Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province,Iran)

As drought occurs in different climates, assessment of drought impacts on parameters such as vegetation cover is of utmost importance. Satellite remote sensing images with various spectral and spatial resolutions represent information about different land covers such as vegetation cover. Hence, the purpose of this study was to investigate the performance of satellite vegetation indices to monitor the agricultural drought on a local scale. In this regard, satellite images including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) data were used to evaluate vegetation cover and their gradual changes effects on agricultural drought. Fars province in Iran with relatively low precipitation values was selected as the study area. Modified Perpendicular Drought Index (MPDI), MPDI1, Vegetation Condition Index (VCI), Normalized Difference Vegetation Index Anomalies (NDVIA), and Standardized Vegetation Index (SVI), were evaluated to select the remote sensing based index with the best performance in drought monitoring. The performance of such indices were investigated during 13 years (2000–2013) for MODIS and 29 years (1985–2013) for AVHRR. To assess the efficiency of the satellite indices in drought investigation, Standardized Precipitation Index (SPI) data of five selected stations were used for 3, 6, and 9 month periods on August. The results showed that NDVI-based vegetation indices had the highest correlation with SPI in cold climate and long-term timescale (6 and 9 month). The highest correlation values between remote sensing based indices and SPI were acquired, respectively, in 9-month and 6-month time-scales, with the values of 43.5% and 40%. Moreover, VCI showed the highest capability for agricultural drought investigating in different climate regions of the study area. Overall, the results proved that NDVI-based indices can be used for drought monitoring and assessment in a long-term timescale on a local time-scale.

     

Dynamic monitoring of drought using HJ-1 and MODIS time series data in northern China

From early November 2008 to February 2009, lack of rainfall led to severe drought in northern China. More than 9.3 million ha of wheat in six major crop production provinces, including Henan, Anhui, Shandong, Shanxi, Gansu, and Shaanxi, were hit by drought. Supported by Chinese HJ-1 satellite images together with NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, dynamic monitoring of the drought was conducted. HJ-1 CCD data with 30-m resolution were used to identify cropland information. Spatial–temporal variation of drought was detected using Vegetation Index and Water Index time series data derived from MODIS visible, infrared, and short-wave infrared bands. The influences of drought were classified into five levels based on MODIS-derived 8-day composite Anomaly Water Index (AWI) and field survey data. The results indicated that the drought deteriorated beginning in November 2008 and became most serious in late January 2009. HJ-1 data together with MODIS data proved to be valuable data sources for monitoring soil moisture and drought at a both regional and national scale.     

The changing characteristics of drought in China from 1982 to 2005

Drought is one of the most detrimental natural disasters. Studying the changing characteristics of drought is obviously of great importance to achieve the sustainable use of water resources at river basin scales. In this paper, the satellite-based Vegetation Condition Index (VCI) and Vegetation Health Index (VH) were firstly calculated by using NDVI and brightness of the Global Vegetation Index dataset derived from Advance Very High Resolution Radiometer for China in growing seasons over 1982–2005. Then, the long-term VCI and VH data were employed to study the variation of droughts in the ten basins covering the whole country. The linear trend of each pixel showed that most parts of China were getting wetter in growing seasons, and the drought areas defined by the number of drought pixels have decreased in most basins. The increasing trend of basin averaged values of VCI and VH also indicates the whole country was generally getting wetter. At last, to better understand the two remote sensing drought indices, the response of the growing-season VCI and VH was compared to that of the Palmer Drought Severity Index and 6-month Standard Precipitation Index. Significant spatial variability of the relationship between the VCI, VH, and the station-based meteorological drought indices was shown, and some more closely related areas were found. The study will be useful for water resources management for each basin in the future.     

