spatial mapping for land use /land cover assessment using ... · spatial mapping for land use /land...

INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES

Volume 2, No 4, 2012

© Copyright 2010 All rights reserved Integrated Publishing services

Research article ISSN 0976 – 4380

Submitted on March 2012 published on May 2012 1069

Spatial mapping for land use /land cover assessment using resourcesat-2

data in the parts of Cuddalore district, east coast of Tamil Nadu, India Kumaravel. S

1, Ramkumar. T

1, Gurunanam. B

2, Suresh. M

3

1- Department of Earth Sciences, Annamalai University, Chidambaram, Tamil Nadu, India

2- Center for Applied Geology, Gandhigram Rural Institute (Deemed University, Dindugul,

Tamil Nadu, India

3- Department of Geology, Periyar University, Salem-11, Tamil Nadu, India

[email protected]

ABSTRACT

Recent land use/land cover estimates from Resourcesat-2 (2012) image and data about spatial

distribution of land use/cover types obtained from outdated cartography. Land use/land cover

pattern in the parts of Cuddalore district coastal areas. A supervised classification was carried

out on the three reflective bands for the image individually with the aid of ground truth data

collected during field trips of the classification results. Based on NRSA (1996) land use / land

cover classification results are agriculture land, built-up land, forest, water bodies, waste land

and others (Level I categories). However, after accuracy assessment six classes have been

further classified into18 classes of Level II categories are classified. Crop land and fallow

land (Agricultural land) was the most dominant class covering 34.97% and 25.43% followed

by Villages (Rural) and Towns/cities (Urban) at 7.16% and 5.46% while Aquaculture Pond

covered only 1.07% of the total area.

1. Introduction

Coastal areas are highly dynamic and undergoing rapid change. The knowledge of land

use/land cover change is very important to understand the natural resources, their utilization,

conservation and management (Nagamani and Ramachandran, 2003). Land use is obviously

constrained by environmental factors such as soil characteristics, climate, topography and

vegetations. But, it also reflects the land as a key and finite resource for most human

activities including agriculture, industry, forestry, energy, settlement, recreation and water

catchments and storage. The main emphasis of agricultural development all over the world

was on increasing productivity per unit area of land used for production to feed the ever

increasing population (Bhat, et al., 2009). It has been tightly coupled with economic growth.

Improper management of land use is causing various forms of environmental degradation.

The remote sensing techniques are used to measure the land cover, from which land use can

be inferred particularly with ancillary data or priority knowledge (Nagamani and

Ramachandran, 2003 and Kachhwala, 1985). Land use/cover studies are multidisciplinary in

nature and thus the participants involved in such work are numerous and varied, ranging from

international wild life and conservation foundation to government researchers and forestry

departments. In addition, facilitating sustainable management of the land, land cover and use

information may be used for planning, monitoring and evaluation of development, industrial

activity or reclamation.

In digital image classification, an interpreter evaluates several characteristics such as tone,

texture, size, pattern, shape and association and his own knowledge about the land cover

distribution in order to identify the components of the image. The majority of these

Spatial mapping for land use /land cover assessment using resourcesat-2 data in the parts of Cuddalore

district, east coast of Tamil Nadu, India

Kumaravel. S, Ramkumar. T, Gurunanam. B, Suresh. M

International Journal of Geomatics and Geosciences

Volume 2 Issue 4, 2012 1070

characteristics are not used in conventional digital image classification. Attempts based upon

different approaches, such as the use of texture (Gong and Howarth, 1990; Palubinskas et al.,

1995; Franklin et al., 2000), object-oriented approaches (Blaschke et al., 2000; Mansor et al.,

2002) and the use of ancillary information (Hutchinson, 1982; Kontoes et al., 1993; Long and

Skewes, 1996; Mas and Ramırez, 1996; Srinivasan and Richards, 1990), have been made in

order to increase the accuracy of spectral classifications.

2. Study area

The study area (Figure 1) lies in the coastal belts and parts of Cuddalore and Chidambaram

Taluk of Cuddalore District, Tamil Nadu, India. It is bounded on the north by Pondicherry

Union Territory, south by Nagapattinam district, east by Bay of Bengal and west by Panruti

and Virudhachalam Taluks of Cuddalore district. It lies between 11°23’57” and 11°48’03” N

latitudes, and 79°38’11” and 79°51’08” E longitudes covering an area of 836.86 km2.

