Spatio-temporal characterization of soil degradation

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Tropical Ecology 43(1): 75-90, 2002 ISSN 0564-3295 © International Society for Tropical Ecology

Spatio-temporal characterization of soil degradation

R.S. DWIVEDI*

National Remote Sensing Agency, Balanagar, Hyderabad – 500 037, India

Abstract: Over exploitation of natural resources for meeting the growing demand for food, fuel and fodder of ever increasing population, has led to soil degradation of varying degrees re-sulting thereby in deterioration of erstwhile healthy ecosystems. Information on the nature, ex-tent, magnitude and the temporal behaviour of soil degradation is a pre-requisite for taking up any ameliorative/preventive measures. Space-borne multispectral data by virtue of providing synoptic view of a fairly large area at regular intervals, offer immense potential for generating above mentioned information on degraded soils in a timely and cost-effective manner. An at-tempt has been made in this article to provide an overview of the utility of space-borne multis-pectral data from both indigenous, namely Indian Remote Sensing Satellite (IRS-1A/1B/-1C/-1D and/-P3) as well as foreign Earth observation missions like Landsat, SPOT, etc. for assess-ment and monitoring of soil degradation; identify gap areas and to project the future scenario.

Resumen: La sobreexplotación de los recursos naturales para satisfacer la creciente de-

manda de alimento, combustible y forraje de una población en constante aumento, ha llevado a la degradación del suelo en grado variable, resultando así en el deterioro de ecosistemas anti-guamente sanos. La información sobre la naturaleza, la extensión, la magnitud y el compor-tamiento temporal de la degradación del suelo es un prerrequisito para poder tomar medidas preventivas y de mejoramiento. En virtud de que los datos multiespectrales transmitidos desde el espacio proporcionan una vista sinóptica de un área suficientemente grande a intervalos regulares, éstos tienen un potencial inmenso para la generación oportuna y económicamente efectiva de la información arriba mencionada sobre la degradación del suelo. En este artículo se intenta ofrecer una visión de conjunto acerca de la utilidad de los datos multiespectrales transmitidos desde el espacio tanto de Satélites de Percepción Remota nativos, es decir, de la India (IRS-1A/1B/-1C/-1D y /-P3), así como de misiones extranjeras de observación de la Tierra tales como Landsat, SPOT, etc. con el fin de evaluar y monitorear la degradación del suelo, identificar áreas carentes de información y proyectar escenarios futuros.

Resumo: A sobreexploração dos recursos naturais para satisfazer as necessidades alimen-

tares crescentes, de lenhas e forragens de uma população em crescimento, conduziu a vários graus de degradação do solo e de que resultou, entretanto, a deterioração dos ricos ecossistemas terrestres. A informação quanto à natureza, extensão, dimensão e comportamento temporal quanto à degradação do solo é um pré-requisito para implantar quaisquer medidas de mel-horamento/prevenção. Os dados da emissão multi-espectral colhidas no espaço, graças às possi-bilidades de proporcionarem uma visão sinóptica de uma área vasta em intervalos regulares, oferecem um potencial imenso para a geração da referida informação sobre a degradação do solo de uma forma tempestiva e a custos eficientes. Neste artigo é feita uma tentativa para proporcionar uma visão global quanto à utilidade da informação multi-espectral obtida, quer pelo satélite indiano de detecção remota (IRS-1A/1B/-1C/-1D e /-P3), quer por satélites es-trangeiros como o Landsat, SPOT, etc., para avaliação e monitorização da degradação do solo: identificação de áreas abertas e para projecção de cenários futuros.

*E-mail: [email protected]

76 SPATIO – TEMPORAL CHARACTERIZATION

Key words: GIS, mining, remote sensing, soil compaction, soil erosion, soil salinization, waterlogging.

Introduction

Soil is an important element of the ecosystem, and plays a crucial role in biochemical and geo-chemical cycling, partitioning of water, storage and release, buffering, energy partitioning, which are essential for supporting ecosystems. The bal-ance among these functions could be changed ei-ther by succession of natural events like volcanic activity, or by continuous large-scale human ac-tivities (Kimpe & Warkentin 1998). Activities like deforestation, overgrazing, cultivation of marginal lands, and introduction of irrigation without pro-viding for adequate drainage, mining and indus-trialization for meeting the increasing demand for food, fuel and fibre for ever-growing population have led to over-exploitation of natural resources, resulting in the deterioration of erstwhile healthy ecosystems. Globally, 1,964.4 million ha of land are affected by human-induced degradation (UNEP 1997). Of this, 1,643 million ha are subject to soil erosion by water, and wind erosion, 239.1 million ha to chemical deterioration, 68.2 million ha to compaction and 10.5 million ha to waterlogging. In addition, an estimated 954.8 million hectares of arable land on earth are effected by soil salinity and/or sodicity (Szabolcs 1992).

In India alone, an estimated 175 million ha land accounting for 53 % of geographical area of our country are subject to various kinds of degra-dation, namely, soil erosion by water and wind, salinization and/or alkalinization, waterlogging, shifting cultivation, etc., which are either lying waste or partially utilized (Anonymous 1976). Of this, an estimated 150 million ha of land are re-ported to be subject to soil erosion by water and wind alone. The problem of waterlogging, saliniza-tion and/or alkalinization and ravine infestation cover an estimated 2.46 million ha (Anonymous 1991), 7.1 million ha (Abrol & Bhumbla 1971); and 3.92 million ha (Bali 1985), respectively.

For sustainable development, available land and water resources need to be utilized based on their potential and limitations. It implies that the productivity of existing degraded soils need to be

restored and of normal cultivated lands to be im-proved. Information on the nature, extent, magni-tude and temporal behaviour of degraded soils is a pre-requisite to achieve the aforesaid goal. Soil surveys provide such information.

Background

Soil degradation is often related to decline in soil quality, and is caused through its misuse by humans. It refers to a decline in the soil’s produc-tivity through adverse changes in nutrient status, soil organic matter, structural attributes, and con-centration of electrolytes and toxic chemicals (Lal & Stewart 1990). Two types of human-induced degradation processes, namely displacement of soil material, and internal soil deterioration are, gen-erally, encountered. The processes included in the first category are water erosion, and wind erosion. Included in the second category of soil degradation are chemical deterioration, physical deterioration, and biological deterioration. The chemical deterio-ration consists of loss of nutrients, pollution and acidification, salinization and / or alkalinization, discontinuation of flood-induced fertility, and other chemical problems. Sealing or crusting of top soil and subsidence of organic soils, soil compaction, deterioration of soil structure, waterlogging com-prise physical deterioration. Biological deteriora-tion includes imbalance of (micro) biological activi-ties. Overgrazing of pasturelands, deforestation, over intensive annual cropping, mining, etc. are causes of soil degradation.

Historical perspective

Until late 1920s, information on soil degra-dation had been generated through conventional approach. Subsequent availability of aerial pho-tographs and the development of aerial photo-interpretation technique helped improving its efficacy. Additionally, the developments in sen-sor technology and concomitant image analysis facilities have further augmented this process. A brief sketch of the study of the soil degradation

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using remote sensing technique is presented hereunder:

Aerial photographs Airborne platforms like balloons and aircrafts

have been used to acquire photographs of the ter-rain using either photographic camera or multis-pectral scanner. Black and white aerial photo-graphs were first used to prepare base maps for a soil survey in Jennings county, Indiana, USA in 1929 (Bushnell 1929), and a vast improvement over the use of plane tables to draw base and soil maps was observed. In India, the chief stimulus for the usage of aerial photographs for soil survey has been the establishment of the erstwhile Indian Photo-interpretation Institute at Dehradun in 1965. Systematic mapping of degraded lands in India started in late fifties and was mostly con-fined to mapping eroded lands and salt-affected soils.

Black and white aerial photographs have been used for watershed management and soil erosion studies (Gorrie 1935; Myers et al. 1966; Iyer et al. 1975). More specifically, aerial photographs have been used for detection of sheet wash and rill ero-sion (Coldwell 1957; Maner 1958). In fact, colour and colour infrared stereo pairs at 1: 600 scale have been found to be useful for detection and in-ventory soil movement on rangeland in the Great Basin (Tueller & Booth 1975). In addition, colour and colour infrared transparencies at three scales, namely 1: 2000, 1: 4000, and 1: 8000 (Frazier & McCool 1981), and Ilford xP1 photographic film in 35-mm format (Frazier & Hooper 1983) have been used for assessment of soil erosion. Apart from sheet wash and rills, aerial photographs have also been used for detection of gullies and ravines. Kamphrost & Iyer (1972) could differentiate four ravine classes based on depth and other mor-phometric features perceived through photo-interpretation and parallax bar measurements.

