10) mining communities in Ijesa land, Nigeria. These are Ibodi, Itagunmodi, Igun, Epe, Ijemogun, Iwara-Odo, Faforiji, Ifewara, Atorin and Iperindo. They lie between latitudes 70 20'N and 70 60'N and longitudes 40 60'E and 40 85'E in Ilesa west, Atakunmosa west and Atakunmosa east Local Government Areas of Osun State, Nigeria (Figure 1). The climate of the study area is tropical with distinct wet and dry season. It has annual rainfall of 1200 to 1500mm. Temperature is high throughout the year ranges from 27°C to 32°C with the maximum temperature around April (Adejuwon, 1979). The relative humidity ranges from 50 to 80 percent (Montgomery, 1962). The original vegetation of the study area is tropical rainforest characterized by big and robust trees as Iroko, Mahogany, Sapele and Tall grasses (Montimore, 1975). The landscape of the study area is punctuated with projecting hills, which range from 366 to 394 metres above the sea level. The area is quite rich in gold, though most of them are yet to be tapped. The Gold in the region is abundantly deposited along its trenches; this explains why the region is disturbed by miners. The relief is steep hence, there are pronounced soil erosion in some locations most especially along the trenches because steeper slopes are more susceptible to erosion than gentle slope (Adeoye, 2006). The area is well drained and the fertility of the area makes farming a prominent economic activity in the area. The dominant ethnic group in the study area is Yoruba and they mainly speak Ijesa dialect. The settlements of the study area are rural in nature while the patterns are both


linear and nucleated especially in few locations. The major occupation of people is basically farming and hunting. Most of the roads are untarred and they become slippery during the wet season. The major source of drinking water in the area still remains streams, rivers and rain water.

Figure 1: Local Government Areas of the study area

MATERIALS AND METHODS

The study utilized Global Positioning System (GPS) receiver to obtain the geographic coordinates of the mining sites and pits. This was superimposed on Landsat ETM+ 2013 for the analysis of spatial pattern of mining sites. The forms of land degradation were captured through field observations and photographs while the levels and extent of the degraded lands were measured with measuring tape and the values were determined using mathematical formula for calculating surface area of a circular shape. The reason for this is that the spatial resolution of the satellite data used was too low to detect the area extent of the degraded areas. Stone was tied on a rope to measure depth of abandoned mining pits and ponds and measuring tape was used to ascertain the values in metres. The selected mining communities include Ibodi, Itagunmodi, Igun, Epe, Iyemogun, Iwara-odo, Faforiji, Ifewara, Atorin and Iperindo. These communities were selected because of the abundance of gold in the area and the devastating effects of gold mine, which has become issues of concern in recent times.

RESULTS AND DISCUSSION Mining sites

The study area is abundantly blessed with gold. The selected communities for this study are not the only settlements where gold are found but the devastating effects of gold mine, which culminated to the hostile behaviours of some members of the communities informed the reason for selecting the area for the study. Thirty-seven (37) mining sites were identified and captured (Table 1). Ibodi, and Faforiji had the highest number of mining sites (5) respectively, while Itagunmodi, Igun, Ifewara, and Iperindo had (4) sites and Epe, Iwara-Odo, and Atorin, recorded only (3) sites. Iyemogun settlement only had (2) mining locations. Observation made during the field work shows that mining activities were concentrated in the farmlands, forested area and sometimes around the settlement, which consequently led to the destruction of farmlands, vegetal cover, pollution of drinking water and land degradation. This corroborates with the findings of Ako, et al., (2014), which reported that mining activity resulted in a lot of physical environmental impacts such as, land degradation, destruction of vegetation, erosion of soils and degrading water quality.


Table 1: Number and the coordinates of Mining Sites

Mining communities

No. of Mining Sites

X Y Z (Elevation In

Meters)

Ibodi

1 2 3 4 5

684750 839837 363

684742 839986 350

684876 839944 354

684893 839768 351

684608 839750 360

Itagunmodi

1 2 3 4

682591 834263 366

682429 834209 356

682528 834111 344

682831 834188 356

Igun

1 2 3 4

684871 832470 309

684962 832444 303

684943 832321 307

684677 832436 305

Epe

1 2 3

685131 839759 375

685274 839782 380

685216 839643 397

Iyemogun

1 2

686961 835816 386

686321 836332 390

Iwara Odo

1 2 3

690236 832908 375

689524 832207 356

691118 832213 345

Faforiji

1 2 3 4 5

688031 806011 246

687746 805988 250

688201 805801 234

688024 805689 278

687846 805744 250

Ifewara

1 2 3 4

685359 826185 347

684700 825813 340

685360 825230 329

686012 826844 350

Iperindo

1 2 3 4

701393 829287 308

701582 828445 306

700558 828441 308

700043 829245 305

Atorin

1 2 3

686228 823594 361

685903 822667 370

686852 823184 370

Total 37


Forms, Levels and Extent of Land Degradation The major form of degradation discovered in the study is mining pits, which were found both on farmlands and forested areas (Plates 1, 2, 3, 4, 5, 6, 7, 8, 9, &10). In all the thirty seven (37) mining sites, a total number of three hundred and fifty four (354) mining pits were discovered and they varied in sizes and depth. As shown in Table 3, Iperindo and Faforiji recorded the highest number of mining pits, which constituted 12.7% while Ibodi, though with five (5) mining locations, recorded the least number of mining pits (6.7%). The reason might not be unconnected with the proximity of the settlement to the headquarters of Ilesa west LGA. It is instructive to note that in Nigeria, the political headquarters is always the seat of agencies including environmental protection agency, which is assigned to supervise the activities of industries and all environmental related organizations. It can therefore, be insinuated that regular visit of the staff of the agency to the settlement had positive influence in reducing the devastating effects of mining activities. Comparing Table 3 with Figure 2, it was observed that settlements that are farther away from Ilesa township were more devastated in terms of the number of mining pits recorded.

A sample of the size and depth of one mining pits was measured in each of the selected mining communities. Most of the mining pits were not expansive as seen in Table 2. Measuring tape was used to measure the length and breadth of the sampled mining pits and the values were determined using mathematical formula for calculating area of a circle, which was later converted to hectares. As observed in the field, the shape of most mining pits was circular rather than triangular; that was why area of a circle was employed. The size of the largest pit was found in Iperindo, which amounted to 3 ha. This was followed by the one in Ifewara (2.8 ha.), while the pit with the least size was found in Ibodi, the estimate of which was 0.5 ha. large.

As shown in Table 2, there is no sampled mining pit that is not deep but the deepest mining pits was found in Igun, which was 3.4 metres deep. As expressed by the villagers, “the pit is a nuisance and a dead trap”. The pit, which situates few metres away from the settlement had claimed life and had become a breeding space for mosquito and other dangerous aquatic animals. It was revealed that there is no single fish in the pond because of the reaction of the element of gold with water, which is poisonous to aquatic life. Table 2: Extent of Mining Activities across mining communities

Mining Communities

No. of Mining Sites

No. of Mining Pits

% Area of Mining pit (Ha)

Depth of a sampled mining Pit (m)

Iperindo 4 45 12.7 3.0* 1.5

Ifewara 4 36 10.2 2.8* 2.4

Faforiji 5 45 12.7 2.7* 2.7

Atorin 3 33 9.3 2.5* 2.1

Iwara-Odo 3 30 8.5 2.3* 2.5

Iyemogun 2 36 10.2 1.8* 2.3

Epe 3 36 10.2 1.6* 2.9

Igun 4 30 8.5 1.3* 3.4

Itagunmodi 4 39 11.0 0.9* 3.2

Ibodi 5 24 6.7 0.5* 3.0

Total 37 354 100 19.4 26

*Formula used in calculating the surface area of a circular pit is 22/7 r².


Plate 1: Mining pit covered with water in Iperindo Plate 2: Mining pit in a farmland in Ifewara & constituting environmental pollution

Plate 3: Mining pit in a farmland in Faforiji Plate 4: Mining pit in a farmland in Atorin with research assistant

Plate 5: Mining pits in Iwara-Odo with research Plate 6: Mining pit in Iyemogun constituting Assistant environmental pollution


Plate 8: Mining Pit that claimed life in Igun Plates 7: Mining Pit constituting health hazards in Epe

Plate 9: Mining Pit becoming pond in Plate 10: Farmland destroyed by illegal Itagunmodi miners for mining operation in Ibodi

Spatial pattern of land degradation The general pattern in Figure 2 is that all the mining sites were accessible to road, though the condition of the roads varied from place to place. Observation made during the field work reveals that settlements closer to the main road and those that are closer to local government headquarters such as, Ibodi, Iyemogun, Itagunmodi, Ifewara and Faforiji had their roads tarred. Some were also tarred but the conditions of the roads were deplorable while few, as the case of Igun were only graded with laterite soils. None of the roads were newly constructed as they have been in existence long ago to ease transportation of people, their agricultural products and/or gold to communities where they are needed. Figure 2 also shows that most of the mining sites were found around the settlement while the few ones located away from the settlement were sited not farther away from the feeder roads. In all the selected mining communities, it was observed that the mining sites were clustered together.


Figure 2

CONCLUSION

Mining of any type in any where brings about exposure of the environment to certain damages depending on the levels of control that show the difference (Gosh, 2002). It is obvious in this study that mining has introduced some adverse effects on the social and physical environment of the study area. This is evident as three hundred and fifty-four (354) mining pits were discovered in thirty seven (37) mining sites in the ten (10) selected mining communities. These pits in various depths and sizes were found on farmlands, forested areas and around the settlements, which posed danger to the inhabitants of the area. Most of the mining sites located around the settlements were clustered together. In conclusion, this study advanced the frontier of our knowledge on the state of mining communities. To ensure sustainable mining environment, there should be adequate monitoring and implementation of environmental law by relevant stakeholders. Also, companies that are allowed to carry out mining operations should reciprocate by providing basic infrastructural facilities in the host communities. References

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activities in Nigeria urban environment: Impetus for community development or environmental deterioration? In: Laryea, S., Leiringer, R. and Hughes, W. (Eds) Procs West Africa Built Environment Research (WABER) Conference, 19-21 July 2011, Accra, Ghana, 747- 760.

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2014. Environmental Impact of Artisanal Gold Mining in Luku, Minna, Niger State, North Central Nigeria, Journal of Geosciences and Geomatics, vol. 2 (1), pp. 28-37, DOI: 10.12691/jgg-2-1-5.

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Nigeria, Journal of Geography and Earth Science 1(1); June 2013 pp. 30-38 Gosh, R., 2002. Impact of Mining on Land, Its Assessment and Management: Course Vol.1, Second

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MAPPING LITHOLOGY USING THE GROUP ENDMEMBER

Kerry Cawse-Nicholson1, Andries Botha1, Andy Lloyd2

1. Southern Mapping Company, [email protected] 2. Rio Tinto Coal Mozambique

KEYWORDS: Classification, Lithology, Endmember, Mapping

ABSTRACT

The traditional approach to mineral mapping is to detect pixels with high concentrations of

specific minerals. With a high confidence threshold, this results in a sparsely populated map that is

accurate at fine scales, but does not adequately show patterns on a global scale. Conversely, the

entire image may be classified so that each pixel is assigned to a particular class. This is a more

comprehensive indicator of regional patterns, but it is very sensitive to the number of classes chosen.

In other words, different classes may incorrectly be combined if the number of classes is set too low,

while random assignments may occur if the number of classes is set too high. In this study, we use

the concept of a group endmember, where this group is a combination of one or more minerals,

rocks and/or vegetation, but this group occurs throughout the image in the same or similar

proportions. Using a small set of these carefully chosen group endmembers as targets enables

accurate lithology mapping on a global scale. We have successfully applied this concept on ASTER

imagery over the Zambian copper belt, where each target group was defined by the spectral

responses in the VNIR, SWIR and LWIR bands in a user-defined area of interest. A similar process was

applied to airborne hyperspectral imagery acquired in Mozambique. Four groups covered over 80%

of the image, and revealed previously under-represented or even unidentified lithologies and

geological structures, through understanding of the global patterns. These results were compared

with a high-resolution lidar-derived digital elevation model (DEM) to validate the detection of these

spatial features. Geological fieldwork in the regions of interest is also ongoing. The advantage to this

technique is that the fieldwork can be significantly reduced to cover only regions of known interest,

to validate the patterns seen in the imagery. The added advantage of multispectral and hyperspectral

imagery is the detection of features not visible to the human eye.

INTRODUCTION

Hyperspectral data is a single image acquired at hundreds of spectral wavelengths. The image is

then represented as a cube, where the z direction gives the spectral information for each pixel. This

spectral information is informative for target detection, since unique spectral signatures may be

identified. Such mineral mapping is well established, and has been applied to aerial and space-borne

multi- and hyperspectral imagery (Chang, 2003). Commonly used target detection algorithms include

spectral angle mapper (SAM) (Kruse, et al., 1993), matched filtering (Turin, 1960), constrained energy

minimization (Harsanyi, 1993), and many others (Chang, 2003).



Although many of these methods are designed for hyperspectral imagery, they are also commonly

applied to multispectral images, which may only acquire data at 4-15 spectral wavelengths. While the

accuracy is understandably lower than in hyperspectral images, multispectral images are more

affordable and easily available, and target detection algorithms have shown good results (Pour &

Hashim, 2014) (Mars & Rowan, 2010).

DATA

Two datasets were used in this study. The first is a hyperspectral image acquired in 2012 by a

ProSpecTIR instrument with a spectral range of 450-2500nm and a spectral resolution of

approximately 5nm, yielding 360 bands. The spatial resolution in this case was 2m. The data was

acquired near Tete, Mozambique. The second dataset is an ASTER image acquired over the copper

belt in 2010 near Lusaka, Zambia. The three visible to near infrared (VNIR) and six short wave

infrared (SWIR) bands were used for the mineral mapping. ASTER has 15m spatial resolution in the

VNIR and 30m spatial resolution in the SWIR. In both cases, mineral mapping was performed, with

the goal of understanding the lithology and structural geology.

The hyperspectral dataset required substantial preprocessing, since there was a considerable

illumination gradient. For each flight line, the across-track illumination was corrected and 81 the

georectified flightlines were then colour balanced and mosaicked. The ASTER radiance image was

converted to reflectance using fast-line-of sight atmospheric analysis of spectral hypercubes

(FLAASH) (Adler-Golden, et al., 1999)

Although no ground truth was available at the time of writing, all results were analysed and

approved by principal geologists with intimate knowledge of the study areas.

Figure 1: A hyperspectral image acquired in Mozambique in 2012.


Figure 2: An ASTER image acquired over the Zambian copper belt.

METHODS

Typical target detection algorithms would use spectral signatures of a particular mineral, taken

either from a spectral library or from a spectrum collected with a handheld spectrometer from a field

sample (Pour & Hashim, 2014). It is also possible to use known locations of outcrops to derive the

spectral signatures from the image itself.

The difference in this case is that we chose pixels that we knew were mixtures of minerals, rocks,

vegetation, etc. This technique enabled us to map similar pixel combinations throughout the image,

allowing us to determine patterns. An iterative process was followed:

First, a feature obvious in the visible image was chosen to derive a representative

spectrum.

Next, spectral angle mapper (SAM) was used to map similar pixels throughout the image.

The threshold was determined by repeatedly increasing from a small threshold, until a

marked increase in the number of classified pixels was observed. The chosen threshold

was the number prior to the one that resulted in such increase.

This classification was overlaid over the original image, and unclassified areas were

evaluated, as above, to select the next target spectrum.

This procedure continued until the majority of the image was classified or the patterns

were clear.

The method was first applied to the hyperspectral image, where the group endmember

classification detected interesting patterns that were validated by comparing with a lidar-derived


digital elevation model (DEM). Structures are clear, where they were not detectable in the visible

part of the image.

Figure 3: The group endmember classification is overlaid over a lidar-derived digital elevation model. The corroboration between the two shows very interesting and detailed features in the

landscape.

Figure 4: The result of a group target detection on an ASTER image, using five spectra chosen from the image.


Figure 5: (Left) An alteration ratio (band 4 / band 3), and (right) an unsupervised classification (K-means with 10 classes) do not show the same level of detail as the group endmember classification

above.

Figure 6: A Jarosite map (spectra from the USGS spectral library []) shows similar regions to the cyan class in Figure 2.

In the ASTER image, Figure 4 shows that the group endmember classification resulted in far more

detail than a standard band ratio (band 4/ band 3 for alteration) and unsupervised classification (K-

means with K=10) shown in Figure 5. In Figure 6, using SAM to map jarosite produced a similar map

to the cyan class in the group endmember classification, but one would have to know the minerals

present in the rest of the scene in order to replicate the rest of the classes. The advantage of the

group endmember classification is that the mineral composition of the scene need not be known in

advance.

While unsupervised unmixing algorithms exist, they are costly, and they map each class according

to unique or extreme pixels. In this case, the classes are defined by mixed pixels, and the

classification is fast.

CONCLUSION

In this study, we have shown the feasibility of a supervised target detection approach, by

explicitly choosing mixed pixels to infer spatial patterns and lithologies in mineral mapping. This


method was demonstrated on hyper- and multispectral images, and showed promising results when

compared to a highly detailed digital elevation model in the hyperspectral case. This method also

showed more detail than unsupervised methods in ASTER data, and was comparable to target

detection algorithms with known endmembers. The advantage in this case is that the pure

substances need not be known prior to unmixing, and the implementation is fast.

REFERENCES

Adler-Golden, S. et al., 1999. Atmospheric correction for short-wave spectral imagery ased on MODTRAN4. s.l., SPIE Proceedings in Imaging Spectrometry. Chang, C.-I., 2003. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. s.l.:Springe. Harsanyi, 1993. Detection and classification of subpixel spectral signatures in hyperspectral image sequences. s.l.:UMI. Kruse, F. et al., 1993. THe spectral image processing system (SIPS) - Interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, Volume 44, pp. 145-163. Mars, J. & Rowan, L., 2010. Spectral assessment of new ASTER SWIR surface reflectance data products for spectroscopic mapping of rocks and minerals. Remote Sensing of Environment, Volume 114, pp. 2011-2025. Pour, A. B. & Hashim, M., 2014. ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration. SpringerPlus, Volume 3, p. 130. Turin, G., 1960. An introduction to matched filters. IRE Transactions on Information Theory, 6(3), pp. 311-329.


WATER CLARITY MAPPING OF LEKKI LAGOON USING REMOTE SENSING AND LEAST SQUARE REGRESSION MODEL

Dupe N. Olayinka1, Emmanuel Luther1, Adzandeh E. Ayila2

1. University of Lagos, Akoka-Yaba, Lagos, Nigeria. Dupe N. Olayinka [email protected] 2. Regional Centre for Training in Aerospace Surveys (RECTAS), Ile-Ife, Osun State, Nigeria

KEYWORDS: Least squares, Lekki Lagoon, Kloiber’s Regression, Turbidity, Water Clarity Mapping.

