Soil Salinity Mapping Model Developed Using RS

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Soil Salinity Mapping Model Developed Using RS and GIS - A Case Study from .... salts move to the surface and salts crystallize through evaporation.
European Journal of Scientific Research ISSN 1450-216X Vol.26 No.3 (2009), pp.342-351 © EuroJournals Publishing, Inc. 2009 http://www.eurojournals.com/ejsr.htm

Soil Salinity Mapping Model Developed Using RS and GIS - A Case Study from Abu Dhabi, United Arab Emirates Mahmoud A. Abdelfattah Soil Scientist, Soil Resources Department Environment Agency Abu Dhabi, P.O. Box 45553, Abu Dhabi, UAE (Permanent position at Faculty of Agriculture Fayoum University, Egypt) E-mail: [email protected] or [email protected] Shabbir A. Shahid Salinity Management Scientist, International Center for Biosaline Agriculture P. O. Box 14660 Dubai United Arab Emirate Yasser R. Othman Remote Sensing and GIS Specialist, Information Technology Department Environment Agency Abu Dhabi, P.O. Box 45553 Abu Dhabi, United Arab Emirates Abstract Soil salinization is one of the most common land degradation processes in arid and semi-arid regions, where precipitation exceeds over evaporation. Under such climatic conditions, soluble salts are accumulated in the soil, influencing soil properties and environment with ultimate decline in soil productivity. Therefore, mapping of saline areas is essential for understanding resource for sustainable soil uses and management. The present study presents a model to map soil salinity using Remote Sensing (RS) and Geographic Information Systems (GIS). The coastal area of Abu Dhabi Emirate, where the issue of salinity is a major concern, was used as pilot study area. The model development consists of a number of phases, salinity detection using RS data, site observations, correlation and verification, and model validation. Multi–temporal Landsat-7 ETM image (Enhanced Thematic Mapper) acquired in 2000 and 2002 were used to detect coastal saline areas. GIS was used to integrate the available data and information, design the model, and to create different maps. A geodatabase was created and populated with 403 observations together with laboratory analyses data. Salinity maps at suborder and great group levels of the USDA Soil Taxonomy were developed. In addition, the relation between salinity and dominant soil types are discussed. The suborder level salinity map reveals that Salids represent an area of 145,823 ha (58% of the total area), whereas, at great group level Haplosalids represent 98,414 ha (39%), and Aquisalids, represent 47,408 ha (19%). The site observations salinity map indicates that 63% of the study area has been classified as strong to extremely saline, whereas, 37% has been classified as slight to moderately saline area. The correlation between the salinity maps developed from visual interpretation of remote sensing data, and site observations shows that 91.2% of the saline areas delineated using remote sensing data fits with those delineated using site observations data. The study confirmed that ground truthing coupled with RS data and GIS techniques are powerful

Soil Salinity Mapping Model Developed Using RS and GIS - A Case Study from Abu Dhabi, United Arab Emirates

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tools in detecting salinity at different levels in hyper-arid conditions. The model can be adopted elsewhere in similar areas that experience salinization problems.

Keywords: Soil salinity, RS, GIS, modeling, Abu Dhabi Emirate, UAE, USDA Soil Taxonomy.

