MONITORING SALT-AFFECTED SOILS IN A REGION IN SAUDI ARABIA USING REMOTE SENSING TECHNIQUES

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1 MONITORING SALT-AFFECTED SOILS IN A REGION IN SAUDI ARABIA USING REMOTE SENSING TECHNIQUES Saleh A. Al-Hassoun Civil Engineering Department King Saud University Riyadh, Saudi Arabia ABSTRACT A Multitemporal Landsat Thematic Mapper (TM) data collected in two-years have been used to monitor the affected soils by salinity in a region north of Saudi Arabia. Results of various remote sensing image techniques of (TM) data were used to (1) show the spectral classes and the corresponding areas of the different land uses covering the region, (2) delineate and map those areas that are salt-affected, and (3) monitor the temporal changes in salinity in terms of its severity and a real extent for the period under investigation. The study has indicated that a serious salinity problem exists and it is getting worse. Moreover, it calls for an urgent salinity management program to control the spread of salinity and to reclaim the damaged areas to be used for economic agriculture. INTRODUCTION Soil salinity in irrigated areas is becoming a serious problem for agriculture, especially in arid and semi-arid climates. Saline soil conditions have resulted in reduction of the value and productivity of considerable areas of land throughout the world. Salinity commonly occurs in irrigated soil because of the accumulations of soluable salts introduced from the continuous use of irrigation waters containing high or medium quantity of dissolved salts. Management of the salt balance to mitigate its adverse effects on agriculture output is required. Management includes application of excessive irrigation water for leaching excess salts, providing soil drainage, and using proper agronomic practices such as growing salt-tolerant crops. Unfortunately, most of these requirements are rarely provided leading the world to problems of salinity. The ability to determine and monitor the effects of salts on soil and plants are of great importance to agriculture. Conventional ground survey procedures are time consuming and costly. Thus the application of satellite imagery to agriculture fields detection and delineation of problematic soil is considered an attractive alternative. Remote sensing techniques can make breakthrough in terms of their efficiency and extent of coverage.

2 Nowadays commercially available earth-observing optical satellite systems loaded with sensors that record broad bands in the visible and infrared spectral regions like Landsat MSS, TM and SPOT, economically provide a wide range of images of increasing spatial resolution ( m). In addition, processing these satellite data which are primarily based on statistical pattern recognition and related topics such as classification, clustering, discriminate analysis, and principal component analysis, have become popular and quite widely accepted. Several researchers have attempted to detect the distribution and severity of soil salinity with either visual or computer remote sensing techniques, or using combination of both methods. Abdel-Hamid et al. [1] presented a study in which Landsat Thematic Mapper (TM) data have been used to identify and map saline areas of Nile delta in Egypt with reasonable accuracy. Moreover, these affected areas were monitored with support of ground information using Geographic Information System (GIS). Using Remote Sensing for mapping and monitoring salt affected soil has also been studied by Verma et al. [7]. Their study concluded that the degree of soil salinity influences the land cover and land-use pattern as a result of which these units exhibit different tone, texture and pattern on the TM image. An attempt has been made by the authors to correlate these variations with the degree o salinity. They also used the image interpretation to assign the amount of gypsum used to overcome the salinity problem. Thompson et al. [5] used different techniques in the delineation and classification of soil salinity. Their study has confirmed that saline affected soil can be reasonably detected using Landsat data. Successful and partially successful techniques are varied and require adaptation according to data quality, size of mine areas to be detected, degree of salinity, environmental conditions, and biophysical setting. On the whole, detection and mapping of salinity related problems using remote sensing have been studied by many other researchers such as Venkataratnam [6], Manchanda [3], Millington et al. [4] and Csillag et al. [2], just to name a few. The objective of this study is to use Landsat TM data for the delineation, mapping, classification and temporal change detection of salt-affected soils in the agriculture area of Skaka, north of Saudi Arabia. Remote sensing using Landsat TM data, coupled with image processing techniques are expected to provide effective and efficient means for inventory and monitoring the extent of this problem. Site Characteristics For the last decade, Saudi Arabia has shown an extraordinary agricultural development. This rapid growth combined with improper agricultural practices have resulted in many soil problems in different areas over the country, Skaka City, north of Saudi Arabia, is an example of those affected areas that are merging from a serious

