Land suitability assessment for perennial crops using remote sensing and Geographic Information Systems: A case study in northwestern Egypt

Transcription

1 Archives of Agronomy and Soil Science June 2006; 52(3): Land suitability assessment for perennial crops using remote sensing and Geographic Information Systems: A case study in northwestern Egypt (Bewertung der Eignung von Standorten zum Anbau von mehrjährigen Fruchtarten mittels Fernerkundung und GIS: eine Fallstudie in Nord-West Ägypten) A. SHALABY, Y. O. OUMA, & R. TATEISHI Center for Environmental Remote Sensing (CEReS), Chiba University, Inage-ku, Chiba, Japan (Received 18 March 2005; accepted 30 January 2006) Abstract The main objective of this study was to develop a Geographic Information Systems-based model for land suitability assessment for guava, olive and date palm in the North-western coast of Egypt. Soil, climatic and landscape database as well as satellite image have been integrated through Geographic Information Systems (GIS). A Landsat ETMþ image dated 2001, was classified using maximum likelihood classifier to produce land use/land cover map. Physical and chemical analyses of 57 soil profiles were interpolated to produce continuous land characteristic maps that are relevant to the requirement of the considered crops. These maps with climate and land cover map were integrated using GIS to produce land suitability maps for guava, olive and date palm. Two types of land suitability maps were produced in this study namely: Continuous land suitability maps and conventional land suitability classified maps. For each of them six land suitability maps were produced for the three crops in which three are for actual land suitability and the other three for potential land suitability. It was found that the suitability was higher for date palm followed by olive and the lowest suitability was assigned for guava. Keywords: Land suitability, perennial crops, remote sensing, GIS, Egypt Introduction Inappropriate land use leads to inefficient exploitation of natural resources, destruction of the land resource, poverty and other social problems. Society must ensure that land is not degraded and that it is used according to its capacity to satisfy human needs for present Correspondence: Adel Shalaby, Center for Environmental Remote Sensing (CEReS), Chiba University, 1 33 Yayoi-cho, Inage-ku, Chiba, Japan, Tel: þ Fax: þ adelnan@yahoo.com ISSN print/issn online Ó 2006 Taylor & Francis DOI: /

2 244 A. Shalaby et al. and future generations while also maintaining the earth s ecosystems. Part of the solution to the land-use problem is land evaluation in support of rational land-use planning and appropriate and sustainable use of natural and human resources (Rossiter 1996). Land evaluation is concerned with the assessment of land performance for specific land utilization purposes and provides a rational basis for taking land-use decisions based on analysis of relations between the land use and land, giving estimates of required inputs and predicted outputs (Food and Agriculture Organization of the United Nations [FAO] 1985; Sys et al. 1991). Geographic Information Systems (GIS) is a powerful tool for collecting, storing, retrieving, transforming and displaying spatial data from the real world (Burrough 1986). GIS have been used for the site-selection of areas such as: Service facilities, recreational activities, retail outlets, hazardous waste disposal sites and critical areas for specific resource management and control practices (Jankowski 1995). GIS have been widely recognized as the most promising tool capable of providing reliable information for both planning and decision-making tasks (Michalak 1993). GIS and expert knowledge has been used for land suitability assessment for Cherimoya in southern Ecuador (Bydekerke et al. 1998). Expert systems and GIS technologies are combined to help with an implementation of a land suitability evaluation model (Kalogirou 2002). Remote sensing is a methodological approach for obtaining information about an object, area, or phenomena through the analysis of data acquired by a device that is not in contact with the object, area, or phenomena under investigation. Applications of remote sensing in agriculture include several aspects such as plant phenology, economic features, and land-use management. These applications have been playing important roles and suggest that remote sensing technology is and will be a powerful tool for monitoring agricultural activities (Allan 1990). Remote sensing has been intensively and routinely used for large-scale crop inventory and yield predictions (Idso et al. 1977, 1980; McDonald & Hall 1980; Vossen & Meyer-Roux 1995). Remote sensing can be used as a tool to gather data set for use in a GIS context (Wilkinson 1996). This paper will outline one way in which remote sensing and GIS technologies are complementary. In Egypt, urban areas are expanding into currently productive agricultural areas causing loss of highly fertile land (Pax-Lenney et al. 1996; Shalaby et al and Shalaby & Tateishi 2004). Considering this, it is necessary to obtain up-to-date land-use information using Landsat ETMþ data to construct databases that can be used to identify suitable areas for crops (guava, olive and date palm). The accurate identification and the characterization of current production areas and potential areas are essential to agricultural research and development (Corbett 1996). The final result of agricultural evaluation is a map, which partitions the landscapes into suitable and unsuitable areas for a particular land use of interest. However, this approach may not represent the continuity of land. Land suitability could be better expressed by continuous land suitability maps. In this study, remote sensing, climatic, topographic and soil data are integrated in GIS in order to assess the suitability of the study area for guava, olive and date palm. These three crops were chosen because they were found to be the most appropriate perennial crops for the highly calcareous nature and the coarse texture of the soil in the study area. The objectives of this study, therefore, are: (i) to produce a land use/land cover map through supervised classification, (ii) to develop a GIS-based model for actual and potential land suitability assessment for guava, olive and date palm in the north-western coast of Egypt, and (iii) to produce continuous land suitability maps that represent the continuity of the soil properties.