ABSTRACT

Water clarity measurement and monitoring information is necessary in body of water with strong presence of water-suspended materials, which account for change in water clarity, reduce photosynthesis and the production of dissolved oxygen. The water clarity of Lekki lagoon in Lagos State is affected by the presence and growth of phytoplankton, algae bloom and indiscriminate dumping of domestic and industrial wastes. It is therefore, imperative to determine water quality and clarity in the lagoon through turbidity values. This study envisage to produce water clarity maps of Lekki Lagoon using remote sensing technique. Turbidity data of thirty (30) sample points within Lekki lagoon were obtained for 2001 and 2006; Landsat multi-spectral data of the study area for 2001 and 2006 were also acquired. The multispectral data for 2001 was used to calculate the reflectance values for each of the sample point in Band 1 and Band 3 from their respective digital number in the bands. These reflectance values serve as independent variables in Kloiber’s Regression model and are related to the natural logarithm of the observed turbidity, serving as the dependent variable of the model. Least squares regression technique was employed to calculate the unknown parameters of the model using the 2001 turbidity measurements and reflectance values of band 1 and band 3 of Landsat multispectral data for 2001. On obtaining the parameters, the model relating the reflectance at band 1, reflectance at band 3 and turbidity was derived. The reflectance values of bands 1 and 3 for 2006 Landsat data were substituted in the established model to derive turbidity values for 2006. The derived turbidity values for 2006 were compared to actual observed turbidity measured in-situ in 2006, and it showed a discrepancy of less than 4 Nephelometric Turbidity Unit. The results of an NDVI colour-coded image classification derived using an unsupervised algorithm show a visual variation of water clarity that is to a very large extent conform to the observations. This study establishes the reflectance-turbidity relationship and indicates reduction in the quality of Lekki lagoon coastal water within the period of study.

INTRODUCTION

The surface water quality of estuarine systems and associated streams, lakes and reservoirs

is an important environmental concern for it determines the overall health of a coastal ecosystem. A

typical body of water comprises of numerous plants, animals species and other substances, including

pollutants. The plants are the major, if not the only, contributors of the chlorophyll content present

in a marine environment thereby responsible for the green nature observed and photosynthesize

activities in such an environment. Specifically, algae and phytoplankton, which according to NASA

account for half of all photosynthetic activities on earth, are plants that contribute immensely to

increase in turbidity and hence water clarity. Turbidity is the amount of suspended particles present



in a water sample and measured with Nephelometric instrument. It causes light to be reflected and

absorbed rather than transmitted in straight lines through the water sample. In addition to the

growth of phytoplankton, turbidity in open water may be caused by human activities that disturb

land, such as construction, which can lead to high sediment levels entering water bodies during rain

storms due to water runoff. Areas prone to high bank erosion rates as well as urbanized areas also

contribute large amounts of turbidity to nearby waters, through storm water pollution from paved

surfaces such as roads, bridges and parking lots. Certain industries such as quarrying, mining and coal

recovery can generate very high levels of turbidity from colloidal rock particles. In drinking water, the

higher the turbidity level, the higher the risk that people may develop gastrointestinal diseases.

Researchers have used remotely sensed data to investigate water clarity and other related

areas. Studies conducted by Kloiber’s et al., 2000; Giardino et al., 2001; Wang et al., 2004; Dewider

and Khedr, 2001; Baban, 1993 are good examples. A typical study of the colour of a body of water

was performed in Upper Lake, Bhopal, India by Singh and Tripathi (2010). They utilized the

methodology that integrates remote sensing, conventional laboratory analysis, Global Positioning

System (GPS) and Geographic Information System (GIS) to establish a relationship between

chlorophyll (chl-a) and reflectance. Singh and Tripathi (2010) employed IRS-IC LISSIII image having

three bands, that is, band 2 (0.52-0.59um), band 3 (0.62-0.68um), band 4 (0.77-0.86um). Knowing

the coordinates of the sampling stations from the GPS observations, reflectance values were

extracted for a 2*2 grid of pixels and the mean value was taken for all the mean reflectance values of

the four bands and the correspondence chl–a (in mg/m3) concentrations at the corresponding

location. The reflectance value of each of the three bands was compared to the chl-a, and band 2 had

the highest correlation. The effort derived a linear model chl (a) =3.73565R2 - 230.667, with

coefficient of determination of 0.895; where R2 is the reflectance in band 2. The model was found to

yield satisfactory result at 5 percent confidence level in Chi-square test.

The project underscored the suitability of band 2 Landsat data in the determination of

chlorophyll concentration in a marine or aquatic habitat. Besides chlorophyll-a concentration, other

studies have been able to exploit image bands in retrieving water quality parameters such as

suspended matter, dissolved organic matter and phytoplankton to mention a few with satisfactory

results. Landsat imagery data may therefore, potentially measure and monitor water quality

measurement in the Lekki Lagoon too, as opposed to in-situ measurements. In this study, in-situ

water clarity measurements were collected in 2001 and 2006 to assist in the building of a regression

model between reflectance and water clarity. First a regression model was derived from the

relationship between the in-situ water clarity measurements and the reflectance values derived from

2001 Landsat image bands 1 and 3. Thereafter, the water clarity values were inferred from the

Landsat 2006 image, using the established model, and compared to the in-situ measurements of

water clarity measured in 2006. Lastly, an unsupervised NDV1 colour-coded image classification was

performed to determine the rate of gain or loss in surface area of turbidity between 2001 and 2006

within the lagoon.

Brief Description of Lekki Lagoon


Lekki lagoon is one of the largest estuarine systems in West Africa. The lagoon is located

between Lagos and Ogun States in Nigeria and lies between longitude 4° 00’E and 4° 15’ E and

between latitude 6° 25’N and 6° 37’N (Figure 1). According to Kusemiju (1973), the lagoon has a

surface area of about 247 km2 and it is mostly shallow (less than 3.0 m deep) and the maximum

depth being 6.4 m. Lekki lagoon is a freshwater environment fed by the rivers Oni and Saga in the

north-eastern part as well the Oshun River in the north-western parts of the lagoon. The lagoon is

transitional in that it connects two south-western states (Ogun and Lagos). The lagoon is part of an

intricate system of waterways made of lagoons and creeks that are found along the coast of South-

western Nigeria from the Dahomey border to the Niger Delta (Emmanuel, 2009). The vegetation

around the Lekki lagoon consists mainly of stilt rooted trees, a dense undergrowth of shrub and

raphia palms and oil palms.

Fig

ure 1: Lekki lagoon in south-western Nigeria with streams connecting to the Lagoon

DATA AND METHODS

In this study, in situ water clarity measurements were collected in 2001 and 2006 to assist in

the building of a regression model between reflectance and water clarity. First, a regression model

was derived from the relationship between the in-situ water clarity or turbidity measurements and

the reflectance values derived from the 2001 Landsat image bands 1 and 3. Thereafter, the turbidity

values for 2006 were determined from the Landsat 2006 image, using the established model, and

compared to the in-situ measurements of water clarity measured in 2006. Lastly, an unsupervised

NDV1 colour-coded image classification was performed to determine the rate of gain or loss in

surface area of turbidity between 2001 and 2006, which is a measure of water clarity, within the

lagoon. The exercise basically used Landsat image data, the ground (in-situ) measurement data,

ESRI’s ArcGIS 9.3 and ITTVIS’, now EXELIS VIS’ ENVI 4.5 majorly to carry out water clarity mapping in

the Lekki Lagoon. The detailed procedure adopted in this study are outlined hereunder.


Data Acquisition

(a) Actual data downloaded: Landsat TM files for path 190 row 56 were downloaded for

2001 and 2006 (LE71900562001048SGS00.tar.gz and LE71900562006363ASN00.tar.gz). These files

were both obtained from GLOVIS, the USGS global visualization viewer GLOVIS site at

http://glovis.usgs.gov/.

(b) Ground data acquired: Ground data used in this study are turbidity data and GPS

coordinates, referenced to Minna datum having UTM zone 31N projection, for 30 points. Water

samples distributed over Lekki Lagoon were well spaced but randomly obtained and their locations

were coordinated using GPS on the boat. Turbidity value was derived from each obtained sample

using the Nephelometric instrument.

METHODS

Image pre-processing

Some raw remotely sensed images contain defects and flaws. Pre-processing of the remotely

sensed images refers to those operations, which are performed before the main analysis. It includes

geometric correction, radiometric correction, masking and filtering. Distortions in remotely sensed

data arise from a variety of factors, including one or more factors such as earth curvature, platform

motion, relief displacement, nonlinearity’s in scanning motion, the platform altitude, and velocity,

rotation of the Earth. To compensate these problems, geometric correction is intended. Although

Landsat images from GLOVIS have standard processing algorithms and terrain correction applied,

other processing algorithm is still applied to them to obtain, if not a perfect image, a near perfect

image.

Landsat calibration

Landsat TM calibration is used to convert Landsat TM or ETM digital numbers to radiance or

exoatmospheric reflectance (reflectance above the atmosphere) using published post-launch gains

and offsets. The spectral radiance (Lλ) was calculated using the following equation (Gyanesh and

Brian, 2003):

LMAX LMINL LMIN QCALQCALMAX

(1)

where QCAL is the calibrated and quantized scaled radiance in units of digital numbers,

LMINλ is the spectral radiance at QCAL = 0, LMAX λ is the spectral radiance at QCAL = QCALMAX, and

QCALMAX is the range of the rescaled radiance in digital numbers. LMIN λ and LMAX λ are derived

from tables provided in the Landsat Technical Notes (August 1986) with the information provided

through the TM calibration parameters dialog in ENVI. QCALMAX is 255 for all TM data and 127 for all

MSS data except for Band 4 (0.8 to 1.1 mm), where 63 is used for certain time periods (data acquired

before February 1, 1979 for Landsat 1-3 and processed before October 22, 1982). The resulting


radiance (Lλ) is in units of watts per square meter per steradian per micrometer (W/(m2*sr*mm)).

The exoatmospheric reflectance (ρp) was calculated using the following equation (Gyanesh and Brian,

2003):

2* **cosp

s

L dpESUN

(2)

where Lλ is the spectral radiance, d is the Earth-Sun distance in astronomical units, ESUNλ is the mean

solar exoatmospheric irradiance, and θs is the solar zenith angle in degrees. ESUNλ is derived from

tables provided in the Landsat Technical Notes (August 1986). The resulting reflectance is unitless.

Figures 2 and 3 show the sample points.

Figure 2: Water sample points and their turbidity reading measured in 2001

Figure 3: Water sample points and their turbidity reading measured in 2006

Normalized difference vegetation index

The Normalized Difference Vegetation Index (NDVI) is one of the oldest, most well-known,

and most frequently used vegetation index. The combination of its normalized difference

formulation and use of the highest absorption and reflectance regions of chlorophyll make it robust


over a wide range of conditions. Algae and phytoplankton that contain chlorophyll are believed to be

partly responsible for the level of turbidity observed in Lekki Lagoon. Their presence and growth will

be efficiently mapped using a chlorophyll mapping technique like the NDVI. NDVI is defined by the

following equation (Rousse et al 1973; Larry, 1997):

r

r

PP

nir ed

nir ed

PNDVIP

(3)

Where Pnir is image band with a center wavelength of 800 nm and Pred is image band with a

center wavelength of 680 nm. The value of this index ranges from -1 to 1. The common range for

green vegetation is 0.2 to 0.8. This enables vegetative cover within an aquatic environment to be

distinctively delineated.

Kloiber’s regression model

The Kloiber’s regression equation was adopted in this study. The regression model gives a

high correlation between the turbidity and the Landsat reflectance or digital number value. Kloiber’s

regression model is expressed as (Kloiber’s et. al, 2002):

1ln ( ) ( 1)2

TmT a b Tm CTm

(4)

Where Tm1 is the reflectance value for band 1, Tm2 is reflectance value for band 3, T is

Turbidity in NTU and In T is the natural logarithm of turbidity. A graph of the in-situ measurement

was plotted against the reflectance values and regression was used to relate turbidity to the

reflectance values of bands 1 and 3 of the Landsat multispectral images. The observation equation

method of solution of least squares was utilized in the determination of solution to the parameters,

which are the coefficients a, b, of the Landsat manipulated bands and the constant, c, of the

KLOIBER’S regression model. Substituting the measured turbidity (T) and reflectance of bands 1 and 3

(Tm1 and Tm2) for all thirty (30) readings in 2001, the parameters a, b, and c are derived using least

squares, as shown below.

1( * ) ( *ln )T Tab A A A Tc

(5)

2.03098316.347957

5.350634

abc

Therefore, the model shown below establishes the relationship between the natural

logarithm of the turbidity of a given point with the reflectance values on band 3 and band 1 of the

same point. Hence, for any given point, the turbidity can be obtained using the reflectance from

band 1 and band 3.


1ln 2.030383( ) 16.347975( 1) 5.3506343

TmT TmTm

(6)

Image classification

The NDVI, which are numeric values for a given multispectral image, obtained using band

math tool is colour mapped. In the colour mapping, the transition of the shades of colour

corresponds to the distribution of the numerical values within the image scene. After colour coding,

unsupervised classification is performed on the obtained NDVI imagery.


The Least square model yields turbidity values that strongly correlate with the in-situ data in

2001 when reflectance are inputted. The model also displayed a high degree of accuracy when

predicting turbidity values at the Lekki Lagoon in 2006. Resemblance ratio between observed

Turbidity (To) and computed Turbidity (Tc) using Kloiber’s derived model was found for 2001 and

2006. Water clarity information of 30 common nodes from observed and computed values were

queried for terrain turbidity. The result from histogram reveals that there exists shift in both

observed and computed turbidity (Figure 4). The shift was seen to be sharp in 2006 at points

603642.67E, 727690.11mN and 615658.48E, 716700.69N with -41.7NTU (TO= 128, TC= 169) and -

74.73NTU (TO= 95, TC= 169), respectively. These errors may be related to errors associated with

humans during field measurement. The accuracy of the model for the computation of turbidity does

not exceed ±4 NTU. This accuracy is an advantage in comparison with the cost of implementing a

turbidity survey of an area, and also, it saves time and eliminates the risk surrounding aquatic

environment. Graph with mini mathematical models (regression equation and RMSE) were

generated for the Lekki Lagoon turbidity for the two epochs (2001 and 2006) (Figures 5 and 6). The R2

for 2001 and 2006 analysis are 0.9999 and 0.7975, respectively.

Figure 4: Deviation in the 30 common nodes queried for observed and computed turbidity for

2001 (left panel) and 2006 (right panel)


Figure 5: 2006 turbidity graph with equation and RMSE

Figure 6: 2001 turbidity graph with equation and RMSE

Unsupervised classification of NDVI colour-coded multispectral image also produced an

output in water colour monitoring that correlates with the situation obtained in reality. The region of

very high turbidity value tends to occur substantially at places where water body is immediately

adjacent a land portion (Figures 9 & 10). The transition of deep shade of red gradually turns greyish

at the centre of the lagoon and takes up reddish colour again towards adjacent land bounds of the

lagoon. Analysis of rate of gain or loss of turbid water (Table 1) reveals that water region with low

turbidity in 2001 was massively reduced, estimated to about 31.93% reduction in surface area. The

medium turbid zone experienced a surface area increment. Similarly, the high turbid zone

experienced a tremendous increase from 2001 – 2006, reaching over 100% increase (Figure: 7). It

shows the high level of discharge of effluents into the Lekki Lagoon. Though there exist a strong

relationship between reflectance values for band 1 and band 3 and the actual turbidity; the result

obtained from NDVI and unsupervised image classification portrays a good relation.


Figure 7: Change in surface area of turbid water (in km2) from 2001 to 2006

Table 1: Comparison between 2001 and 2006 extracts

Features AREA (km2)

Percentage (Gain or Loss)

Loss in area occupied by low turbid region -52.18 -31.93

Gain in area occupied by medium turbid region 13.78 28.37

Gain in Area occupied by high turbid region 33.82 101.38

Area occupied by very High turbid region 4.58 10.03

Figure 9: Turbidity map for 2001


Figure 10: Turbidity map for 2006

CONCLUSIONS

Remote sensing and least square techniques have been put to test in this study for mapping

water clarity in the Lekki Lagoon. Water clarity data and Landsat multispectral data for 2001 and

2006 were collected. Reflectance of band 1 and band 3 of Landsat were utilized. An efficient and

practical method to extract satellite data for individual ground observation site was ensured to

increase accuracy. Regression technique was adopted to obtain the parameters for a model that

accurately relates reflectance value of Landsat bands 1 and 3 to in-situ water clarity, turbidity

measurements. Unsupervised NDV1 colour-coded image classification was adopted in determination

of the rate of gain or loss in surface area of turbidity, which is a measure of water clarity, within the

lagoon. The results of the study suggest the proliferation of phytoplankton, implying the decline in

the quality of coastal water. Since fishes depend largely on phytoplankton, the availability of

phytoplankton and its growth suggest an influx of fishes into the lagoon. Analysis from Kliober’s

model with parameters obtained using least squares shows a strong correlation with the in-situ data

in 2001. The accuracy of the model for the computation of turbidity does not exceed ±4 NTU. This

accuracy is an advantage in comparison with the cost of implementing a turbidity survey of an area,

and also, it saves time and eliminates the risk surrounding aquatic environment. Water region with

low turbidity in 2001 was seen to experience about 31.93 percent reduction in surface area while the


medium turbid zone experienced a surface area increment. There was a tremendous increase in high

turbid zone between 2001 – 2006, reaching over 100 percent increase. This shows the high level of

discharge of effluents into the Lekki Lagoon.

REFERENCES

Baban S. (1993): Detecting water quality parameters in Norfolk Broads, UK., using Landsat imagery. Int. J. Remote Sensing; 14: 1247-1267.

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Emmanuel, B. E. (2009). The Artisanal Fishing Gears, Crafts Technology and Their Efficiency in the Lekki Lagoon, Nigeria. PhD Thesis. University of Lagos, Nigeria. 256pp.

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Kusemiju, K. (1973). A Study of the Catfishes of Lekki Lagoon with Particular Reference to the Species Chrysichthys walkeri Bagridae. PhD Thesis. University of Lagos, Nigeria.

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Kloiber’s S, Anderle T, Brezonik P, Olmanson L, Bauer M, Brown D. (2000): Trophic state assessment of lakes in the Twin Cities (Minnesota, USA) region by satellite imagery. Archive Hydrobiologie special Issues Advances in Limnology; 55: 137-151.

