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Combined GIS and Remote Sensing techniques in MappingDesertification Sensitivity in the North of the Western

Desert, Egypt

GAD, A. and LOTFY, INational Authority for Remote Sensing and Space Sciences, Egypt

[email protected]

Abstract

The rain fed grazing area at the northern coast of Egypt is

subjected to erosion and weathering. Environmental systems aregenerally in a state of dynamic equilibrium with external drivingforces. Desertification of an area proceeds if certain landcomponents are brought beyond specific threshold, where furtherchange produces irreversible alterations. In the appliedmethodology, three quality indices were computed (i.e. Soil QualityIndex, Vegetation Quality Index and Climatic Quality Index. ETMsatellite images, geologic and soil maps were used as mainsources for calculating the indices of Environmental Sensitivity

Areas (ESAs) for desertification. The obtained results revealed thatthe study area is characterized by sensitive quality indices for soil,

vegetation and climate. The produced 1:100,000 scale maps of theESAs show that 55.25% of study area is classified as verysensitive areas, while 42.57% as sensitive and 2.19% asModerately sensitive. It can be concluded that implementing themaps of sensitivity to desertification is rather useful in the arid andsemi arid areas as they give more likely quantitative trend forfrequency of sensitive areas.

Keywords: Remote sensing, GIS, Desertification, Western Desert, Egypt


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1. Introduction

Desertification includes a set of important processes, which areactive in the arid land due to scarcity of land and water resources. Thenorthwestern area of Egypt (Fig. 1) was known as the breadbasket during theGreeks and Romanian civilization periods. In the recent decades a lot ofchanges in land use and land cover have been induced mostly by man andresulted in acceleration of different degradation processes. Many descriptivestudies are available, however the quantification of the land degradationissues is clearly missed. Recently, many scientific efforts are done toenhance the more likely quantification nature of the situation. In the context ofthe EU Mediterranean Desertification and Land Use project (MEDLUS), a

distinction has been made between degradation processes in EuropeanMediterranean environments and the more arid areas. Physical loss of soil bywater erosion, and associated loss of soil nutrient status are identified as thedominant problems in the European Mediterranean region. However, Winderosion and salinisation problems are most often in the arid Mediterraneanareas. Mismanagement of land cover and land use may cause the landsurface to be more susceptible to erosion driving forces.

Fig. (1) Location of study area in the north of western desert,Egypt.


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Environmental systems are generally in a state of a dynamic

equilibrium with external driving forces. Small changes in the driving forces,such as climate or imposed land use tend to be accommodated partially by asmall change in the equilibrium and partially by being absorbed or buffered bythe system. Desertification of an area will proceed if certain land componentsare brought beyond specific threshold, beyond which further change producesirreversible change (Tuckeret al. 1991; Nicholson et al. 1998). For example,climate change cannot bring a piece of land to a desertified state by itself, butit may modify the critical thresholds, so that the system can no longermaintain its equilibrium (Williams & Balling, 1996). Environmentally Sensitive

Areas (ESAs) to desertification around the Mediterranean region exhibitdifferent sensitivity status to desertification for various reasons. For example

there are areas presenting high sensitivity to low rainfall while other areas aresensitive due to low vegetation cover, low resistance of vegetation to drought,steep slopes and soft parent material (Ferrara et al, 1999).

Desertification indicators are those, which indicate the potential riskof desertification while there still time and scope for remedial action. Regionalindicators should be based on available international source materials,including remotely sensed images, topographic data (maps or DEMs),climate, soil and geologic data (Woodcock et al, 1994; Pax-Lenney et al,1996). At the scale ranging 1: 25,000 to 1:1,000,000 the impact of socio-economic drivers is expressed mainly through pattern of land use. Eachregional indicator or group of associated indicators should be focused on asingle desertification process. The various types of ESAs to desertificationcan be distinguished and mapped by using certain key indicators forassessing the land capability to withstand further degradation, or the landsuitability for supporting specific types of land use. The key indicators fordefining ESAs to desertification can simply be based on four broadcategories defining the qualities of soil, climate, vegetation, and landmanagement (Kosmas et al, 1999). This approach includes parameters,which can easily be extracted from reports on soil, vegetation and climate.