3 Land suitability assessment using remote sensing and GIS 245 Materials and methodology Study area The study area is located between and North and to East in the north-western coast of Egypt (Figure 1), bordered by the Mediterranean Sea to the north, Alexandria governorate to the east, El-Alamein town to the west and Khashm El-Eish ridge to the south. The area is formed of sedimentary rocks, of the Tertiary and Quarterly geologic ages. The study area covers 3750 km 2 and the elevation varies from 0 to about 100 m above sea level. The north-western coast of Egypt can be subdivided into the following three geomorphic units; firstly, Coastal plain which is bordered by the Mediterranean Sea to the north and by the plateau to the south, taking east-west orientation. The width of the coast varies from one site to another, controlled by the geologic formations. Secondly, Pedomental plain: The interference zone between the coastal plain and the plateau, where the rains flow from the plateau and collect at the depressions. It appears clearly between Ras El-Hekma and Ras Alam El-Room and in the area of El-Salloum. This area has good potential for agriculture Figure 1. Location of the study area.

4 246 A. Shalaby et al. expansion under suitable management of water and land. And thirdly, Plateau rocky plateau, mostly covered by a thin layer of soil. A short rainy season and a long dry summer characterize the climate of the area, with limited fluctuation in daily temperature. The yearly mean maximum air temperature is 248C and the yearly mean minimum air temperature is 168C. The amount of precipitation is about 180 mm per year. The amount of rain decreases from 30 50% on a distance of about 15 km from the coast. Wind speed ranges from m/sec The prevailing wind is mostly from the north. The soils of the north-western coast are highly calcareous due to the calcareous nature of the parent material from which these soils were formed plus the insufficient and limited leaching. Concerning soil classification, only two orders are found in the soils of the north-western coast of Egypt; namely the Entisols, which includes Psamments and Orthents suborders, and the Aridisols, which include Calciorthids, Paleorthids and Salorthids suborders. Landsat Thematic Mapper (ETMþ) was acquired on 20 May 2001; topographic map with scale of 1:25 000, climatic database of the study area and chemical and physical analysis of 57 soil profiles were used in this study. Image processing Geometric correction. Accurate per-pixel registration of remote sensing data is essential to correct for the distortion occurred during acquisition and for the resulting image to be overlaid with other geographic data coverage. The RMS error should not exceed 0.5 pixels (Lunetta & Elvidge 1998). In this study geometric correction was carried out using ground control points from topographic maps to geocode the TM image and the RMS was less than 0.4 pixels which is acceptable. Image enhancement and visual interpretation. The goal of image enhancement is to improve the visual interpretability of an image by increasing the apparent distinction between the features. The process of visually interpreting digitally enhanced imagery attempts to optimize the complementary abilities of the human mind and the computer. The mind is excellent at interpreting spatial attributes on an image and is capable of identifying obscure or subtle features (Lillesand & Kiefer 1994). Contrast stretching was applied on the image and False Colour Composite (FCC) was produced. The FCC was visually interpreted using on screen digitizing in order to delineate land cover classes that could be easily interpreted such as water bodies, urban Sabkha and road networks. Image classification. Land cover classes are typically mapped from digital remotely sensed data through the process of a supervised digital image classification (Campbell 1987; Thomas et al. 1987). The overall objective of the image classification procedure is to automatically categorize all pixels in an image into land cover classes or themes (Lillesand & Kiefer 1994). The maximum likelihood classifier quantitatively evaluates both the variance and covariance of the category spectral response patterns when classifying an unknown pixel so that it is considered to be one of the most accurate classifier since it is based on statistical parameters. Supervised classification was done using ground checkpoints and digital topographic maps of the study area. The area was classified into eight main classes: Seawater, salt marshes, Sabkha, cropland, grassland, bare land, urban and quires. Then accuracy assessment was carried out using 200 points from field data and existing land cover maps. In order to increase the accuracy of land cover mapping of the images, ancillary data and the result of visual interpretation was integrated with the classification result using GIS.