松嫩平原荒漠化的EOS-MODIS 数据研究*

文章应用EOS-MODIS数据对松嫩平原的荒漠化问题进行了研究。首先计算出标准化植被指数(NDVI)和植被覆盖指数(VCI),反演求得荒漠化指数(DI),得到荒漠化面积;采用多种方法和复杂步骤,首次获得了松嫩平原碱质荒漠化、沙质荒漠化的面积,分别为160.30×10⁴hm²和50.56×10⁴hm²,分别占该区面积的24.35 ％ 和7.84 ％ ;应用数字高程模型(DEM)对荒漠化的分布规律和成因进行了分析和讨论,指出松嫩平原以碱质荒漠化为主, 当前荒漠化的发展程度已处于临界状态,对区域可持续发展构成了严重威胁,亟需采取行之有效的防治措施。     

Soil erosion risk assessment of the Keiskamma catchment,South Africa using GIS and remote sensing

This paper examines the soil loss spatial patterns in the Keiskamma catchment using the GIS-based Sediment Assessment Tool for Effective Erosion Control (SATEEC) to assess the soil erosion risk of the catchment. SATEEC estimates soil loss and sediment yield within river catchments using the Revised Universal Soil Loss Equation (RUSLE) and a spatially distributed sediment delivery ratio. Vegetation cover in protected areas has a significant effect in curtailing soil loss. The effect of rainfall was noted as two pronged, higher rainfall amounts received in the escarpment promote vegetation growth and vigour in the Amatole mountain range which in turn positively provides a protective cover to shield the soil from soil loss. The negative aspect of high rainfall is that it increases the rainfall erosivity. The Keiskamma catchment is predisposed to excessive rates of soil loss due to high soil erodibility, steep slopes, poor conservation practices and low vegetation cover. This soil erosion risk assessment shows that 35% of the catchment is prone to high to extremely high soil losses higher than 25 ton ha⁻¹ year⁻¹ whilst 65% still experience very low to moderate levels of soil loss of less than 25 ton ha⁻¹ year⁻¹. Object based classification highlighted the occurrence of enriched valley infill which flourishes in sediment laden ephemeral stream channels. This occurrence increases gully erosion due to overgrazing within ephemeral stream channels. Measures to curb further degradation in the catchment should thrive to strengthen the role of local institutions in controlling conservation practice.     

Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve

Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R ²) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale.     

An assessment of soil erosion probability and erosion rate in a tropical mountainous watershed using remote sensing and GIS

Remote sensing data and Geographical Information System (GIS) has been integrated with the weighted index overlay (WIO) method and E ₃₀ model for the identification and delineation of soil erosion susceptibility zones and the assessment of rate of soil erosion in the mountainous sub-watershed of River Manimala in Kerala (India). Soil erosion is identified as the one of the most serious environmental problems in the human altered mountainous environment. The reliability of estimated soil erosion susceptibility and soil loss is based on how accurately the different factors were estimated or prepared. In the present analysis, factors that are considered to be influence the soil erosion are: land use/land cover, NDVI, landform, drainage density, drainage frequency, lineament frequency, slope, and relative relief. By the WIO analysis, the area is divided into zones representing low (33.30%), moderate (33.70%), and high (33%) erosion proneness. The annual soil erosion rate of the area under investigation was calculated by carefully determining its various parameters and erosion for each of the pixels were estimated individually. The spatial pattern thus created for the area indicates that the average annual rate of soil erosion in the area was ranging from 0.04 mm yr⁻¹ to 61.80 mm yr⁻¹. The high soil erosion probability and maximum erosion rate was observed in areas with high terrain alteration, high relief and slopes with the intensity and duration of heavy precipitation during the monsoons.     

Soil erosion determinations by using 137Cs technique in the agricultural regions of Gediz Basin, Western Turkey

Gediz Basin is one of the regions where intense agricultural activities take place in Western Turkey. Erosion and soil degradation have long been causing serious problems to cultivated fields in the basin. This work describes the application of two different ¹³⁷Cs models for estimating soil erosion rates in cultivated sites of the region. Soil samples were collected from five distinct cultivated regions subject to soil erosion. The variations of ¹³⁷Cs concentrations with depth in soil profiles were investigated. Soil loss rates were calculated from ¹³⁷Cs inventories of the samples using both proportional model (PM) and simplified mass balance model (SMBM). When PM was used, erosion and deposition rates varied from −15 to −28 t ha⁻¹ year⁻¹ and from +5 to +41 t ha⁻¹ year⁻¹, respectively; they varied from −16 to −33 t ha⁻¹ year⁻¹ and from +5 to +55 t ha⁻¹ year⁻¹ with SMBM. A good agreement was observed between the results of two models up to 30 t ha⁻¹ year⁻¹ soil loss and gain in the study area. Ulukent, a small representative agricultural field, was selected to compare the present data of ¹³⁷Cs techniques with the results obtained by universal soil loss equation (USLE) applied in the area before.     

