USING STATE-OF-THE-ART RS AND GIS FOR MONITORING WATERLOGGING AND SALINITY - Salman Asif and Mubeen-ul-Din Ahmad

Salman Asif and Mubeen-ul-Din Ahmad
GIS specialists, International Water-Management Institute
Lahore, Pakistan

INTRODUCTION

At present rates of population growth, global food needs will increase while soil and water resources for new crop production will be limited and their quality will diminish. Protection of soil resources and conservation of water are high on the agenda, because irrigated agriculture is one of the biggest producers of food. In arid and semi-arid regions, irrigation has brought about environmental problems of waterlogging and salinity. According to the United Nations Economic and Social Commission for Asia and the Pacific (UN-ESCAP), affected areas exceed 1 million ha in Cambodia and Thailand, 3 million ha in Afghanistan, Bangladesh, Mongolia and Malaysia, 10 million ha in Pakistan and Indonesia, 20 million ha in China, India and Iran and over 350 million ha in Australia. The total area affected by salinity is estimated to exceed 500 million ha (Szabolcs, 1979).

SALINITY PROBLEMS IN THE INDUS BASIN IRRIGATION SYSTEM

The problem faced in Pakistan is of staggering proportions and is the major factor preventing agricultural productivity increases. Geologically, this part of the sub-continent, including the Indus plain, was formed from alluvium deposited by rivers into a shallow sea. The receding sea left residues of salts in soil profiles and aquifers. Minerals in parent rocks released significant quantities of salts into the soil through weathering. Under prevailing climatic conditions, salts released through weathering are not leached out of soils. Irrigation in arid and semi-arid areas leads to accumulations of salt at the soil surface. Salinization occurs naturally - fossil salinity - and as a consequence of poor irrigation practices - secondary salinity.

Fossil salinity occurs at margins of natural depressions, where rain and floodwater accumulate. The soil becomes loaded with salts because of capillary rise of water to the surface. Land affected by fossil salinity is generally severely saline and is not easily reclaimed. Secondary salinity, resulting from modern irrigation, occurs because of accelerated redistribution of salts in the profile by high watertables or use of insufficient water to leach salts out of the soil. Insufficient storage capacity with existing dams has led to conjunctive use of surface and groundwater in most areas. Water from tubewells contains varying amounts of salts, and injudicious use has contributed significantly to salinization of surface soils after periods of capillary rise or when fluctuating watertables reach the surface (Khan, 1993). The process of secondary salinization is represented in Figure 1.

Figure 1. Vicious cycle of salinization and watertable rise

PROBLEMS ASSOCIATED WITH SALINITY AND WATERLOGGING MANAGEMENT

"If you can't measure it, you can't manage it."

Pakistan has tried to overcome the menace of salinity and waterlogging by establishing the following bodies responsible for measuring its extent and taking remedial measures:

• The Directorate of Land Reclamation (DLR) makes salinity assessments through visual surveys during December, January and February, when salinity is most visible; information is collected at field or killa level.

• The Soil Survey of Pakistan (SSP) is mainly responsible for soil mapping: soil-series maps are produced based on colour and drainage properties at a scale of 1:250 000 using aerial photographs.

• WAPDA is a federal organization responsible for water resources development and management and power generation and distribution. Integrated reporting on the extent of land affected by waterlogging and soil salinity has only been done twice: in the mid-1960s under the aegis of the Lower Indus Basin Plan (LIP) report, which was less detailed than the 1976-79 WAPDA Master Planning and Review (MPR) division surveys by aerial photography. Detailed assessments of waterlogging and soil salinity for smaller areas, such as the Right Bank Master Plan (RBMP) study, have been completed and published individually. Integrated assessment remains elusive. WAPDA MPR surveys have mapped at 1:250 000 scale the distribution of waterlogging and soil salinization across the lower Indus basin.

Conventional techniques such as geochemical measurement are expensive and time consuming, and accuracy is questionable for large areas. A list of salinity surveys is given in Table 1.

Table 1. Salinity surveys in Pakistan

* Water and Soil Investigation Division

Variation of salt-affected land does not necessarily reflect the dynamics of salinization; it could result from different approaches to sampling and salinity assessment, including:

• processes and phenomena identified at different survey dates by different indicators;

• subjectivity of visual indicators because of high costs of soil analysis;

• different survey areas and inclusion or exclusion of barren areas;

• difficulties in data management and information retrieval.