5 Land suitability assessment using remote sensing and GIS 247 Digital soil properties mapping and DEM The accuracy of three different spatial interpolation techniques namely; Proximal, weighted average and Kriging was tested by Van Kullenburg et al. (1982); the RMS error of both weighted average and Kriging was smallest compared to that of proximal. At the same time the difference in accuracy between Kriging and the relatively simple weighted average technique could be neglected in practice. In the absence of a soil map of the study area, 57 soil profiles had been examined and soil samples from different layers has been collected for laboratory analysis to determine the physical and chemical characteristics of the existing soil in the study area. The locations of the 57 soil profiles were chosen based on the visual interpretation of the satellite image in order to represent the different photo-morphological units. Digital continuous soil properties maps were produced by means of a distance-weighted average interpolation procedure for each of the different soil properties. This technique was chosen because of the few available reference points (57 soil profiles), it gives similar accuracy to that of Kriging, in addition, it is a simple and accurate mathematical process of estimating the Z value of a surface at locations where the Z value is unknown (Eastman 1997; Alejandro & Jorge 2003). For producing a digital elevation model (DEM), contour lines from a topographic map of the study area had been digitized and then interpolation was applied to produce DEM from which a slope map had been derived since slope is a main factor for the determination of the suitability of an area for growing crops. Land suitability assessment The FAO framework for land evaluation (FAO 1976) was carried out according to the guidelines for land evaluation for irrigated agriculture (FAO 1985) and (Sys et al. 1991). Climatic and soil requirements tables of Sys et al. (1993) were used for olive and guava (Tables I and II) after modification to suit the local conditions while the requirement table for date palm (Table III) was established by the author depending on the available literature reviews mainly Ecocrop (FAO 1994) and date palm cultivation (FAO 2002). The land properties maps were given a rating according to Tables I, II and III and then, overlaid to produce the final suitability maps. Two types of land suitability maps were produced in this study namely, continuous land suitability maps and conventional land suitability classified maps. The conventional land suitability maps are classified into four classes with sharp boundaries between these classes while the continuous land suitability maps are a gradual change of the suitability value which represent the gradual change of soil properties. For each of them, six land suitability maps were produced for the three crops in which three are for actual land suitability and the other three for potential land suitability after soil reclamation including removal of salts and improving the drainage conditions. The land use/land cover map that has been produced through supervised classification of the ETMþ image was used to mask out areas that are physically unavailable for agriculture such as Sabkhas, water bodies and settlements. All subsequent analysis was carried out only for areas that are not masked. Flow chart of the methodology is shown in Figure 2. Results and discussion Image processing The colour composite generated from bands 4, 3 and 2 (Figure 3) was visually interpreted through on-screen digitizing. The visual interpretation gave a general idea about the forms of