Gadilam River flows through the town and separates the Cuddalore Old town from the new

one. River Uppanar is one of the rivers passing through the industrial coastal town of

Cuddalore in southeast coast of India along with River Gadilam in the north, which drains

into the Bay of Bengal. The river runs parallel to the coast south of Cuddalore to a distance of

about 20 km and the tidal influence extends to about 1.5 km. A number of surface water

bodies are found in this region, of which, Perumal Eri (Lake) in the western side is connected

with the river and a large thermal power plant effluent finds its way into the river through this

water body. During the past two decades, industrial development has increased three times

with many large and small-scale industries being established along the Uppanar river bank.

The coastal zone of Cuddalore includes production of fertilizers, dyes, chemicals and mineral

processing plants, and metal-based industries. The Pitchavaram Mangrove forest is an

important eco-tourist spot. Cuddalore is known for its picturesque beaches, particularly

Silver Beach and Samiyarpettai beach.

2.1 Data products

The Survey of India Toposheet map Nos. 58 M/9 (1970), 10 & 14 (1971), 11 (1971), 13

(1973), & 15 (1970) and Resourcesat-2 (March, 2012) LISS IV MX path 102 row 065-D and

spatial resolution is 5.8m. The spectral resolution is 0.52-0.59 (B2), 0.62-0.68 (B3) and 0.77-

0.86 (B4) include geocoded FCC digital data was imported from CD to ERDAS system as an

image format.

3. Materials and methods

It is very limited on the east coast by using Resourcesat-2 digital data of 2012. As the digital

data did not corrected using ground control points viz. road–road intersection, etc. were taken

from the Survey of India Toposheet using ERDAS IMAGINE 9.2 image processing package.

False Color Composite of the study area was generated with the band combinations of 3, 2,

and 1 in Red Green Blue data (Figure 2). The displayed image with the above classes was

spectrally enhanced by histogram have real earth coordinates, data were geometrically

intersection, road–rail intersection, canal–road equalization method. Land use/land cover map

of the study area was prepared by digital image interpretation method using ERDAS

IMAGINE 9.2.






Figure 1: Key map of study area

Supervised classification was carried out on the three reflective bands for the image

individually with the aid of ground truth data collected during field trips. The classification

results are agriculture land, built-up land, forest, water bodies, waste land and others (Level I)

corresponding to the NRSA (1996) classification scheme. However, after accuracy

assessment Level I categories are further classified into18 classes of Level II categories are

classified. Different land use/land covers classes like crop land, fallow land and plantation

(Agriculture land), land with scrub, land without scrub, barren rocky/ stony waste, sandy area,

sandy area: beach, sandy area: beach ridge and Sandy area: sand dunes (Waste land), river,






tank, canal and sea (Water bodies), mangrove forest (Forest), Villages and Towns/cities

(Built-up land) and Aquaculture Pond (Others) were identified using digital image

interpretation elements. Shape file of the Land use/land cover features are converted into GIS

platform and prepared the spatial distribution map.

Figure 2: Area of interest Resourcesat-2 data – 2012

4. Results and discussion

Land cover mapping serves as a basic inventory of land resources for all levels of

government, environmental agencies and private industry throughout the world (Vijith and

Satheesh 2007). Cuddalore coastal and its surrounding east coast areas are rapid

developments; there is a need for real time monitoring of the land based/changes. Land use

classes can be effectively delineated from the digital remote sensing data (Ram and Kolarkar,

1993; Vijayakumar, 2004). The various land use/land cover features in the study area was

depicted using digital image interpretation of the Resourcesat-2 satellite data and was

described with the areal coverage.






4.1 Classification of the 2012 resourcesat-2 image

Supervised classification of the 2012 Resourcesat-2 data yielded results with most of the

classes mixed. Land without scrub was the most confused class and mixed with other classes

like sandy area, beach and beach ridges. Crop land has well separated with most of the other

classes but it was mixed with plantation and land with scrub. Fallow land was very confused

class and mixed with aquaculture pond, while some dry areas were classified as sandy area.

Settlement was the most confused class and mixed with all other classes. The classification

resulted into a land use/land cover map (Figure 3) with eighteen classes. Crop land and fallow

land was the most dominant class covering an area of 34.97% and 25.43% followed by

Villages (Rural) and Towns/cities (Urban) at 7.16% and 5.46% while Aquaculture Pond

covered only 1.07% of the total area (Table 1).