The result is divergence of conclusions as to the nature and extent of saline areas.

It is becoming increasingly important to use standardized techniques to assess salinity. GIS and RS techniques can play an important role in monitoring soil to locate and evaluate the extent of saline areas and improve the situation through enhanced understanding.

THE ROLE OF RS IN IDENTIFYING WATERLOGGING AND SALINITY

Satellite images can help in assessing the extent of waterlogged and saline areas and monitoring the extent and severity of changes in real time.

Direct estimates

Saline fields are often identified by the presence of spotty white patches of precipitated salts. Such precipitates usually occur in elevated or unvegetated areas, where water evaporates and leaves salt behind. Such salt crusts, which can be detected on satellite images, are not reliable evidence of high salinity in the root zone.

Indirect estimates

Salinity is a dynamic process. To assess the extent of salinity, modelling is often required. One of the main problems of dealing with large areas is lack of information about water-balance components. RS can provide useful information for large-area water and salt balances and identification of parameters such as evapo-transpiration, rainfall distribution, interception losses and crop types and intensities that can be used as indirect measures of salinity and waterlogging and as evidence for direct estimates (Ahmad, 1999).

ATTEMPTS TO IDENTIFY WATERLOGGING AND SALINITY WORLDWIDE

IWMI has already reviewed different RS applications for water-resources management (Bastiaanssen, 1998). Part of the section related to salinity is summarized here.

Changes in the reflectance, composition and morphology of a single leaf can be used to detect salinity effects at an early stage. Mougenot, Pouget and Epema (1993) noted that "visible reflectance of leaves from plants growing on salt-affected soils is lower than reflectance of non-salt-affected leaves before plant maturation and higher after. Near-infrared reflectance increases without water stress due to a succulent (cell thickening) effect and increases in other cases."

Bands in the near- and middle-infrared spectral bands give information on soil moisture and salinity (Mulders, 1987; Agbu, Fehrenbacher and Jansen, 1990). Steven et al. (1992) confirmed this by showing that near- to middle-infrared indices are indicators for chlorosis in stressed crops (normalized difference for Thematic Mapper [TM] bands 4 and 5). This new ratio is immune to colour variations and provides an indication of leaf water potential. Steven et al. (1992) showed that chlorotic canopies could be distinguished from healthy canopies. Biophysical response to a salty environment is manifested in low fractional vegetation cover, low leaf-area index (LAI), high albedo, low surface roughness and high surface resistance compared with healthy crops.

RS investigations on soil salinity can be divided into identification of salt-affected and cropped soils and mineralogy.

Salinized and cropped areas can be identified with a salinity index based on greenness and brightness that indicates leaf moisture influenced by salinity, with classical false-colour composites of separated bands or with a computer-assisted land-surface classification (Kauth and Thomas 1976; Hardisky, Klemas and Daiber, 1983; Steven et al., 1992; Vincent et al., 1996). A brightness index detects brightness appearing at high levels of salinity. The contribution of false-colour composites and visual interpretations is demonstrated in studies from India given in Table 2. Geomorphological patterns are helpful in distinguishing salinization. Aerial photography, often mentioned as a suitable solution, falls outside the scope of this review on satellite RS.

Table 2 shows that TM bands 5 and 7 are frequently used to detect soil salinity or drainage anomalies (Mulders and Epema, 1986; Menenti, Lorkeers and Vissers, 1986; Zuluaga, 1990; Vincent et al., 1996). The physiological condition of a crop is shown best at TM 5 and 7; TM bands 3 and 4 are better suited to describing overall crop development. Most of the studies in Table 2 are based on multispectral scanner (MSS) and TM data, because the Satellite pour l'Observation de la Terre (SPOT) and the Indian Remote Sensing Satellite (IRS) have no bands greater than 1.7 mm. Joshi and Sahai (1993) found that TM, which had an accuracy of 90 percent for soil salinity mapping, was better than MSS, which was 74 percent accurate. Goossens, et al. (1993) compared the accuracy of TM, MSS, and SPOT and found TM to be the best multispectral radiometer for soil-salinity mapping.