6 248 A. Shalaby et al. Table I. Climatic and soil requirements for olive (Olea europacae). Suitability class S1 S2 S3 N1 N2 Rating scale Climatic characteristic Mean annual temp. (8C) Average minimum temperature to to to of coldest month (8C) Land characteristics Slope % Flooding Fo F1 Drainage good, GW 4150 cm good, GW 4150 cm moderate imperfect poor but drainable poor but not drainable Physical soil characteristics (s) Texture/structure L, SCL, SL SC, SiL, Si CL, C 5 60s, LcS Cm, cs, SiCm SiCL, LfS, LS C 4 60s, fs Coarse fragments (volume %) Soil depth (cm) CaCO3 (%) any Gypsum (%) Salinity and alkalinity (n) ECe (ds/m) ESP (%) GW, ground water table; L, loam; SCL, sandy clay loam; SL, sandy loam; SC, sandy clay; SiL, silt loam; Si, silt; CL, clay loam; SiCL, silty clay loam; LfS, loamy fine sand; LS, loamy sand; C 5 60s, clay less than 60% 0 2 m, blocky structure; LcS, loamy coarse sand; C 4 60s, clay more than 60% 0 2 m, blocky structure; fs, fine sand; Cm, clay, massive; cs, coarse sand; SiCm, silty clay, massive; Fo, no flood limitation; F1, slight.

7 Land suitability assessment using remote sensing and GIS 249 Table II. Climatic and soil requirements for guava (Psidium guijava). Suitability class S1 S2 S3 N1 N2 Rating scale Climatic characteristics Mean annual temp. (8C) Land characteristics Slope % Flooding Fo F1 F2 Drainage good, GW 4150 cm good, GW 4150 cm moderate imperfect poor but drainable poor but not drainable Physical soil characteristics (s) Texture/structure SiCs, SiCL, C 4 60s, SC, C 4 60v, SL, LfS, LS, Cm, SiCm,S C 5 60s, Si, C 5 60v, L SCL LcS, fs SiL, Co, CL Coarse fragments (volume %) Soil depth (cm) CaCO3 (%) any Gypsum (%) any Salinity and alkalinity (n) ECe (ds/m) ESP (%) GW, ground water table; SiCs, silty clay, blocky structure; SiCL, silty clay loam; C 5 60s, clay less than 60% 0 2 m, blocky structure; Si, silt; SiL, silt loam; Co, clay, oxisol structure; CL, clay loam; C 4 60s, clay more than 60% 0 2 m, blocky structure; SC, sandy clay; C 5 60v, clay less than 60% 0 2 m, vertisol structure; L, loam; C 4 60v, clay more than 60% 0 2 m, vertisol structure; SL, sandy loam; SCL, sandy clay loam; LfS, loamy fine sand; LS, loamy sand; LcS, loamy coarse sand; fs, fine sand; Cm, clay, massive; SiCm, silty clay, massive; S, sand; Fo, no flood limitation; F1, slight; F2, moderate.

8 250 A. Shalaby et al. Table III. Climatic and soil requirements for date palm (Phoenix dactylifera L.). Suitability class S1 S2 S3 N1 N2 Rating scale Climatic characteristics Number of heat units (8C) Mean maximum temperature of the growing period (8C) Relative air humidity (average from August to October) Land characteristics Slope % Flooding Fo F1 F2 F3 Drainage good moderate imperfect poor and aeric poor but poor but drainable not drainable Physical soil characteristics (s) Texture/structure L, SCL, SiCL, SC C 5 60s, S, C 4 60v, Cm, SiCm SL, LS SiL, Si C 4 60s C 5 60s Coarse fragments (volume %) Soil depth (cm) Ca CO 3 (%) any Gypsum (%) any Salinity and alkalinity (n) ECe (ds/m) ESP (%) L, loam; SCL, sandy clay loam; SL, sandy loam; LS, loamy sand; Si CL, silty clay loam; SC, sandy clay; SiL, silt loam; Si, silt; C 5 60s, clay less than 60% 0 2 m, blocky structure; S, sand; C 4 60s, clay more than 60% 0 2 m, blocky structure; Cm, clay, massive; SiCm, silty clay, massive; Fo, no flood limitation; F1, slight; F2, moderate; F3, severe.