Figure 4: Land use/land cover map of Resourcesat-2 Data – 2012

4.2 Agricultural land

It is defined as the land primarily used for farming and for production of food, fiber, and

other commercial and horticulture crops. It includes land under crops (irrigated and un-

irrigated), fallow, plantations, etc. It includes those lands with standing crop (per se) as on the






date of the satellite imagery. The crops may be of either Kharif (June-September) or Rabi

(October-March) or Kharif Rabi seasons. Crop land has occupied in the study area of 189.64

km2 in the study period. It is described as agricultural land which is taken up for cultivation

but it is temporarily allowed to rest un-cropped for one or more seasons, but not less than one

year. The total area occupied under agricultural fallow land was 137.91 km2. It is defined as

an area under agricultural tree crops, planted adopting certain agricultural management

techniques. It includes Coconut, Banana, Eucalyptus, Cashew and Casuarinas plantation. In

satellite imagery, it appears as dark red to red with a small areal extent when compared to

crop land. Along the coastal plains of the study area coconut, Cashew and Casuarinas

plantation has been observed. The total area occupied under agricultural plantation was 46.75

km2 (5.59%) in the study period.

4.3 Waste land

It is described as degraded land, which can be brought under vegetative cover with

reasonable water and soil management or on account of natural causes. Wastelands can

result from internal/imposed constraints such as, by location, environment, chemical and

physical prosperities of the soil or financial or management constraints. Land with scrub is a

type of wasteland. Very small portion of the study area has land with scrub. It is identified in

the satellite data by its light blue or light green color, medium to coarse texture but no pattern

and as isolated patches spread over the investigation area occupied 2.10% of the study area.

Land without scrub is another type of wasteland inferred by its distinct light grey to white

color and medium to coarse texture. It is devoid of vegetation and observed in isolated

patches around the scrub land. It is defined as the rock exposures of varying lithology often

barren and devoid of soil cover and vegetation and not suitable for cultivation. It is identified

in the satellite data is showing light brown color with sparse or no vegetation. Small patches

are found in the northwestern part of the study area and their areal extent is 10.24 km2.

Generally the sandy area appears as bright white to yellow with bluish tone in the satellite

imagery. In the study area, the entire shore and backshore region are occupied by extensive

sandy beach which is in the form of small pocket beaches. The total area covered by this

category is given in table 1.

4.4 Water bodies

It is an area of impound water, areal in extent and often with a regulated flow of water. It

includes man-made lakes/tank/canals, besides natural river/stream and creeks. It is a natural

or man-made enclosed water body with a regulated flow of water. Tanks/Lakes are used for

generating irrigation and for flood control. Canals are inland waterways used for irrigation

and sometimes for navigation. Rivers/streams of an area are given in table 1.

Table 1: Result of 2012 land use/land covers class area in km2

Sl. No. Land use/Land cover Class 2012 Resourcesat-2

data Area in km2

Area in %

1 Crop Land 189.64 22.66

2 Fallow Land 137.91 16.48

3 Plantations 46.75 5.59

4 Land with scrub 17.59 2.10






5 Land without scrub 8.52 1.02

6 Barren Rocky/ Stony Waste 10.24 1.22

7 Sandy area 2.67 0.32

8 Sandy area: Beach 3.09 0.37

9 Sandy area: Beach ridge 0.65 0.08

10 Sandy area: Sand dunes 2.19 0.26

11 River 26.67 3.19

12 Tank 15.96 1.91

13 Canal 1.31 0.16

14 Sea 294.50 35.19

15 Mangrove Forest 4.92 0.59

16 Villages (Rural) 38.81 4.64

17 Towns/cities (Urban) 29.62 3.54

18 Aquaculture Pond 5.83 0.70

Total Area 836.87 100%

4.5 Forest

It is an area with the notified boundary bearing an association predominantly of trees and

other vegetation types capable of producing timber and other forest produce. It is described as

a dense thicker or woody aquatic vegetation or forest cover occurring in tidal water near

estuaries and along the confluence of delta in coastal areas. It includes species of the general

Rhizophora and Aviccunia. The satellite imagery shows bright red to red tone. Majority of

the forest (Mangrove forest) occur in southern part of the study area. The mangrove forests

covered in the study area are 4.92 km2. Mangroves protect the coastline against erosion

(Dahdouh-Guebas 2002).

4.6 Built-up land (village, town/cities)

It is defined as an area of human habitation developed due to non-agricultural use and that

which has a cover of buildings, transport, communication utilities in association with water,

vegetation and vacant lands. Moreover they are sparsely present in the entire study area. The

built-up-land occupies in the study area is 8.18% in the year of 2012 respectively.

4.7 Others categories

It includes all those, which can be treated as miscellaneous because of their nature of

occurrence, physical appearance and other characteristics. The aquaculture pond is generally

occupies in near to the sea shore. It is identified by its light blue or white color, medium to

coarse texture and well developed pattern. The aquaculture pond occupies an area is very

small.