Johnston and Barson (1990) reviewed RS applications in Australia. They found that identification of saline areas was most successful during peak vegetation growth. In other periods, low fractional vegetation cover in salinized areas could not be distinguished from areas that were bare because of overgrazing, erosion or ploughing. Siderius (1991) concluded the opposite: salinity is best seen at the end of irrigation or the rainy season when the plots are bare. Goossens et al. (1993) analysed the growing season in the western Nile delta, showing that mono-temporal images are suitable for detecting severely salinized soils but that more gradations can be determined through a multitemporal approach. Venkataratnam (1983) used MSS images of pre-monsoon, post-monsoon and harvest seasons to map soil salinity in the Punjab. He concluded that the spectral curves of highly and moderately saline soils change considerably during the annual cycle, which significantly complicates the time-compositing procedure.

The investigations of Vidal et al. (1996) in Morocco and Vincent et al. (1996) in Pakistan are based on a classification-tree procedure. The first treatment is to mask vegetation from non-vegetation using normalized difference vegetation index (NDVI). Then the brightness index is calculated to detect moisture and salinity on fallow land and abandoned fields. The approach of Vincent et al. (1996) was suitable for locating blocks that had malfunctioning drainage networks. Two classes based on levels of soil salinity could be mapped with an accuracy of 70 percent. Areas of high salinity were 66 percent accurate and nonsaline areas were 80 percent accurate.

Table 2. Literature on detection of soil salinity

Chaturvedi et al. (1983) and Singh and Srivastav (1990) used microwave brightness and thermal infrared temperatures synergistically. The microwave signal was interpreted physically by means of a two-layer model of fresh and saline groundwater. Synergetic use of satellite measurements to map soil salinity physically is a new concept; although results were not perfect, the integration of multiple-sensor data has set new directions for research on soil salinization. The physical conditions of the surface soil can be obtained with optical and passive microwave data. The larger L-band and P-band wavelengths are capable of penetrating soil and retrieving information from deeper layers rather than just from the surface.

Goossens et al. (1993) presented an example of a contextual classifier for soil salinity mapping. They built a GIS to link the location of the irrigation feeders and drainage master canals in the western Nile delta with digital elevation data and satellite classifications. Soil-salinity risks are considered to be proportional to distance and elevation differences of fields with respect to main irrigation canals. TM bands 2, 3, 4, 5, 6 and 7 were used to classify three different stages of waterlogging.

Although the combined GIS/RS approach published by Cialella et al. (1997) for predicting soil drainage classes does not focus on soil salinity, its method is worth mentioning. Soil drainage was studied by means of a classification-tree analysis using airborne NDVI data, digital elevation data and soil types.

Verma et al. (1994) combined the TM false-colour composite (FCC) bands 2, 3 and 4 with thermal data at 10.4-12.5 mm to solve the problem of spectral similarity where the dull-white tone of salt-affected and sandy soils has been difficult to distinguish. They found the data between March and first week of April significantly better because of maximum contrast. They classified salt-affected soils in Etah, Aligarh, Mainpuri and Mathura districts into S1: <10 percent of the salt-affected area, S2: 10-30 percent, S3: 30-50 percent, S4: 50-75 percent and S5: >75 percent, using the integrated approach to image interpretation.

Dwivedi and Rao (1992) adopted a quantitative approach to identify the most appropriate three-band combination of Landsat TM reflective-band data for identifying salt-affected soils. They used the standard deviation and correlation coefficient values of TM data to compute a statistical parameter called the optimum index factor (OIF), indicative of the variance of the data. Of the 20 possible 3-band combinations, the combination of 1, 3, and 5 was found to be the best in terms of information content. The validation of results revealed a mixed relationship between ranking obtained from OIF values and estimated accuracy.

Dwivedi and Sreenivas (1998) demonstrated the potential of image transformations such as principal-component analysis (PCA), ratioing, and image differencing to detect changes in extent and distribution of salt-affected soils, using Landsat MSS data for 1975 and 1992 to study the alluvial plains of Uttar Pradesh. Results indicated that the third principal component, image differencing and ratioing of the first two bands provided substantial information about behaviour of salt-affected soils over time in the two periods.

Metternicht and Zinck (1997) applied a synergistic approach to map salt-affected surfaces, combining digital image classification with field observation of soil-degradation features and laboratory determinations. Landsat TM bands1, 2, 4, 5, 6 and 7 were combined to obtain the highest separability between salt- and sodium-affected soils. Overall accuracy was 64 percent; for some soils 100 percent accuracy was obtained. The main reasons for low accuracy are:

• type and abundance of vegetation cover, which control reflectance values when the surface is not bare;

• topsoil texture: high amounts of silt plus clay obscure the effects of variable salt and sodium content;

• mixture of topsoil properties under field conditions, where salt, particle size, organic matter, crusting and soil moisture mutually interfere.