2. Methodology

The satellite mosaic image of the study area (Fig. 2) was composedfrom a number of LANDSAT-TM scenes. The main input data for calculatingthe environmental indices were driven from the analysis of the image,CONOCO (1989) geologic map of Egypt, climatic data recorded by theMinistry of Agriculture. An image processing system (i.e. ERDAS IMAGINE8.3) and a GIS system (i.e. Arc GIS 9) were the main tools in indicescomputations and ESAs mapping. The following three quality indices werecomputed;


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2.1 Mapping of Soil Quality Index (SQI)

A number of four soil parameters were considered at the currentinvestigation (i.e. parent material, soil texture, soil depth and slope gradient).Weighting factors were assigned to each category of the consideredparameters, on basis of Basso et al, (1998) and Observatory of the Saheland Sahara (OSS, 2003). The used algorithm was adapted from Medalusproject methodology (European Commission, 1999). Tables (1 to 4)demonstrate the assigned indexes for different categories of each parameter.The soil Quality Index (SQI) was calculated on basis of the followingequation, and classified according to categories shown in table (5).

SQI = (Ip * It * Id * Is)

Ip index of parent material, It index of soil texture, Id index of soil depth, Isindex of slope gradient)

Table (1) Classes, and assigned weighting index for parent material

Class Description Score

1) Coherent: Limestone, dolomite, non-friable sandstone,hard limestone layer.

Good 1.0

2) Moderately coherent: Marine limestone, friablesandstone

Moderate 1.5

3) Soft to friable: Calcareous clay, clay, sandy formation,alluvium and colluvium

Poor 2

Note: In case of deep Aeolian deposits over a rocky parent material, theAeolian sediments are considered as parent material.

Table (2) Classes, and assigned weighting index for soil depth

Class Description Score

Very deep Soil thickness is more than 1 meter 1

Moderately deep Soil thickness ranges from


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Table (3) Classes, and assigned weighting index for soil texture

TextureClasses

Description Score

Areas dominatedby water erosion

Areas dominatedby wind erosion

Not very lightto average

Loamy sand, Sandyloam, Balanced

1 1

Fine toaverage

Loamy clay, Clayeysand, Sandy clay

1.33 1.66

Fine Clayey, Clay loam 1.66 2

Coarse Sandy to very Sandy 2 2

Table (4) Classes, and assigned weighting index for Slope gradient

Classes Description Score

< 6% Gentle 1

6 18 % Not very gentle 1.33

19 35 % Abrupt 1.66

> 35 % Very abrupt 2

Table (5) Classification of soil quality index (SQI)

Class Description Range

1 High quality 1.46

2.2 Mapping Vegetation quality index (VQI)

Vegetation quality, according to Basso et al (2000) is assessed interms of three aspects (i.e. erosion protection to the soils, drought resistanceand plant cover). The TM satellite images mosaic covering the north of thewestern desert area (Fig. 2) is the main material used to map vegetation andplant cover classes. Rating values for different vegetation aspects were

adapted on basis of OSS (2003) as shown in table (6). Vegetation QualityIndex was calculated according the following equation, while VQI wasclassified on basis of the ranges indicated in table (7).