Air photos have also been used for detection and delineation, and studying the temporal behav-iour of features associated with wind erosion. Us-ing 1: 25,000 scale aerial photographs over coastal New Zealand for 1956 and 1979, Stephens & Cock (1991) observed a significant positive change in vegetation cover over erstwhile sand-covered ar-eas. Pandey et al. (1964) studied the movement of sand dunes in the central Luni basin, Rajasthan, India using 1: 40,000 scale aerial photographs.

Additionally, a comparison of aerial photographs acquired at different time intervals over Qatar has enabled Embabi & Asour (1992) estimating the annual mean rate of movement of sand.

Studies on salt-affected soils with respect to their spatial extent and temporal behaviour have been carried out from aerial photographs of differ-ent types and scales (Hilwig & Karale 1973; Man-chanda & Khanna 1979). Dale et al. (1986) ob-served that the colour infrared aerial photographs taken in autumn with high red reflectances pro-vide maximum discrimination between the classes of salt marsh in southeast Queensland, Australia.

Land subject to waterlogging has also been de-lineated using aerial photographs. Wallace et al. (1993) used 1: 10,000 scale aerial photographs over part of Western Australia for detection and map-ping of waterlogged areas. In India, Sahai & Kalubarme (1985) used black-and-white and colour infrared aerial photographs at 1: 30,000 and 1: 50,000 scale for delineation of waterlogged areas with water table within 1.5-3.0 m in the Ukai command area in Gujrat, western India.

Spectral reflectance studies

Spectral reflectance studies are fundamental to understanding the spectral behaviour of any feature or phenomena including soil degradation, which facilitates their recognition and delineation. Latz et al. (1984) studied the spectral reflectance characteristics of selected eroded Alfisols of United States. Reflectance from A horizon was found to be low, and reflectance spectra had a concave curve between 0.5 and 0.8 µm which is typical of soils having high organic matter content. Erosion of the Alfisols exposed B-horizon that had higher iron contents and lower organic matter contents than the upper A horizon. Reflectance from B horizon was higher, and the spectra had a convex curve between 0.5 and 0.8 µm which is typical of soils having low organic matter content and high iron content. Wiesmiller et al. (1985) has reviewed the use of spectral reflectance data for soil erosion in-vestigations. While studying the spectral response pattern of eroded black cotton soils of the Indian peninsula in the Landsat-MSS, TM and SPOT-HRV MLA spectral bands (Kumar et al. 1997) ob-served the maximum spectral response from se-verely eroded black soils followed by moderately eroded and nil to slightly eroded soils.


Salt-affected soils with salt encrustation at the surface are, generally, smoother than non-saline surface and cause high reflectance in the visible and near infrared bands (Everitt et al. 1988; Rao et al. 1995). Based on in situ spectral measurement, Kalra & Joshi (1994) observed the maximum spec-tral response from natural or in situ salt-affected soils with salt encrustation at the surface, followed by sodic or alkali soils formed due to irrigation with high residual sodium carbonate (RSC) water, natural saline soils, and saline soils formed due to irrigation with saline water.

In the visible and near infrared regions, salt-affected soils do not exhibit any characteristic spectral response. Hunt et al. (1971) reported an almost featureless spectrum of halite (NaCl 433B from Kansas). However, based on a laboratory spectra of mixtures of SiO2 and NaCl +MgCl2, Hick & Russell (1990) observed significant features as-sociated with two of the water absorption bands at around 1.4 and 1.9 µm. Endorsing the previous observations, Szilagyi & Baumgardner (1991) em-phasized that high resolution laboratory spectra are required for characterizing salinity status. Csillag et al. (1993) found the visible (550 to 570 nm), near infrared (900-1030 nm and 1270-1520 nm) and middle infrared (1940–2150 nm, 2150–2310 nm and 2330–2400 nm) portion of the spec-trum at 20 nm, 40 nm, and 80 nm spectral resolu-tion as the key spectral bands in characterizing the salinity status of soils.

Mougenot (1993) noted that in addition to an increase in reflectance with salt content, high salt content might mask ferric ion absorption in the visible region. Soil salinity causes moisture stress and a reduction in transpiration, which results in an increase in the thermal infrared radiation. In addition, the thermal infrared region registers fea-tures caused by energy absorption of sulphates, phosphates and chlorides (Mulders 1987; Siegal & Goetz 1977). Brightness temperature decreases with an increase in soil salinity especially at low frequencies especially L-band (1.44GHZ) under varying terrain conditions. Furthermore, better results are obtained at lowest moisture content (Chaturvedi et al. 1983).

The significant difference in the imaginary part of dielectric constant (I∈) between pure water and saline water at microwave frequencies less than 7 GHz (Ulaby et al. 1986) has been used to derive information on soil salinity. Bell et al. (2001) have used Small Perturbation Model (SPM),

Physical Optics, and Dubius Dielectric Retrieval models for mapping soil salinity in the Alligator River Region of the Northern Territory, Australia using C-(5.33GHZ), L-(1.25GHZ) and P-(440MHZ) bands airborne SAR data (Colwell 1983).

The presence of moisture in the soil affects its spectral response considerably. Clark (1981) noted a dramatic decrease in the albedo, and other changes related to water and lattice-OH from dry to wet condition of montmorillonite at room tem-perature. Further, adding water to montmorillo-nite sample enhanced the water OH features at 0.94,1.2,1.4, and 1.9 µm, due to relatively high sur-face area and a corresponding high content of ad-sorbed water. While studying the reflectance spec-tra of a representative soil (Typic Hapludalf) over various water tensions, Baumgardner et al. (1985) observed a decrease in the general albedo and the area under 1.4 –1.9 µm water absorption features at 1.4 and 1.9 µm. In Western Australia, McFarlane et al. (1992) used portable field spec-trometer (PFS) operating in the 0.4 to 2.5 µm to delineate waterlogged areas of wheat, oat and pas-tures. The PFS measures the spectra in 256 con-tinuous bands over the visible and reflected infra-red wavelengths (0.4 to 2.5 µm). For wheat crop there was a poor separation in the visible part of the spectrum. But a good separation between 0.80 and 1.8 µm in the near infrared region of the spec-trum. Further, there were separations between 1.2 and 1.38 between 1.46 and 1.38 and between 1.94 and 2.5 µm. The PFS spectra for waterlogged and non-waterlogged oat canopies showed that there was slightly better separation in the visible part of the spectrum (0.40-0.65 µm) than for wheat. There was an overlap between the spectra of two waterlogged and two non-waterlogged pas-ture canopies in October. In, general, pasture spec-tra were more variable than crop spectra.

Wallace et al. (1993) used a 13-channel air-borne multispectral scanner (GEOSCAN Mark1) operating in the visible and near infrared region (0.45-0.97 µm), short-wave (1.98-2.40 µm) and thermal infrared region (8.5-12.5 µm) of the spec-trum to detect and map areas in cereal crops where growth has been affected by waterlogging in Western Australia. Near infrared and thermal channels were found to be important in the dis-crimination between waterlogged and non-waterlogged areas.

Occasionally, waterlogging is accompanied by salinisation. Some attempts have been made to

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study the salt concentration of lands subject to wa-terlogging. Based on features in SWIR region at 1.63, 1.70, 2.08, and 2.17 µm, Hirschfield (1985) was able to predict the NaCl concentration in a water solution. Similarly, Lin & Brown (1992) ob-served a very good linear correlation between the absorption [log (1/T); T = transmittance] at 1.7809 µm and varying NaCl concentrations (0.1 to 5M).

O’Neill (1994) observed the spectral features of microphytic crust between 2.08 and 2.10 µm and at-tributed to the presence of cellulose. In another study, Karnieli & Tsoar (1994) showed that the mi-crophytic crust caused a decrease in overall albedo in the soils whereas the spectral response related to the biogenic crust permits linear mixing models. Cipra et al. (1971) observed higher spectral reflectance values between 0.43 and 0.73 µm from a crusted soil relative to the same soil with the crust broken.