9 Land suitability assessment using remote sensing and GIS 251 Figure 2. Flow chart of the methodology.

10 252 A. Shalaby et al. land cover that exist in the study area. Many summer resorts were easy to delineate. Roads and irrigation channels were also delineated. On-screen digitizing was carried out for Sabkha, salt marshes, road networks and urban land-cover classes. Supervised classification using all reflective bands of the TM image acquired on 20 May 2001 was carried out using maximum likelihood classifier. Figure 4 shows the result of the classification. Table IV shows the area in hectares of different land cover classes. Figure 3. False colour composite image of Landsat ETMþ (2001) bands 4, 3, 2. Figure 4. Land use/cover classification map of northwestern coast of Egypt, using Landsat ETMþ Table IV. Area of different land cover classes based on supervised classification of ETMþ image of Class Area (ha) Percentage Salt marshes 6, Sabkha 6, Cropland 77, Grassland 76, Bare land 193, Urban 11, Quires 1,

11 Land suitability assessment using remote sensing and GIS 253 In order to increase the accuracy of land-cover mapping of the image, ancillary data and the result of visual interpretation was integrated with the classification results using GIS. This overlying of the visual interpretation on the result of the classification led to the increase in the overall accuracies by about 10%. This could be explained by the fact that the resort houses are spectrally confused with cropland class because resort houses are surrounded by gardens and the small cities are confused with bare land class because these building are made from the surrounding local materials. A standard overall accuracy for land-cover and land-use maps is set between 85 (Anderson et al. 1976) and 90% (Lins & Kleckner 1996). In this study the overall classification accuracy was 92.3%. Details of single class accuracy for the image of 2001 can also be found in Table V. Digital soil properties maps Soil properties maps for all relevant characteristics were produced using GIS. These maps were produced using interpolation technique from point data. This resulted in continuous soil properties maps. These maps were explored using GIS. The salinity map was divided into classes, the area in hectares and percentage of each class was extracted using GIS (Table VI). The advantage of the continuous maps is that it takes into consideration the spatial variability of different soil attributes in contrast to the traditional method which supposes that the area within the land unit or soil polygon is homogenous. In the case of continuous soil properties maps each pixel is given a unique value. The pixel size chosen to produce these maps was 30 m to correspond with the resolution of the ETMþ image that have been used to produce the land cover map of the study area. These maps were then used to produce continuous land suitability maps for different crops. Land suitability classification Actual and potential land suitability maps were produced for the study area. The actual land suitability maps express the suitability of the area for olive, guava and date palm under the present condition of the soil while the potential suitability represent the suitability of the area for the same three crops but after correcting the limiting factors such as salinity and drainage. The potential suitability was calculated and the maps were produced in order to find out the feasibility of conducting the soil reclamation process and what areas should be reclaimed and what is the effect of the reclamation on the suitability of the area for the crops under consideration. Table V. Accuracy statistics for the classification result of 2001 ETMþ image. Class name Producer s accuracy User s accuracy Kappa statistic Salt marshes 100.0% 100.0% 1.00 Sabkha 100.0% 100.0% 1.00 Cropland 97.8% 86.3% 0.84 Urban 87.5% 87.5% 0.87 Bare land 88.8% 95.4% 0.92 Grass land 83.3% 81.6% 0.78 Quires 100.0% 100.0% 1.00