Regional soil erosion risk mapping using RUSLE,GIS, and remote sensing: a case study in Miyun Watershed,North China

This paper applied the Revised Universal Soil Loss Equation (RUSLE), remote-sensing technique, and geographic information system (GIS) to map the soil erosion risk in Miyun Watershed, North China. The soil erosion parameters were evaluated in different ways: the R factor map was developed from the rainfall data, the K factor map was obtained from the soil map, the C factor map was generated based on a back propagation (BP) neural network method of Landsat ETM+ data with a correlation coefficient (r) of 0.929 to the field collected data, and a digital elevation model (DEM) with a spatial resolution of 30 m was derived from topographical map at the scale of 1:50,000 to develop the LS factor map. P factor map was assumed as 1 for the watershed because only a very small area has conservation practices. By integrating the six factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the upper watershed of Miyun reservoir was obtained by the RUSLE model. The results showed that the annual average soil loss for the upper watershed of Miyun reservoir was 9.86 t ha⁻¹ ya⁻¹ in 2005, and the area of 47.5 km² (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.88% very low, 21.90% low, 6.19% moderate, 2.90% severe, and 1.84% very severe. Among all counties and cities in the study area, Huairou County is in the extremely severe level of soil erosion risk, about 39.6% of land suffer from soil erosion, while Guyuan County in the very low level of soil erosion risk suffered from 17.79% of soil erosion in 2005. Therefore, the areas which are in the extremely severe level of soil erosion risk need immediate attention from soil conservation point of view.     

Remote sensing monitoring the spatio-temporal changes of aridification in the Mongolian Plateau based on the general Ts-NDVI space, 1981–2012

This paper has developed a general Ts-NDVI triangle space with vegetation index time-series data from AVHRR and MODIS to monitor soil moisture in the Mongolian Plateau during 1981–2012, and studied the spatio-temporal variations of drought based on the temperature vegetation dryness index (TVDI). The results indicated that (1) the developed general Ts-NDVI space extracted from the AVHRR and MODIS remote sensing data would be an effective method to monitor regional drought, moreover, it would be more meaningful if the single time Ts-NDVI space showed an unstable condition; (2) the inverted TVDI was expected to reflect the water deficit in the study area. It was found to be in close negative agreement with precipitation and 10 cm soil moisture; (3) in the Mongolian Plateau, TVDI presented a zonal distribution with changes in land use/land cover types, vegetation cover and latitude. The soil moisture is low in bare land, construction land and grassland. During 1981–2012, drought was widely spread throughout the plateau, and aridification was obvious in the study period. Vegetation degradation, overgrazing, and climate warming could be considered as the main reasons.     

Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework,Chania, Northwestern Crete,Greece

Soil erosion is a growing problem in southern Greece and particularly in the island of Crete, the biggest Greek island with great agricultural activity. Soil erosion not only decreases agricultural productivity, but also reduces the water availability. In the current study, an effort to predict potential annual soil loss has been conducted. For the prediction, the Revised Universal Soil Loss Equation (RUSLE) has been adopted in a Geographical Information System framework. The RUSLE factors were calculated (in the form of raster layers) for the nine major watersheds which cover the northern part of the Chania Prefecture. The R-factor was calculated from monthly and annual precipitation data. The K-factor was estimated using soil maps available from the Soil Geographical Data Base of Europe at a scale of 1:1,000,000. The LS-factor was calculated from a 30-m digital elevation model. The C-factor was calculated using Remote Sensing techniques. The P-factor in absence of data was set to 1. The results show that an extended part of the area is undergoing severe erosion. The mean annual soil loss is predicted up to ∼200 (t/ha year⁻¹) for some watersheds showing extended erosion and demanding the attention of local administrators.     