Spaceborne multispectral data

Spaceborne multispectral data have been used for deriving information on soils subject to various kind of degradation. Globally, the major opera-tional usage of space-borne multi-spectral data has been in the soil degradation mapping at 1:5 million scale (Food and Agriculture Organization 1978). In India, the mapping of wastelands of entire country at 1:1 M scale was concluded in 1985 using Land-sat MSS data (National Remote Sensing Agency 1985). The follow-up of this endeavour led to dis-trict-wise wasteland mapping at 1: 50,000 scale using Landsat-TM/ Indian Remote Sensing Satel-lite (IRS-1A/1B) Linear Imaging Self-scanning Sensor data (Ministry of Rural Development and National Remote Sensing Agency 2000). A total of 63.85 million ha lands were found to be lying waste. Subsequently, a soil degradation map of entire country was prepared at 1:4 million scale (Sehgal & Abrol 1994). Besides, Landsat TM data at 1: 250,000 scale have been used in a national-level project titled “Mapping saline/alkali soils of India” (National Remote Sensing Agency 2001a) for mapping salt-affected soils. The individual process–specific studies carried out using space-borne multispectral data are cited hereunder:

Soil erosion The studies carried out for deriving informa-

tion on soil erosion by water and wind using satel-lite data are discussed below:

Water erosion Qualitative assessment and delineation and

mapping of eroded lands (Dwivedi et al. 1997a & b; Rao et al. 1980) was attempted using Landsat, MSS/TM, SPOT-PLA/MLA, and IRS ISS-I/II data. Besides, Landsat-MSS data have been used for predicting soil loss in the rangelands of western Australia (Pickup & Chewings 1986). Further-more, using Landsat data and Geographic Infor-mation System (GIS), Mallawaarachchi et al. (1996) estimated the cost of productivity that had lost due to soil erosion in Lachlan catchment in New South Wales, Australia.

Information on the extent, spatial distribution and morphometry of ravines is of paramount importance for taking up any reclamative measures. Landsat-MSS/TM data have been used for mapping ravines (Karale et al. 1987; Singh & Dwivedi 1989).

Attempts have also been made to prioritize the watersheds for treatment and to study the impact of the implementation of soil and water conserva-tion measures on terrain conditions especially vegetation cover. Based on climate, physiography, slope, land use/land cover, current soil erosion status and soil conservation measures employed, Karale et al. (1989) have attempted prioritization of watersheds after integrating them in a comput-erized software module- ‘WEIGHT’. Besides, the Landsat TM data have been used to derive the sediment yield index values in a GIS domain which in turn has been used for watershed priori-tization (Dohare et al. 1985). Wu et al. (1997) used Landsat TM data and GIS to evaluate and monitor the impact of soil conservation measures in Finney county, Kansas, USA. Using remote sensing and GIS McCloy (1995) evaluated the risk of soil ero-sion for the Harbour creek catchment in New South Wales, Australia.

Wind erosion Spaceborne multispectral data hold a great

promise in deriving information on features asso-ciated with the wind erosion. Using brightness and redness indices derived from the Nimbus Coastal Zone Colour Scanner (CZCS) data over northern Africa, Escadafal (1992) distinguished sand seas with different sand types (pale to reddish) apart from other features. In India, several workers have studied the separability of terrain features related to wind erosion. Dwivedi et al. (1992) used Landsat


TM data and its various transforms, principal component, Normalized Difference Vegetation In-dex (NDVI), Soil Brightness Index (SBI) and Per-pendicular Vegetation Index (PVI) derived there-from over central Luni Basin in Rajasthan, and have shown that the information on sand dunes and other associated features generated by the conjunctive use of the standard data product i.e. false colour composite prints generated from green, red and near-IR spectral bands, and the trans-forms far exceeds the one derived from the former alone. While working in the Indira Gandhi canal command area in Rajasthan, western India, Goossens et al. (1992) could discriminate parabolic dunes from interdunal depressions using Landsat MSS data of 1973 and 1986 coinciding with the before and after commissioning of the canal. Addi-tionally, Singh (1992) and Mitra & Bhoj (1992) could delineate several land forms in western Ra-jasthan associated with the wind erosion using spaceborne multispectral data. Kharin (1986) has reviewed the potentials of space images in study-ing the wind erosion with special reference to Kara Kum desert.

Salt-affected soils Though the acquisition and availability of

space-borne multispectral data became possible with the launch of the first Landsat satellite in July 1972, mapping of salt-affected soils was al-ready attempted earlier using Apollo-9 (Wiegand et al. 1971) and Skylab (Everitt et al. 1977) data. In India, the Landsat-MSS data was used for the first time at the National Remote Sensing Agency, Hyderabad for mapping salt - affected soils (Singh et al. 1977). Subsequently, Landsat-MSS (Sharma & Bhargava 1988; Singh & Dwivedi 1989), Landsat-TM (Metterricht & Zinck 1997; Verma et al. 1994; Wheaton et al. 1992), SPOT-MLA (Leonardo et al. 1996; Sharma & Bhargava 1987) and the Indian Remote Sensing Satellite (IRS-1A/-1B,Linear Imaging Self-scanning Sensor (LISS-I and –II) data (Dwivedi & Venkataratnam 1992; Sharma et al. 2000) were used for mapping salt-affected soils. For mapping salt-affected soils in the Indo-Gangetic alluvial plains Dwivedi & Rao (1992) identified a three-band combination from Landsat-TM data viz. bands-1 (0.45-52 µm), -3 (0.63-0.69 µm) and-5 (1.55-1.75 µm). With the improvement in spatial resolution of 80 m from Landsat MSS to 30 m and

20 m from Landsat TM and SPOT MLA, respec-tively the level of information that could be de-rived improved tremendously. While only nature of salt-affected soils in terms of saline, saline-alkali and alkali could be derived from Landsat MSS data, Landsat TM and SPOT MLA data could afford the delineation of the magnitude of soil salinity and/or alkalinity in terms of slight, moderate and strong categories. Apart from in-ventory of salt-affected soils, studies were also carried out to study their temporal behaviour us-ing concurrent and historical satellite data/aerial photographs (Dwivedi 1992; Singh 1994; Venkatratnam 1981).

Water-logged areas Landsat TM digital data were used to map wa-

terlogged crops in East Yornaning catchment in Australia (McFarlane 1992). Using space-borne multispectral measurements made in the reflective portion of the spectrum, waterlogged areas with surface ponding or a thin film of water on the sur-face or wetness of the surface layer could be de-lineated (Sahai & Kalubarme 1985; Sharma & Bhargava 1988). Such a capability has been opera-tionally used for delineating and monitoring wa-ter-logged areas apart from salt-affected soils for entire Sharda canal system covering 6.7 million ha in Uttar Pradesh, northern India (National Re-mote Sensing Agency 1995). However, waterlog-ging on account of rising ground water table are not easily amenable to be detected using spectral measurements in the reflective portion of the spec-trum.

State-of-the-art

With the launch of IRS-1C in 1995 and IRS-1D in 1997, space-borne spectral measurements made at the same point of time with an unique combina-tion sensors, namely Panchromatic (PAN) camera with 5.8 m spatial resolution, Linear Imaging Self-scanning Sensor (LISS-III) with 23.5 m spatial resolution, and Wide Field Sensor (WiFS) with 188 m spatial resolution, and with different swath width became available. A pilot study conducted using IRS-1C LISS-III data for delineation of salt-affected soils revealed a relatively poor overall ac-curacy from this data as compared to the one de-rived from using IRS-1B LISS II data with 36.25 m spatial resolution (Dwivedi & Sreenivas 1998). It

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was attributed to higher intra-class spectral varia-tions resulting from high spatial resolution. In another study mapping of salt - affected soils and waterlogged areas at 1:12,500, 1:25,000 and 1:250,000 scales over part of Uttar Pradesh was attempted using IRS-1C, PAN LISS III and WiFS data (Dwivedi et al. 1998). By superimposing ca-dastral map after digital/optical reduction to com-patible scale over salt-affected soil map at 1:12,500 scale derived from IRS IC LISS-III and PAN-merged data, individual fields having salinity and alkalinity problem could be identified (National Remote Sensing Agency 1997).