12 254 A. Shalaby et al. Table VI. Salinity classification of the study area. Salinity rank EC (ds/m) Area (ha) Percentage Interpretation Low 0 2 5,316 6 Very little chance of damage on all plants Moderate , Sensitive plants and seedlings of others may show damage High , Most non-salt tolerant plants will show damage; salt-sensitive plants will show severe damage Excessive ,458 3 Salt-tolerant plants will grow; most others show severe damage V. Excessive Very few plants will tolerate and grow Actual land suitability mapping. A rating was calculated for each soil properties map for olive, guava and date palm taking into consideration the parameters: Flooding, drainage, texture, structure, coarse fragments, soil depth, CaCO 3, gypsum, salinity and alkalinity and slope. The rating maps were then overlaid to produce the land suitability maps following two procedures using IDRISI Kilimanjaro software: (1) Storie method (Storie 1976) I ¼ A B 100 C 100 where A, B, C,..... ratings; I: index. (2) Square root method (Khiddir 1986) rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi A B I ¼ Rmin where: I: index; Rmin: minimum rating; A, B,...: other ratings beside the minimum rating. These land suitability maps were overlaid with the land cover/land use map to mask out areas that are physically unavailable for agriculture such as Sabkhas, water bodies and settlements. The final actual suitability maps are presented as: Figure 5 continuous land suitability maps, and Figure 6 conventional land suitability classified maps. The areas in hectares of different land suitability classes for the three crops are presented in Table VII. From Figures 5 and 6 and Table VII it was found that, under the present condition without major land reclamation, date palm is the most suitable crop where 30.5% of the study area is very suitable and moderately suitable, followed by olive with 21.1% very suitable and moderately suitable while only 21.2% of which only 0.3% of the area is very suitable and 20.9% is moderately suitable for guava. The climatic requirements of all the three crops are met and the study area is very suitable from the climatic point of view for the three crops. The climatic suitability index is 96 for olive, 92 for guava and 90 for date palm. When evaluating the land characteristics it was found that the slope is suitable for the three crops since the slope is less than 2% for most of the area. However, the main limiting soil characteristics for guava are the salinity and texture. Guava is considered sensitive for

13 Land suitability assessment using remote sensing and GIS 255 Figure 5. Actual continuous land suitability map for (A) guava, (B) olive and (C) date palm. salinity. If the salinity level of the soil is more than 5 ds/m it becomes unsuitable for growing guava. Olive is considered to be moderately tolerant for salinity and it can tolerate up to 12 ds/m while the soil depth is the main limiting factor besides the drainage in some parts of the study area. Date palm is considered the most suitable for the study area since it is one of the most tolerant crops for salinity and it can sustain up to 25 ds/m especially in sandy soils.

14 256 A. Shalaby et al. Figure 6. Actual land suitability classified map for (A) guava, (B) olive and (C) date palm. Table VII. Actual suitability classes for guava, olive and date palm (area in ha). Suitability classes Guava % Olive % Date palm % Very suitable (S1) , , Moderately suitable (S2) 17, , , Marginally suitable (S3) 40, , , Unsuitable (N) 19, , , Physically unavailable 6, , ,