Introduction The main problems associated with arid and semi-arid regions are salinization and desertification. Soil salinization is a major form of land degradation in agricultural areas, where information on the extent and magnitude of soil salinity is needed for better planning and implementation of effective soil reclamation programs. Statistics about the extent of world salt-affected areas vary according to authors; however, general estimates are close to 1 billion hectares, which represent about 7% of the earth’s continental extent. In addition to these naturally salt-affected areas, about 77 million hectares (mha) have been salinized as a consequence of human activities (secondary salinization), with 58% in irrigated areas. On average, 20% of the world’s irrigated lands are affected by salts, but this figure increases to more than 30% in countries such as Egypt, Iran and Argentina (Ghassemi et al., 1995). To keep track of changes in salinity and anticipate further degradation, mapping and monitoring is essential for proper and timely decisions to be made to adjust the management practices or to undertake proper reclamation and rehabilitation measures. Mapping and monitoring of salinity means first identifying the areas where salts concentrate and secondly, detecting the temporal and spatial changes in this occurrence. Both largely depend on the peculiar way of salts distribution at the soil surface and within the soil mantle, and on the capability of the remote sensing tools to identify salts (Zinck, 2001). For monitoring and eventual control of salinity problems, remote sensing techniques are very useful, especially for the study of soils in arid and semi-arid environments due to sparse vegetation cover. Satellite data have been used for detection and mapping of saline soils using different techniques such as principal component analysis (Marchanda, 1981), a combination of spectral classification and physiographic maps (Tricatsoula, 1988), and spectral correlation and classification (Abdel-Hamid and Shrestha, 1992). Remotely sensed data and geoinformatics caused a revolution in research related to agriculture, land, water, marine and geomorphology. It helps the researchers to facilitate the investigations, assessments and may lead to more understanding of sustainable development. Remote Sensing and GIS techniques can be an excellent tool for mapping saline and waterlogged soils (De Dapper et al., 1997). Ghabour and Daels (1993) concluded that detection of soil degradation by conventional means of soil surveying requires a great deal of time, but remote sensing data and techniques offer the possibility for mapping and monitoring these processes more efficiently and economically. Wiegand et al. (1994) carried out a procedure to assess the extent and severity of soil salinity in fields in terms of economic impact on crop production and effectiveness of reclamation efforts. Their results illustrate practical ways to combine image analysis capability, spectral observations, and ground truth to map and quantify the severity of soil salinity and its effects on crops. The objective of the present study is to develop a model that integrates remote sensing data with GIS techniques to assess, characterize and map the state and behavior of soil salinity. The coastal area of Abu Dhabi Emirate, where the issue of salinity is a major concern, has been used as a pilot study area.

The Study Area Abu Dhabi (Fig. 1 and 2) is the largest Emirate of the United Arab Emirates occupying 84% (77,000 km2) area of the country. Abu Dhabi’s major ecosystems comprise the coast, numerous islands, mountainous areas, gravel plains, and sand desert (Boer, 1998). Nearly 80% of the Abu Dhabi Emirate

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Mahmoud A. Abdelfattah, Shabbir A. Shahid and Yasser R. Othman

area is desert, to the north and west is an extensive area of coastal salt flats, locally known as “sabkha". Isolated interdunal sabkha exists in the desert away from coastline. The sandy desert begins behind the coastal sabkha, with little white dune ripples eventually forming an expanse of large orangered dunes in the southwest. About 100 kilometers inland, towering dunes rising to 200 meters (mega dunes) are common. These form part of the Empty Quarter or “Rub Al-Khali”, a vast desert which stretches beyond the UAE’s southern border (UAE Yearbook, 2008). Gravelly plains also cover wide areas in both the far west and east of the Emirate. Mountains are absent, a notable exception being the impressive Jebel Hafit near Al Ain, an outlier of the Hajar mountain range (Brown, 2008). Abu Dhabi Emirate experiences extremely high temperatures (Table 1) during summers (45-50ºC) with mean temperature being 28ºC and short mild winters with temperature as low as 3ºC (Alsharhan and Kendall, 2002). Humidity is the highest along the coastal fringes and decreases inland. The mean annual rainfall is about 111 mm. Figure 1: Location of Abu Dhabi Coastal area

Soil Salinity Mapping Model Developed Using RS and GIS - A Case Study from Abu Dhabi, United Arab Emirates

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Figure 2: Location of United Arab Emirates