Modelling of stream run-off and sediment output for erosion hazard assessment in Lesser Himalaya: need for sustainable land use plan using remote sensing and GIS: a case study

Assessment and inventory on soil erosion hazard are essential for the formulation of successful hazard mitigation plans and sustainable development. The objective of this study was to assess and map soil erosion hazard in Lesser Himalaya with a case study. The Dabka watershed constitutes a part of the Kosi Basin in the Lesser Himalaya, India, in district Nainital has been selected for the case illustration. The average rate of erosion hazard is 0.68 mm/year or 224 tons/km²/year. Anthropogenic and geo-environmental factors have together significantly accelerated the rate of erosion. This reconnaissance study estimates the erosion rate over the period of 3 years (2006–2008) as 1.21 mm/year (398 tons/km²/year) in the barren land having geological background of diamictite, siltstone and shale rocks, 0.92 mm/year (302 tons/km²/year) in the agricultural land with lithology of diamictite, slates, siltstone, limestone rocks, while in the forest land, it varies between 0.20 mm/year (66 tons/km²/year) under dense forest land having the geology of quartzwacke and quartrenite rocks and 0.40 mm/year (132 tons/km²/year) under open forest/shrubs land having geological setup of shale, dolomite and gypsum rocks. Compared to the intensity of erosion in the least disturbed dense forest, the erosion rate is about 5–6 times higher in the most disturbed agricultural land and barren land, respectively. The erosion hazard zones delineated following scalogram modelling approach. Integrated scalogram modelling approach resulted in severe classes of soil erosion hazard in the study area with numerical values of Erosion Hazard Index (EHI) ranging between 01 (very low hazard) and 5 (very high hazard).     

Influence of temperature decrease and soil drought on the geochemical fractionation of 60Co and 137Cs in fluvisol and cambisol soils

Consideration of the impact of substantial changes in soil temperature or moisture regime on the geochemical forms of radionuclides is important for more accurate assessment of the environmental risk posed by radionuclide migration and potential biological availability, especially in the first months after their release into the environment. This paper presents the results from a study of the influence of cooling, freezing and soil drought on the migration and potential bioavailability of ⁶⁰Co and ¹³⁷Cs in two soils (a fluvisol and a cambisol, according to the World Reference Base for Soil Resources/FAO) from Bulgaria. The changes in the geochemical fractionation of ⁶⁰Co, the exchangeable ¹³⁷Cs and water-soluble forms of both radionuclides were examined under different storage conditions up to 5 months after their introduction into the soils in solution form. Freezing or soil drought resulted in a significant increase of the water-soluble forms of ⁶⁰Co in the fluvisol soil, defining higher mobility and potential bioavailability. No influence of the storing conditions on the water-solubility of ⁶⁰Co in the cambisol soil was established. The cooling, freezing and soil drought caused an increase of the exchangeable ¹³⁷Cs in both soils.     

Application of the <Superscript>137</Superscript>Cs tracer technique to study soil erosion of alpine meadows in the headwater region of the Yellow River

The ¹³⁷Cs tracer technique was used to study soil erosion of alpine meadow grassland in two small river basins in the headwater region of the Yellow River. The results show that the levels of ¹³⁷Cs in soil samples from this alpine meadow vegetation zone exhibit an exponential distribution, generally within a depth of approximately 20 cm. Due to strong winds, freeze-thaw cycles and water, soil erosion was found to be stronger on the upper slope than on the lower slope, and except for the slope crest, the intensity of soil erosion at other sites was as follows: upslope < midslope < downslope. There was a significant negative correlation between the intensity of soil erosion and the extent of alpine meadow vegetation cover (P < 0.01). The mean soil erosion modulus exhibited a linear reduction trend with an increase in vegetation cover, and the correlation coefficient R ² was ≥ 0.997. The higher the degradation degree of the alpine meadow grassland, the greater is the soil erosion. The mean erosion modulus in the severely degraded meadow zone was 2.23 times greater than the one in the slightly degraded zone, and the maximum erosion modulus reached 2.96 × 10⁶ kg/km²/a.     