Subsequently, digitally merging LISS-III and PAN data were through Intensity, Hue and Satu-ration (IHS) transformation, and its classification using per-pixel Gaussian maximum likelihood classification algorithm has resulted in a deterio-ration in the overall accuracy of salt-affected soils derived from LISS-III data as compared to IRS-1B LISS-II data owing to an improvement in the spa-tial resolution (23.5 m for LIS-III versus 36.5 m for LISS-II) leading to enhanced intra-class spectral variability. The PAN and LISS-III hybrid data without any transformation ranked the last in terms of overall accuracy. Overall accuracy figures for LISS-II, LISS-III, and PAN and LISS-III hy-brid data with IHS transformation have been in the order of 89.6%, 85.9% and 81.5%, respectively (Dwivedi et al. In Press).

Multi-temporal LISS III and PAN data were used for monitoring the success and progress of the ongoing land reclamation programme under a World Bank-aided project for reclamation of salt- affected soils (Verma & Singh 1999). The IRS-1C PAN data were used for mapping and monitoring ravinous lands in western Uttar Pradesh (Singh et al. 1998). The IRS1C/1D LISS-III data have been operationally used for assessment and monitoring of soil erosion, and to study the impact of soil and water conservation measures employed in the catchments (National Remote Sensing Agency 2000a).

Besides, in a pilot study, the reclamative grouping of ravines (a network of gullies) was made using IRS 1D LISS-III data (National Re-mote Sensing Agency 2000b).

Case studies

In order to illustrate the potentials of space-

borne multispectral data for inventory and moni-toring of lands subject to soil degradation, two case studies: one dealing with the qualitative assess-ment and monitoring of soil erosion by water , and another relating to monitoring spatial extent of salt-affected soils and waterlogged areas are dis-cussed hereunder:

Assessment and monitoring of eroded lands The study was taken up in the Ramganga

catchment which covers an area of 3.11 lakh ha, and forms parts of Almora, Chamauli, Nainital, Pauri Garhwal and Tehri Garhwal districts of newly-formed Uttaranchal State, north India. The annual rate of soil loss from the catchment has been observed as 25.95 t ha-1 as against an as-sessed value of 6.44 t ha-1, which is quite alarm-ing. Realising the seriousness of the problem, a study was taken up at the instance of the Ministry of Agriculture, Government of India using pre- and post-monsoon historical (1985-86) and concurrent (1999-2000) satellite data to assess the impact of soil and water conservation measures employed in the catchment on agricultural land use, soil ero-sion status and biomass. The approach essentially involves generation of NDVI images, preparation of agriculture land use and soil erosion maps from historical (1985-86) and concurrent (1999-2000) satellite data, and change detection data therefrom in a Silicon Graphics work station using ERDAS/ IMAGINE version 8.4 software after radiometric normalization.

Soil conservation measures taken up in the area, generally, result in (i) arresting soil loss, thereby an improvement in soil erosion status, and (ii) improving soil moisture status, which subse-quently leads to establishment for improvement in vegetation cover/ biomass. A sample map showing eroded lands in the Rg2h sub-watershed of the catchment during the periods 1985-86 and 1999-2000 is portrayed as Fig. 1. As evident from the Figure, there has been a substantial shrinkage in the spatial extent of moderately eroded lands with concomitant increase in the slightly eroded cate-gory (National Remote Sensing Agency 2000b). During 1985-86 an estimated 691 ha of land was subject to moderate soil erosion. By 1999-2000 it has shrunken to 458 ha while the slightly eroded category has expanded to 1128 ha from mere 901 ha during 1985-86 (Table 1).


Temporal behaviour of salt-affected soils and waterlogged areas

Development of soil salinity and/ alkalinity is the major problem in the irrigated commands of the arid and semi-arid regions. The study was taken up at the instance of the Ministry of Water Resources, Government of India to delineate salt-affected soils and waterlogged areas in Sri Ram Sagar Project (SRSP) command area in part of An-dhra Pradesh, southern India using Landsat-TM and IRS 1C/1D LISS-III data for the periods 1986 and 1998-99, respectively. The methodology in-volves radiometric normalization of temporal satel-lite data, detection and delineation of and change detection in the spatial extent of salt-affected soils and waterlogged areas in a Silicon Graphics work-station using ERDAS/ IMAGINE version 8.4 soft-ware. Fig. 2 shows the spatial distribution of salt-affected soils around Korutla in part of the com-mand. It is clear from the Figure in this part of the

command only saline-alkali soils are predominant. Further, five categories of saline-alkali soils, namely slightly saline-alkali, slightly saline and moderately alkali, slightly saline and strongly al-kali, moderately saline-alkali, and moderately sa-line and strongly alkali soils are encountered (Na-tional Remote Sensing Agency 2001b). Further, there has been a significant increase in the spatial extent of slightly saline and strongly alkali soils during the period 1985-86 to 1998-99. In fact the area under slightly saline and strongly alkali soils

Fig. 1. Temporal behavioural of eroded lands in Rg2h sub-watershed.

Table 1. Temporal behaviour of eroded lands.

Spatial extent (ha) S. N. Category

1985-86 1999-2000 1. Slightly eroded 901 1128 2. Moderately eroded 691 458 3. Severely to very

severely eroded 354 354

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has been doubled i.e. from 47 ha to 98 ha, during 14 years period.

Future perspective

In spite of tremendous development in the sen-sor technology, and data processing and analy-

sis/interpretation techniques of remote sensing data, there are certain specific issues related to soil degradation studies which could not be ad-dressed with the currently available remote sens-ing data, and require immediate attention. Impor-tant amongst them are enumerated hereunder:

Quantification of soil loss Hitherto, the assessment of soil erosion has

been mostly qualitative. For objective assessment of soil loss, quantitative information is required. Studies on soil loss have been confined mostly to individual plot level or to a micro watershed level using empirical models like USLE or WEPP. The value of ‘K’ (the soil erodibility factor), ‘L’ (the slope length factor), ‘S’ (the slope gradient factor) and ‘C’ (the crop management factor) could be de-rived using high resolution LISS III and PAN ste-reo data from IRS-1C/1D; and by using the values of ‘R’ and ‘P’ factor from in situ measurements, quantitative soil loss could be estimated through empirical USLE model. Some pilot studies have

Fig. 2. Salt affected soils of part of Sri Ram Sagar command area.

Table 2. Temporal behaviour of salt-affected soils.

Spatial extent (ha.) Category

1985-86 1999-2000 1. S1N1 31 31 2. S1N2 14 14 3. S1N3 47 98 4. S2N2 48 48 5. S2N3 108 108

N.B.: S1N1 = Slightly saline-alkali, S1N2 = Slightly sa-line moderately alkali, S1N3 = Slightly saline strongly alkali, S1N3 = Slightly saline strongly alkali, S2N2 = Moderately saline-alkali, S2N3 = Moderately saline strongly alkali.


already been carried out in this direction. Saha et al. (1991) have attempted to quantify soil loss us-ing the spaceborne multispectral data have been used for deriving information ‘C’ factor of the Uni-versal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE).

Mapping salt-affected soils under varying terrain conditions

Owing to a large variation in the surface condi-tion of salt-affected soils on account of the presence of moisture, organic matter, vegetation-both live and dead, and similarity in its spectra with other non-salt affected soils, namely sandy soils and black soils due primarily to similarity in colour, these soils do not exhibit unique spectra. Conse-quently, the detection and delineation of salt-affected soils using remote sensing data is fraught with several problems. Vegetation is one of the major factors marking spectral response from soils. When, vegetation cover is more than 30-40% of an area, the spectral response is mostly from vegeta-tion, whereas below this level, the signal is from a mixture of soil and vegetation. Furthermore, the situation becomes more complicated in the pres-ence of both live and dead vegetation (Aase & Ta-naka 1983). Decaying vegetation tissues had a greater impact on soil spectra than living vegeta-tion (Murphy & Wadge 1994). Even within un-vegetated terrain, only a portion of the soils has unaltered surface layer. Hence in most of the cases, soil spectra need to be derived from the mix-ture of soil-vegetation signals.