15 Land suitability assessment using remote sensing and GIS 257 Potential land suitability mapping. The potential land suitability classification relates the suitability of land for the use in question at some future date after major improvements have been effected where necessary (Sys et al. 1991). These improvements include the leaching of the excessive salt, replacing the sodium on the soil complex by calcium and magnesium through adding gypsum and improving the drainage conditions. Saline soils have EC of more than 4 ds/m and ESP of less than 15, while Sodic soils have an ESP of more than 15 and EC of less than 4 ds/m. Saline soils are relatively easy to reclaim for crop production if adequate amounts of low-salt irrigation water are available, internal and surface drainage are present, and salt disposal dump areas are available. Potential land suitability maps are produced for the three crops. Figure 7 shows potential conventional land suitability classified map after improving the adverse soil conditions. Areas of different land suitability classes in hectares as well as the percentage of different land suitability classes are presented in Table VIII. From Figure 7 and Table VIII it was found that after correcting the unfavorable soil conditions, about 56.2% of the study area becomes very suitable and moderately suitable for date palm, compared to 55.3% for olive and only 41% for guava. This result could be explained by the fact that the main limitation for date palm and olive which is (drainage, salinity) could be corrected while soil depth cannot be corrected. While for guava, the main limitation, in addition to salinity and drainage, soil texture and soil depth cannot be corrected. Land evaluation is mostly applied on a traditional soil map which does not account for the spatial variation within land unit. With traditional soil maps only representative soil profiles can be used, one profile for each soil type or average of more than one profile, while the proposed method take the spatial variation into account and all soil profiles can be used. The proposed methodology uses soil information and land-cover information in a GIS environment to facilitate the exploration of different data coverage and to present the result spatially. In continuous soil suitability maps produced by this methodology, each pixel is given a suitability index. In that way we can distinguish, for example, between pixels moderately suitable (S2) with value of 74 and S2 with a value of 51 which will produce different yield although all are considered S2 in conventional soil suitability maps. Conclusion and recommendations Remote sensing can provide valuable information for land suitability assessment especially in arid areas in the absence of detailed soil maps. The land cover map was produced through supervised classification and then used as a layer in the land suitability model. GIS has been used to produce continuous soil properties maps which are relevant to land suitability for different crops. These maps were then used to produce continuous land suitability maps. While using traditional soil maps for land suitability assessment, only representative soil profiles can be used, one profile for each soil type or average of more than one profile, the proposed method take the spatial variation into account and all soil profiles can be used. The proposed methodology uses soil information and land cover information in a GIS environment to facilitate the exploration of different data coverage and to present the result spatially. The produced continuous land suitability maps account of the spatial variability of the suitability for different crops and these maps could be easily used to produce conventional land suitability classified maps.

16 258 A. Shalaby et al. Figure 7. Potential land suitability classified map for (A) guava, (B) olive and (C) date palm. Table VIII. Potential suitability classes for guava, olive and date palm (area in ha). Suitability classes Guava % Olive % Date palm % Very suitable (S1) , , Moderately suitable (S2) 35, , , Marginally suitable (S3) 28, , , Unsuitable (N) 12, , , Physically unavailable 6, , ,