VQI = (I Ep * I Dr* I Vc)1/3

Where: IEp index of erosion protection, IDr index of drought resistance and IVcindex of vegetation cover)


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2.3 Mapping of Climatic quality index (CQI)

Climatic quality is assessed by using parameters that influencewater availability to plants such as the amount of rainfall, air temperature andaridity, as well as climate hazards, which might inhibit plant growth (Thornes,1995). Table (8) reveals the classification categories of climatic quality indexaccording to OSS, 2003. The Climate quality index is evaluated through the

Aridity Index (AI), using the methodology developed by FMA in accordancewith the following formula in the current study, rainfall and evapotranspirationdata on a number of 33 metrological stations were used to calculate the CSIas follows;

CQI = P/PET

Where: P is average annual precipitation and PET is average annualPotential Evapo-Tanspiration

Table (6) Classes, and assigned weighting index for different vegetationparameters

Class Description IEp IDr IVc

1 Perennial cultivation 1 1 1

2 Halophytes 1.33 1 1.333 Temporal and orchards, mixed withcrop land

1.66 1.33 1.66

4 Saharan vegetation < 40% 2 1.66 1

5 Saharan vegetation > 40% 2 1 1

Table (7) Classification of vegetation quality index (VQI)

Class Description Range

1 Good < 1.2

2 Average 1.2 to 1.4

3 Weak 1.4 to 1.6

4 Very weak > 1.6


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Table (8) Classification of Climatic quality index (CQI)

2.4 Mapping Environmentally Sensitive Areas (ESAs) to Desertification

ArcGIS9 software was used to map ESAs to Desertification(Kosmas et al, 1999) by integrating all data concerning the soil, vegetationand climate. Different quality indices were calculated and displayed as GIS-ready maps from which class areas were deduced. The DesertificationSensitivity Index (DSI) was calculated in the polygonal attribute tables linkedwith the geographic coverage according to the following equation;

DSI = (SQI * VQI * CQI) 1/3

Table (9) Ranges and classes of desertification sensitivity index (DSI)

Classes DSI Description

1 < 1.2 Non affected areas or very lowsensitive areas to desertification

2 1.2 < DSI < 1.3 Low sensitive areas todesertification

3 1.3 < DSI 0.65 1


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clay and colluviums materials). The coherent parent materials are limited in

the study area. The soil depth (Fig. 3) was also evaluated on basis of bothgeologic map (CONCO, 1989) and soil map of the Land Master Plan(Ministry of Development, 1986). The soils are mostly characterized byshallow to very shallow soil depth representing 71% of study area. Thosesoils characterized by deep profiles do not exceed more than 29% of thewhole territory, located mainly in the wadies and sandy plains. The verydeep soils are limited to the western extension of the Nile Delta and particularspots along the coastline.

Fig. (3) Categories of soil depth as contributing in soil quality index

The soil texture was assessed on basis of the geomorphology,deduced from the ETM satellite mosaic. Fig. (4) shows that the most sensitivecoarse textured soils amount 40% of study territory. The wadi and undulatinglandscape are exhibited by very light to average textured soils, covering 47%of all soils. The colluviums (13%), brought by the alluvial fans and ravines, atthe fringes of the high land, are exhibited by fine to average textured soils.

Fig. (4) Categories of soil Texture classes as contributing in soil quality index


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The slope gradient (Fig. 5) was classified, on basis of topographic

maps and digital elevation model (DEM).

Fig. (5) Categories of slope gradient as contributing in soil qualityindex

Calculating the soil quality index (Fig. 6 and table 10) reveal thatthe moderate quality soil index characterizes 52.3% of the study area in parts

of the coastal plain, including the wadi soils and Siwa oasis. Also, the westernextension of the Nile Delta is classified as moderate soil quality index. Thevery low and low soil quality classes characterize most of the western desertsoils, covering shallow and rugged landscaped soils at the coastal plain andplateau. They exhibit 46.7% and 1% respectively.