In the presence of vegetation cover In the presence of native vegetation, there ex-

ists a relationship between its spectra and under-lying salt-affected soils. Some attempts have al-ready been made to derive information on salt-affected soils in partially or fully vegetated terrain (Dalsted et al. 1979; Taylor et al. 1994; Worcester & Seelig 1976). More specifically, the type and composition of vegetation has been used as indica-tor for delineation of salt-affected soils (Battle et al. 1988; Hardisky et al. 1983; Wiegand et al. 1994). Gausman et al. (1970) pointed out that cot-ton leaves grown in saline soil had a higher chlo-rophyll content than that of leaves grown in low-salt soils. In the absence of vegetation, the major influence of salt is on structure of the upper soil surface. Based on this, Hirschfield (1985) sug-

gested that high spectral resolution data are re-quired. Besides, Wiegand et al. (1996) studied the effects of soil salinity on growth and yield of sugar-cane (Saccharum spp hybrid) using digital video-graphic or SPOT HRV data, and developed multi-ple linear regression equations to relate the re-sponse in the three spectral bands of SPOT MLA to both weighted electrical conductivity (WEC) and yield at plant and soil sampling sites.

In case of agro-ecosystems, the influence of vegetation on soil spectra need to be accounted for. The spectral region 0.63 to 1.3 µm of soils is the region most affected by green vegetation, as a re-sult of the steep rise in reflectance caused by vege-tation (Ammer et al. 1991). The low reflection of green vegetation beyond 1.4 µm indicates that if soil-vegetation mixture exists, most of the spectral information relates to rock and soil material (Sie-gal & Goetz 1977). For deriving information on soil spectra from a mixture of soil-vegetation mixture, non-linear models are typically used (Goetz 1992; Ray & Murray 1996). Huete (1988) developed an index termed as soil adjusted vegetation index (SAVI) which accounts for soil brightness and shadow. Furthermore, Liu & Huete (1995) devel-oped a modified NDVI (MNDVI) which accounts for atmospheric attenuation as well. The SAVI was shown to minimize soil-related problems. Though these studies were experimental in nature, they nevertheless did provide an insight into the possi-bility of generating information on salt-affected soils in the vegetated terrain.

In black soil and sandy terrain The spectral similarity between normal and

salt-affected black soils, and sandy areas precludes their delineation using currently available space-borne multispectral data. It may attribute to the broad band spectral measurements made by cur-rently operating space-borne sensors. Spectral measurements made in the narrow spectral bands of the order of a few nanometres may enable detec-tion of otherwise spectrally similar features. As pointed out earlier, Taylor et al. (1994) observed absorption at 1400, 1900 and 2500 nm due to un-combined water in saline soils having MgCl2 while measuring the spectral response from vegetated and bare salt-affected soils with a 24-channel field spectrometer operating in the short-wave infrared region. In addition, using AVRIS spectra the dis-tribution of, and relationship between soil se-

DWIVEDI 85

quences and other surface materials have been mapped (Fox et al. 1990; Palacios-Orueta & Ustin 1996). Szilagyi & Baumgardner (1991) reported the use of high resolution laboratory measure-ments to identify salinity status of soils. Working in the southern Apennines (Fortore beneventano) in Italy, Leone & Sommer (2000) showed that mul-tivariate analysis of high resolution reflectance spectra offers great potential for discriminating between soil development and soil degradation states within the specific environmental condi-tions.

Detection of water-logging due to rising ground water table and in the presence of

vegetation cover When ground water table rises and reaches

very close (within 2 m) to the surface, the growth of most of the mesophytic plants begins to be af-fected. Being a sub-surface phenomenon, its detec-tion in the optical region of the spectrum is, gener-ally, not feasible. However, indirect detection has been made by assessing the vegetation conditions from spectral measurements made in thermal re-gion (Heilman & Moore 1982; Huntley 1978). Re-sults have, however, not been encouraging. In ad-dition, attempts have also been made to detect the shallow ground water tables using ground pene-trating radar (GPR) in part of Massachusetts (Doolittle 1987). Furthermore, an index based on visible green, near and mid-infra-red region of Landsat-TM bands was used to delineate the de-gree and extent of water-logging in crop lands in part of Australia (Wallace & Wheaton 1990). Cialella et al. (1997) conjunctively used Normal-ized Difference Vegetation Index (NDVI) derived from Advanced Visible and Infrared Imaging Spec-trometer (AVRIS) and digital elevation model (DEM) over Howland, Maine to develop a tech-nique for predicting soil drainage classes which are very important from waterlogging point of view.

Generating information on third dimension of degraded soils

For taking up any reclamative or preventive measures for salt-affected soils, information on sub-surface layers like clay pan, kankar pan, gyp-sum layer is a pre-requisite. Ground Penetrating Radar (GPR) with the sub-surface imaging capabil-ity on the order of less than 0.5 m in fine-textured

soils to about 25 m in coarse-textured soils under un-saturated conditions have been found useful in recording the depth and extent of such sub-surface layers (Doolittle 1987; Tarussov et al. 1994).

Creation of digital data base on degraded soils

Though voluminous information on degraded lands in the form of maps and attributes (physical and chemical properties, geographic location, cur-rent land use, etc.) is available with various or-ganizations, there is no organized digital data base at the state or national level available to concerned users. It is , therefore, necessary to develop a cen-tralized digital database in a Geographic Informa-tion System (GIS) domain on degraded lands and related terrain parameters covering the location of the soil observation, if made, using Global Posi-tioning System (GPS) with adequate accuracy, and incorporated into the data base. It would enable studying the variations in properties of degraded lands in space and time. By incorporating the Digi-tal Elevation Model (DEM) in the database, an-other dimension would be added which would be very useful for taking up any reclamative meas-ures for degraded lands. In India, a beginning in this direction has already been made under the National (Natural) Resources Information System (NRIS) which aims at creating a digital data base (both spatial and attribute data) on natural re-sources at 1: 50,000 and 1: 250,000 scales.

Development of intelligent GIS In order to standardize the methodology for

mapping degraded lands and generating trained manpower, there is a need to develop an intelligent GIS by integrating GIS, expert system, artificial intelligence and approaches derived from physics and mathematics of chaos. Some efforts in this di-rection has already been made by developing proto-type expert systems on degraded lands (Na-tional Remote Sensing Agency 1996).

Acknowledgements

The author is extremely thankful to Dr R. R. Navalgund, Director, Dr. D.P. Rao, Ex-Director, and Prof. S.K. Bhan, Deputy Director (Applica-tions), National Remote Sensing Agency (NRSA) for providing necessary facilities during prepara-


tion of manuscript. Further, valuable input pro-vided by Dr. K. V. Ramana, Scientist/Engineer ‘SE’, Agriculture and Soils Group, NRSA is also gratefully acknowledged.

References

Aase, J. A. & D. L. Tanaka. 1983. Effect of tillage prac-tice on soil and wheat reflectance. Journal of Agron-omy 76: 814-818.

Abrol, A.P. & D.R. Bhumbla. 1971. Saline and alkali soils in India - their occurrence and management. World Soil Resources. Report No.41-42, FAO, Rome.

Ammer, U., B. Koch, T. Schneider, H. Witt-meier.1991.High resolution spectral measurements of vegetation and soil in field and laboratory. Pro-ceedings of the 5th International Colloquium on Physical Measurements and Signatories in Remote Sensing. Courchevel, France. 1: 213-218.

Anonymous.1976. National Commission on Agriculture. Chapter-V, Resources Development. Ministry of Ag-riculture, Government of India, New Delhi.

Anonymous.1991.Waterlogging, Soil Salinity and Alka-linity. Report of the Working Group on Problem Identification in Irrigated Areas with Suggested Remedial Measures. Ministry of Water Resources, Government of India, New Delhi.

Bali, J.S.1985. Problems of gullied and ravinous area - Policy, programmes and progress. Proceedings Na-tional Seminar on Soil Conservation and Water-shed Management, New Delhi, September17-18, 1985.

Battle, J., J. Guauzzio & M. Moral. 1988. Remote sens-ing applied to soil salinity survey in the central re-gion of Spain. pp. 142-149. In: Solonetz Soils: Problem, Properties and Utilization. Proceedings of the ISSS Sub-commission Symposium. Osijek.

Bell, D., C. Menges, W. Ahmad & J.J. van Zyl. 2001. The application of dielectric retrieval algorithms for mapping soil salinity in a tropical coastal environ-ment using polarimetric SAR. Remote Sensing of Environment 75: 375-384.