Geospatial assessment of soil erosion intensity and sediment yield: a case study of Potohar Region,Pakistan

Estimation of spatial extent of soil erosion, one of the most serious forms of land degradation, is critical because soil erosion has serious implications on soil fertility, water ecosystem, crop productivity and landscape beauty. The primary objective of the current study was to assess and map the soil erosion intensity and sedimentation yield of Potohar region of Pakistan. Potohar is the rainfed region with truncated and complex topography lying at the top of the Indus Basin, the world’s largest irrigation networks of canals and barrages. Spatially explicit Revised Universal Soil Loss Equation (RUSLE) Model integrated with Remote Sensing-GIS techniques was used for detecting/mapping of erosion prone areas and quantification of soil losses. The results show that the Potohar region is highly susceptible to soil erosion with an average annual soil loss of 19 tons ha^?1 year^?1 of which the maximum erosion (70–208 tons ha^?1 year^?1) was near the river channels and hilly areas. The sediment yield due to the erosion is as high as 148 tons ha^?1 year^?1 with an average of 4.3 tons ha^?1 year^?1. It was found that 2.06% of the total area falls under severe soil erosion, 13.34% under high erosion, 15.35% under moderate soil erosion while 69.25% of the area lies in the low (tolerable) soil erosion. Chakwal and Jhelum districts of the region are seriously affected by erosion owing to their topography and soil properties. The information generated in this study is a step forward towards proper planning and implementation of strategies to control the erosion and for protection of natural resources. It is, hence, necessary that suitable water harvesting structures be made to control water to prevent soil erosion and provision of water in the lean season in this region. Tree plantation and other erosion control practices such as strip cropping can also minimize soil erosion in this region.     

Assessment of soil erosion risk using SWAT model

Soil erosion is one of the most serious land degradation problems and the primary environmental issue in Mediterranean regions. Estimation of soil erosion loss in these regions is often difficult due to the complex interplay of many factors such as climate, land uses, topography, and human activities. The purpose of this study is to apply the Soil and Water Assessment Tool (SWAT) model to predict surface runoff generation patterns and soil erosion hazard and to prioritize most degraded sub-catchment in order to adopt the appropriate management intervention. The study area is the Sarrath river catchment (1,491 km²), north of Tunisia. Based on the estimated soil loss rates, the catchment was divided into four priority categories for conservation intervention. Results showed that a larger part of the watershed (90 %) fell under low and moderate soil erosion risk and only 10 % of the watershed was vulnerable to soil erosion with an estimated sediment loss exceeding 10 t?ha^?1?year^?1. Results indicated that spatial differences in erosion rates within the Sarrath catchment are mainly caused by differences in land cover type and gradient slope. Application of the SWAT model demonstrated that the model provides a useful tool to predict surface runoff and soil erosion hazard and can successfully be used for prioritization of vulnerable areas over semi-arid catchments.     

Monitoring rangeland ground cover vegetation using multitemporal MODIS data

The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data. At first, various preprocessing steps were applied to a topography map. The atmospheric and topographic corrections were applied using subtraction of the dark object method and the Lambert method. Image processing, including false-color composite, principal component analysis, and vegetation indices were employed to produce land use and pasture production maps. Vegetation sampling was carried out over a period of 4 months during June–September 2008, using a stratified random sampling method. Twenty random sampling points were selected, and herbage production was estimated and verified with the double-checking method. Four MODIS data sets were used in this study. The models for image processing and integrating ground data with satellite images were processed, and the resulting images were categorized into seven classes. Finally, the land covers were verified for accuracy. A postclassification analysis was carried out to verify the seven class change detections. The results confirmed that Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) maps had a close relationship with the field data. The indices produced with shortwave infrared bands had a close relationship with field data where the ground cover and yields were high. The R ² value observed was 0.85. The changes in the pasture vegetation were high during the growing season in more than 90 % of the pastures. During the growing season, most changes in the pastures belonged to class 5 and 2 in the NDVI and SAVI index maps, respectively.     