Fig. (6) Categories of Soil Quality Index (SQI) classes


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Table (10)Area percentages of Soil Quality Index (SQI) classes

3.2 Vegetation Quality Index (VQI)

Hyperid classification of ETM images resulted in identifying a

number of four vegetation classes. Each of these classes was given ascore evaluating vegetation cover, erosion protection and droughtresistance. Calculating the vegetation quality index, on basis of the previousparameters (Fig. 7 and table 11) reveal that the 76.8% of the vegetationcover, spread as desert shrubs, is very weak and sensitive to desertification.The good vegetation index class, which may resist desertification,represents only 2.1% of the vegetation cover and mostly restricted atnortheastern corner, on the Nile Delta western extension. The averagevegetation Index characterize the orchard vegetation at the coast and thenewly reclaimed areas, irrigated from the new El-Hamam canal.

g

Fig. (6) Different categories of vegetation quality index (VQI) classes

Class Area %

Very Low Quality 46.7

Low Quality 01.0

Moderate Quality 52.3

Total 100


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Table (11) Area percentages of vegetation quality index (VQI) classes

Classes Areas (%)

Good 2.1

Average 21.3

Very Weak & weak 76.7

3.3 Climate Quality Index (CQI)

The interpolation of rainfall and potential evapo-transpiration data

resulted in obtaining climatic thematic map layers (Figs. 7 and 8). Theclimatic sensitivity index (CQI) was calculated and displayed in a GIS readymap (Fig. 9). Most rainfed areas are located in the northern coastal regionand dont exceed 200 mm. annually. The average annual rainfall drops downto almost zero, at less than 50 150 km distance south of the Mediterraneancoast. The average annual potential evapo-transpiration is relatively high inthe whole country, however increases southwards. Table (12) shows theareas of climatic quality index classes. The hyper arid climatic conditionscharacterize 89.3% of the whole study area, while 10.7% is characterized byarid climatic conditions.

Fig. (7) Average annual precipitation in the northwestern area of Egypt,on basis of meteorological data


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Fig. (8) Average annual potential evapo-transpiration in thenorthwestern area of Egypt, on basis of meteorological data

Fig. (9) Different classes of climate quality (Aridity) index (CQI) in thenorthwestern area of Egypt

Table (12) Areas of different climatic quality index (CQI) classes

Class %

Hyper-arid 89.3

Arid 10.7

Total 100


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3.4 Environmentally Sensitive Areas (ESAs) to Desertification

The three previous indices were used together for the assessmentof the environmentally sensitive areas (ESAs) to desertification, on basis ofthe calculated Desertification Sensitivity Index (DSI). Fig. (10) shows thedistribution of ESAs, while table (13) demonstrates their areas. It is clearthat most of the study area is very sensitive and sensitive to desertification;these classes exhibit 55.2 and 42.6% of the total area respectively. Thewestern extension of the Nile Delta, covering 2.2% of the area, is classifiedas moderately sensitive, as its moderate quality soils are protected by goodquality vegetation. The oases, as Siwa oasis, lie within the areas vulnerableto high desertification sensitivity index.

Fig. (10) Environmentally sensitive areas (ESAs) for desertificationin North of Western desert, Egypt

Table (13) Occurrence of Environmentally sensitive areas (ESAs)

Classes Areas (%)

Moderately sensitive 2.2Sensitive 42.6

Very sensitive 55.2

4. Conclusions and Recommendations

It can be concluded that the quantitative approach for assessing thedesertification is rather important for planning sustainable development


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programs. Desertification sensitivity index demonstrates a clearer vision of

risk state, thus, reliable priority actions can be planned. Remote sensing, inaddition to thematic maps, may supply valuable information concerning thesoil and vegetation quality at the general scale. However, field validation willbe necessary to insinuate on ground truth information. The GeographicInformation System (GIS) is a valuable tool to store, retrieve and manipulatethe huge amount of data needed to compute and map different qualityindices to desertification.

The Northwestern coast of Egyptian is susceptible to very high to highdesertification sensitivity. Action measures are essential for the sustainableagricultural projects located in the rainfed area at north of western desert and

its oases.

It can be recommended that mathematical modeling should be developedfor the operational monitoring of different elements contributing indesertification sensitivity. Multi scale an multi-seasonal mapping of ESAsare needed to point out the risk magnitude and causes of degradation,specially for desert oases.