Larry Ryan (1997). Creating a Vegetation Index Using MultiSpec. The GLOBE Program. 161 Morse Hall OPAL/EOSUniversity of New Hampshire. Durham, NH 03824. 1-14. Retrieved from: www.globe.unh.edu/MultiSpec/NDVI.pdf. Accessed:March 14, 2013.

Nitin, K.T. and Singh, P. (2010): Mapping chlorophyll-a in upper lake, Bhopal using IRS-1C data. Accessed 7/07/2013. Retrieved from: http://www.gisdevelopment.net/Application/Environment/Water/watq0002pf.htm.

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Wang Y., Xia H, Fu J, Sheng G. (2004): Water quality change in reservoirs of Shenzhen, China: Detection using Landsat / TM data. Science of Total Environment; 328: 195-206.

http://www.globe.unh.edu/MultiSpec/NDVI.pdf


EXPLORING MODIS IMAGERY IN MONITORING WATER QUALITY ON LAKE VICTORIA

Anthony Gidudu1, Zainab Mpakiraba1,2, Herbert Kalibbala1

1. Makerere University, UGANDA [email protected] 2. National Water and Sewerage Corporation, Kampala, Uganda

KEYWORDS: MODIS, Water Quality, Lake Victoria , Validation

ABSTRACT

At 68,800 km2, Lake Victoria is the largest fresh water lake in Africa. It is a trans-boundary water

resource supporting the livelihoods of over 20 million people directly and indirectly. It is a source of

food, recreation, domestic and industry use. This has rendered its monitoring of paramount interest

to several environmental agencies in Uganda, Kenya and Tanzania as well as along the river Nile

basin. Traditionally, the monitoring of water quality is carried out at specific points of the lake by

carrying out in-situ measurements or collection of water samples for laboratory testing. This

traditional approach of determining water quality is cumbersome, expensive and does not give a

synoptic perspective of the whole lake. This has inspired the consideration of satellite imagery as a

tool to monitor water quality on the lake. Satellite imagery offers the advantage of providing

regularly collected data, giving a synoptic view of the water quality of the whole lake. The aim of this

paper was to therefore investigate how satellite derived water quality parameters compare with in

situ measurements in a bid to operationalise the use of satellite images in monitoring water quality

on the lake. To wit, in-situ lake surface temperature at specific points was measured and water

samples of those points were taken to the lab to test for Chlorophyll_a. These samples were

collected within 4 hours of satellite overpass. These results were then compared with water quality

parameters derived from MODIS imagery. The results showed that there is a moderate to strong

correlation (R2 = 0.68) between satellite derived lake surface temperature and in-situ measurements

implying that MODIS satellite imagery can be depended to accurately model the spatial variation of

lake surface temperature. Unfortunately because of cloud cover coinciding with the day of in-situ

observations, no similar comparisons could be made regarding Chlorophyll_a, thus portraying one of

the challenges of operationalizing the use of satellite imagery in monitoring water quality on lake

Victoria. Given the potential of satellite imagery as a reliable source of water quality information,

further studies are urgently needed to validate it for Lake Victoria.

INTRODUCTION

Remote sensing of inland lakes has in the recent past gained currency and increasingly studies are

being undertaken to use satellite imagery operationally (Zhu et al. 2004). This is mainly motivated by

the fact that in-situ measurements are costly, do not give a synoptic perspective of the lake, are

irregularly done and are cumbersome (Watkins, 2009). Satellites on the other hand regularly orbit

the globe collecting information about the earth’s surface. This information is usually archived and in

many instances is freely available, thus enabling historical studies of a lake’s characteristics. Current

research is increasingly focussing on how to best relate imagery radiances to water geophysical

parameters (Teillet, 1997). Whereas the use of satellite imagery to monitor water quality parameters



on oceans has reached maturity, its adoption for inland lakes is mainly experimental due to the high

degree of optical complexity of inland waters (Zhu et al, 2004). Some of the water quality parameters

that have been monitored using satellite imagery include Chlorophyl_a, lake surface temperature,

water transparency, dissolved organic matter etc (Fengyun, 2010).

The interest in Chlorophyll_a stems from the fact that it is contained in all species of

phytoplankton (Thiemann and Kaufmann, 2000) and is indicative of the level of eutrophication of the

lake (Koponen et al., 2001). Lake Surface Temperature (LST) is important for bulk hydrodynamic and

ecological variables (Watkins, 2009). It also is used to mark surface water temperature fronts (Ullman

et al., 1998) and to develop upwelling indices (Platnner et al, 2006) which is of particular interest to

the fisheries industry. Lake Surface Temperature is also important because it gives an indication of a

lake’s biological and chemical activity (MacCallum and Merchant, 2012; Stefouli and Charou, 2012).

This paper focuses on attempts by the authors to explore and validate the use of satellite imagery

in monitoring water quality on lake Victoria using the Moderate-resolution Imaging

Spectroradiometer (MODIS) Imagery, with particular focus on the Murchison Bay in Kampala,

Uganda. The choice of MODIS Imagery is motivated by the fact that it has a daily temporal resolution,

synoptically covers the whole lake at a go, and is freely available online. Lake Victoria is the largest

lake in Africa covering an expanse of 68,800 km2 (Carvalli, et al., 2009). By its size, any water quality

monitoring strategy is challenging due to costs involved. This is further complicated by the fact that

Lake Victoria is trans-boundary, surrounded by Uganda, Kenya and Tanzania. All these factors build

the case for the need of satellite imagery in developing an operational strategy of monitoring water

quality for the lake. This paper therefore discusses the challenges in using and validating satellite

derived water quality parameters for Lake Victoria.

METHODOLOGY

The main thrust of the research was to validate satellite derived water quality parameters with

traditionally determined water quality parameters. The water quality parameters considered were

Lake Surface Temperate (LST) and Chlorophyll_a. Endeavours were made to collect the water

samples within three - four hours of the MODIS overpass. Satellite overpass was predicted using the

NASA’s Ocean Color website (http://oceandata.sci.gsfc.nasa.gov/cgi/overpass_pred). In the case of

Lake Victoria, MODIS (aqua) traverses at about 11 am. At each sampling point, location was

determined using a handheld GPS while LST was measured using a thermometer to measure the

‘skin’ surface temperature (i.e. top surface of the lake) as this is the temperature that the sensor

detects (Robinson, 2010). At each of these locations, water samples were collected and stored in cool

boxes for further testing and analysis of Chlorophyll_a concentrations in the laboratory. There being

no guide as to how to sample the locations, points were randomly selected at about 1 km apart

within the Murchison Bay. Figure 1 depicts the sampling points in Murchison Bay of Lake Victoria in

Uganda

http://oceandata.sci.gsfc.nasa.gov/cgi/overpass_pred


Lake Victoria in East Africa

Murchison Bay

Sample Points in Murchison Bay

Figure 1: Study area (Courtesy of Google maps – Copyright: Map Data 2014 Google)

MODIS imagery of Lake Victoria for the days that samples were taken was retrieved from NASA’s ocean color web site (http://oceandata.sci.gsfc.nasa.gov). ‘Level 2’ data has been processed by NASA from raw spectroradiometer data to estimate surface water temperature and Chlorophyll_a. LST was extracted from the imagery by using MODIS bands 31 and 32 at 11µm and 12µm. The algorithm for computing LST from observed brightness temperatures is shown in equations 1 - 5 (Franz, 2006): For dBT<= 0.5

LST = a00 + a01*BT11 + a02*dBT*bLST + a03*dBT*( (1)

For dBT>= 0.9

LST = a10 + a11*BT11 + a12*dBT*bLST + a13*dBT* (2)

For 0.5 <dBt< 0.9

LST(lo) = a00 + a01*BT11 + a02*dBT*bLST+ a03*dBT* (3)

http://oceandata.sci.gsfc.nasa.gov)./


LST(hi) = a10 + a11*BT11 + a12*dBT*bLST + a13*dBT* (4)

LST = LST(lo) + (5)

Where:

BT11 = Brightness temperature at 11 μm, in deg-C (i.e. band 31) BT12 = Brightness temperature at 12 μm, in deg-C (i.e. band 32) dBT = BT11 - BT12

LST (lo)= Lake Surfac Temperature when dBT>= 0.5 LST (hi) = High Lake Surface Temperature when dBT>= 0.9 bLST = Baseline Lake Surface Temperature Cos(ø) = Cosine of sensor zenith angle

The coefficients a00, a01, a02, and a03 and a10, a11, a12, and a13 are based on match-ups between the satellite retrievals of brightness temperature and field measurements of sea surface temperature.

Chlorophyll_a was extracted from the imagery using the Ocean Colour algorithm version 5 (OC3v5) (O’reily et al. 2000). The algorithm form describes the polynomial best fit that relates the log-transformed geophysical (in this case Chl-a) variable to a log-transformed ratio of remote-sensing reflectances (of the MODIS imagery):

Log10(Chla)=0.241-2.477r+1.530r2+0.106r3-1.108r4 (6) where

r = Log10 {(Rrs 443>Rrs490)/Rrs555} Rrs – electromagnetic wavelengths used for Chl-a extraction The input radiances are in the form of either remote sensing reflectance or normalized water leaving radiance.

WIMSOFT software was used to extract the satellite derived water quality parameters in order to facilitate comparisons with the in-situ measurements. To extract the LST and Chl_a concentration at a given location of interest from the MODIS imagery, the 3 x 3 pixel mean is calculated of a 1 x 1 km pixel. The Pearson product-moment correlation coefficient was then used to determine the degree of correlation between satellite derived and in situ water quality parameters using a 5% (p ≤ 0.05) level of significance.


The table 1 shows the days when samples were collected and the corresponding status of imagery

Table 1: Sampling expedition vis a vis status of imagery

Date of expedition Status of Imagery

1st June 2013 Imagery available, however cloud cover made it impossible to

obtain satellite derived Chlorophyll_a, though some LST was

determined

15th June 2013 Study area overcast hence no corresponding satellite derived water

quality parameters

31st January 2014 Imagery available, however cloud cover made it impossible to

obtain satellite derived Chlorophyll_a, though some LST was

determined


7th June 2014 No imagery for the study area

Unfortunately none of the sampled points Chl_a could be compared with satellite derived Chl_a

because of cloud cover on the days of sampling. Lake Victoria is located along the equator and is

notoriously bedevilled by cloud cover. Since MODIS uses the optical bands to derive Chl_a, it poses a

big hindrance in its extraction. This evidently presents a challenge in the adoption of satellite

imagery in monitoring quality, as all samples collected during cloud covered days could not be

correlated with satellite derived water quality parameters. The second reason why the study may

have yielded no results could have been based on the fact that the sampling points were close to the

shore. The choice of Murchison bay as a case study in this research was motivated by, among other

reasons, that it is the main abstraction point for the National Water and Sewerage Corporation

(NWSC), thus rendering NWSC a potential beneficiary of this study as they depend on regular water

quality information for their operations. These abstraction points are less than 1 km from the shores,

and unfortunately water quality information collected here could not be used to validate satellite

derived water quality information because of the so called ‘adjacency effect’. In other studies such as

(Watkins, 2009) it is suggested that for better assessment of Chlorophyll_a, more reliable results

could be obtained by separating sampling between nearshore (< 30m depth) and offshore (> 30m).

Figure 2 depicts an annual satellite derived Chlorophyll_a for Lake Victoria for 2003 and as can be

seen the areas closer to the shore often times don’t have any Chl_a values, probably indicating that

whereas MODIS can be effective in depicting synoptic Chl_a distribution, it seems handicapped in

determining Chl_a distribution in bays which define the lake shore’s outline. The downside of this is

that it is in the bays that there is most pollution, it is where water abstraction occurs and there is

need to continually monitor these areas. Future expeditions will have to be made further into the

lake if validation of satellite derived chl_a is to be successfully validated for Lake Victoria.

Figure 2: 2003 Annual Satellite derived Chlorophyll_a distribution

To determine LST, only two images could be used of which only 14 out of 30 sampled locations

could be correlated with satellite derived LST. The figure 3 show cases the relationship between in-

situ and satellite derived LST. The Pearson Correlation Coefficient for the Satellite derived vis a vis the

in-situ LST gives R2 = 0.68 which is significant at the p = 0.05 level. This depicts moderate to strong

positive correlation, indicating that there is a tendency for satellite derived LST to vary positively with


in-situ LST measurements. Similar studies on other lakes have presented higher R2 Pearson

Correlation Coefficients. This research could be further improved by using a bigger sample size.

Figure 3: Comparison of satellite derived LST with in-Situ measured LST

CONCLUSIONS

From this research, it is evident that there is need to further explore the applicability of satellite

imagery in monitoring water quality on lake Victoria. For this occur, a bigger sample will be required,

with samples preferably collected at least 1 km from the shores. Secondly, the influence of cloud

cover is not trivial, nonetheless there is need to explore means by which this challenge can be

circumvented. To increase on the possibility of collecting samples on cloud free dates,

precipitation/cloud prediction models may have to be considered to increase the possibility of

collecting verifiable satellite derived water quality data. The strong moderate to strong correlation

between satellite derived and in situ LST demonstrates that satellite imagery can be relied upon to

determine spatial variation of LST on the lake. This will be further ascertained when more data is

collected.

ACKNOWLEDGEMENT

The authors would like to acknowledge the anonymous reviewers whose comments have gone a long way in improving this manuscript. The authors would like to acknowledge the Presidential Initiative of the College of Engineering, Design Art and Technology of Makerere University, under whose auspices this presentation has been made possible.

REFERENCES

Cavalli, R., M. Laneve, G., Fusilli, L., Pignatti, S., and Santini, F., 2009. Remote sensing water observation for supporting Lake Victoria weed management. Journal of Environmental Management 90:2199 – 2211


Fengyun, M. 2010. Progress in Water Quality Monitoring Based on Remote Sensing and GIS. In Proceedings of the International Conference on Challenges in Environmental Science and Computer Engineering. 6th – 7th March. Wuhan, China pp 208 – 211

Franz Bryan, 2006. Implementation of SST Processing within the OBPG, http://oceancolor.gsfc.nasa.gov/DOCS/modis_sst/

Koponen, S., Pulliainen, J., Servomaa, H., Zhang, Y., Hallikainen, M., Kallio, K., Vepsalainen, J., Pyhalahti, T. and Hannonen T. 2001. Analysis on the feasibility of multi-source remote sensing observations for chl-a monitoring in Finnish lakes. The Science of the Total Environment 268:95-106

MacCallum, N. S. and Merchant, J. C. 2012. Surface Water Temperature Observations of Large Lakes by Optimal Estimation. Canadian Journal of Remote Sensing, Vol. 38 (01): pp. 25-45

O’Reily, JE and 24 co-authors.2000. SeaWIFS post launch Calibration and Validation Analyses part 3. NASA/TM-2000-206892, vol.11, NASA Goddard Space Flight Center, Green belt, Maryland.

Plattner, S., Mason, D. M., Leshkevich, G. A., Schwab, D. J., & Rutherford, E. S. 2006. Classifying and forecasting coastal upwellings in Lake Michigan using satellite derived temperature images andbuoy data. Journal of Great Lakes Research, 32, 63−76.

Robinson, I. 2010. Discovering the Ocean from Space – The Unique Applications of Satellite Oceanography. Springer-Verlag Berlin Heidelberg.

Stefouli, M. and Charou, E., 2012.Ohrid Lake Monitoring using Meris and Landsat Images. In Proceedings of the 5th International Conference on Water, Climate and Environment. 28th May – 2nd June, 2012, Ohrid, Republic of Macedonia

Teillet P., M. 1997. A Status Overview of Earth Observation Calibration/Validation for Terrestrial Applications. Canadian Journal of Remote Sensing 23(4): 291 – 298

Thiemann, S., and Kaufmann, H. 2000. Determination of Chlorophyll Content and Trophic State of Lakes Using Field Spectrometer and IRS-1C Satellite Data in the Mecklenburg Lake District, Germany. Remote Sensing of the Environment. 73:227–235

Ullman, D., Brown, J., Cornillon, P., and Mavor, T., 1998. Surface Temperature fronts in the Great Lakes. Journal of Great Lakes Research 24(4), 753 - 775

Watkins, J., M. 2009. Comparison of shipboard and Satellite Measurments of Surface Water Temparature and Chlorophyll a in Lake Ontario. Aquatic Ecosystem Health and Management 12(3): 271 - 280

Zhu, L., Shixin, W., Zhou, Y, Yan, F., and Zhou, W. 2004. Water Quality Monitoring in Taihu Lake Using MODIS Image Data. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Anchorage, Alaska, Sept. 20-24, 2004, pp 2314 - 2317


Estimation of Potential Crop Evapotranspiration Using Remote Sensing Techniques Mohammed A. El-Shirbeny1, Nasser H. Saleh1, Abd-Elraouf M. Ali1

1. National Authority for Remote Sensing and Space Sciences (Egypt), Mohammed A. El-Shirbeny,

[email protected]

KEYWORDS: Reference evapotranspiration (ETo), Normalized Deference Vegetation Index (NDVI),

Crop coefficient (Kc), Landsat8, and arid region

ABSTRACT

In arid and semi-arid regions Potential crop evapotranspiration is a good index for crop water requirements calculation. Irrigation in north Sinai during winter depends on rainfall, but in summer depends on underground water or/and water harvested during rainfall season. Drip irrigation is common irrigation system in study area. The aim of this paper is determining potential crop evapotranspiration (ETc) using satellite data. ETc estimated from ETo and Crop coefficient (Kc). Reference evapotranspiration (ETo) estimated using agro-meteorological data according to FAO-Penman-Monteith and Hargreaves methods. FAO-Penman-Monteith method used to calibrate Hargreaves under the same conditions. The difference between air temperature (Tair) and Land Surface Temperature (LST) varies particularly by surface water status. Linear relation between Tair and LST was established and R2 was 0.86. LST used to predict maximum, minimum, and mean Tair (oC). Red (R) and Near Infra-Red (NIR) measurements used to calculate Normalized Difference Vegetation Index (NDVI). NDVI extracted from NOAA/AVHRR and landsat8 satellite data to calculate emissivity as an intermediate step for producing LST. ETo estimated using predicted maximum, minimum, and mean Tair according to Hargreaves after calibration with FAO-Penman-Monteith method. In this paper Crop coefficient; Kc=2*NDVI-0.2 will represent the relation between Kc and NDVI. Kc and ETo used to estimate ETc in study area.

INTRODUCTION

In Arid and semi-arid conditions, irrigated agriculture is the major consumptive user of water. Most large irrigation schemes have extremely high losses, with crop evapotranspiration accounting for only 20-35% of water supplied with remainder being wasted (Tanton and Heaven, 1999).