Materials and Methods The development of the salinity model has been structured under four main phases: salinity detection using remote sensing data, site observations (ground truthing), correlation and verification (intersection between salinity map produced from visual interpretation of remotely sensed data and salinity map produced from site observations), and model validation. Figure (3) shows the construction of the model. Remotely sensed data, multi–temporal Landsat-7 ETM image (Enhanced Thematic Mapper) acquired in 2000 and 2002 has been used to detect coastal saline areas. Two datasets were used to delineate the saline boundaries; each dataset consisted of three satellite images acquired in the same path and row, but in different years. In the initial phase, remote sensing data was intensively used to identify and map salt-affected areas. The main factors affecting the reflectance are the quantity of salts, soil moisture, soil color and terrain roughness. Salts influenced surface features are crusts without or with only little evidence of salt, thick salt crusts and puffy structures (Fig. 3). A visual interpretation of the processed satellite image data was carried out to delineate boundarues of the saline areas. Salinity maps at suborder and great group levels of the USDA Soil Taxonomy (Soil Survey Staff, 1999, 2002, 2003 and 2004) were developed. Soil samples were collected from 403 observation sites and analyzed in the laboratory; the weighted average method has been applied to calculate salinity at a depth of 050cm. The results have then been plotted on the map and interpolated to produce salinity map with different classes. ERDAS imagine and ArcGIS were used as main GIS packages for building the model and running its functions including input, output, analysis and processing. Raster and vector GIS datasets were used to create different maps based on overlaying, crossing and interpolation techniques. The verification between mapping salinity using visual interpretation of remote sensing data and mapping salinity through site observations were determined through intersection. A correlation was developed between both the maps to arrive at a final method of integrating remote sensing data with spatial modeling.

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Mahmoud A. Abdelfattah, Shabbir A. Shahid and Yasser R. Othman Figure 3: The main phases of the developed salinity model Phase 1

Phase 2

Salinity detection

Site observations

acquired RS data

Soil sampling

R S

Image processing

Laboratory analyses

&

Visual interpretatio

Interpolation

Saline areas delineation

Salinity classification

G

Salinity map

Salinity map

S

&

G I S

R S

I

Phase 3 Correlation & verification Phase 4 Model validation

Results and Discussions Salinization Status in the Study Area Salinization constitutes the major process in the coastal areas of Abu Dhabi Emirate. The process was recognized by the presence of surface salt crust in different shapes; polygonal and hexagonal patterns, sealed surfaces, and upturned salt flakes (Fig. 4) associated with a high water table with an electrical conductivity (EC) reaching more than 200 dS m-1. In Aquisalids (under great group level of USDA Soil Taxonomy), the water table remains within the upper 1 meter and through capillary rise water and salts move to the surface and salts crystallize through evaporation. X-ray diffraction analysis identified halite as the dominant salt mineral accompanied with gypsum, anhydrite, and calcite (Shahid et al., 2004, 2007; Abdelfattah and Shahid, 2007).

Soil Salinity Mapping Model Developed Using RS and GIS - A Case Study from Abu Dhabi, United Arab Emirates

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Figure 4: Common salinity features in the coastal area of Abu Dhabi Emirate

Phase 1: Salinity Detection Using Remote Sensing Data Color composite of Landsat TM image bands 123 RGB showing salty areas overlaid with visual interpretation boundaries is shown in Figure (5). Table 1 shows classification of saline and non-saline areas at great group level of USDA Soil Taxonomy using visual interpretation of satellite image data. The results of the salinity mapping under the suborder level of the USDA Soil Taxonomy reveals that Salids represent an area of 145,823 ha (57.76% of the total area), whereas, at great group level Haplosalids represent 98,414 ha (38.98%), and Aquisalids represent 47,408 ha (18.78%). On the other hand, non-saline areas (Haplogypsids, Petrocalcids, Torriorthents, and Torripsamments) represent 42.24%. Figure 5: Visual interpretation of saline area boundaries overlaid processed remote sensing image.

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Table 1:

Saline and non-saline areas at great group level of USDA Soil Taxonomy using Remote Sensing data Saline areas

Soil Type (Great Group Level) Aquisalids

Haplosalids

Non-saline areas

Map Units Codes Gypsic Aquisalids 01 Gypsic Aquisalids 02 Typic Aquisalids 01 Gypsic Haplosalids 01 Gypsic Haplosalids 02 Gypsic Haplosalids 03 Typic Haplosalids 01 Typic Haplosalids 02 Typic Haplosalids 03 Typic Haplosalids 04 Typic Haplosalids 05