5. References

Basso F., Bellotti A., Bove E., Faretta S., Ferrara A., Mancino G., PisanteM., Quaranta, G., Taberner M., (1998). Degradation processes in the AgriBasin: evaluating environmental sensitivity to desertification at basin scale.Proceedings International Seminar on 'Indicator for Assessing Desertificationin the Mediterranean'. Porto Torres, Italy 18 - 20 September. Edited by G.Enne, M. D'Angelo, C. Zanolla. Supported by ANPA via Brancati 48 - 00144Roma. pp 131-145

Basso F., Bove E., Dumontet S., Ferrara A., Pisante M., Quaranta, G.,Taberner M., (2000). Evaluating Environmental Sensitivity at the basin scalethrough the use of Geographic Information Systems and Remote Senseddata: an example covering the Agri basin (southern Italy). Catena 40 : 19-35

CONOCO Inc. (1989). Startigraphic Lexicon and explanatory notes to thegeological amp of Egypt 1- 500,000, eds. Maurice Hermina, Eberhard klitzschand Franz K. List, pp. 263, Cairo: CONOCO Inc., ISBN 3-927541-09-5.

European Commission (1999). The Medalus project Mediterraneandesertification and land use- Manual on key indicators of desertification andmapping environmentally sensitive areas to desertification, pp. 84, Eds. C.kosmas, M. Kirkby and N. Geeson, European environment and climate


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research program Theme: Land resources and the threat of desertification

and soil erosion in Europe (Project ENV4 CT 95 0119).

Ferrara A., Bellotti A., Faretta S., Mancino G., Taberner M. (1999).Identification and assessment of Environmentally Sensitive Areas by RemoteSensing. MEDALUS III 2.6.2. - OU Final Report. King's College, London.Volume 2: 397-429

Kosmas C., Ferrara A., Briasouli H., Imeson A. (1999). Methodology formapping Environmentally Sensitive Areas (ESAs) to Desertification. In 'TheMedalus project Mediterranean desertification and land use. Manual on keyindicators of desertification and mapping environmentally sensitive areas to

desertification. Edited by: C. Kosmas, M.Kirkby, N.Geeson. European Union18882. pp:31-47 ISBN 92-828-6349-2

Ministry of Development (1986). Land Master Plan, Joint project (Kingdomeof the Netherlands Ministry of Foreign Affairs, Directorate General forInternational Co-operation and Arab Republic of Egypt, Ministry ofDevelopment, General Authority for Rehabilitation Projects and AgriculturalDevelopment.

Nicholson, S.E, C.J Tucker, and M.B Ba. (1998). Desertification, Drought andSurface Vegetation: an example from the West African Sahel. Bulletin of the

American Meteorological Society79 (5): 815-829.

Pax-Lenney, M., Woodcock, C.E., Collins, J. and Hamdi, H. (1996). Thestatus of agricultural lands in Egypt: The use of multitemporal NDVI featuresderived from Landsat TM. Remote Sensing of Environment. In Press

OSS (2003). Map of sensitivity to desertification in the Mediterranean basin-Proposal for the methodology for the final map, Rome: Observatory of theSahara and Sahel (OSS).

Thornes J.B. (1995). Mediterranean desertification and the vegetation cover.

In EUR 15415 - "Desertification in a European context: Physical and socio-economic aspects", edited by R.Fantechi, D.Peter, P.Balabanis, J.L. Rubio.Brussels, Luxembourg: Office for Official Publications of the EuropeanCommunities. 169-194

Tucker, C.J, Dregne, H.E, Newcomb WW (1991). Expansion and Contractionof the Sahara Desert from 1980 to 1990. Science 253: 299-301.

Woodcock, CE., El-Baz, F., Hamdi, H. et. al (1994), Desertification ofAgricultural Lands in Egypt by Remote Sensing. Final Report

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