Limited water resources and water scarcity in Egypt are obstructing the agriculture horizontal expanding. In the same time over population increases and agriculture land decreases. As a result, quantities of food will decrease. Reference crop evapotranspiration (ETo) mainly depends on water availability and incoming solar radiation and then reflects the interactions between surface water processes and climate (Sobrino, el al., 2007). However, it can be calculated by using pan evaporation from a free water surface; models based on climatological parameters. FAO-penman-monteith is most accurate for ETo estimation in both humid and arid climatic conditions, it provides ETo estimates for planning and efficient use for agricultural water resources (Yin, et al., 2008). The determination of ETc at the farm level has traditionally depends on two steps approach. The first one is ETo, and the second is a semi-empirical coefficient (crop coefficient, kc) which applied to represents other crop and environmental factors (Magliulo, et al., 2003).

Dealing with large scale, needs to use a new technique. At last decades remote sensing and geographic information system (GIS) involved in this work. Remote sensing provides spatial coverage by measurement of reflected and emitted electromagnetic radiation, across a wide range of wavebands, from the earth’s surface and surrounding atmosphere (Sivakumar, et al., 2004). Various studies on NDVI and LAI using remote sensing techniques were done in Egypt for agricultural sustainability purposes (Aboelghar et al., 2010; Aboelghar et al., 2011; El-Shirbeny et al., 2014a; El-



Shirbeny et al., 2014b; El-Shirbeny et al., 2014c). (El-Shirbeny, et al., 2014a and 2014b) studied Evapotranspiration in Egypt using remote sensing techniques. Regional scale water management has historically relied on relatively coarse approaches to estimate crop water requirement (Hess, 1996). Recent developments in GIS and enhanced computational capabilities presented the opportunity to apply physically based soil water balance and simulation models at a regional scale to enhance water supply management (Satti and Jacobs, 2004). To estimate ET using remote sensing you have to use thermal bands to get Land Surface temperature (LST).

Sobrino et al. (1993) used a combination of the channels 4 and 5 of the AVHRR sensor to estimate LST. Pinheiro et al., (2006) developed a new 6-year daily, daytime and nighttime; NOAA-14 AVHRR based on LST dataset over continental Africa for the period 1995 - 2000. This algorithm requires as input values of surface emissivity in AVHRR channels 4 and 5. The objective of this paper is to use NOAA/AVHRR and Landsat8 satellite data to estimate ETc.

MATERIALS AND METHODS

1. Study area

The target area chosen for the implementation of this paper is located in the northern east of Egypt; latitude 31o 03'56.33" and 31o18' 38.07" N and longitude 33o 39' 37.53" and 34o 16' 02.57" E and total area is 67175.24 fed (282.136 Km2) as shown in figure (1).

Figure 1. shows the study area location.

2. Climate conditions

Generally, the prevailing Macro-climatic parameters in the north Sinai may be characterized with low rainfall, high temperature, moderate wind, and high relative humidity. The data were collected from El-Arish meteorological station. It was installed at 2m above ground level at latitude 31.27 N,


longitude 33.75 E and altitude 15 meter. It recorded values of Tair, relative humidity, wind speed and rainfall.

3. Different Remote Sensing data

Satellite images from NASA database which available on web were used to cover the study area (Coastal area from El Arish to Rafah). NOAA/ AVHRR (advanced very high resolution radiometric) was used from the 1st June to 30th June 2006 and 2007 with 1100 m ground resolution. Landsat8 data acquired on 25 June 2013 which used to calculate Kc and ETo.

4. Extracting LST and NDVI

NOAA data were considered by the following steps:- a) Clear-sky satellite overpasses selection; b) Georeferencing data; c) 10 days average NDVI data generation; d) Computing of the 9 pixels mean LST over the ground-based stations position. The Sobrino et al. (1993) (equation 1) use the retrieved emissivity computed by Valor and Caselles (1996) (eq. 2, 3 and 4) algorithm, by means of the NDVI as representative of the emissive characteristics. The emissivity factor was retrieved as follows:- LST=T4+ [0.53+0.62(T4-T5)] (T4-T5) +64(1- ε) (1)

Where: T4 and T5 = brightness temperature for channels 4 and 5 of AVHRR, and ε = mean emissivity for channels 4 and 5, (ε4 + ε5)/2. ε = 0.985Pv + 0.96(1-PV) + 0.06Pv (1-PV) (2)

Thus:

(3)

And k is:

(4)

Where: i, ig, i ν = NDVI, NDVI bare soil, NDVI vegetated surface, ρ1 and ρ2 = channels 1 and 2 reflectance's of AVHRR, and ν and g = indexes for vegetation and bare soil. The monthly mean NDVI values for bare soil and vegetated areas are presented in the Table (1).

Table 1. illustrates NDVI values for bare soil and vegetated area from NOAA/AVHRR.

Month

NDVI

Bare soil Vegetation

June, 2006 0.03 0.12

June, 2007 0.04 0.12

For landsat8 data, the recorded digital numbers (DN) and converted to radiance units (Rad) using the calibration coefficients specific for each band. Band 10 used to extract LST as follow:- Radiance = 0.0003342* DN+ 0.10000 (5)

Surface emissivity (Eo) was estimated from the NDVI using the empirical equation developed from raw data on NDVI and thermal emissivity (Valor and Caselles, 1996). Eo = 0.9932 + 0.0194 ln NDVI (6)

The radiant temperature (To) can be calculated from band 10 radiance (Rad10) using calibration constants K1=774.89 and K2=1321.08. To = K2/ ln((K1/Rad10) +1) (7)


The resulting temperature (Kelvin) is satellite radiant temperature of the viewed Earth atmosphere system, which is correlated with, but not the same as, the surface (kinetic) temperature. The atmospheric effects and surface thermal emissivity have to be considered in order to obtain the accurate estimate of surface temperature from satellite thermal data (Norman et al., 1995). LST is calculated from the top of atmosphere radiant temperature (To) and estimated surface emissivity (Eo) as: LST = To/Eo (8)

5. Reference evapotranspiration estimation

ETo was calculated from meteorological data using FAO-Penman-Montieth method equation (9) prepared by (Allen et al., 1998).

(9)

Where; ETo, reference evapotranspiration [mm day-1], Rn, net radiation at the crop surface [MJ

m-2 day-1], G, soil heat flux density [MJ m-2 day-1], T, mean daily air temperature at 2 m height [°C], u2 ,wind speed at 2 m height [m s-1], es, saturation vapour pressure [kPa], ea ,actual vapour

pressure [kPa], es - ea, saturation vapour pressure deficit [kPa], , slope vapour pressure curve [kPa

°C-1], , psychrometric constant [kPa °C-1]. Hargreaves method used to estimate ETo from predicted hourly Tair equation (10) after

calibration with FAO-Penman-Montieth under the same conditions. FAO organization recommended this method in case of low data availability like north Sinai. This method uses minimum data; maximum, minimum, average Temperature, number of the day and latitude. The meteorological parameters used in this equation were considered from predicted data from Landsat8 satellite data. ETo = 0.0023(Tmean + 17.8)(Tmax - Tmin)0.5 *Ra (10)

Where; Tmean is average of daily temperature (oC), Tmax is maximum temperature (oC), Tmin is minimum temperature (oC), and Ra is the extraterrestrial radiation, for each day of the year and for different latitudes could be estimated from the solar constant.

Ra is the extraterrestrial radiation, for each day of the year and for different latitudes could be estimated from the solar constant, the solar declination and the time of the year by:-

(11)

Where; Ra is extraterrestrial radiation [MJ m-2 day-1], Gsc is solar constant (0.0820 MJ m-2 min-

1), dr is inverse relative distance Earth-Sun, s is sunset hour angle [rad], j is latitude [rad] , d is solar decimation [rad]. Ra is expressed in the above equation in (MJ m-2 day-1). The corresponding equivalent evaporation in (mm day-1) is obtained by multiplying Ra by 0.408. The latitude (j) expressed in radians is positive for the northern hemisphere and negative for the southern hemisphere. The conversion from decimal degrees to radians is given by: Radians = Л/180*(decimal degrees) (12)

The inverse relative distance Earth-Sun (dr) and the solar declination (δ) are given by: dr = 1+0.033*cos (2* Л*J/365) (13) δ = o.409*sin((2* Л*J/365)-139) (14)

Where: J is number of the day in the year between 1 (1 January) and 365 or 366 (31 December). J Values for all days of the year.

The sunset hour angle, s, is given by:

s = arccos [-tan (j) tan (d)] (15) As the arccos function is not available in all computer languages, the sunset hour angle can also

be computed using the arctan function:


Mean Air Temp. (June)

15

17

19

21

23

25

27

29

31

1 3 5 7 9 11 13 15 17 19 21 23

Day hours

Te

mp

era

ture

(oC

)

Relation between LST and Tair (June)

y = 0.3233x + 15.119

R2 = 0.7598

10

15

20

25

30

35

10 15 20 25 30 35 40 45 50 55

LST (oC)

Tair

(o C)

s = (Л/2)-arctan[-tan(φ)*tan(δ)/X0.5] (16) Where:-

X = 1 - [tan(j)]2 [tan(d)]2 (17) and X = 0.00001 if X £ 0

6. Crop coefficient

Kc is a dimensionless number (usually between 0.1 and 1.2) which used to calculate (ETc). The resulting ETc helps in irrigation scheduling; timing of irrigation and quantity of water should be applied. In this paper, the relation between Kc and NDVI represented by equation (18) which established by (El-Shirbeny el al., 2014b) and evaluated by (El-Shirbeny et al., 2014c) for wheat.

(18)

Where; the value of 1.2 is maximum Kc; 0.6 is the difference between minimum and maximum NDVI value for vegetation and 0.2 is minimum NDVI value for vegetation.


1. Minimum, Maximum and Mean Tair

The difference between Tair and LST varies particularly with the surface water status, the roughness length and the wind speed. Such physical conditions often make the relation between Tair and LST into the biosphere thermodynamics understanding a lot obscure. LST was lower than Tair at night but at day it was the opposite, because of the surface energy emitted during the day more than during the night and Tair affected by wind speed and air humidity. Quantities of data during night were lower than during day because of clouds. Figure (3) shows the relation between Tair (oC) and LST (oC) derived from NOAA/AVHRR 17 and 18 in El-Arish region. This relation used to predict Tair from LST. The average of hourly Tair (figure 4) used to predict min, max and mean Tair.

Figure 3. shows the relation between Tair (oC) and LST (oC) In El-Arish region


y = 0.5825x - 0.1056

R2 = 0.7829

0

2

4

6

8

10

0 2 4 6 8 10 12 14 16 18

ETo Hargreeves

ET

o P

en

ma

n-M

on

tie

th

Figure 4. shows the hourly air temperature average.

2. Hargreaves method compared with FAO-Penman-Monteith

ETo estimated using agro-meteorological data according to Hargreaves and FAO-Penman-Monteith methods for five years (2002-2006) under the same conditions. Most of ETo values calculated using Hargreaves were very high. Because of Hargreaves method depends on few parameters, so it must be calibrated with a standard method like FAO-Penman-Monteith or Lysimeter. Also, because of nearest of study area from the Mediterranean Sea, hence other factors like wind speed and relative humidity will affect. (Allen et al., 1989) evaluated popular forms of the penman equation, and general relationships for estimating ETo. They did a relationship between FAO-24 corrected penman equation and Lysimeter. They also did a relation between 1982 Kimberly-Penman and Penman-Monteith vs. Lysimeter. All methods used in the daily analysis, besides the Penman-Monteith, were adjusted for Lysimeter vegetation type using the 1.15 multiplier. Compatibility with (Allen et al., 1989) and FAO56 paper recommendations, Evapotranspiration by Hargreaves method calibrated with FAO-Penman-Monteith method under the same conditions and time. Figure (2) shows the relation between ETo by Hargreaves method and FAO-Penman-Monteith method. It gave a good correlation where R2 = 0.78 and well distribution around the straight line.

Figure 2. shows the relation between ETo by Hargreaves and FAO-Penman-Monteith.

3. ETo, Kc and ETc using satellite data

(Bois et al., 2008) used FAO-Penman-Monteith method, Hargreaves method and Radiation method to estimate reference evapotranspiration based on satellite data and ground stations. ETo extracted form Landsat8 data according to Hargreaves after calibration with FAO-Penman-Monteith. Figure (5) shows ETo estimated by Hargreaves after calibration. It varies from 0 mm/day to 12 mm/day.


Kc depends on stage of canopy height, crop growth, architecture and cover (Allen et al., 1998). The relation between Kc and NDVI is highly correlated. NDVI calculated from Red and NIR bands in Landsat8 data which acquired on June 25th 2013. The NDVI equation produces values in the range of -1.0 to 1.0, where vegetated areas typically have values greater than 0.2 and less values indicate non-vegetated surface features such as water, barren, ice, snow, or clouds. NDVI vary according to crop age, planting density and chlorophyll activity. It seems like Kc varying from planting to senescence. Figure (6) illustrates Kc values variation in study area according to Landsat8 data. Kc in study area varies from 0 to 0.8.

ETo and Kc used to estimate ETc. ETc values varies from 0 to 5 mm/day. Figure (7) shows ETc distribution in

study area.

Figure 5. ETo estimated by Hargreaves after calibration with FAO-Penman-Monteith.

Figure 6. Kc extracted from Landsat8 according to equation NO (18)


Figure 7. ETc distribution in study area.

CONCLUSION

ETo extracted form Landsat8 data according to Hargreaves after calibration with FAO-Penman-Monteith. Kc estimated from NDVI. ETo and Kc used to estimate ETc. ETc values varied from 0 to 5 mm/day.

ACKNOWLEDGEMENT

I would like to thank NASA for data availability. This research has been partially funded by the

World Bank, funded project P130801-TF 12960, "Regional coordination on improved water

resources management and capacity building program".

REFERENCES

Aboelghar, M., Arafat, S., Saleh, A., Naeem, S., El-Shirbeny, M. A., Belal, A. (2010). Retrieving leaf area index from SPOT4 satellite data. The Egyptian Journal of Remote Sensing and Space Science, 13(2), 121-127.

Aboelghar, M., Arafat, S., Abo Yousef, M., El-Shirbeny, M. A., Naeem, S., Massoud, A., Saleh, N. (2011). Using SPOT data and leaf area index for rice yield estimation in Egyptian Nile delta. The Egyptian Journal of Remote Sensing and Space Science, 14(2), 81-89.

Allen, R. G.; Jenesen M. E.; Wright J. M.; and Burman R. D., 1989. Operational estimates of reference evapotranspiration, Agronomy journal, 81.

Allen, R, G.; Perrier, L. S.; Raes, D. and Smith, M.., 1998. Crop evapotranspiration: guidelines for computing crop requirements. Irrigation and drainage paper No. 56, FAO, Rome, Italy.

Bois B.; Pieri P.; Van Leeuwen C.; Wald L.; Huard F.; Gaudillere J. P. and Saur E., 2008. Using remotely sensed solar radiation data for reference evapotranspiration estimation at a daily time step, Agricultural and Forest Meteorology, 148: 619-630.

El-Shirbeny, M. A., Aboelghar, M. A., Arafat, S. M., and El-Gindy, A. G. M., 2014a. Assessment of the mutual impact between climate and vegetation cover using NOAA-AVHRR and Landsat data in Egypt. Arabian Journal of Geosciences, 1-10.

El-Shirbeny, M. A., Ali, A. M., and Saleh, N. H., 2014b. Crop Water Requirements in Egypt Using Remote Sensing Techniques. Journal of Agricultural Chemistry and Environment, 3: 57.

El-Shirbeny, M. A., Alia, A. M., Badra, M. A., Bauomy, E. M., 2014c. Assessment of Wheat Crop Coefficient Using Remote Sensing Techniques. World Research Journal of Agricultural Sciences, Vol. 1(2): 12-17.

Hess, A., 1996, A microcomputer scheduling program for supplementary irrigation. Comput. Electron. Agric., 15: 233-243.

Magliulo, V., d’Andria, R., and Rana, G., 2003. Use of the modified atmometer to estimate reference evapotranspiration in Mediterranean environments, Agricultural Water Management, 63: 1-14.

Norman, J. M., Divakarla, M., and Goel, N. S., 1995. Algorithms for extracting information from remote thermal-IR observations of the Earth’s surface, Remote Sens. Environ., 51: 157-168.

Pinheiro, A. C. T.; Mahoney, R.; Privette, J. L and Tucker, C. J., 2006. Development of a daily long term record of NOAA-14 AVHRR land surface temperature over Africa, Remote Sensing of Environment, 103: 153-164

Satti, S. R. and Jacobs, J. M., 2004. A GIS-based model to estimate the regionally distributed drought water demand. Agric. Wat. Manage., 66: 1-13.


Sivakumar, M. V. K.; Roy, P.S.; Harmsen, K. and Saha, S.K., 2004. Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, Proceedings of the Training Workshop 7-11 July, Dehra Dun, India

Sobrino, J. A.; Caselles, V. and Coll, C., 1993. Theoretical split-window algorithms for determining the actual surface temperature. Il Nuovo Cimento, Verona, 16C, 3, 219-236.

Sobrino, J. A.; Gomez, M.; Jimenez-Munoz, J.C. and Olioso, A., 2007. Application of a simple algorithm to estimate daily evapotranspiration from NOAA–AVHRR images for the Iberian Peninsula, Remote Sensing of Environment, 110: 139-148.

Tanton, T. W., and Heaven, S., 1999. Worsening of the aral basin crisis: Can there be a solution. Journal of Water Resources Planning and Management, 125: 363-368.

Valor, E. and Caselles, V., 1996, Mapping land surface emissivity from NDVI: Application to European, African and South American Areas, Remote Sensing of Environment, 57: 167-184.

Yin, Y., Wu, S., Du Zheng, and Yang O., 2008. Radiation calibration of FAO56-Penman–Monteith model to estimate reference crop evapotranspiration in China, agricultural water management, 95: 77-8 4.

Part 5Education, Capacity Building, Policy and Economy

469


REMOTE SENSING EDUCATION AND RESEARCH SITUATION IN NIGERIA: AN OVERVIEW TOWARDS

ENHANCING CAPACITY BUILDING

Raimi Abidemi Asiyanbola, [email protected]

Olabisi Onabanjo University, Nigeria

KEYWORDS: Remote sensing, education, research, capacity building, Nigeria

ABSTRACT

This paper reviews remote sensing education and research in Nigeria. The questions addressed are: (i)

what is the situation of remote sensing education and research in Nigeria? (ii) what are the broad areas

of research and topics that are addressed? (iii) what type of remote sensing and GIS software are used?