Total

Area (ha) 4582 800 42025 29991 1962 221 62350 558 147 2274 909

1.22 0.21 11.15 7.95 0.52 0.06 16.54 0.15 0.04 0.6 0.24

145823

57.76

%

Soil Type (Great Group Level) Haplogypsids

Petrocalcids Torriorthents

Torripsamments

Map Units Codes Leptic Haplogypsids 01 Leptic Haplogypsids 02 Leptic Haplogypsids 03 Leptic Haplogypsids 04 Typic Petrocalcids 01 Typic Petrocalcids 02 Typic Torriorthents 01 Typic Torriorthents 02 Typic Torriorthents 03 Typic Torripsamments 01 Typic Torripsamments 02 Typic Torripsamments 03

Area (ha) 705 6291 1088 2890 538 1661 31340 538 1699 47680 9975 2211 106623

% 0.19 1.67 0.29 0.77 0.14 0.44 8.31 0.14 0.45 12.65 2.65 0.59 42.24

Phase 2: Site Observations (Ground Truthing) The salinity of the 403 collected soil samples were interpolated using an Inverse Distance Weighted (IDW) technique. Salinity map with different salinity classes (0-10, 10-20, 20-30, 30-40, 40-50, and >50 dS m-1) were produced (Fig. 6 and Table 2). The results indicate that 63.34% of the study area has been classified as strongly to extremely saline areas, whereas, 36.65% have been classified as slight to moderate saline areas. Figure 6: Salinity map produced from interpolated site observations data

Table 2:

Salinity data extracted from interpolation of site observations data

Salinity Classes

Salinity Ranges (dS/m)

Area (m2)

Area (ha)

Percent

1 2 3 4 5 6

0-10 10-20 20-30 30-40 40-50 > 50

508766696.16 416600071.33 319028501.03 369076655.40 464542757.90 446450318.19

50876.67 41660.01 31902.85 36907.67 46454.28 44645.03

20.15 16.50 12.64 14.62 18.40 17.68

Salinity status (%) Slight to Moderate Strongly to Extremely (20dS/m) 36.65 63.34

Phase 3: Correlation and Verification The intersection between salinity map produced from visual interpretation of remote sensing data and salinity map produced from site observations was carried out (Fig. 6) to confirm how much of the delineated saline areas fit with the interpolated site observation saline areas. The results indicate that 91.2% of the saline areas delineated through remote sensing data fits with those processed from site observations.

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Figure 6: Intersection between salinity maps produced from remote sensing and site observation data.

Phase 4: Model Validation In terms of correlation between the saline areas delineated using remote sensing data and those developed through site observations, the results indicate that 91.2% of the saline areas delineated using remote sensing data fits with those delineated using site observations, which gave a very good indication for the validity of the model. In terms of boundaries fitting between the two maps, it appears that the lines don’t fit completely, however they are close to each other. Notably, this model can only be used in the hyper-saline areas that are easily detectable using remote sensing data and would give much accurate results with more observations.

Conclusions The sequence of the model from detection, site observations, correlation and verification, and model validation has proved to be applicable for mapping salinity using RS and GIS techniques. From the above results it is concluded that the use of remote sensing data followed by site observations is a powerful tool in detecting saline areas. The model shows that 91.2% of the saline areas delineated using remote sensing data fits well with those delineated using site observations data, which gave a good indication for the validity of the model. The model can be used in similar areas that experience salinization problems.

Acknowledgment The authors highly acknowledge His Excellency Majid Al Mansouri, Secretary General of Environment Agency–Abu Dhabi, for his continuous support and encouragement to complete the present study. The assistance provided by Khaliq-ur-Rehman Arshad, Mohamed Al Meharibi, Anil Kumar and Environment Laboratory Department staff is highly appreciated.