3 salinity crisis. Skaka city is located in Al-Jouf region north of Saudi Arabia. It is centered between 40 N and 30 E with an area of km 2 (24.5 x 25 km). The area has a typical desert climate which is very hot in summer and very cold in winter. The topography of the area consists of sand dunes covering the northeast part of the city and medium to small mountains scattered in the northern west. The rest of study area, in general, is relatively flat mainly used for agriculture purposes. METHODOLOGY An attempt to map and monitor the salt affected soil in the region under investigation using an efficient technique, remote sensing approach was utilized as opposed to the conventional ground surveying methods. Nowadays, remote sensing is, with no doubts, one of the most efficient technologies used in gathering information, particularly after the tremendous developments which affected cameras, photographic techniques, films, aviation and satellites. The procedure employed in the salinity evaluation process of the irrigated lands in Skaka is briefed in the followings: 1) Requesting and obtaining at least two images representing the area spatially and temporally. The source of the images depends highly on cost and resolution required. In this project Landsat TM images covering the area under investigation were collected for two years: 1987 and During the time of conducting the study, the 1993 image was the most recent one conveniently available; 2) Performing an explorative unsupervised classification for the 1993 image to be used as a guideline for field data collection. Minimum amount of field data should be collected to provide training sets for the more accurate Supervised classification phase. Data collected include soil types, crop types, and water quality; 3) Using the analyzed field data to create a supervised classification map for the study area for the year The resultant map is employed to trace salt affected soil; 4) Producing a 1987 map using the unsupervised classification technique for the purpose of detecting general landuse changes and salinity spread as compared to 1993 conditions. Field Studies and Laboratory Analysis With the Guidance of the 1993 unsupervisedly processed image, the site was carefully surveyed to observe and collect necessary information needed for the analysis. Detailed data such as farm establishment date, well digging date, evapotranspiration, water quality deterioration, bare soil types, and cultivated crop types were obtained for almost all the farms in the Skaka area. The data acquired was used first for the salinity mapping, which is the scope of this paper, and will be used later for the development of a salinity management program.

4 The soil chemical and physical properties and the water characteristics (PH and electric conductivity, EC) for all the samples collected were analyzed. Examples of the results are presented in Table 1. Land Table 1 Soil Properties in Chosen Areas Soil Analysis (grain size distribution) % % % % % Gravel Sand Silt Clay Fine Soil Testing Electric Conductivity (EC) (mmhos) % Organic Matter Farm 1 (F1) Farm 2 (F2) Farm 3 (F3) Farm 4 (F4) Farm 5 (F5) Farm 6 (F6) Soil A (SA) Soil B (SB) Soil C (SC) Soil D (SD) Soil E (SE) Soil F (SF) Image Interpretation The Classification process involved the use of two techniques: supervised and unsupervised classifications. Both classification schemes were conducted using the well known image processing package ERDAS. The interpretation process involved the following: One. Loading and displaying the contents of the tapes, that storing the digital maps of the area for the years 1987 and 1993, on a high speed workstation. The produced images were used for visually determining the extent of the area to be analyzed and then extracting that portion out of the file. Two. Image Enhancing: Histogram Equalization, which is a nonlinear sketch, was applied on both images to enhance the distinction among its feature. Three. Color composing: Colors were composed using three bands Red, Green, and Blue. The red, green and blue lights were added together to produce a wide variety of colors ne4eded for features geographic recognition. Different color combinations were used to specify different classes for each land use. The signature divergence process was used to choose image colors.

5 Four. Performing supervised classification using the selected band combination for the (1993) image, see Figure 1. In addition, the same band combination was also used for performing unsupervised classifications for the (1987) image, as seen in Figure 2. The (1993) image was used in the field survey stage as mentioned earlier. Five. Using Maximum Likelihood process: This process is used to get color combination and classes classifications. The maximum likelihood decision rule is based on the probability that a pixel belongs to a particular class. The basic equation assumes that these probabilities are equal for all classes, and that the input bands have normal distributions. RESULTS AND DISCUSSION In this study, two Landsat TM images for the years 1987 and 1993 for Skaka city were used to monitor landuse changes and salinity affected soil. SPATIAL AND TEMPORAL LANDUSE DETECTION Results of the unsupervised classification for 1987 and 1993 are presented in Table 2. Comparison of the results shows noticeable change in landuse during the six years period. Results also show that some of the landuses have expanded such as urban setting while some others like bare soils have decreased. Class # Table 2 Unsupervised Classification for Study Area Image 1987 Image 1993 Class % Area Cover/ (km 2 ) Full % Cover Full Class Area (km 2 ) Land 1987 Land SF SF Urban Urban Sand Sand Urban Urban F3 F Urban Urban SA SA Urban Urban F4 F F5 FI+F2+F SB F SD F Sand Sand SD SE SE+SF SE Total

6 Results of the supervised classification of the 1993 image, on the other hand, are presented in Table 3. In this procedure, signatures of predefined areas using field data were determined on the image. Accordingly each pixel (i.e. either farm or bare soil) on that same image was then classified. Percentage of each class along with its size and class type are displayed. Results in Tables 2 and 3 were used to classify the area according to the land use. The classification is based on soil type. Therefore, three categories were defined: Cultivated, bare soil, and urban. Table 4 shows the size and percentage of cover to the total area of each category for the unsupervised and supervised classification, respectively. It is clear that a growth in the cultivated areas was noticed. The area has grown from 11.7 to km 2. On the other hand, bare soil areas decreased from 88% to about 72%. Also, urban setting has increased during the six years period to about double. Generally the results of landuse changes are as expected in any developing own. Table 3 Supervised Classification for Study Area Class Image 1993 Image 1993 Land Class # Cover/Full Class Area (km 2 ) 1993 Color F2 Green F3 Red F4 Sand FI Ochre SB Maghenta SA Cream SC Blue SD White SE Blue-Green SF Yellow Urban+Sand Black Shadow F5 Brown F6 Orange Total