5. Conclusion

This paper described how the technologies of satellite remote sensing and GIS were

combined to assessment of land use/land cover features in parts of the east coast of Cuddalore

district, Tamil Nadu, during the year of 2012 using Resourcesat-2 satellite data. The present

study also found that remote sensing coupled with GIS can be effectively used for real time

and long term monitoring of the environment. The baseline information generated on land

use/land cover pattern of the area would be of immense help in formulation of policies and

programmes required for developmental planning.

6. References

1. Bhat, N.R., M.K. Suleiman and M. Abdal, (2009), Selection of crops for sustainable

utilization of land and water resources in Kuwait. World journal of agricultural

sciences, 5(2), pp 201-206.

2. Blaschke, T., Lang, S., Lorup, E., Strobl, J., Zeil, P., (2000), Object oriented image

processing in an integrated GIS/remote sensing environment and perspectives for

environmental applications. In: Cremers, A., Greve, K. (Eds.), Environmental

information for planning, Politics and the public, vol. II, Metropolis-Verlag,

Marburg, pp. 555e570, http://www.geo.sbg.ac.at/staff/tblaschk/ publications/

UI2000_Blaschke_et_al.pdf.

3. Dahdouh-Guebas, F., (2002), The use of remote sensing and GIS in the sustainable

management of tropical coastal ecosystems, Environment, development and

sustainability, 4, pp 92-112

4. Franklin, S.E., Hall, R.J., Moskal, L.M., Maudie, A.J., Lavigne, M.B., (2000),

Incorporating texture into classification of forest species composition from airborne

multispectral images. International journal of remote sensing, 21(1), pp 61-79.

5. Gong, P., Howarth, P.J., (1990), The use of structural information for improving

land cover classification accuracies at the ruraleurban fringe. Photogrammetric

engineering and remote sensing 56(1), pp 67-73.

6. Hutchinson, C.F., (1982), Techniques for combining Landsat and ancillary data for

digital classification improvement. Photogrammetric engineering and remote

sensing, 8 (1), pp 123-130.

7. Kachhwala, T.S., (1985), Temporal monitoring of forest land for change detection

and forest cover mapping through satellite remote sensing. In the proceedings of the

6th Asian conference on remote sensing, Hyderabad, pp 77-83.

8. Kontoes, C., Wilkinson, G., Burril, A., Goffredo, S., Me´ gier, J., (1993), An

experimental system for the integration of GIS data in knowledge-based analysis for

remote sensing of agriculture. International journal of geographical information

system, 7 (3), pp 247-262.






9. Long, B.G., Skewes, T.D., (1996), A technique for mapping mangroves with

Landsat TM satellite data and geographic information system. Estuarine, Coastal

and shelf science, 43, pp 373-381.

10. Mansor, S., Tai Hong, W., Rashid Mohamed Shariff, A., (2002), Object oriented

classification for land cover mapping, http:// www.gisdevelopment.com/application/

environment/overview/envo0010.htm

11. Mas, J.F., Ramı´rez, I., (1996), Comparison of land use classifications obtained by

visual interpretation and digital processing. ITC journal 1996-3/4, pp 278-283.

12. Nagamani, K. and S. Ramachandran, (2003), Land use/land cover in Pondicherry

using remote sensing and GIS. In the proceedings of the third international

conference on environment and health held Chennai, India, pp 300-305.

13. Navalgund, R.R., V. Jayaraman and P.S. Roy., (2007), Remote sensing application:

An overview. Current science, 93(12), pp 1747-1766.

14. NRSA (1996), Integrated mission for sustainable development-technical guideline.

National remote sensing agency, Department of space, India.

15. Palubinskas, G., Lucas, R.M., Foody, G.M., Curran, P.J., (1995), An evaluation of

fuzzy and texture-based classification approaches for mapping regenerating tropical

forest classes from Landsat-TM data. International journal of remote sensing, 16(4),

pp 747-759.

16. Ram, B. and A.S. Kolarkar, (1993), Remote sensing application in monitoring land

use changes in arid Rajasthan. International journal of remote sensing, 14(17), pp

3191-3200.

17. Srinivasan, A., Richards, J.A., (1990), Knowledge-based techniques for multi-

source classification. International journal of remote sensing, 3(3), pp 505-525.

18. Vijayakumar, S.P., S.P. Rai and D.S. Rathore., (2004), Land use mapping of Kandi

belts of Jammu region. Journal of Indian society of remote sensing, 32(4), pp 323-

328.

19. Vijith, H. and R. Satheesh., (2007), Evaluation of land use pattern and

geomorphology of parts of western Ghats using IRS P6 LISS III Data. IE (I)

Journal-AG, 88, pp 14-18.

spatial mapping for land use /land cover assessment using ... · spatial mapping for land use /land...

Documents