Baumgardner, M.F., L.F. Silva, L.L. Biehl & E.R. Stoner. 1985. Reflectance properties of soils. Ad-vances in Agronomy 38: 1-44.

Bushnell, T. M. 1929. Aerial photographs of Jennings County. Proceedings Indiana Academy of Sciences 39: 229-230.

Chaturvedi, L., K. R. Carver, J. C. Harlan, G. D. Han-cock, F. V., Small & K. J. Dalstead .1983. Multispec-tral remote sensing of saline seeps. IEEE Transac-tions on Geosciences and Remote Sensing 21: 239-250.

Cilella, A. T., R. Dubayah, W. Lawrence & E. Le-vine.1997. Predicting soil drainage class using re-motely sensed and digital elevation data. Phot-grammetric Engineering and Remote Sensing 63: 171-178.

Cipra, J.C., M.F. Baumgardner, E.R. Stoner & R.B. McDonald. 1971. Measuring radiance characteris-tics of soils with a field spectro-radiometer. Proceed-ings American Society of Soil Science 35: 14-17.

Clark, R.N. 1981. The reflectance of water-mineral mix-ture at low temperatures. Journal of Geophysical Research 86: 3074-3086.

Colwell, R.N. 1983. Manual of Remote Sensing. Volume II. Interpretation and Analysis. American Society of Photogrammetry. 2nd edition.

Coldwell, A.E. 1957. Importance of channel erosion as a source of sediment. American Geophysical Union Transactions 38: 908-912.

Csillag, F., L. Pasztor & L.L. Biehl. 1993. Spectral band selection for the characterization of salinity status of soils. Remote Sensing of Environment 43: 231-242.

Dale, P.E.R., K. Hulsman & A.L. Chandica. 1986. Clas-sification of reflectance on colour infrared aerial photographs and sub-tropical salt-marsh vegetation types. International Journal of Remote Sensing 7: 1783-1788.

Daslted, K.J., B.K. Worcester & L.J. Brus.1979. Detec-tion of saline seeps by remote sensing techniques. Photogrammetric Engineering and Remote Sensing 45: 285-291.

Dohare, D.D., S. Ali & P.G. Shanware. 1985. Evaluation of sediment yield index using Landsat data and Geographic Information System. pp. 412-417. Pro-ceedings Asian Conference on Remote Sensing, Hy-derabad.

Doolittle, J.A. 1987. Using ground – penetrating radar to increase the quality and efficiency of soil surveys. pp. 11-32. In: Soil Survey Techniques. Soil Science Society of American Spectral Publication No. 20. Madison, Wisconsin, USA.

Dwivedi, R.S., T. Ravi Sankar, L. Venkataratnam & D.P. Rao. 1992. Detection and delineation of vari-ous desert terrain features using Landsat-TM de-rived image transforms. Journal of Arid Environ-ment 25: 151-162.

Dwivedi, R.S. 1992. Monitoring and the study of the ef-fects of image scale on delineation of salt-affected soils in the Indo-Gangetic alluvial plains. Interna-tional Journal of Remote Sensing 13: 1527-1536.

Dwivedi, R.S. & B.R.M. Rao.1992. The selection of the best possible Landsat-TM band combinations for de-lineating salt-affected soils. International Journal of Remote Sensing 13: 2051-2058.

DWIVEDI 87

Dwivedi, R.S. & L.Venkataratnam.1992. Land degrada-tion studies using IRS-1A data. pp. 153-169. In: R.L. Karale (ed.) Natural Resources Management; A New Perspective. National Natural Resources Man-agement Systems (NNRMS), Department of Space, Government of India, Bangalore.

Dwivedi, R.S., T. Ravi Sankar, L. Venkataratnam, R.L Karale, S.P. Gawande, K.V.S. Rao, S. Senchuand-hary , K.R. Bhaumik & K.K. Mukherjee. 1997a. The inventory and monitoring of eroded lands using remote sensing data. International Journal of Re-mote Sensing 18: 107-119.

Dwivedi, R.S., A.B. Kumar & K.N. Tewari. 1997b. The utility of multi-sensor data for mapping eroded lands. International Journal of Remote Sensing 18: 2303-2318.

Dwivedi, R.S. & K. Sreenivas. 1998. Delineation of salt-affected soils and waterlogged areas in the Indo-Gengetic plains using IRS-1C LISS-III data. Inter-national Journal of Remote Sensing 19: 2739 - 2751.

Dwivedi R.S., K.V. Ramana & S.S. Thammappa. 1998. Evaluation of regional - level mapping of salt-affected soils and waterlogged areas using IRS-1C WiFS data. pp. 109-116. Remote Sensing and Geo-graphic Information System for Natural Resources Management. A joint publication of Indian Society of Remote Sensing and NNRMS, Department of Space, Government of India, Bangalore.

Dwivedi, R.S., K.V. Ramana, S.S. Thammappa & A. N. Singh. Mapping salt-affected soils from IRS-1C LISS-III and PAN data. Photogrmmetric Engineer-ing and Remote Sensing. (In Press).

Embabi, N.S. & M.M. Asour. 1992. Sand dunes in Qatar. Proceedings of the International Symposium on Evolution of Deserts. Ahmedabad, India, Febru-ary 11-19, 1992.

Escadafal, R. 1992. Remote sensing of north African sand seas with medium resolution satellite imagery (Nimbus-CZCS). International Symposium on Evo-lution of Deserts. Ahmedabad, India, February 11-19, 1992.

Everitt, J.H., A.H. Gertermann, & J.Q. Cuellar. 1977. Distinguishing saline from non-saline range lands with Skylab. Photogrammetric Engineering and Remote Sensing 43: 1041-1047.

Everitt.J., D.E. Escobar, A.H. Gerbermann & M. Alaniz. 1988. Detecting saline soils with video imagery. Photogrammetric. Engineering and Remote Sensing 54: 1283-1287.

Food and Agriculture Organization. 1978. The Applica-tion of Landsat Imagery Soil Degradation Mapping at 1: 5,000,000. FAO Summary Report AGL Bulle-tin, 1/78.

Fox, L., A.F. Fischer, A.R. Gillespie & M.R. Smith. 1990. Using spectral mixture analysis of AVIRIS high di-mensional data for distinguishing soil consequences. pp. 23-32. Proceedings of the Third Airborne Visi-ble/Infrared Imaging Spectrometer (AVIRIS) Work-shop. JPL Publications,91-28. Jet Propulsion Labo-ratory, Pasadena, California.

Frazier, B.E. & D.K. McCool. 1981. Aerial photographs to detect rill erosion. Transactions of the American Society of Agriculture Engineers 24: 1168-1171 and 1176 - 1181.

Frazier, B. E.&G. K. Hooper.1983. Use of chromogenic film for aerial photography of erosion features. Pho-togrammetric Engineering and Remote Sensing 49: 1211-1217.

Gausman, H.W., W.A. Allen, R. Cardenas & R.L. Brown. 1970. Colour photos, cotton leaves and soil salinity. Photogrammetric Engineering and Remote Sensing 36: 454-459.

Goetz, A.F.A. 1992. Principles of narrow band spec-trometry in the visible and near IR: instrumenta-tion and data analysis. pp. 21-32. In: F. Tosseli & J. Bodechtel (eds.) Imaging Spectroscopy: Fundamen-tals and Prospective Applications. ECSE, EEC, EAEC Brussels and Luxembourg.

Goossens, E.B., de Roover & R. Goossens.1992. A digi-tal approach to separation of parabolic sand dune area from interdunal using Landsat MSS data. Proceedings of the International Symposium on Evolution of Deserts. Ahmedabad, India, February 11-19, 1992.

Gorrie, R.M., 1935. The Use and Misuse of Land. Oxford Forestry Memoirs.

Hardisky, M.A., V. Klemas & R.M. Smart. 1983. The influence of soil salinity, growth form and leaf mois-ture on the spectral radiance of Spartina al-terniflora canopies. Photogrammetric Engineering and Remote Sensing 39: 77-83.

Heilman, J.L. & D.G. Moore. 1982. Evaluating depth to shallow ground water using Heat Capacity Mapping Mission (HCMM) data. Photogrammetric Engineer-ing and Remote Sensing 48: 1903-1906.