7 Land Image 1987 (unsupervised) Class area (km 2 ) Table 4 Land Use Categories % Cover/ Full Image 1993 (unsupervised) Class area (km 2 ) % Cover/ Full Image 1993 (supervised) Area (km 2 ) % Cover Cultivated Bare Soil Urban+ Sand Shadow Total Salinity Analysis Analysis of the supervised and unsupervised images with support of field data were performed in order to first assist the existing salinity conditions and then calculate the rate of increase of salinity during the period of study. The salinity and alkalinity conditions for each category (either farm or bare soil) were determined based on field data and using a standard classification system. The results are presented in Table 5. Table 5 Salinity and Alkalinity Conditions for Sample Areas Land EC (mmhos) Salinity Condition Water ph Alkalinity condition Farm Low 8.0 Low Farm 2 26 Severe 8.0 Low Farm Medium 7.9 Low Farm Medium 7.8 Low Farm V. High 8.0 Low Farm Medium 7.7 Low Soil A 9 High - Soil B 6.5 Medium Soil C 0.4 V. Low Soil D 0.6 V. Low Soil E 14 High Soil F 3.4 Low From Table 5 it is noticed that alkalinity is low for all farms. On the other hand, salinity ranges from very low (Soil C and D which are sand dunes) and low (Farm 1) to very high (Farm 5) and severe (Farm 2). These salinity indicators for each category were then used to reclassify the two 1987 and 1993 images. Later the size of the affected soil associated with each salinity level was determined as shown in Table 6 which reveals that salinity has increased during

8 the study period in cultivated areas. This increase can be visualized clearly by noticing that only 17.40% of the land have very high salinity in 1987, while in 1993 a 45.2% of the land (using unsupervised classification) got affected with very high salinity. These results prove that the cultivated areas in the study area (Skaka) are affected by salinity and need an urgent management program to determine the factors causing this damage and the correct methods for mitigation. Salinity Conditions Table 6 Salinity Conditions in Cultivated Areas Unsupervised 1993 Unsupervised % of Area cultivated (km 2 ) areas ( ) Area (km 2 ) % of cultivated areas ( ) Medium V. High Severe CONCLUSIONS Several conclusions can be drawn from this study: One. Landsat Thematic Mapper (TM) data could be used for delineating and monitoring soil salinity. Accuracy could be significantly improved by using computer enhancement techniques. Lots of effort, time, and money have been saved by using remote sensing techniques. Yet very reasonable descriptive results were achieved. Two. It has been proved that the majority of the cultivated lands in the study area are affected by salinity at different levels. Three. Temporal analysis revealed that a rapid increase in salinity level and extent have been detected during the six year study period which is presumed short. Four. In terms of providing guidance to ground based money, use of remote sensing data may make significant improvement in monitoring the spread of the saline solid. RECOMMENDATIONS Based on the results the following are recommended: One. More samples data should be collected to improve the accuracy of results.

9 Two. When studying soil related problems, it is better to use images that were taken when there is no heavy vegetation cover such as right after crop cultivation. Three. An enhanced supervised classification can be achieved when samples (field data) are collected within the time the satellite image was taken for the study area. Four. Five. It is advisable to regularly monitor lands that are potentially affected by salt to get good periodical estimates of the extent and severity of salinity. Related authorities should initiate the necessary soil amendments and the suitable reclamation program to control salinity damage of cultivated areas. REFERENCES 1. Abdel-Haniid, M.A., Sherestha, D. and Valenzuela, C. 1992, Delineating, Mapping and Monitoring of Soil Salinity in the Northern Nile Delta (Egypt) using Landsat Data and a Geographic Information System, Egypt, J. Soil Sci. 32. No Csffiag, F., L. Pasztor and L. Biehl. 1993, Spectral Band Selection for the Characterization of Salinity Status of Soils, Remote Sensing Environ, Vol. (43). 3. Machanda, M.L., 1984, Use of R.S.T. in the Study of Distribution of Salt Affected in North-West India, J. Indian Soc., Soil, Vol. (32). 4. Millington, A.C., N.A. Drake, J.R.G. Townshend, N.A. Quannby, J.J. Settle and A.J. Reading, 1989, Monitoring Salt Playa Dynamics Using TM Data, IEE Trans. on Geoscience and R.S., V(27), No Thampson, M.D., N.A. Prout, and T.G. Sommer Fedt, 1981, Landsat of Delineation and Mapping of Saline Soil in Dryland Area in Southern Alberta, The 7 th Canadian Symposium on Remote Sensing, Winnipeg, Manitoba. 6. Venkataratnam L. 1983, Monitoring of Soil Salinity in Indo Gauetic Plains of North W. India 17 th Intl. Symposium on Remote Sensing of Environ., Ann Arbor, MI., U.S.A. 7. Verma, K.S. R.K. Saxena, A.K. Barthwal and S.N. Deshmukh, 1993, Remote Sensing Technique for Mapping Salt Affected Soils, Int. J. Remote Sensing, V(15), No. 9.

10 Fig. 1. Image of Skaka (1993) Supervised class

11 Fig. 2. Image of Skaka (1987) Unsupervised class