17 Land suitability assessment using remote sensing and GIS 259 The actual suitability of the study area for guava, olive and date palm cultivation ranges from very suitable to unsuitable. The main limitation was found to be due to texture, soil depth, drainage and salinity. About 25% of the study area ranges from high to excessive in salinity while 69% is slightly saline and only 6% of the area is low in salinity. From the potential suitability maps that have been produced it is clear that the suitability and productivity of the soil will be improved if soil reclamation is carried out. It should be mentioned that, due to the limited financial resources for the field work and chemical and physical analysis, only 57 reference points have been used to produce the soil properties maps. The accuracy of the soil properties maps and consequently the suitability maps could be improved if the number of reference point is increased. References Alejandro C, Jorge L Delineation of suitable areas for crops using a Multi Criteria Evaluation approach and land use/cover mapping: A case study in Central Mexico. Agricult Syst 77: Allan JA Sensors, platforms and applications; acquiring and managing remotely sensed data. In: Steven MD, Clark JA, editors. Applications of remote sensing in agriculture. London, UK: Butterworths. pp Anderson JR, Hardy EE, Roach JT, Witmer RE A land-use and land-cover classification system for use with remote sensor data. Washington DC: US Geological Survey Professional Paper 964. Burrough PA Principles of geographical information systems for land resources assessment. Oxford: Oxford University Press. p 194. Bydekerke L, Van Ranst E, Vanmechelen L, Groenemans R Land suitability assessment for cherimoya in southern Ecuador using expert knowledge and GIS. Agric Ecosyst Environ 69: Campbell JB Introduction to remote sensing. New York: Guilford. Corbett JH Dynamic crop environment classification using interpolated climate surfaces. In: Goodchild MF, Steyaert TL, Parks OB, editors. GIS and environmental modeling: Progress research issues. Fort Collins: GIS World Book. pp Eastman JR IDRISI for Windows User s Guide. Version 2.0. Clark Laboratories for Cartographic Technologies and Geographic Analysis, Clark University, Worcester, MA. Food and Agriculture Organization of the United Nations (FAO) A framework for land evaluation. Soils bulletin, No. 32. Rome: FAO. Food and Agriculture Organization of the United Nations (FAO) Guidelines: Land evaluation for irrigated agriculture. Soils Bulletin 55, Rome, Italy: FAO. 231 pp. S590. F68 no. 55. Food and Agriculture Organization of the United Nations (FAO) Ecocrop Version 1.1. Rome: FAO. Food and Agriculture Organization of the United Nations (FAO) Date palm cultivation, Plant Production and Protection paper. 156 Rev. 1. Rome: FAO. Idso SB, Jackson RD, Reginato RJ Remote sensing of crop yields. Science 196:9 25. Idso SB, Pinter PJ Jr, Jackson RD, Reginato RJ Estimation of grain yields by remote sensing of crop senescence rates. Remote Sens Environ 9: Jankowski P Integrating geographical information systems and multiple criteria decision-making methods. Int J Geograph Info Syst 9(3): Kalogirou S Expert systems and GIS: An application of land suitability evaluation. Comp Environ Urban Syst 26: Khiddir SM A statistical approach in the use of parametric systems applied to the FAO framework for land evaluation. (PhD Thesis). Belgium: State University of Gent. p 141. Lillesand T, Kiefer RW Remote sensing and image interpretation, 4th ed. New York: John Wiley and Sons. Lins KS, Kleckner RL Land cover mapping: an overview and history of the concepts. In: Scott JM, Tear TH, Davis F, editors. Gap analysis: A landscape approach to biodiversity planning. Bethesda, MD: American Society for Photogrammetry and Remote Sensing. pp Lunetta RS, Elvidge CD Remote sensing change detection. Michigan: Ann Arbor Press. McDonald RB, Hall FG Global crop forecasting. Science 280: Michalak WZ GIS in land use change analysis: Integration of remotely sensed data into GIS. Appl Geograph 1:28 44.

18 260 A. Shalaby et al. Pax-Lenney M, Woodcock CE, Collins JC, Hamdi H The status of agricultural lands in Egypt: The use of multitemporal NDVI features derived from Landsat TM. Remote Sens Environ 56(1):8 20. Rossiter DG A theoretical framework for land evaluation. Geoderma 72: Shalaby A, Aboel Ghar M, Tateishi R Desertification impact assessment in Egypt using low resolution satellite data and GIS. Int J Environ Studies 61(4): Shalaby A, Tateishi R Remote sensing and GIS for land cover change detection in the northwestern coast of Egypt. Proceedings of annual conference of Japan Society for Photogrammetry and Remote Sensing, Tokyo, Japan June pp Storie RE An index soil rating (revised 1978). Spec Publ Div Agric Sci No Berkley: University of California. Sys C, Van Ranst E, Debaveye J Land evaluation Part I and II crop requirement. Belgium General Administration for Development Cooperation. Agricultural Publications No. 7. Sys C, Van Ranst E, Debaveye J, Beernaert F Land Evaluation Part III crop requirement. Belgium General Administration for Development Cooperation. Agricultural Publications No. 7. Thomas IL, Benning VM, Ching NP Classification of remotely sensed images. Bristol, UK: Adam Hilger. Van Kullenburg J, De Gruijter JJ, Marsman BA, Bouma J Accuracy of spatial interpolation between point data of soil moisture supply capacity was compared with estimates from mapping units. Geoderma 27: Vossen P, Meyer-Roux J Crop monitoring and yield forecasting activities of the MARS Project. In: King D, Jones RJA, Thomasson AJ, editors. European Land Information Systems for Agro-Environmental Monitoring. Luxembourg: Official Publications of the EU. pp Wilkinson GG A review of current issues in the integration of GIS and remote sensing. Int J Geograph Info Sci 10(1):