WORK IN PAKISTAN

The SSP has long experience of interpreting aerial photographs for soil surveys. Salt-affected soils are identifiable by a patchy salt pattern (slightly saline) and areas with a more continuous surface cover of salt (highly saline). Salt-affected areas on aerial photographs show up as light-coloured areas. Dark-coloured areas can be identified as waterlogged or consisting of clay soils. Chouchri et al. (1978) used aerial photographs from 1953-54 and 1976 to compare differences in salt-affected and waterlogged lands before and after implementation of the SCARP-1 programme. They concluded that the waterlogged areas had decreased in extent but had often become saline. Large areas with salt-affected soils were still out of cultivation; only small patches of slightly saline soils were under cultivation.

WAPDA, in collaboration with the World Bank, carried out a study in the Khairpur Pilot Project Sindh to identify salinity by means of Landsat data using visual and digital interpretation (Farooq and Nur ud Din, 1980). Visual interpretation identified only highly saline areas; computer analysis identified moderately saline areas. Areas that were only slightly affected could not be identified because of their small size and patchy pattern. The research showed that multi-temporal images are a prerequisite because of seasonal variations in salinity and effects of vegetation cover. They found that the most valuable months for salinity surveys were February and March.

In 1984, WAPDA conducted a study on the applicability of Landsat imagery for monitoring soil-salinity trends in two areas in Punjab and Sindh. Black-and-white mosaics of Landsat MSS band 5, scale 1: 250 000 taken in March, April and December were visually interpreted and compared with 1:250 000 surface salinity maps published by WAPDA. Results were not promising, because it was largely impossible to identify WAPDA salinity categories (slight, moderate and strong) and their extent. For some small areas in Punjab it was possible to distinguish slightly saline areas from strong and moderate.

In 1988, Euroconsult studied an area near Dera Ghazi Khan, Punjab, using the Système pour l'Observation de la Terre (SPOT) satellite XS images from May and SPOT PAN from April to identify waterlogged and saline areas, which are reflected in 1and use. The images were first interpreted visually, but because results were unsatisfactory, computer compatible tapes were processed by methods including composition of three bands into false-colour images, image enhancement and linear stretching. Filtering was used instead of spectral-reflectance analysis to enhance texture differences and make it easier to differentiate sand dunes, saline areas and waterlogged areas. After preliminary interpretation and field surveys, the boundaries of the map units could be indicated.

The Space Upper Atmosphere Research Commission (SUPARCO) conducted research to distinguish saline areas in Punjab and Sindh, using Landsat MSS and then, because of better spatial resolution, SPOT images. Khan and Siddiqui (1987) studied three test sites and compared digital interpretation results with WAPDA surface-salinity maps. Visual interpretation of images - Landsat MSS, PAM, 1:250 000, bands 5 and 7 - was used to identify waterlogged and saline areas. Digital interpretation identified four classes: non-saline, slightly saline, moderately saline and highly saline. Maps prepared with RS techniques corresponded to the WAPDA surface-salinity maps for two of the three areas.

A major attempt to classify salinity in Pakistan with RS indices was by Tabet (1995 and 1999) and Vidal et al.(1998), conducted as part of an IWMI-Cemagref collaboration in the Chishtian district in southeastern Punjab. Vegetation and brightness indices derived from SPOT XS were used to classify salinity for vegetative and non-vegetative areas. The resulting classification allows identification of highly saline and non-saline areas, but areas with low to medium salinity levels are difficult to distinguish. Confusion about salinity classes results mainly from the fact that uneven crop growth may be due to salinity or agricultural practices.

CONCLUSION

Most applications of RS for irrigation management in Pakistan have been isolated studies, mainly to identify salinity-affected areas, in which visual interpretation gives rapid identification only of highly saline areas. Digital interpretation is necessary for more accurate identification of saline areas.

There are constraints to RS that need to be considered. Only surface features can be sensed, so subsurface information has to be obtained indirectly. The spatial resolution of satellite images limits them to detection of cropping patterns and monitoring of salinity on fields. Various factors cause spectral reflectance changes during the year.

RS information can be improved when it is integrated with other data, for which a GIS is an appropriate tool.

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