Hick, R.T. & W.G.R. Russell.1990. Some spectral con-siderations for remote sensing of soil salinity. Aus-tralian Journal of Soil Research 28: 417-431.

Hilwig, F.W. & R.L. Karale. 1973. Physiographic sys-tems and elements of photo-interpretation as ap-plied to soil survey in Ganges plain. Journal of In-dian Society of Soil Science 21: 205-212.

Hirschfield, T. 1985. Salinity determination using NIRA. Applied Spectroscopy 39: 740-741.

Huete, A.R. 1988. Soil-adjustednvegetation index (SAVI). Remote Sensing of Environment 25: 47-57.


Hunt, G.R., J.W. Salisbury & C.J. Lenholff. 1971. Visi-ble and near infrared spectra of minerals and rocks: halides, phosphates, arsenates, vandates, and bo-rates. Modern Geology 3: 121-132.

Huntley, D. 1978. On the detection of shallow aquifers using thermal infrared imagery. Water Resources Research 14: 1075 - 1083.

Iyer, H.S., A.N. Singh & Ram Kumar. 1975. Problem soil inventory of part of Hoshiarpur district through photo-interpretation. Journal of Indian Society of Soil Science 11: 79-85.

Kalra, N.K. & D.C. Joshi. 1994. Spectral reflectance characteristics of salt-affected soils of Rajasthan. Journal of Indian Society of Soil Science 22: 183-193.

Kamphrost, A & H.S. Iyer. 1972. Application of aerial photo-interpretation of ravine surveys in India. Proceedings of the Twelfth Congress of the Interna-tional Society of Photogrammetry. Ottawa, Canada.

Karale, R.L., K.K. Narula, V. Dayal & K.M. Saini. 1989. Database management system for erosivity values. Journal of Indian Society of Remote Sensing 77: 23.

Karale, R.L., K.M. Saini, & K.K. Narula. 1987. Mapping and monitoring ravines using remotely sensed data. Journal of Soil and Water Conservation, India 32: 75.

Karnieli, A. & H. Tsoar. 1994. Spectral reflectance of biogenic crust developed on desert dune sand along the Israel-Egypt border. International Journal of Remote Sensing 16: 369-374.

Kharin, N.G. 1986. Multipurpose desertification maps and a methodology for compiling them from space images. Soviet Journal of Remote Sensing 5: 69-80.

Kimpe, C.R. de & B.P. Warkentin. 1998. Soil functions and the future of natural resources. In: H.-P. Blume, H. Eger, E. Fleischhauer, A. Hebel, C.Reij, K. G. Steiner (eds.) Towards Sustainable Land Use: Fur-thering Co-operation between People and Institutions. Vol.I Advances in Geoecology 31-Aco-operating series of the International Society of Soil Science.

Kumar, A.B., K.N. Tewari, R.S. Dwivedi & K. Karuna-kar. 1997. Spectral behaviour and spectral separa-bility of eroded lands using multi-sensor data. Land Degradation and Development 8: 27-38.

Lal, R. & B.A. Stewart. 1990. Soil degradation: A global threat. Advances in Soil Science 11: 13-17.

Latz, K.R.A., G.E. Weismiller, Van Scoyoc & M.F. Baumgardner. 1984. Characteristic variations in spectral reflectance of selected eroded Alfisols. Soil Science Society of America Journal 48: 1130-1134.

Leonardo, P.M., C.L Weigand, S.S. Hector & R.Z. Car-los. 1996. Using Landsat - TM and SPOT satellite images to identify salinity in irrigated areas of Mex-ico. Proceedings of the Sixteenth International Con-gress on Irrigation Drainage. Cairo Egypt, Sept. 15 - 22, 1996.

Leone, A.P. & S. Sommer. 2000. Multivariate analysis of laboratory spectra for the assessment of soil devel-opment and soil degradation in the Southern Apen-nine (Italy). Remote Sensing of Environment 72: 346-359.

Lin, J. & C.W. Brown. 1992. Near- IR spectroscopic de-termination of NaCl in aqueous solution. Applied Spectroscopy 46: 1809-1815.

Liu, H.Q. & A. Huete.1995. A feedback-based modifica-tion of the NDVI to minimize canopy background and atmospheric noise. IEEE Transactions on Geo-sciences and Remote Sensing 33: 457-465.

Mallawaarachchi, T., P.A. Walker, M.D. Young & R.E. Smyth. 1996. GIS-based modelling system for natu-ral resources management. Agricultural Systems 50: 169-189.

Manchanda, M.L. & S.K. Khanna. 1979. Use of aerial photographs for study of soil salinity and landscape relationship in parts of Haryana. Journal of Indian Society of Remote Sensing 7: 27-34.

Maner, S.B. 1958. Factors affecting sediment delivery rates in the Red Hills physiographic area. American Geophysical Union Transaction.

McCloy, K.R. 1995. Resource Management System: Process and Practice. Taylor and Francis, London.

McFarlane, D.J., G.A. Wheaton, T.R. Negus & J. F. Wal-lace. 1992. Effects of waterlogging on crop and pas-ture production in the Upper Great Southern, West-ern Australia.Technical Bulletin No. 86, Depart-ment of Agriculture and Western Australia.

Metternicht, G. & J.A. Zinck. 1997. Spatial discrimina-tion of salt and sodium affected soil, soil surfaces. International Journal of Remote Sensing 18: 2571–2586.

Ministry of Rural Development and National Remote Sensing Agency. 2000. Wastelands Atlas of India.

Mitra, D.S. & R. Bhoj. 1992. Dune forms in space im-ages-A case study from Shahgarh and Myajlar areas of western Rajasthan. Proceedings of the Interna-tional Symposium on Evolution of Deserts. Ahmeda-bad, India, February 11-19, 1992.

Mougenot, B. 1993. Effects of salts on reflectance and remote sensing of salt affected soils,Cab ORSTOM Series on Pedology 28: 45-54.

Mougenot, B., M. Pouget & G.F. Epema. 1993. Remote sensing of salt-affected soils. Remote Sensing Re-views 7: 241-259.

Mulders, M.A. 1987. Remote Sensing in Soil Science. Developments in Soil Science 15, Elsevier Amster-dam.

Murphy, R.J. & G. Wadge. 1994. The effects of vegeta-tion on the ability to map soils using imaging spec-trometer data. International Journal of Remote Sensing 15: 63-86.

DWIVEDI 89

Myers, V.I., L.R. Ussery & W.J. Rippert. 1966. Photo-grammetry for detailed detection of drainage and salinity problems. Transactions of the ASCE 6: 332-334.

National Remote Sensing Agency.1985. Wasteland Map-ping of India using Landsat False Colour Composite on 1.1 Million Scale. Project Report.

National Remote Sensing Agency. 1995. Study of land degradation problems in Sharda Sahayak command area for sustainable agriculture development. Pro-ject report National Remote Sensing Agency, De-partment of Space, Government of India.

National Remote Sensing Agency. 1996. Development of an Expert System for Detection of Salt-affected Soils. Project report National Remote Sensing Agency, Department of Space, Government of India.

National Remote Sensing Agency. 1997. Evaluation of IRS-1C data for mapping soil resources and de-graded lands. Technical Report. National Remote Sensing Agency, Department of Space, Government of India.

National Remote Sensing Agency. 2000a. ABC Catego-ries of Wastelands. Project Report National Remote Sensing Agency, Department of Space, Government of India.

National Remote Sensing Agency. 2000b. Evaluation of the Impact of Implementation of Soil Conservation Measures in RVP and FPR Catchments: Ramganga catchment. Project Report National Remote Sensing Agency, Department of Space, Government of India.

National Remote Sensing Agency. 2001a. Mapping Sa-line/alkali Soils of India. Draft wrap up report, Na-tional Remote Sensing Agency, Department of Space, Government of India.

National Remote Sensing Agency. 2001b. Satellite Re-mote Sensing-based Evaluation Study of Sri Ram Sagar Project Command Area, Andhra Pradesh. Project Report National Remote Sensing Agency, Department of Space, Government of India.

O’Neill, A. L. 1994. Reflectance spectra of microphytic soil crust in semi-arid Australia. International Journal of Remote Sensing 15: 675-681.

Palacios-Orueta, A. & S.L. Ustin. 1996. Multivariate statistical classification of soil spectra. Remote Sens-ing of Environment 57: 108-118.