(iv) what are the sources of satellite images used? (v) what are the institutional affiliation and

departments of the researchers? (iv) what are the research coverage areas and publication outlets? and

(vii) what are the major findings from the remote sensing images? The data used in the study come from

an extensive published article search covering the period from 2009 to 2013. Descriptive statistics such

as frequencies and percentages were used to analyse the data. It is observed that there have been an

increasing number of researchers and publications on remote sensing in Nigeria. This is an indication of

healthy development and suggests a growing interest in remote sensing in Nigeria. This also suggests a

bright future prospect for remote sensing and GIS business in Nigeria. The study shows that most of the

remote sensing research is on analysis/applications; most of the research topics relate to land use/land

cover; most of applications are on change detection/ landuse and land cover patterns; most used remote

sensing and GIS software is ILWIS followed by ArcGIS 9, ArcView 3, Idrisi, ERDAS Imagine, ArcGIS 10, and

ENVI. Sources of images are diverse, prominent among which are Global Land Cover Facility (GLCF),

Google Earth, National Space Research and Development (NASRDA) Abuja, and National Centre for

Remote Sensing (NCRS) Jos. The study shows that the majority of researchers are from departments of

Geography, Environmental Management, Planning and, Geographic Information System. The majority of

researchers come from university institutions, especially Federal universities. The highest percentage of

research focused on the South West area of Nigeria followed by research on North Central, North West,

South South (Niger Delta), North East and South East areas. The majority of the articles is published in

recent journals, i. e in journals whose first issue is between 2008 and 2013 and which are characterized

by open access and fast publication schedule. Research findings from remote sensing images include

high rate of land use/land cover change, steady decline in vegetation areas, and increasing areal extent

of built-up areas in Nigeria. The study concludes that there is a need to strengthen the research

capabilities of educational institutions and research centres by equipping laboratories and libraries with

state of the art equipment and providing adequate funds for maintenance and staff training, and to

encourage indigenous technology industries and companies on remote sensing and GIS method

development.



INTRODUCTION

Until recently, aerial photo-interpretation was the only remote sensing technique available and

taught in schools in Nigeria. Observation in the literature shows that up till 1986 there was fairly wide

coverage of aerial photography of Africa (Areola, 1986). Despite this fact, aerial photo-interpretation

techniques were not fully integrated into the routine data gathering systems in many countries.

According to Areola, this may be due to a generally held perception that aerial photo-interpretation is a

highly specialized technique meant for the use of only a small group of specialists in the field. Areola also

notes that aerial photo-interpretation is not taught in most schools and colleges. Other reasons for the

gross underutilization of aerial photo-interpretation techniques in Africa include the prevailing high cost

of aerial photographs and the fact that aerial photographs are not made readily available for general use

by the survey departments – the restriction on the circulation of aerial photographs being ostensibly for

security reasons (Areola, 1986:12).

In an attempt at building remote sensing capabilities in Nigeria, the National Agency for Science and

Engineering Infrastructure (NASENI) set up a committee to formulate a National Space Science and

Technology Policy for the country in 1993. The policy recommended, among other things, the creation of

centres of excellence for the development of space science and technology, and the capability

enhancement of institutions offering space related courses of study in the country. In 1999, the National

Space Research and Development Agency (NASRDA) was established to pursue the development and

application of space science and technology for the socio-economic benefits of the nation. In 2001, the

government approved the National Space Policy and Programmes to serve as roadmaps for transforming

Nigeria from the status of a consumer nation to an active participant in space technology and allied

fields. The main thrust of the National Space Policy and Programmes was to make space research and

development activities part of the overall strategies for sustainable national development. The objectives

of the policy include development of human resources and capacity building in various areas of space

science and technology; developing and building competence in space technologies of direct relevance

to national development; developing strategies and space applications; national resource management;

defence, national security and law enforcement; study of the earth environment; communication and

information; education and training; support to universities and other academic institutions in space

related research and development projects; promoting private sector participation in the space industry

and, promoting international cooperation. In order to achieve these, six centres were created to act as

the operational limbs of the National Space Research and Development Agency (NASRDA). These are:

a. Centre for Satellite Technology Development (CSTD)

b. National Centre for Remote Sensing (NCRS)

c. Centre for Space Science and Technology Education (CSSTE)

d. Centre for Space Transport and Propulsion

e. Centre for Geodesy and Geodynamics (CGG)

f. Centre for Basic Space Science (CBSS)

To date, five satellites have been launched by the Nigerian government into outer space. One of

these, however, was a failure. NigeriaSat-1 (first satellite), NigeriaSat-2 (third satellite) and NigeriaSat-X


(fourth satellite) are part of the world-wide Disaster Monitoring Constellation (DMC). Their objectives

are to give early warning signals of environmental disasters; to help detect and control desertification in

the northern part of Nigeria; to assist in demographic planning; to help at establishing relationships

between malaria vectors and the environment that breeds malaria, and to give early warning signals on

future outbreaks of meningitis; to provide the technology needed to bring education to all parts of the

country through distant learning; and to aid in conflict resolution and border disputes by mapping out

state and international borders.

NigComSat-1, the second Nigerian satellite, ordered and built in China in 2004, was Africa’s first

communication satellite. It failed in orbit after running out of power and was replaced by NigComSat-1R

(fifth satellite). Table 1 describes all satellites in more detail.

Table 1. Description of Nigeria satellites

Satellite Description

NigeriaSat-1 A microsatellite for earth observations, the NigeriaSat-1 was the first Nigerian

satellite and built by a United Kingdom based satellite technology company, Surrey

Space Technology Limited (SSTL ltd) for $30 million under the Nigerian government

sponsorship. The satellite was launched by Kosmos-3M rocket from Russian

Plesetsk spaceport on 27th September 2003. NigeriaSat-1 was part of the world-

wide disaster monitoring constellation system. It has 100kg mass and carries an

optical imaging payload developed by SSTL to provide 32m ground resolution with

an exceptionally wide swath width of over 640km. The payload uses green, red and

near infrared bands equivalent to Landsat TM+ bands 2, 3 and 4. Images are stored

in a 1-gigabyte solid-state data recorder and returned via an 8-Mbit/s S-band

downlink. NigeriaSat-1 can image scenes as large as 640 x 560 km, providing

unparalleled wide-area, medium-resolution data. It has a revisit period of 3-5 days.

The design lifespan was 5 years. As at 2009, total Scenes = 3,757 Scenes, data

requests: government – 2,037 requests, private – 1,225 requests.

NigeriaSat-2 NigeriaSat-2, launched in 2011, is the third satellite, with 300 kg mass, was built as

a high resolution earth satellite by SSTL for DMC system. It has 2.5-metre

resolution panchromatic (very high resolution), 5-metre multispectral (high

resolution, NIR red, green and red bands), and 32-metre multispectral (medium

resolution, NIR red, green and red bands) antennas.

NigeriaSat-X NigeriaSat-X, launched in 2011, is the fourth satellite. NigeriaSat-X is based on the

SSTL-100 and is developed by SSTL in association with a team of Nigerian trainee

engineers at SSTL's facilities in the UK under their supervision. The key features of

the NigeriaSat-X include: 22 m next generation imager with improved resolutions

and optics over the same swath areas as DMC+, Swath of 600 km @ 8 bit, High rate

X-band downlink set to 20 Mbit/s, Low rate S-band at 8 Mbit/s, 2 x 2 GByte data

recorders. The NigeriaSat-X sensor provides 22 m multi-spectral (R, G, NIR)

imagery. The data volume is estimated to be a handful of images per day. The on-

board storage is different between NigeriaSat-X and NigeriaSat-2: NigeriaSat-X


uses SSDRs (Solid State Data Recorders), while NigeriaSat-2 uses HSDRs (High

Speed Data Recorders).

NigComSat-1 A Geostationary Satellite, NigComSat-1, built in China in 2004, was Nigeria’ second

satellite and Africa’s first communication satellite. It was launched on 14th May

2007, aboard a Chinese Long March 3B carrier rocket, from the Xichang Satellite

Launch Centre in China. It was over 5 Tons Wet Mass. It was based on the Chinese

DFH-4 satellite bus, and carries a variety of transponders: 4 C-band; 14 Ku-band; 8

Ka-band; and 2 L-band. It was designed to provide coverage of many parts of Africa

and Middle East, and the Ka-band transponders would also cover Italy. It was to

have a lifespan of 15 years. NigComSat-1 failed in orbit after running out of power

due to an anomaly in its solar array. On 10th November 2008 (0900 GMT), the

satellite was reportedly switched off for analysis to avoid a possible collision with

other satellites.

NigComSat-IR NigComSat-IR replaces the lost NigComSat-1 and was launched by China on 19th

December 2011 with no cost to Nigeria.

Source: Mohammed (2010); Wikipedia Encyclopaedia accessed: 21/10/2013; eoPortalDirectory

https://directory.eoportal.org/web/eoportal/satellite-missions/n/nigeriasat-x, accessed

11/12/2013

RESEARCH QUESTIONS, SOURCE OF DATA AND METHOD OF DATA ANALYSIS

The questions addressed in this paper are:

1) What is the situation of remote sensing education and research in Nigeria?

2) What are the broad areas of research and topics that are addressed?

3) What type of remote sensing and GIS software are used?

4) What are the sources of satellite images used?

5) What are the institutional affiliation and departments of the researchers?

6) What are the research coverage areas and publication outlet? and

7) What are the major findings from the remote sensing images?

The study uses data from an extensive search on articles published between 2009 and 2013. The total

number of articles used in the study is seventy-seven (77). Descriptive statistics including frequencies

and percentages, were employed to analyse the data.

FINDINDS

It is observed that there have been an increasing number of researchers and publications on remote

sensing in Nigeria (Table 2). This is an indication of healthy development and suggests a growing interest

in remote sensing as well as a bright future prospect for remote sensing and GIS business in Nigeria.

Table 2. Year of the publication of articles, number of authors and percentage

Year of the publication No (n=77) % Number of Authors (n=166) %

https://directory.eoportal.org/web/eoportal/satellite-missions/n/nigeriasat-x


Year of the publication No (n=77) % Number of Authors (n=166) %

2009 6 7.8 7 4.2

2010 8 10.4 24 14.5

2011 15 19.5 36 21.7

2012 22 28.6 44 26.5

2013 26 33.7 55 33.1

It is found that most of the remote sensing research in Nigeria is on analysis/applications (Table 3),

most research topics are on land use/land cover (Table 4), most of the broad topics applications are on

change detection/ land use and cover patterns (Table 5). The remote sensing and GIS software mostly

used, is ILWIS followed by ArcGIS 9, ArcView 3, Idrisi, ERDAS Imagine, ArcGIS 10, and ENVI (Table 6).

Table 3. Broad areas of research

Broad Areas of research No. (n=77) %

Analysis/Application 77 100.0

Method Development 0 0.0

Observation from the literature shows that African countries have been mainly consuming nations;

there has been low scientific research output from African countries. In East African countries, the

greatest knowledge, skill and productivity gap identified is remote sensing and GIS method development

(Simons, 2013). It is observed from a concept note of a recent workshop on Expanding and Sustaining

Excellence in Doctoral Programmes in Sub-Saharan Africa: What needs to be done? organized by South

Africa’s National Research Foundation and the Carnegie Corporation of New York, held in South Africa,

that while rapidly expanding economies elsewhere had more than doubled their rates of scientific

publication in the past decades, Sub-Saharan Africa contributed only 0.7% to world scientific output, and

this percentage was decreasing and only three countries in Africa – South Africa, Egypt and Nigeria –

produced three quarters of Africa’s output (MacGregor, 2013).

Table 4. Broad topics

Broad Topics No (n=77) %

Land use/land cover 67 87.0

Socioeconomic 4 5.2

Disaster Management 6 7.8

Table 5. Broad topic applications

Broad topics applications No (n=77) %

Change detection/use and cover patterns 33 42.9

Damage assessment/vulnerability 7 9.0

Population 1 1.3


Urban and regional planning 14 18.2

Agriculture 6 7.8

Health 1 1.3

Geology 9 11.7

Climatology 5 6.5

Energy 1 1.3


Table 6. Remote Sensing and GIS Software used by authors

Software used No. (n=82) %

ILWIS 29 35.4

ArcView 3 15 18.3

ArcGIS 9 16 19.5

ArcGIS 10 2 2.4

ERDAS IMAGINE 7 8.5

Idrisi 11 13.4

ENVI 2 2.4

Did not mention the software used (n=77) 15 19.5

Sources of images are diverse, prominent among which are Global Land Cover Facility (GLCF) of the

University of Maryland, USA, Google Earth, National Space Research and Development (NASRDA) Abuja,

and National Centre for Remote Sensing (NCRS) Jos, in that order (Table 7).

Table 7. Sources of satellite images used by the authors

Sources of satellite images used No (n=54) %

United States National Aeronautical and Space Administration (NASA) 3 5.6

National Centre for Remote Sensing, Jos, Nigeria 7 13.0

Global Land Cover Facility (GLCF) of the University of Maryland, Maryland, USA

10 18.5

National Space Research and Development (NARSDA), Abuja, Nigeria 9 16.7

Google Earth imagery 10 18.5

www.digitalglobe.com 2 3.7

Institute of Food Security, Environmental resources and Agricultural Research (IFSERAR), University of Agriculture, Abeokuta (UNAAB)

1 1.9

National Population Commission of Nigeria 1 1.9

Ministry of Agriculture and Rural Development, Federal Department of Forestry (FORMECU), Nigeria

4 7.4

Regional Centre for Training in Aerospace Surveys, (RECTAS) Obafemi Awolowo University, Ile-Ife, Nigeria

2 3.7

Department of Geography, Ahmadu Bello University, Zaria 1 1.9

Department of Geography, Obafemi Awolowo University, Ile-Ife 1 1.9

United States Geological Survey EROS Data Centre 2 3.7

National Emergency Management Agency (NEMA) 1 1.9


The study reveals that the majority of the researchers are from Departments of Geography,

Geography and Environmental Management, Geography and Planning and, Geographic Information

Systems (Table 8); the majority of the researchers are from universities, particularly Federal universities

(Table 9).

Table 8. Departments of researchers

Departments No (n=166) of authors

%

Department of Urban and Regional Planning 15 9.0

Department of Geography/ Geography and Environmental Management/ Geography and Planning/ Department of Geographic Information System

78 47.0

Department of Environmental Management/ Environmental Management & Toxicology

8 4.8

Department of Surveying and Geoinformatics 8 4.8

Department of Geology and Mineral Sciences /Department of Applied Geology/ Earth Science/Geoscience

15 9.0

Department of Physics and Solar Energy/ Pure and Applied Physics 4 2.4

Research Institutes/Agencies/Centres 20 12.1

Others 18 10.8

Table 9. Types of institutional affiliation of authors

Types of Institution No (n=166) of authors %

University 135 81.3

Polytechnic 3 1.8

College of Education 0 0.0

Research Institute/Agencies/Centres 20 12.1

Others 8 4.8

The study reveals that few university institutions in Nigeria are featured out of which most are

Federal universities (Table 10).

Table 10. Type of university institution affiliation of researchers

Type of university institution affiliation of researchers

No. of university affiliation of

% of the total number of such

% of the total number of universities

Total % of the universities in Nigeria


researchers by type

institutions in Nigeria

in Nigeria (n=129)*

(n=129)*

Federal University (n=40)* 18 45.0 14.0 25.6 State University (n=38)* 10 26.3 7.8

Private University (n=51)* 5 9.8 3.9

*The source of these figures is from the website of the National University Commission of Nigeria.

The study shows that the highest percentage of research is on the South West area of Nigeria

followed by research on the North Central, North West, South South (Niger Delta), North East and South

East areas of Nigeria. Appendix 1 shows the list of the articles by research coverage area, year of

publication and the name of the author(s). Findings from the publications used in the study, include a

high rate of land use/land cover change, steady decline in vegetation areas, and an increasing areal

extent of built-up areas (Asiyanbola, 2014) (Table 11).

Table 11. Publications by research coverage area and some of the major findings from the

remote sensing images

Research coverage area

Publication Numbers

% (n=77)

Some of the major findings from the remote sensing images

South South

13 16.9 Findings from the images show a high rate of land use/land cover change; areas of oil spills are increasing; area covered by mangrove forest is declining, and urban land use is increasing in areal extent.

South West

19 24.7 Findings from the images reveal a steady decline in forest area and land use intensification with the expansion in farmlands, fallow ground and built up/residential areas.

South East 5 6.5 Findings from the images show that a high rate of forest and agricultural land has been decreasing, built-up areas have been increasing.

North Central

17 22.1 Some of the major findings from the images revealed that while the built-up area increased, vegetation cover decreased at an alarming rate.

North West

15 19.5 Some of the major findings from the images revealed significant rapid growth in city/residential land use while the vegetation is diminished. Open spaces constituted the most extensive type of land use/land cover. Cultivated land is also observed to be decreasing.

North East 6 7.8 Some of the major findings from the images show increase in built up areas/land areas put under man’s use, and decrease in woodlands - the only natural vegetation cover in the area.

Others 2 2.6


It is further observed that the majority of the articles is published in recent generation journals, i. e. in

journals whose first issue is between 2008 and 2013 and which are characterized by open access and fast

publication schedules (Table 12).


Table 12. Publication numbers, research coverage area and publication outlets

Research coverage area

Publication Numbers

Articles in old generation journals (% per coverage area)

Articles in recent generation journals (% per coverage area)

% of articles in old generation journals (n=77)

% of articles in recent generation journals (n=77)

South South 13 15.4 84.6

24.7

75.3

South West 19 31.6 68.4

South East 5 40.0 60.0

North Central

17 29.4 70.6

North West 15 6.7 93.3

North East 6 33.3 66.7

Others 2 50.0 50.0

POLICY IMPLICATIONS AND CONCLUSION

Findings of this study show that there is a need to encourage and promote indigenous technology

industries and firms, particularly on remote sensing and GIS method development. To achieve this, there

is a need to strengthen the research capabilities of educational institutions and research

centres/agencies in Nigeria. Research laboratories and libraries in various educational institutions should

be equipped with state of the art research equipment, and adequate funds should be provided for the

maintenance of research laboratories and for training and re-training of staff.

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Fashae A. Oluwatoyin, Moshood N. Tijani, Abel O. Talabi and Oluwatola I. Adedeji 2013. Delineation of groundwater potential zones in the crystalline basement terrain of SW-Nigeria: an integrated GIS and remote sensing approach. Appl. Water Sci DOI 10.1007/s13201-013-0127-9

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Appendix 1: List of the articles by area of coverage, year of publication and name of the author(s)

S/N AREA COVERAGE

RESEARCH TITLE YEAR PUBLISHED

AUTHOR(S)

1 Niger Delta Remote sensing and geographic information techniques: veritable tools for land degradation assessment

2012 Abbas I.I. and Fasona M.J.,

2 Niger Delta An assessment of land use/land cover changes in a section of Niger Delta, Nigeria.

2012 Abbas I.I.,

3 Niger Delta Application of remote sensing (RS) and Geographic Information Systems (GIS) to Environmental Impact Assessment (EIA) for sustainable development.