Calibrating a rainfall erosivity assessment model at regional scale in Mediterranean area

A simplified regression model is here calibrated on the basis of rainfall data records of Sicily (southern Italy), in order to show the model reliability in assessing the R-factor of the Universal Soil Loss Equation and its revised version (RUSLE) and to provide an estimate of long-term rainfall erosivity at medium-regional scale. The proposed model is a rearrangement of a former simplified model, formulated for the Italian environment, grouping three easily available rainfall variables on various time scales, which has been shown to be more successful than others in reproducing the rainfall erosive power over different locations of Italy. A geostatistical interpolation procedure is then applied for generating the regional long-term erosivity map with associated standard error. Areas with severe erosive rainfalls (from 2,000 up to more than 6,000 MJ mm ha⁻¹ h⁻¹) are pointed out which will correspond to areas suffering from severe soil erosion. Solving the problem of calculating the R-factor value in the RUSLE equation by means of such a simplified model here formulated will allow to predict the related soil loss. Moreover, given the availability of long time-series of concerned rainfall data, it will be possible to analyse the variability of rainfall erosivity within the last 50 years, and to investigate the application of RUSLE or similar soil erosion models with forecasting purposes of soil erosion risk.     

Recent trends on glacier area retreat over the group of Nevados Caullaraju-Pastoruri (Cordillera Blanca,Peru) using Landsat imagery

The Cordillera Blanca, located in the central zone of the Andes Mountains in Peru, has shown a retreat in its glaciers. This paper presents a trend analysis of the glacier area over the groups of Nevados Caullaraju-Pastoruri from 1975 to 2010 using Landsat-5 Thematic Mapper (TM) imagery. In the case of the Nevados Pastoruri/Tuco, the study period was extended back to 1957 by using an aerial photograph taken that year. The extent of clean glacier ice was estimated using Normalized Difference Snow Index (NDSI) thresholds. Moreover, the estimation of debris-covered glacier ice was retrieved by means of a decision tree classification method using NDSI, Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). Area estimations derived from Landsat imagery were compared to the glacier ground-truth data in 1975 and 2010. Results show a statistically significant (p < 0.05) decreasing trend over the whole study area. Total glacier area decreased at a rate of 4.5 km² per decade from 1975 to 2010, with a total loss of 22.5 km² (58%). Lower decreasing rates were found for the period 1987–2010: 3.5 km² per decade with a total loss of 7.7 km² (32.5%). In the case of the Nevados Pastoruri/Tuco, decreasing rates of clean ice extent were constant for the periods 1957–2010, 1975–2010 and 1987–2010, with values close to 1.4 km² per decade and a total loss between 1957 and 2010 estimated at about 5 km² (54%). This work shows an evident area decrease in the Caullaraju-Pastoruri tropical glaciers, which needs to be included in a future hydrological scenario of local adaptability and water management.     

Spatial prediction of soil erosion risk by remote sensing,GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley,Kerala, India

Siruvani watershed with a surface area of 205.54 km² (20,554 hectare), forming a part of the Western Ghats in Attapady valley, Kerala, was chosen for testing RUSLE methodology in conjunction with remote sensing and GIS for soil loss prediction and identifying areas with high erosion potential. The RUSLE factors (R, K, LS, C and P) were computed from local rainfall, topographic, soil classification and remote sensing data. This study proved that the integration of soil erosion models with GIS and remote sensing is a simple and effective tool for mapping and quantifying areas and rates of soil erosion for the development of better soil conservation plans. The resultant map of annual soil erosion shows a maximum soil loss of 14.917 t h⁻¹ year⁻¹ and the computations suggest that about only 5.76% (1,184 hectares) of the area comes under the severe soil erosion zone followed by the high-erosion zone (11.50% of the total area). The dominant high soil erosion areas are located in the central and southern portion of the watershed and it is attributed to the shifting cultivation, and forest degradation along with the combined effect of K, LS and C factor. The RUSLE model in combination with GIS and remote sensing techniques also enables the assessment of pixel based soil erosion rate.