Pandey, S., S. Singh & B. Ghosh. 1964. Orientation, distribution and origin of sand-dunes in the central Luni basin. Proceedings of the Symposium on Prob-lems of Indian Arid Zone.Jodhpur(Rajasthan), 23rd November to 2nd December 1964.

Pickup, G. & V.H. Chewings. 1986. A grazing gradient approach to land degradation assessment in arid areas from remotely-sensed data. International Journal of Remote Sensing 15: 597-617.

Rao, B.R.M., T. Ravi Sankar, R.S. Dwivedi, S.S. Tham-mappa, L. Venkataratnam, R.C. Sharma & S.N. Das. 1995. Spectral behaviour of salt-affected soils. International Journal of Remote Sensing 16: 2125-2136.

Rao, K.V.S., Y.P. Bali & R.L. Karale. 1980. Remote sensing for soil conservation and watershed man-agement. Proceedings of the National Seminar on Applications of Photo-interpretation and Remote Sensing Techniques for Natural Resources Surveys and Environmental Analysis. Dehradun, India.

Ray, T.W. & B.C. Murray. 1996. Non-linear spectral mixing in desert vegetation. Remote Sensing of En-vironment 55: 59-79.

Saha. S.K, M. Kudrat & S.K. Bhan. 1991. Erosional soil loss prediction using satellite data and universal soil loss equation. pp 369-372. In: Sunji Murai (ed.) Application of Remote Sensing in Asia and Oceania - Environmental Change Monitoring. Asian Associa-tion on Remote Sensing.

Sahai, B. & M.H. Kalubarme. 1985. Ecological studies in the Ukai command area. International Journal of Remote Sensing 6 : 401-409.

Sehgal, J.L. & I.P. Abrol. 1994. Soil Degradation in In-dia : Status and Impact. Oxford & IBH Publication.

Sharma, R.C. & G.P. Bhargawa. 1987. Operational use of SPOT-1 image in mapping and management of alkali soils. Proceedings of the National Symposium on Remote Sensing in Land Transformation and Management. Hyderabad.

Sharma, R.C. & G.P. Bhargawa. 1988. Landsat imagery for mapping saline soils and wetlands in north-west India. International Journal of Remote Sensing 9: 39-44.

Sharma, R.C., R.K. Saxena & K.S. Verma. 2000. Recon-naissance mapping and management of salt-affected soils using satellite images. International Journal of Remote Sensing 21: 3209-3218.

Siegal, B. S. & A. F. H. Goetz. 1977. Effects of vegeta-tion on rock and soil type discrimination. Photo-grammetric Engineering and Remote Sensing 43: 191-196.

Singh, A.N. & R.S. Dwivedi. 1989. Delineation of salt affected soils through digital analysis of Landsat-MSS data. International Journal of Remote Sensing 10: 83.

Singh, A.N. 1994. Monitoring change in the extent of salt-affected soils in Northern India. International Journal of Remote Sensing 19: 2739 - 2751.

Singh, A.N., M.F. Baumgardner & S.T. Kristof. 1977. Delineating salt affected soils in part of Indo- Gangetic plain by digital analysis of Landsat data. Technical Report 111477, LARS, Purdue University, West Lafayette, Indiana, USA.


Singh, A.N., Y.K. Sharma & S. Singh. 1998. Evaluation of IRS-1C PAN data for monitoring gullied and rav-inous lands of western U.P. Remote Sensing and Geographic Information System for Natural Re-sources Management. A joint publication of Indian Society of remote sensing and NNRMS, Department of Space, Bangalore.

Singh, S. 1992. Aeolian geomorphology of the Indian desert through remote sensing. Proceedings of the International Symposium on Evolution of Deserts. Ahmedabad, India, February 11-19,1992.

Stephens, P. & J. Cock. 1991. Changes in vegetation and sand-dunes from digitized aerial photographs and SPOT data: changes over a period of 23 years. pp. 175-179. In: Shunji Murai(ed.) Applications of Remote Sensing in Asia and Oceania: Environ-mental Change Monitoring. Asian Association on Remote Sensing.

Szabolcs, I. 1992. Salinization of soil and water and its relation to desertification. Desertificaiton Control Bulletin 21: 32-37.

Szilagyi, A. & M. F. Baumgardner.1991.Salinity and spectral reflectance of soils. Proceedings of American Society of Photogrammetry and Remote Sensing Symposium, Baltimore, 25-29 March, 1991, APRS, Baltimore.

Tarussov, A., F. Bonn & A. Judge. 1994. Soil survey from light research aircraft using a ground pene-trating radar. Proceedings of the First International Airborne Remote Sensing Conference and Exhibi-tion, Strasbourg, France, 11 - 15 September, 1994.

Taylor, G.R., B.A. Bennett, A.H. Mah & R.D Hewson. 1994. Spectral properties of salinized land and im-plications for interpretation of 24-channel imaging spectrometry. Proceedings of the First International Airborne Remote Sensing Conference and Exhibi-tion, Strasbourg, France. 11-15 Sept., 1994.

Tueller, P.T. & D.T. Booth. 1975. Large scale aerial pho-tographs for erosion evaluation on rangeland water-sheds in the Great Basin. Proceedings of the Fall Convention, American Society of Photogrammetry. Falls Church , Virginia 220046.

Ulaby, F.T., R. K. Moore & A.K. Fung. 1986. Microwave Remote Sensing Active and Passive-From Theory to Applications. 3. Dedham, Massachusetts: Artech House.

UNEP, 1997. World Atlas of Desertification. 2nd edition, Arnold, London.

Venkataratnam, L. 1981. Delineation and mapping of problem soils with remotely sensed data. Interactive Workshop on Applications of Remote Sensing for Rice Production, Hyderabad. September 9-13, 1981.

Verma, D. & A.N. Singh. 1999. Monitoring change in the status of sodic lands in part of Uttar Pradesh plains consequent upon reclamation. Proceedings of the National Symposium on Remote Sensing Appli-cations for Natural Resources; Retrospective and Perspective. Jan 19-21, 1999, Bangalore.

Verma, K.S., R.K. Saxena, A.K. Barthwal & S.N. Deshmukh. 1994. Remote sensing techniques for mapping salt-affected soils. International Journal of Remote Sensing 15: 1901 - 1914.

Wallace, J & B. Wheaton. 1990. Mapping the extent of waterlogged crops using satellite imagery West Aus-tralian Journal of Agriculture 31: 48-57.

Wallace, J., N.A. Campbell, G.A. Wheaton & McFarlane. 1993. Spectral discrimination and mapping of waterlogged cereal crops in Western Australia. International Journal of Remote Sensing 14: 2731-2743.

Weismiller, R.A., P.E. Van Scoyoc, S.E. Pazar, K. Latz, & M.F. Baumgardner. 1985. Use of soil spectral properties for monitoring soil erosion. In: Soil Ero-sion and Conservation. Soil Conservation Society of America. ANKENY,IA.

Wheaton, F.A., J.F. Wallace, D.J. McFarlane & N.A. Campbell. 1992. Mapping salt-affected land in Western Australia. Proceedings of the Sixth Austra-lian Remote Sensing Conference, New Zealand.

Wiegand, C.L., R.W. Leamer, D.A. Weber & A.H. Ger-bermann. 1971. Multibase and multi-emulsion space photos for crops and soils. Photogrammetric Engineering 27: 147-156.

Wiegand, C.L., J.D. Rhoades, D.E. Escobar & J.H. Everitt. 1994. Photographic and videographic ob-servations for determining and mapping the re-sponse of cotton to soil salinity. Remote Sensing of Environment 49: 212-223.

Wiegand, C.L., G. Anderson, S. Lingle & D.E. Escobar. 1996. Soil salinity effects on crop growth and yield of- Illustration of an analysis and mapping method-ology for sugarcane. Journal of Plant Physiology 148: 418-424.

Worcester, B.K. & B. D. Seelig.1976. Plant indicator of saline seep. N.D. Farm Research 34: 18-20.

Wu, J., M.D. Nellis, K.P. Ransom & S.L. Egbert. 1997. Evaluating soil properties of CRP land using remote sensing and GIS inFinney county, Kansas. Journal of Soil and Water Conservation 52: 352-358.

Spatio-temporal characterization of soil degradation

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