2009 Abbas I.I. and Ukoje J.A.

4 Niger Delta Trends in vegetation cover changes in Bonny area of the Niger Delta.

2013 Akuro A.

5 South South

An assessment of the threats to global warming in Akwa Ibom State, Nigeria.

2013 Ekpenyong Etim Robert

6 Niger Delta An Assessment of Recent Changes in the Niger Delta Coastline Using Satellite Imagery

2010 Jimmy O. A., Mofoluso F., Godstime J. and Ganiyu A., Ologunorisa T.E.

7 Niger Delta Landuse and Landcover Change Analysis Using Satellite Remote Sensing: A case Study of the Upper Niger Delta Region of Rivers State, Nigeria.

2013 Monte J. and Farhan A.

8 South South

Spatial image data mining using K-Means analysis: a case study of Uyo Capital City, Nigeria.

2013 Ndehedehe Christopher, Ogunlade Simeon and Akwaowo Ekpa

9 South South

An integrated remote sensing and GIS approach in monitoring urban expansion in Benin City, Nigeria.

2013 Ogunbodede Emman Funmilayo and Balogun Toju Francis

10 South South

Interpretation of structural features based on Nigeriasat1 imagery of Benin City and its environs.

2012 Okereke C.N., Nwagbara J., Onyekuru S., Okoro R., Chinemelu E.S.

11 South South

Analysis of the rate of change of Mangrove Forest Ecosystem in Calabar South, Nigeria.

2013 Okpilya F.I., Effiong E.B., and Udida A.A.

12 Niger Delta Forest transition in an ecologically important region: patterns and causes for landscape dynamics in the Niger Delta.

2011 Onojeghuo O. Alex and C. Alan Blacburn

13 South South

An assessment of the physical impact of oil spillage using GIS and Remote Sensing technologies: Empirical evidence from Jesse town, Delta State, Nigeria.

2013 Oyinloye M. A. and Olamiju O.I.

14 South West

Disposal sites and transport route selection using geographic information system and remote sensing in Abeokuta, Nigeria.

2012 Achi H.A., Adeofun C.O., Ufoegbune G.C., Gbadebo A.M. and Oyedepo J.A.


S/N AREA COVERAGE


AUTHOR(S)

15 South West

Geospatial analysis of deforestation and land use dynamics in a region of Southwestern Nigeria

2012 Adeoye N.O., Abegunde A.A., and Adeyinka S.

16 South West

Assessment of deforestation, biodiversity loss and the associated factors: case study of Ijesa-Ekiti of Southwestern Nigeria

2011 Adeoye N.O. and Ayeni B.

17 South West

An assessment of the growth of Ile-Ife, Osun State Nigeria, Using Multi-Temporal Imageries.

2013 Ajala O.A. and Olayiwola A.M.

18 South West

Fracture distribution within Bowen University permanent site and its hydrogeologic implication.

2013 Alagbe O.A., Sunmonu L.A., and Adabanija M.A.

19 South West

Tectono-Hydrological Study of Akure Metropolis, Southwestern Nigeria.

2012 Anifowose A.Y.B. and Kolawole F.

20 South West

Waste disposal site selection using remote sensing and GIS: A study of Akure and its environs, Southwest-Nigeria.

2011 Anifowose Y.B., Omole K.E. and Akingbade O.

21 South West

Spatial analysis of soil fertility estimates and NDVI in south-western Nigeria: a new paradigm for routine soil fertility mapping.

2013 Fabiyi O.O., Ige-Olumide O. and Fabiyi A.O.

22 South West

Spatial and social dimensions of post conflict urban reconstruction programme in south-western Nigeria: The case of Ile-Ife, Nigeria.

2012 Fabiyi O., Thontteh O.E., and Borisade P.

23 South West

Delineation of groundwater potential zones in the crystalline basement terrain of SW-Nigeria: an integrated GIS and remote sensing approach.

2013 Fashae A. Oluwatoyin, Moshood N. Tijani, Abel O. Talabi and Oluwatola I. Adedeji

24 South West

Impact of climate change on sea level rise in Lagos, Nigeria.

2011 Fashae Adeola Olutoyin and Olumide David Onafeso

25 South West

A Study of Land Degradation Pattern in the Mahin Mud-beach Coast of Southwest Nigeria with Spatial-statistical Modelling Geostatistics.

2011 Mayowa F., Ademola O. & Alabi S.

26 South West

Developing natural resources database with Nigeriasat-1 satellite data and geographical information systems.

2012 Ogunbadewa Yemi Ebenezer

27 South West

Resultant land use and land cover change from oil spillage using remote sensing and GIS.

2013 Omodanisi E.O.

28 South West

Urban expansion and urban land use in Ado Ekiti, Nigeria.

2013 Oriye Olusegun

29 South Geospatial analysis of urban growth – The 2013 Oyinloye M.A.


S/N AREA COVERAGE


AUTHOR(S)

West case of Akure, Nigeria.

30 South West

Analysis of Landuse, Landcover change and urban expansion in Akure, Nigeria.

2011 Oyinloye M.A. and Kufoniyi O.

31 South West

Status of flood vulnerability area in an ungauged basin, southwest Nigeria.

2013 Sobowale A. and Oyedepo J.A.

32 South West

The application of remote sensing and geographic (GIS) information system for monitoring deforestation in Southwestern Nigeria.

2012 Yohanna P., Innocent R., and Emmanuel B.

33 South East Application of Geographic Information System in Mapping of Groundwater Quality for Michael Okpara University of Agriculture Umudike and its Environs, Southeastern Nigeria.

2012 Akaninyene O.A. and Magnus U.I.

34 South East The role of satellite remote sensing data and GIS in population census and management in Nigeria: A case study of an enumeration areas in Enugu, Nigeria.

2009 Chijioke G.E.

35 South East Changing Pattern of Land Use in the Calabar River Catchment, Southeastern Nigeria.

2011 Efiong J.

36 South East Detection and Mapping of Land Use and Land Cover Classes of a Developing City in Southeastern Region of Nigeria, using Multi-band Digital Remotely-sensed Data.

2010 Njoku J.D., Ebe T.E., and Pat-Mbano E.C.

37 South East Detecting changes in landuse/cover of Umuahia, South-Eastern Nigeria using remote sensing and GIS technique.

2011 Ujoh F., Ifatimehin O.O., and Baba A.N.

38 North Central

Characterization of Structural Composition and Diversity of Vegetation in the Kpashimi Forest Reserve, Niger State, Nigeria.

2013 Abdullahi J. and Idris A. J.

39 North Central

Monitoring urban sprawl in the Federal Capital Territory of Nigeria using remote sensing and GIS techniques

2013 Ade M.A. and Afolabi Y.D.

40 North Central

Spatio-Temporal Analysis of Land Use/Cover Change of Lokoja – A Confluence Town.

2012 Adeoye N.O.

41 North Central

Geological investigation of Tagwai Dams using remote sensing technique, Minna Niger State, Nigeria.

2012 Akinrinmade A.O., Ibrahim K.O. and Abdurrahman A.

42 North Central

Urban sprawl, pattern and measurement in Lokoja, Nigeria

2009 Alabi M.O.

43 North Central

Spatial Growth Assessment with Remote Sensing Data for Central Nigeria.

2011 Alaci, D.S.A., Amujabi F.A., Baba A.N. and Daniel O.


S/N AREA COVERAGE


AUTHOR(S)

44 North Central

Remote sensing and GIS assessment of flood vulnerability of Nigeria’s confluence town.

2012 Dukiya J.J.

45 North Central

GIS Analysis of Peri-Urban Agricultural Land Encroachment in (FCT), Nigeria.

2013 Etim N.E., and Dukiya J.J.

46 North Central

An analysis of temperature variations using remote sensing approach in Lokoja Area, Nigeria.

2010 Ifatimehin O.O., Ishaya S., and Fanan U.

47 North Central

Application of Remote Sensing and GIS Techniques in Mapping Fadama Farming Areas in a part of Abuja, Nigeria.

2009 Ishaya S. and Ifatimehin O.O.

48 North Central

Using Remote Sensing Data to Improve Rice Production in Kutigi, Niger State, Nigeria.

2012 Jibril M. and Yunusa M.B.

49 North Central

Geospatial techniques for the assessment and analysis of flood risk along the Niger-Benue basin in Nigeria.

2013 Nkeki Felix Ndidi, Philip John Henah and Vincent Nduka Ojeh

50 North Central

Agricultural land use in sub-urban Lafia of Nasarawa State, Nigeria.

2013 Nuhu Zainab and Ahmed M.

51 North Central

Geospatial application in mapping gully erosion sites in Jos, Plateau State, Nigeria.

2013 Ogwuche Jonathan and Bulus Joshua

52 North Central

Structural analysis the ropp complex, North Central Nigeria, using magnetic anomaly and Landsat ETM imagery.

2012 Olasehinde A., Ashano E.C., and Singh G.P.

53 North Central

Applications of geographic information system and remote sensing in road monitoring in Minna and environs, Nigeria.

2013 Onuigbo I.C. and Orisakwe K.U.

54 North Central

Urban Expansion and Vegetal Cover Loss in and around Nigeria’s Federal Capital City.

2011 Ujoh F.1, Kwabe I. D. and Ifatimehin O.O.

55 North West

Application of remote sensing and geographic information system in parcel mapping for irrigation farm scheme in Pampaida millennium village, Ikara, Kaduna State, Nigeria.

2012 Abbas I.I. and Anger R.T.,

56 North West

Green area mapping of Ahmadu Bello University main Campus, Zaria, Nigeria using remote sensing (RS) and geographic information system (GIS) techniques.

2012 Abbas I.I. and Arigbede Y.A.,

57 North West

Sustainable land management in Zaria using remote sensing and GIS techniques.

2011 Abbas I.I. and Arigbede Y.A.,

58 North West

Mapping land use-land cover ad change detection in Kafur Local Government, Kastina, Nigeria (1995-2008) using remote sensing and GIS.

2010 Abbas I.I., Muazu K.M. and Ukoje J.A.,

59 North Analysis of land use/land cover changes to 2012 Abubakar Mahmud and


S/N AREA COVERAGE


AUTHOR(S)

West monitor urban sprawl in Keffi-Nigeria Anjide Simon Achide

60 North West

Assessment of Revised Universal Soil Loss Equation (RUSLE) in Katsina Area, Katsina State of Nigeria using Remote Sensing (RS) and Geographic Information System (GIS).

2010 Adediji A., Tukur A.M., and Adepoju K.A.

61 North West

Mapping the spatial distribution of Rabies in Kaduna State, Nigeria (1999-2009) using geographic information system technology.

2012 Asabe A.D. and Abbas I.I.

62 North West

The impact of spatial distribution of solid waste dumps on infrastructure in Samaru, Zaria, Kaduna State, Nigeria using geographic information systems (GIS).

2011 Benedine A., Robert T.A. and Abbas I.I.

63 North West

A remote sensing and geographic information systems approach to estimating electric power consumption: a case study of Sokoto metropolis, Sokoto State, Nigeria.

2012 Eniolorunda N.B., Dankani M. and Yusuf N.

64 North West

Application of remote sensing (RS) and geographic information systems (GIS) in flood vulnerability mapping: case study of River Kaduna.

2013 Ismail Muhammed and Iyortim Opeluwa Saanyol

65 North West

Space technology application for disaster management

2012 Ohamobi S.I.

66 North West

Determination of transport route network in Sokoto Area, Nigeria, using remote sensing and GIS.

2012 Ohamobi S.I.

67 North West

Change Detection Analysis of Landuses in Hadejia township of Jigawa State of Nigeria.

2013 Orisakwe K.U.

68 North West

Characterization of gold mineralization in Garin Hawal area, Kebbi State, NW Nigeria, using remote sensing.

2010 Ramadan M. Talaat, and Abedel Fattah F. Mohammed

69 North West

An assessment of the changing climate in Northern Nigeria using Cokriging.

2013 Usman U., Yewa S.A., Gulumbe S.U. and Danbaba A.

70 North East Assessment of land cover change in the North Eastern Nigeria 1986 to 2005.

2013 Garba Samuel and Tim Brewer

71 North East Integrated remote sensing approach to desertification monitoring in the crop-rangeland area of Yobe State, Nigeria.

2010 Haruna D.M. and Bukar S.

72 North East Assessment of human impacts on landuse and vegetation cover changes in Mubi region, Adamawa State, Nigeria; remote sensing and GIS approach.

2009 Ikusemoran M.

73 North East The use of remote sensing and GIS technique for appraisal of spatial growth in

2011 Nuhu H.T., Yohanna P. and Geoffrey N.


S/N AREA COVERAGE


AUTHOR(S)

Mubi metropolis, Adamawa State, Nigeria.

74 North East Application of remote sensing and geographic information system (GIS) in revising township map: a case study of Mubi metropolis, Adamawa State, Nigeria.

2011 Yohanna P. and Nuhu H.T

75 North East Evaluation of The Impact Of Urban Expansion On Surface Temperature Variations Using Remote Sensing-Gis Approach.

2010 Zemba A.A., Adebayo A.A. and Musa A.A.

76 The whole country

An overview of land cover and changes in Nigeria, 1975 – 2005

2009 Abbas I.I.

77 NNE-SSW Remote sensing analysis of a dextral discontinuity along Ifewara-Zungeru area, Nigeria, West Africa.

2011 Kolawole F., and Anifowose A.Y.B.


CHALLENGES IN CAPACITY BUILDING AND EDUCATION IN GEOSPATIAL TECHNOLOGY IN AFRICA

Mahamadou S. KEITA Deputy Executive Director

Regional Centre for Training in Aerospace Surveys (RECTAS) Obafemi Awolowo University Campus

PMB 5545, Ile-Ife, Nigeria E-mail: [email protected], [email protected]

KEY WORDS: Capacity building, Education, Geospatial technology

ABSTRACT

The effectiveness of geospatial technology as a major decision making tool depends on the

availability of skilled manpower with high technical expertise in applying it. Currently, most African

nations have a high deficit of skilled man power in the geospatial technology industry. However,

the few training and capacity building institutions available nationally as well as regionally have

been failing for many years to produce adequate personnel in the geospatial technology

applications sector. Hence, there is an urgent need to develop a strategy of strengthening the

existing geospatial technology capacity development and education institutions. This paper; i)

presents the status of existing capacity development institutions in Geospatial Technology in

Africa, ii) it evaluates the main challenges faced by capacity development institutions in improving

the acquisition of knowledge and skills in the application geospatial technologies. Moreover, it

proposes a framework of developing methods for rapidly improving geospatial technology

applications for sustainable development in Africa.

RÉSUMÉ

L'efficacité de la technologie géospatiale comme un outil majeur de prise de décision dépend de

la disponibilité de la main-d'œuvre hautement qualifiée qui a connaissance de la technologie et

peuvent en faire usage afin de conseiller les décideurs de manière appropriée. Malheureusement,

en Afrique, depuis de nombreuses années, les quelques institutions de formation et de

renforcement des capacités disponibles aux niveaux national et régional ne sont pas à mesure de

relever les défis de l'acquisition de compétences en technologie géospatiale. A l'heure actuelle, la

plupart des pays africains ne peuvent pas satisfaire la masse critique nécessaire par l'éducation

dans les pays développés. Par conséquent, le besoin urgent d'élaborer une stratégie de

renforcement des institutions d'éducation et de développement des capacités existantes dans la

technologie géospatiale s’impose. Cet article présente l'état des institutions de formation en

technologie géospatiale existantes en Afrique. La deuxième partie énumère les principaux défis et

propose un cadre pour une amélioration rapide des institutions de renforcement de capacités, des

méthodes d’acquisition de compétences et de connaissances et des applications de la technologie

géospatiale pour le développement durable de l’Afrique.

MOTS CLÉS: Renforcement de capacités, Education, Technologie géospatiale.




INTRODUCTION

Geospatial Technologies have grown to become the major pivot for national development. The

United States of America has classified geospatial technologies as one of the fourteen (14) growth

industries of the future (Offor-Amoah - 2004). Africa stands to benefit from geospatial

technologies especially in confronting its numerous challenges.

However, the effectiveness of geospatial technologies especially as a major decision-making tool

depends on highly skilled human resources with theory and practical expertise in those

technologies so as to advise decision makers on appropriate applications for solving problems.

For many years, some African countries depended on the developed countries (Europe and US

essentially) for manpower development in the area of geospatial technology. Since most of the

countries could not afford to send their staff abroad for training, the capacity to use geospatial

technologies remained weak for a long period. In addition to the few experts trained in the

European and American institutions, the efforts of building capacity in the area of geospatial

technologies was limited to specific projects. The contribution of universities and training

institutions ineffective due to the lack of adequate infrastructure, equipment and well trained

lecturers.

Evaluative reports on the state of geospatial technologies in Africa indicate that in spite of the

growing number of institutions that were using geospatial technologies on the continent, the

human and organisational capacity are still a necessity if an effective utilisation of these

technologies is to be achieved.

In view of the challenges facing manpower development, there is urgent need to strengthen and

coordinate the activities of existing higher education and training institutions focusing geospatial

technology.

This has been generally difficult to achieve in developing countries due to a lot of constraints some

of which have been addressed by different authors in different forums.

In order to tickle the afore-mentioned geospatial technologies requisite, the following three (3)

platform levels described in Japan International Cooperation Agency report (JICA 2004) are being

proposed.

(i) Individual or human capacity referring to the will and ability of an individual to set

objectives and to achieve them using one’s knowledge and skills;

(ii) Organisational capacity referring to anything that will influence an organisation’s

performance;

(iii) Environment or Societal capacity referring to the environment and conditions necessary

for demonstrating capacity at the individual and organisational levels.

This paper will highlight the role of Geospatial Technology in developing Africa, the capacity building

efforts, the main challenges and the need for a suitable capacity development framework. It calls for

a very high level commitment of universities, training institutions and other entities for full

incorporation of geospatial technology as part of the university curriculum and a key tool in all the

decision making processes.


THE ROLE OF GEOSPATIAL TECHNOLOGY IN AFRICA DEVELOPMENT

Over the past decades, geospatial technologies, as a set of tools and methods used in acquiring,

processing, analysing, integrating and sharing geospatial data, have gained increasing importance in

both the public and private sectors. These technologies were used in different areas of endeavour

including land administration, public and private facilities management, planning and management of

transportation systems, management of environmental problems, promotion and protection of

human health.

The importance of Geospatial technologies in social and economic development is based on the ability

to help in making decisions. The decisions are about allocating and utilising limited resources to meet

greater needs that tend to be geographically distributed (Offor-Amoah, 2004). Most decisions are

about “who gets what” , “where” and it is in this regard that geospatial technology becomes crucial.