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References [1]

[2]

[3]

[4] [5]

[6] [7] [8]

[9] [10]

[11]

[12]

[13] [14] [15]

[16]

[17] [18]

Abdelfattah, M.A., and S.A. Shahid. 2007. A comparative characterization and classification of soils in Abu Dhabi Coastal Area in relation to arid and semi-arid conditions using USDA and FAO soil classification systems. Arid Land Res. Manage. 21(3):245– 271. Available at http://www.informaworld.com/smpp/content~content=a779704000~db=all~order=page (accessed 9 Sept. 2007, verified 14 Nov. 2007). Abdel-Hamid, M. A. and Shrestha, D.P. 1992. Soil salinity mapping in the Nile Delta, Egypt using remote sensing techniques. International Archives of Photogrammetry and Remote Sensing, 29, (B7), 783–787. ISPRS Commission VII/4, Washington D.C., USA. Alsharhan, A. S. and C.G.St. C. Kendall. 2002. Holocene carbonate-evaporates of Abu Dhabi, and their Jurassic ancient analogs, pp. 187–202, in H. J. Barth and B. Boer, eds., Sabkha ecosystems. Kluwer Academic Publishers, Dordrecht, The Netherlands. Boer, B. 1998. Ecosystems, anthropogenic impacts, and habitat management techniques in Abu Dhabi. Ph.D. Dissertation, the University of Paderborn, Germany. Brown, G. 2008. Flora and vegetation of Abu Dhabi Emirate. Marine and environment of the UAE. Book Chapter in the Terrestrial Environment of Abu Dhabi. Environment Agency – Abu Dhabi. ISBN 978-9948-408-33-8. De Dapper, M., R. Gossens and E. van Ranst. 1997. Soil salinity and waterlogging in the Ismailia Governorate, Egypt. Telsat III/13 project. Belgium Egypt Scientific Collaboration. Ghabour, T.K. and Daels, L. 1993. Mapping and monitoring of soil salinity of ISSN. Egyptian Journal of Soil Science 33: (4)355-370. Ghassemi, F., A. J. Jakeman & H. A. Nix. 1995. Salinization of land and water resources: human causes, extent, management and case studies. Canberra, Australia: The Australian National University. Wallingford, Oxon, UK: CAB International. Manchanda, M. L. 1981. Study of Landsat imagery and aerial photos for the evaluation of saltaffected soils of north-west India. Unpublished MSc thesis, Soils Division, ITC, Enschede. Shahid, S.A., M.A. Abdelfattah, and K.R. Arshad. 2004. Soil survey for the coastline of Abu Dhabi Emirate. Volume I: Reconnaissance survey. Volume II: Soil maps. Environment Agency–Abu Dhabi, UAE. Shahid, S. A., M. A. Abdelfattah, and M. A. Wilson. 2007. A Unique Anhydrite Soil in Coastal Sabkha of Abu Dhabi Emirate. Soil Survey Horizons (SSH), Soil Science Society of America (SSSA). Volume No. 48, Issue No. 4, pp. 75-79. Soil Survey Staff. 1999. Soil Taxonomy-A Basic system of soil classification for making and interpreting soil surveys. Agriculture Handbook Number 436, Second Edition, pp. 869. U.S. Gov. Print Office, Washington. Soil Survey Staff. 2002. Field book for describing and sampling soils. Version 2.0. U.S. Gov. Print. Office, Washington, DC Soil Survey Staff. 2003. Keys to Soil Taxonomy. 9th ed. U.S. Government Printing Office, Washington, DC. Soil Survey staff. 2004. Soil survey laboratory methods manual, Soil Survey Investigations Report No. 42, U.S. Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Center. Tricatsoula, E. 1988. Digital image processing and geographic information system for soil and land use mapping with emphasis on soil moisture and salinity/alkalinity determination. Unpublished MSc thesis, Soils Division, ITC, Enschede. UAE Yearbook. 2008. Ministry of information and culture. Trident Press Ltd., UK. Wiegand, C. L., Rhoades, J.D., Escobar, D. E., and Everitt, J. H. 1994. Photographic and videographic observations for determining and mapping the response of cotton to soil salinity. Remote Sensing of Environment, 49, 212-223.

Soil Salinity Mapping Model Developed Using RS and GIS - A Case Study from Abu Dhabi, United Arab Emirates [19]

351

Zinck, J. A. 2001. Monitoring salinity from remote sensing data. In R. Goossens & B. M. De Vliegher (Eds.), Proceeding of the 1st Workshop of the EARSeL Special Interest Group on Remote Sensing for Development Countries (pp. 359-368). Belgium: Ghent University.