The role of geospatial technologies in economic development also lies in the fact that improvements

that result from the economic development process must not only be structural but also spatial. Thus,

transformations in income, unemployment, and social structures of the country must be accompanied

by spatial changes including location of economic activities, distribution of population, and

decentralisation of government and private services (Gilbert 1974). This transformation requires

making appropriate spatial decisions, which cannot be made without spatial data and spatial tools.

In Africa, geospatial technologies have been identified as a critical risk analysis, characterisation, and

evaluation tool. They are currently being recognised and utilised in most developed and developing

countries. Different scientists, in collaboration with geospatial technologies experts, have paid

significant attention to the utilisation of these technologies in the management of natural resources

and disasters, peace and security, oil and gas as well as other areas. In many situations, geospatial data

has been used to help government and business entities meet the challenges during emergencies. Fire,

police, public works, building and safety, water, engineering, utilities, telecommunications, and many

other disciplines have long recognised the utility of Geospatial technology in emergency response. The

government and other sectors have also discovered how some previously challenging tasks can be

performed more efficiently and how some tasks which previously seemed to impossible or impractical

to do can now be easily addressed.

A few of the benefits of using geospatial technology include, fast response capabilities, timely, fast

access to information, integrated of numerous layers of data, strong analytic capabilities, and powerful

visualisation and dissemination.

GEOSPATIAL TECHNOLOGY CAPACITY BUILDING IN AFRICA

Geospatial technologies are more than computer hardware, software and data. The effectiveness of

geospatial technologies as data and information, and decision making tools depends on the availability

of human resources with a theoretical and technical-know how in the application of those

technologies. These are immensely resourceful in an informed decision-making processes.

In most African countries, the geospatial capacity building and educational system follows the three

known components that is human resource development, organisational strengthening and

institutional strengthening). In addition to the national institutions, the role of capacity development


in geospatial technology is mainly being played by interregional and intergovernmental organisations,

as well as international organisations.

The national capacity building and education in geospatial technology involves universities,

polytechnics, surveys schools, space agencies and national remote sensing centres. If geospatial

technologies have been integrated into the curriculum of learning institutions, the implementation

these technologies will be inevitable. There is often a challenge of lack of resources and infrastructure

such as equipped laboratories as well as experts and specialised lecturers. Another predominant

capacity building effort in the application of geospatial technology in Africa for a long time, are project-

based trainings. In this case, the sponsor/funding agency selects a limited number of staff from a

certain institution to undergo a short term course in USA or Europe. There is absolutely no transfer of

technology.

Regional and interregional organisations are also involved in geospatial technology capacity building

in Africa. These are usually United Nations and United Nations Economic Commission for Africa

(UNECA) institutions and networks. Other institutions that have played a significant role in capacities

development in Africa. These include the Regional Center for Mapping Resources for Development

(RCMRD), the Regional Centre for Training in Aerospace Surveys (RECTAS), the Southern and Eastern

Africa Mineral Centre (SEAMIC), the African Centre for Meteorological Applications and Development

(ACMAD), the African Regional Centre for Space Science and Technology in French Language (CRASTE-

LF) and the African Regional Centre for Space Science and Technology Education in English (ARCSTE-

E). In addition to those institutions, we have also the Agrhymet Regional Centre (CRA). Most of those

institutions were involved in training and research in geoinformatics and its applications. Some of

them stopped their activities due to lack of resources, like the Regional Centre for Remote Sensing of

Ouagadougou (CRTO).

The following table (Table 1.) shows some of the institutions in Africa where geospatial technologies

capacities could be acquired.

Table 1. Geospatial technology training and capacity building institutions in Africa

S/N Institution Capacity development programs and activities

1. Regional Centre for Training in Aerospace Surveys (RECTAS) - Ile-Ife, NIGERIA

Short courses, Professional Masters, MSc, PhD, Research, Seminars and Workshops, Consultancy and Advisory Services

2. Regional Centre for Mapping Resources for Development (RCMRD) - Nairobi, KENYA

Short courses, Project implementation, Maintenance of equipment, Consultancy and Advisory services

3. Southern and Eastern Africa Mineral Centre (SEAMIC)

Short Term Courses

4. African Centre for Meteorogical Applications and Development (ACMAD) - Niamey, NIGER

Short Term Courses and Workshops

5. Agrhymet Regional Centre - “Centre Regional Agrhymet” - CRA - Niamey, NIGER

Short courses, Professional Masters, MSc, Research, Workshops, Consultancy and Advisory Services


6. African Regional Centre for Space Science and Technology in French Language - “Centre Regional Africain des Sciences et Technologies de l’Espace” (CRASTE-LF) - Rabat, MOROCCO

Professional Masters, Masters, Short courses, Seminars and Workshops

7. African Regional Centre for Space Science and Technology Education in English (ARCSTE-E) - Ile-Ife, NIGERIA

Professional Masters, MSc, Seminars and Workshops

8. National Remote Sensing Centres Certificate courses, Workshop programmes and Advisory Services

9. Space Agencies & Centres (ALGERIA, MOROCCO, NIGERIA, SOUTH AFRICA )

Certificate courses and Workshop programmes

10. National/Federal Surveys Schools (African countries)

Certificate courses, Technologist and Postgraduate training programmes

International agencies have so far been the major contributors in geospatial technologies capacity

building in Africa. Many African professionals were trained with the sponsorship of GIS-based projects

or through collaborative projects. Some of those agencies are the former “Groupement pour la

Teledetection Aerospatiale” (GTDA) in Toulouse - France, the International Institute for

Geoinformation Science and Earth Observation ITC) in Enschede - Netherlands, the International

Development Research Center (IDRC) in Canada through the Canadian International Development

Agency (CIDA), the Geographical Surveys Institute (GSI) in Tsukuba - Japan, through the Japan

International Cooperation Agency (JICA), United States. Other contributors in capacity building of

geospatial technology in Africa include the private sector/organisations and professional associations

such as AARSE, ISPRS, FIG, ICA, IAG, etc. through professional exchanges during periodical meetings,

conferences and workshops.

CHALLENGES FACED BY AFRICA IN RAPID DEVELOPING HUMAN RESOURCES FOR GEO-

SPATIAL TECHNOLOGY APPLICATIONS

Slow rate of manpower development in geospatial technologies in Africa has been attributed to many

factors (Ruther, 2001). These include:

- low enrolment in geospatial technology education in African institutions of learning;

- few institutions run geospatial technology course;

- absence of uniform academic standards, obsolete curriculum and limited modern equipment;

- rigid curriculum that does not give room for intake of serving personnel to undergo training;

- lack of coordination on applied research and development;

- lack of financial resources for overseas training. Many organisations in African countries

cannot afford to send their members of staff for training abroad due to financial constraints,

especially considering the number of people to be trained

To overcome these constraints, it is necessary to redesign the courses curriculum and encourage

networking of educational institutions.

THE NEED FOR A GEOSPATIAL CAPACITY DEVELOPMENT FRAMEWORK


Geospatial technologies, as fast developing and new techniques, are emerging very frequently and

rapidly often beyond the capacity of many African countries. It is on record that African countries have

been introduced to geospatial technologies for three decades, yet most of these countries are still at

the level of elementary and peripheral stages of geospatial applications compared to other countries

in Asia, Europe and US which are at a highly technical and integrated level in solving problems using

geospatial technologies.

Today, in Africa, many projects that are using geospatial technologies still have inputs from Europe and

USA. When foreign consultants are engaged in Geospatial project implementation, they often leave

technologies and systems without capacities from local experts. In addition, the capacity building

programmes which are project-based usually depend on the availability of grants from foreign

institutions. These usually lack sustainability after the grants for the projects have ceased.

The cost of training experts in geospatial technologies is still very high and most African countries lack

resources to send their staff members for training in institutions that offer better geospatial technical

expertise and techniques.

Considering the above mentioned factors as well as the critical need of geospatial technologies in

addressing Africa’s challenges, there is urgent need for; strengthening the existing capacity building

institutions, to organise continuous education and refresher programmes, to incorporate geospatial

technology into African universities’ curriculum and building infrastructure to offer robust capacity

development programmes.

PROSPECTS FOR GEOSPATIAL TECHNONOGIES CAPACITY BUILDING IN AFRICA

In addition to the numerous challenges, the increasing interest of African countries in Space Science

and Technology development and geospatial data infrastructure development have brought forth the

need for pro-active capacity development efforts. However, the number of capacity development

potentials available, such as institutions and organisations currently engaged in training and research

activities, illustrates the efforts exerted into mass capacity development in geospatial technologies in

Africa.

Recent studies have indicated that the few geospatial technology experts in Africa were trained in

other contents. Though the capacity development opportunities in Africa are still limited, efforts are

underway to strengthen and reposition and integrate the geospatial technology community in Africa

for better performance and effectiveness in conducting daily activities. One example is the action taken

by the Regional Centre for Training in Aerospace Surveys (RECTAS) whose activities include training

and researching in geoinformatics, conducting seminars and short courses, consulting and advising.

RECTAS has embarked on the improvement of its capacity development programmes. The table below

(Table-2) shows the available programmes.

Table 2. RECTAS’ capacity development programmes

S/N Programme / Degree Duration Availability

1. Technologist Diploma in Geoinformation production and Management

4 Semesters English/French

2. Professional Master in Geoinformation production and Management

12 months English/French

3. Professional Master in Cartography and Geospatial Techniques

12 months English/French


4. Professional Master in Geoinformation and Environmental Management

12 months English

5. M.Tech - Master in Geoinformation Technology (offered in collaboration with the Federal University of Technology Akure, Nigeria)

4 Semesters English

6. MSc - Master in Geoinformation Science (offered in collaboration with the University of Abomey-Calavi, Cotonou, Benin Republic

4 Semesters French

7. MSc - Master in Cartography and Geospatial Science

4 Semesters English/French

8. PhD in Geoinformation Science / Cartography / Photogrammetry and Remote Sensing

3-5 Years English/French

THE WAY FORWARD

The framework for African capacity development in Geospatial Technologies can be achieved through

a designed Strategic Alliance among African countries which can be initiated by the United Nations

Economic Commission for Africa (UNECA). The proposed framework (Strategic Alliance for African

Capacity Development in Geospatial Technologies - SACDGT) is highly authoritative in ensuring

adequate capacity development in Geospatial technologies in Africa. It is necessary to harness and

strengthen available national and regional resources in Africa to develop capacities in Geospatial

technologies and advanced applications of African countries.

UNECA is pivotal in the proposed framework to help member countries in formulating policies in

Geospatial technologies, emphasising on its appropriate integration in the society. The proposed

framework for capacity development is presented in four “I”s (4i s) as follows.

Intuiting - This is the process of developing new insights and ideas about procedures in the

implementation of Geospatial technologies and strategies. Africans must generate new ideas,

approaches and add on technologies that are indigenous rather than focusing on foreign ideas,

technologies and approaches to knowledge creation and knowledge sharing. Capacity development

institutions in African should upgraded from knowledge dissemination only to knowledge creation.

Interpreting - The knowledge created locally must be well interpreted in the context of the peculiar

challenges of each African country and must be disseminated through a platform that every member

country can buy in and practically implement.

Integrating - Integrating refers to steps taken to unite nations and share knowledge as well as expertise

among groups which allows for coherent and collective action the development of Africa. The

education, research community, the industry and the public sectors must be linked together in the

knowledge creation and tacit transfer in Africa before any meaningful development can be realised in

the region not only in the Geospatial technologies but in all other scientific disciplines for sustainable

development.

Institutionalising - relates to implementation of the actions through development of regional

geospatial policies, rules and procedures that will fast track the development of human capacities in

geospatial technologies in Africa. These include the implementation of robust all-encompassing


Geospatial policies in African countries and empowering practitioners already in the industries

applying Geospatial technologies.

CONCLUSION - RECOMMENDATION

To achieve the rapid development of African nations through the efficient use of geospatial

technology, it is essential to make available the critical mass of skilled manpower required. This

involves the development of a strategy of strengthening the capacity building institutions, improving

the curriculum of universities and making available the necessary equipment and enabling

infrastructure. In addition to individual capacity development in Geospatial technology, the capacity

at organisational and institutional levels should be addressed for if rapid capacity development of skills,

the creation of knowledge focusing on peculiar applications in African situations and environment has

to prevail. This is the best way to nature and strengthen a technological revolution of Africa.

It is recommended that African nations have to recognise the role and importance of geospatial

technologies in decision-making processes, in their development projects as well as good governance.

Therefore, priority should be given to the allocation of reasonable budgets for the improvement and

strengthening of existing capacity building institutions in Africa. It is proposed that UNECA be

responsible for the sensitisation of governments in the region.

REFERENCES

Kufoniyi O., (1999) - Education requirements in Geospatial Information Technology. In. Proc.

Workshop on Surveying and Spatial Information Technology. University of Lagos, Nigeria. 13p.

UNECA, (2000) - The future Orientation of Geoinformation Activities in Africa. Committee on

Development Information (Geo-Information Subcommittee). United Nations Economic Commission

for Africa (UNECA), Addis Ababa. 37p.

Bereens, S. J. J. (2002) - Capacity Building for Geospatial Information Handling in Africa - The ITC

perspective. Presentation to Committee on the Geographic Foundation for Agenda 21.

Ofori-Amoah, B. (2004) - “Developing and Implementing a Geographic Information System Training

Program for Local Government in Uganda” - Report of the Planning Meeting Organised by the Ministry

of Local Government, Kampala, Uganda and Makerere University Economic Policy Research Center

(EPRC), Kampala, Uganda, Sponsored by The Rockefeller Foundation, Nairobi - Kenya.

Taylor, D. R. F. (2004) - “Capacity Building and Geographic Information Technologies in African

Development” In S. D. Brunn, S. L. Cutter, and J. W. Harrington (Eds) Geography and Technology

Dordrecht Kluwer Academic Publishers. Pp 491-519

Kufoniyi O., Gerrit H. & John Horn - (2005) - Human and Institutional Capacity Building in

Geoinformatics Through Educational Networking.


APPLICATION OF GEOGRAPHIC INFORMATION SYSTEMS (GIS) AND REMOTE SENSING (RS) IN MONITORING AND EVALUATION: A CASE STUDY OF NATIONAL PLANNING COMMISSION (NPC),

ABUJA, FCT, NIGERIA

Ademola Adeyemi1, Saleem Falowo1, Olabanji Kolade1

1. GIS/MIS/QA Division, Monitoring and Evaluation Department, National Planning

Commission, Abuja, FCT, Nigeria E-mail: [email protected]

Abstract The most important indicator of good governance and government socio-economic

development policies and programmes is the measure of direct translation of the former to the lives of the citizens. To this end, there are various machineries (strategies) put in place by the Federal Government of Nigeria in ensuring good governance such as the Transformation Agenda (TA), the Nigeria Vision (NV) 20:2020, the National Implementation Plans (NIP) and the National Integrated Infrastructure Master Plan (NIIMP). The implementation of these strategies depends largely on the social micro and macro-economic statistical data gathering and processing through various government agencies such as the National Planning Commission (NPC), National Population Commission (NPopC) and the National Bureau of Statistics (NBS).

This article describes the structure of the already deployed Geographic Information Systems (GIS) and Remote Sensing (RS) Solution for monitoring and evaluation of federal government policies, programmes and projects in Nigeria. NPC GIS-Based programmes and projects monitoring and evaluation solutions, as well as the NPC National Integrated Infrastructure master plan database solutions are being discussed. NPC GIS-Based support for macro-economic index analysis towards NV 20:2020 are also highlighted.

Finally, the article stressed briefly the organisational structure challenges and proposed pragmatic solutions towards achieving a successful monitoring and evaluation of projects, programmes and policies through the application of GIS and RS Technologies.

Keywords: Monitoring and Evaluation, Nigeria Vision 20:2020, Transformation Agenda, National Integrated Infrastructure Master Plan, Geographic Information Systems, Remote Sensing, National Planning Commission

1.0 INTRODUCTION One of the most important areas of social and economic development and good governance

is the dated capturing and analysis of data most especially on indicators such as employment, infant and maternal mortality rates, life expectancy and gross domestic products. These overall indicators determine people’s standard of living. Generally, researchers, statisticians and economists have used several computer packages with inbuilt statistical analysis models for analysing socio-economic data in a tabular form for decades, but lack the ability to link the figures to geographic locations whereby more intelligent questions can be raised and answered; questions such as where, how, extent and trend could be answered. In this regard, Geographic Information Systems (GIS) and Remote Sensing (RS) have come to play a vital role in solving the problems faced by other professionals in terms of location analysis for monitoring and evaluation of policies, programmes and projects.

GIS is the latest system concerned with linking survey figures with geographic phenomena. According to ITC (2004), GIS is considered as a computerised integration of hardware, software, data and procedure used in the acquisition, management, transformation and analysis of geographically


referenced data. RS, on the other hand, is defined as the capturing of geographic data from a remote distance through the use of instruments such as the aerial camera mounted on aircraft or space satellite in recording objects’ electromagnetic radiation properties thereby enabling us to access remote data that could have ordinarily been difficult to reach. Also made available for use by remote sensing are the sub-surface mineral resources, our oceans and invisible data such as noise and shadow direction, heat levels and chlorophyll levels in green plants (Sivakumar, 2003). GIS and RS have served as a good support to statistical data analysis and have become an invaluable tool in data analysis and statistics in a geographic medium that is clear to understand and enables data and policy analysts to identify patterns and trends that would otherwise be difficult to determine if presented only in raw figure/data sets, or in standard graphs and tables (Goodchild, 1990).

Using a country’s geography to illustrate differences in trends in key micro and macro social, economic, political, geographic and environmental statistics, GIS and RS solutions have successfully helped to present clearly, topics such as education attainment across a country, access to sanitation and its management, income distribution, access to roads, schools, hospitals and distances from main centres of commerce. In addition, it has even highlighted potential geographic and other constraints that might hamper development efforts. GIS and RS solutions have been instrumental in highlighting areas and issues that have either been inadequately addressed in some areas of a country, or highlight those areas which have achieved the most successes. These types of information have, in turn, enabled both policy analysts and decision-makers to make informed decisions on where and how to better concentrate development efforts, and/or to identify those success stories that could be replicated in other areas. Overall, GIS and RS solutions have become a critical tool utilized by many developing countries to improve and better target development efforts. In Nigeria, the Federal Government has developed plans and policies for the achievement of its promise of good governance and economic development to the citizens. It does this through the establishment of ministries, departments and agencies for the implementation of policies, programmes and projects. Thus the National Planning Commission (NPC) was established by Act No. 12 of 1992 and later amended by Act 71 of 1993, with the mandate to determine and advise the President of the Federation on matters relating to national development and the overall management of the economy. Hence, NPC has been on top of its activities in ensuring the success of the Federal Government strategies for sustainable socio-economic development such as the Transformation Agenda (TA), the NV20:2020, the 1st National Implementation Plans (1st NIP) and the National Integrated Infrastructure Master Plan (NIIMP).

The research methodology employed in this article is purely descriptive, hence it deviates a little bit from the laid down procedures as approved by NPC for any article to be presented and for subsequent publication. The basic reason is that, the questionnaires to be administered for this study has to pass through many protocols before they could be responded to adequately for analysis and synthesis for this topic. In view of this handicap, the main thrust of this paper therefore is to highlight areas where GIS and RS are and can distinctly be beneficial for socio-economic development with regards to the National Planning Commission of Nigeria efforts in applying the technologies in Monitoring and Evaluation of Federal Government policies, programmes and projects.

The article will chronologically x-ray the essence of GIS and RS in the following areas; NPC GIS-Based programme and projects monitoring and evaluation NPC National Integrated Infrastructure Master Plan (NIIMP) Database NPC National Data Mining Framework NPC GIS-Based support for Macro-economic Index Analysis towards NV 20:2020 Challenges and Solution Data Acquisition Framework at NPC Funding and Infrastructure


Conclusion 2.0 NPC GIS-Based Programmes and Projects Monitoring and Evaluation Solutions

One of the mandates of NPC is to support Federal Government monitoring and evaluation of projects, programmes and policies and recently the performance contracts signed by all the Ministers of Federal Ministries, Departments and Agencies (MDAs). To achieve this, NPC embarked on the GIS-based Monitoring and Evaluation framework and solutions. The main reason is that the Federal Government has interest in monitoring and evaluation of policies, programmes and projects that were hitherto done manually. Because of the large data involved, the NPC tends to do Monitoring and Evaluation scientifically to improve effective decision making process in the country. What is monitoring and evaluation? It is composed of two separate words taken as one. According to Kusek and Rist (2008), Monitoring represents an on-going activity to track project progress against planned tasks. It aims at providing regular oversight of the implementation of an activity in terms of input delivery, work schedules and targeted outputs, while Evaluation is a systematic and objective assessment of on-going or completed projects or programs in terms of their design, implementation and results. Monitoring and Evaluation (M&E) are two different management tools that are closely related, interactive and mutually supportive. The main difference here is that Monitoring is continuous while Evaluation is one time event.

Various techniques and strategies are available globally for a successful project/programme M&E among which are Result-based M&E, Benchmarking and Cost-Benefit technique. Result-based M&E was adopted at NPC due to the inherent nature of Nigeria projects. It was discovered that in the past, a lot of projects have been sponsored by the Federal Government with no significant direct impact on the citizens, thus this has been portraying government efforts as futile.

Result-based M&E stems from Managing for Development Results (MfDR) Memorandum of the Marrakech Roundtable on Managing for Results in 2004, which is a management strategy focused on development performance and sustainable improvement in country outcomes. It provides a coherent framework for development effectiveness in which performance information is used to improve decision making, and it includes practical tools for strategic planning, risk management, progress monitoring and outcome evaluation. NPC has built a robust M&E solution that is GIS/RS-based. It has started implementing this solution based on pilot study of five (5) Federal MDAs namely; Federal Ministries of Works, Transport, Health, FCT and Agriculture with SURE-P which is an intervention programme and thus needs an immediate implementation of the GIS-Based M&E solution. Therefore, in the application of a Result-Based M&E system that can generate data for decision making for NPC, the ten steps listed below are always adhered to strictly:

Step one conducting a readiness assessment

Step two agreeing on outcomes to monitor and evaluate

Step three develop key indicator to monitor outcomes

Step four gathering baseline data on indicators

Step five planning for improvement i. e. setting realistic targets

Step six monitoring for results

Step seven evaluate information to support decision making

Step eight analyzing and reporting findings

Step nine using findings

Step ten sustaining the M&E system within the organization

At NPC, we have developed a GIS-Based Monitoring templates and framework where Officers go to the project sites for ‘on-the-spot’ inspection with camera based Global Positioning System (GPS) machine and embedded GIS Applications linked to the M&E web application. The solution is equipped with the state-of-the-art project location, delineation verification and security techniques


such as Geo-Fencing and Secured Socket Layer (SSL) for the web security with different user access levels.

Figure 1 below shows the efficacy of the system established at NPC to monitor and evaluate projects and programmes throughout the country. This example shows 3km (16%) Completed Part of a 49.36km Planned Construction of Abaji to Kotonkarfe Road, by applying GIS-Based monitoring and evaluation technology.

Figure 1: Using GIS/RS to monitor development Source: NPC M&E

3.0 NPC National Integrated Infrastructure Master Plan (NIIMP) Database Infrastructure is the physical assets, which are defined as the “Fundamental Facilities and

Systems serving the country, city, area or neighborhood, such as transportation and communication systems, power, plants and schools” (Leong, 2004). It is the stock of fixed capital equipment in a country including factories, schools and hospitals, which are considered as a backbone of economic growth of a country.

The importance of GIS/RS application in monitoring and evaluation of NIIMP is being pursued rigorously by NPC, since infrastructural development is the heart of a nation’s sustainable socio-economic development. It is therefore inevitable to apply GIS/RS in the monitoring and evaluation including planning and execution of infrastructure in this modern era. At present, Nigeria lacks credible and up-to-date central database of all its Engineering, Environmental, Infrastructures and projects data, and where data exists, it is scattered in various files, and in uncoordinated individual firms’ or corporation database which are not in a ready-to-use form.

According to Ofunne (1998), scientific information is the backbone of infrastructural development of any nation. Therefore NPC has been able to appreciate the importance of the storage of RS images of the areas of interest, in a central geo-spatial database that ensures a centralized national data as regards infrastructure. The application of GIS can be used to fetch data for analysis such as national land suitability analysis and services planning. This can be linked to a web application for the prospecting investors to access and query. Hence this improves investors’ trust in the country’s system and consequent economic development. Besides, having geo-spatial


data centrally helps the country know the status of her critical data via NPC. Though NPC has not reached this stage, but it aspires to get to this stage as soon as possible. This is due to the challenge of acquiring digital and remotely sensed data from constituted institutions such as the National Space Research and Development Agency (NASRDA) and the Office of the Surveyor General of the Federation (OSGOF).

Figure 2: National Infrastructure Databank Source: NPC M&E Department

Centralizing the storage of information concerning country’s assets and other infrastructures

in a central database that is geo-enabled will enhance the ease of locating and identifying information about a particular infrastructure for possible status update. Using GIS and Remote Sensing to create this geo-repository will enable GIS analysis such as location-Allocation Efficiency Analysis (optimizing placement of government infrastructure and services). Figure 3 below shows the conceptual framework diagram of the proposed National Infrastructure Databank. Infrastructure data is stored in a central database from various dedicated workstations over a network spread across the country. GIS applications are used to fetch data for analysis and the resulting information returned to the databank for various organisations and other stakeholders to access.

Figure 3: National Infrastructure Databank Organisational Framework

GIS

Procedures

Data

Hardware

SoftwarePeople

National Infrastructures DataBase

Private organizations

Govt. Organizations’

Foreign Investors and Donors

GIS

Procedures

Data

Hardware

SoftwarePeople


Source: NPC M&E Department 4.0 NPC National Data Mining Framework

This system is aimed at providing a centralized infrastructure and socio-economic data acquisition with quality assurance. It is the platform where theories and models about development efforts are concerted to ensure that the country’s development data are subjected to globally acceptable scientific standard scrutiny in terms of its technical and organisational sources and structure. National data mining framework will enable NPC to have a detailed and quality data bank of the national development data starting from policies, programmes and projects, and socio-economic data that is pluggable to the expected National Geospatial Data Infrastructure (NGDI) in the nearest future. 5.0 NPC GIS-Based support for macro-economic index analysis towards NV 20:2020

Several strategic frameworks are currently in place in Nigeria, all aimed at moving the country to a better level in socio-economic development. NV 20:2020 is a Federal Government Vision which serves as the foundation for socio-economic development. Its main objective is to place Nigeria among the top 20 economies measured by GDP by the year 2020. For example, the 1st NIP, MDGs, Government’s Transformation Agenda and some medium term implementation plans for the actualization of the NV20:2020 lack some verifiable and internationally accepted data. The Vision stressed the importance of data and information gathering as a key factor in documenting information needed to actualize the agenda’s activities.

At NPC, M&E department has developed a solution for the M&E data gathering and some statistical analysis for the government on the current level of the nation’s economy within a given period under study. Therefore, utilising M&E data and remotely sensed data such as housing and urban distribution maps, that reflects areas of actual census counts, road construction, rivers and rails networks maps linked to other social and public opinion survey statistics, combined with other verified standard institutions and organisations data, will help to bring out more information to identify important gaps in government efforts for effective decision making.

The application of GIS techniques will help support government with GDP results that is not only based on figures but also on the reality on ground about area of count, perception, location of people and distribution of goods and services; and eventual level of translation of government policies and programs to the lives of the citizens (De Soto, 2006). The full utilisation of the combination of M&E data, remotely sensed data coupled with other data sources that were scientifically credible will no doubt help to support the Federal government Transformation Agenda. 6.0 Challenges and Solutions

Globally, the sophistication of RS and GIS in socio-economic development is not without some challenges, especially in Nigeria where accurate and reliable data acquisition techniques and standardization are facing problems of credibility. However, these challenges are not insurmountable. At NPC, the challenges are been tackled with some standard techniques and strategies which include the use of Global Positioning Systems receiver (GPS), Smart phones, iPad and Global Mapper, to collect real time geo-spatial data including the images, that are sent to the NPC server through wireless technologies. In addition, the NPC Web-Based geo-information system has the ability to receive data from different MDAs without hindrance for on-the-spot analysis and reporting. Other challenges such as institutional and organisational structures were also tackled with corresponding frameworks such as developing a symbiotic relationship and understanding with two major actors of these geo-spatial data acquisition which are the Office of the Surveyor General of the Federation (OSGOF) and National Space Research and Development Agency (NASRDA).

With respect to the hardware inadequacies, the SURE-P office is assisting with the acquisition of the required hardware gadgets. The NPC is seeking assistance from European Union


and other international donors or organizations to make sure that the NPC GIS-Based M&E System meets up with the minimum acceptable standards international standard. As at the moment, the NPC is deficient in the following areas; Data Mining and Knowledge discovery, Spatial Econometrics and Statistics, BIG Data management just to mention a few. These areas are very new and germane to the success of NPC decision making process, therefore there is the need to train and re-train NPC staff in these areas in order to meet the 21st century modern day technology. NPC is currently lacking adequate geo-database in the area of infrastructure, both on and underground, and other facilities that could aid in making precise decision that will help in sustainable development. Though the NPC is rigorously working to attaining these status of having a scientific database in its purview, yet the M&E data that could be relied on are not usually referred to and accessible for decision making process, hence the government should fund NPC to collect M&E data for decision making process. 7.0 Data Acquisition frame work at NPC

The Data acquisition technical framework for NPC GIS-Based M&E in Figure 6 below shows the flow of data in a network of computers, people, organisation and administration. In this framework, geospatial data prepared by NPC M&E officers and other MDAs from various workstations such as Desktops and mobile devices are used to fetch data in and out of the database for stakeholders to access, perform GIS analysis after necessary data clean-up to fit for various decision makings. 8.0 Funding and Infrastructure

At present, in NPC, there is inadequate funding to purchase up-to-date high resolution images where projects can be tracked and evaluated continuously. GIS and Remote Sensing need more attention from all stakeholders in terms of funding. GIS/RS infrastructures cannot be funded by government alone. In terms of the remote sensing image acquisition, a lot of collaboration is expected from NARSDA because currently the 2.5 meter resolution of Nigeria Sat 2 satellite image which we could utilise for some projects is still not accessible in a ready to use form. NPC, therefore, has no other choice than to settle for the acquisition of commercially available and very expensive high resolution images that are highly useful for GIS and RS applications. This in turn, creates overhead with increase in cost of establishing and managing GIS and RS M&E projects.

STAKEHOLDERS &

MDAs REMOTE

TERMINALS

INTERNET

DESKTOP GIS

DISTRIBUTED

DATABASE

& MAP

SERVER

LIGHT WEIGHT

DB

FILTER

AND

APP SERVER

DATA

ACQUISITION

& CLEANUP

MODELS WEB GIS

MOBILE GIS FUTURE NGDI


Figure 6: Data acquisition Technical Framework of NPC GIS-Based M&E Source: NPC M&E

9.0 Conclusion The NPC is rigorously executing the Federal Government development plans using different

technical and institutional instruments. In this regard, GIS/RS is one of the valuable technical instruments being used by the NPC M&E Department. The intrinsic advantages of using GIS and RS data and applications are valuable in socio economic development of every nation. GIS and RS application are being appreciated in the area of housing, land administration and management, commerce and industries.

NPC has been able to harmonise its mandate with the government development plans such as the TA, the NIIMP and NV 20:2020 through the establishment of a GIS Unit under the M&E Department. NPC’s implementation of GIS and RS applications for monitoring and evaluation has been useful in collecting M&E data for sustainable development. Finally, it is important to know that the challenges Nigeria is facing today in information intelligence gathering is largely because of lack of adequate geo-information system stemming from the absence of a centralized location geospatial database. The central geospatial database is where data about National infrastructure and natural resources are stored. These include data about food, security, unemployment and road infrastructures etc. All these can be tackled when a country is able to track its geospatial data, linked to the population data. Nigeria has to know its citizens, where they are and the proportion of the population in a particular area at a particular period.

Therefore, NPC has jumpstarted the use of GIS and RS solutions to harness the country’s resources into a central database, thereby providing a decision support that helps the country execute its development plans for sustainable development. Furthermore, the institutionalisation of monitoring and evaluation at National level and gradually cascading to the state and local governments will help in acquiring M&E data through the application of GIS/RS technologies. This will bring about accountability, transparency, efficiency and good governance.

REFERENCES De Soto, H. (2006) The Mystery of Capital: Why Capitalism Triumphs in the West and Fails Everywhere Else. Goodchild, M. F. (1990) GIScience Ten Years after Ground Truth. Transactions in GIS, 10:687-692 ITC Educational Textbook Series; 1, Principles of Geographic Information Systems Rolf A. by (Ed.). Second edition ISBN 90–6164–200-0 ITC, Enschede, The Netherland. Kusek, J. Z. and Rist, R. C. (2008) Ten steps to Results-Based Monitoring and Evaluation Systems, In: Bridging the gap. The role of monitoring and evaluation in evidence- based policy making. Leong, K. C. (2004) The Essence of Asset Management – A Guide. Published by United Nations Development Programme, EROPH and APIGAM Marrakech Roundtable on Managing for Results (2004) Available at Available at http://www.focusintl.com/RBM051-SMES1_TM_02_Module_1_Intro_M&E.pdf, Accessed: 14. May 2014. Nigeria Vision 20:2020 (2009) Economic Transformation Blueprint. National Planning Commission Ofunne, G. C. (1998) The Nigerian Guardian Newspaper Wednesday march 11, 1998, page 36. Sivakumar, M.V.K. (2003) Satellite Remote Sensing and GIS Applications in Agricultural Meteorology Proceedings of the Training Workshop 7-11 July, 2003, Dehra Dun, India. The International Bank for Reconstruction and Development / The World Bank, World Bank, 1818 H Street NW, Washington.

List of Presenters

Mohamed Abd Elbasitl University of Johannesburg, South Africa

Khaled Abutaleb University of Johannesburg, South Africa

Nathaniel Adeoye Obafemi Awolowo University, Nigeria

Raimi Asiyanbola Olabisi Onabanjo University, Nigeria

Amidu Ayeni CSIR Pretoria, South Africa

Kerry Cawse-Nicholson Southern Mapping Company, South Africa

Moses Azong Cho CSIR Pretoria, South Africa

Selouna Chouaf University of Science and Technology Houari Boume-diene, Algeria

Mercy Phaphana University of Pretoria, South Africa

Antoine Rover Flemish Institute for Technological Research (VITO), Bel-gium

Mohammed El-Shirbeny National Authority for Remote Sensing and SpaceSciences (RARSS)

Jeanine Engelbrecht CSIR Meraka Institute, South Africa

Mohamed Ouessar Institut des Régions Arides (IRA), Tunisia

Tijani Garba Abubakar Tafawa Balewa University, Nigeria

Anthony Gidudu National Water and Sewerage Corporation

Muhire Innocent University of Johannesburg

Mahamadou Keita Regional Centre for Training in Aerospace Surveys(RECTAS)

Priscilla Kephe North West University, South Africa

Ademola Adeyemi GIS/MIS/QA Division, National Planning Commis-sion, Nigeria


Giovanni Laneve DIAEE Università di Roma, Italy

Eric Maluta University of Venda, South Africa

Lee Maropeng NO INSTITUTE

Amit Mishra University of Cape Town, South Africa

Chiedza Musekiwa Council for Geoscience, South Africa

Xolile Ncipha University of Kwazulu Natal, South Africa

Adeline Ngie University of Johannesburg, South Africa

Enj Njoku California Institute of Technology, USA

Adolph Nyamugama Nelson Mandela Mettropolitan University, SouthAfrica

Robert Olabanjo Nigerpet Structures Ltd. Uyo, Nigeria

Olayinka Dupe Nihinlola University of Lagos, Nigeria

Galal Omer University of Kwazulu-Natal, South Africa

Omoleomo Omo Irabor Federal University of Petroleum Resources, Nige-ria

Zakariyyaa Oumar KZN Department of Agriculture and Rural Develop-ment, South Africa

Soumya Ourabia University of Sciences and Technology Houari Boume-diene (USTHB)

Lobina Palamuleni North West University, South Africa

Abel Ramoelo CSIR, South Africa

Hibrahim Rijasoa Ravonjimalala Centre National de Recherche sur l’Environnement(CNRE), Madagascar

Roselyne Ishimwe University of Johannesburg, South Africa

Colin Schwegmann CSIR, South Africa

Lerato Shikwambana CSIR, South Africa

Bolelang Sibolla CSIR, South Africa

Rebekah Singh CSIR, South Africa

Vhusomuzi Sithole Nelson Mandela Metropolitan University, South Africa


Olalekan Taiwo University of Johannesburg, South Africa

Andrew Onwusulu National Space Research and Development (NASRDA),Nigeria

Riaan van den Dool CSIR, South Africa

Heidi van Deventer CSIR, South Africa

Jaun van Loggerenberg North West University, South Africa

Willem Vorster SANSA, South Africa

Konrad Wessels CSIR, South Africa


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