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Not AvailableArunachal Pradesh, the largest mountainous state of India, is situated in the northeastern part of the Himalayan region and characterized by high annual rainfall, forest vegetation and diversity in soils. Information on the soils of the state is essential for scientific land use planning and sustainable production. A soil resource inventory and subsequent database creation for thematic mapping using a Geographical Information System (GIS) is presented in this paper. Physiographically, Arunachal Pradesh can be divided into four distinct zones: snow-capped mountains (5500 m amsl); lower Himalayan ranges (3500 m amsl); the sub-Himalayan Siwalik hills (700 m amsl); and the eastern Assam plains. Soils occurring in these physiographic zones are lnceptisols (37 percent), Entisols (35 per- cent), Ultisols (14 percent) and Alfisols (0.5 percent). The remaining soils can be classed as miscellaneous. Soil resource inventory studies show that the soils of the warm perhumid eastern Himalayan ecosystem, with a ‘thermic’ temperature regime, are lnceptisols and Entisols; and that they are highly acidic in nature. Soils of the warm perhumid Siwalik hill ecosystem, with a ‘hyperthermic’ temperature regime, are also Entisols and lnceptisols with a high to moderate acidic condition. The dominant soils of the northeastern Purvachal hill ecosystem, with ‘hyperthermic’ and ‘thermic’ temperature regimes, are Ultisols and Inceptisols. lnceptisols and Entisols are the dominant soils in the hot and humid plain ecosystem. Steeply sloping landform and high rainfall are mainly responsible for a high erosion hazard in the state. The soil erosion map indicates that very severe (20 percent of TGA) to severe (25 percent of TGA) soil erosion takes place in the warm per-humid zone, whereas, moderate erosion takes place in the Siwalik hills and hot, humid plain areas. This is evident from the soil depth class distribution of Arunachal Pradesh, which shows that shallow soils cover 20 percent of the TGA of the state. Most of the the state is covered by hills and agri- cultural practices are limited to valley regions, However, the soils of other physiographic zones (lower altitudinal, moderately hilly terrain) provide scope for plantations, such as orange, banana and tea plantations.Not Availabl

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Not AvailableAn attempt has been made to study drainage morphometry and its influence on landform processes, soil physical and land erosion characteristics in Vena river basin of basaltic terrain (Deccan traps), Nagpur district, Maharashtra, Central India. High Spatial Resolution Indian Remote Sensing Satellite (IRS)-ID Linear Image Self Scanning (LISS)-III sensor data of 7 March 2000 in conjunction with Survey Of India (SOI) topographical sheets (1:50,000 scale) were used for systematic analysis of various morphometric, lithological and landform characteristics of the river basin. Morphometric analysis was carried out at subbasin level using Spatial Analysis System (SPANS ver. 7.0) GIS system to analyze the influence of drainage morphometry on landforms, soil depth, drainage, available water holding capacity (AWC) and land erosion characteristics. Ten distinct landforms were identified in the basin based on visual interpretation of satellite sensor data. These are dissected ridges, isolated mounds, linear ridges, escarpments, plateau spurs, subdued plateau, rolling plains, foot slopes, narrow valleys and main valley floor. Very shallow soils exists on dissected ridges, isolated mounds, linear ridges, escarpments and plateau spurs covering the sub basins nos. 1, 5–7, 9, 15 and 16 and are associated with high drainage density (Dd), impermeable geology and high runoff conditions. High drainage density, high bifurcation ratio (Rb) and steep slopes are the main causative factors for the development of well drained soils. The AWC is low in the soils of higher elevations covering the sub basins 1, 2, 3, 4, 7 and 9 whereas, it is very high at lower elevation in the sub basins nos. 12, 13 and 19. Sub basins nos. 1, 2, 5, 15 and 16, associated with high drainage density, stream frequency (Fu) and texture ratio (T) showed very severe to severe erosion. The analysis reveals that the influence of drainage morphometry is very significant in understanding the landform processes, soil physical properties and erosional characteristics. The study demonstrates that remotely sensed data and GIS based approach is found to be more appropriate than the conventional methods in evaluation and analysis of drainage morphometry, landforms and land resources and to understand their inter-relationships for planning and management at river basin level.Not Availabl

Extent of land degradation and status of wastelands in Rajasthan (NW India) with a focus on the Bhilwara District

Geographically, the Rajasthan is the largest state of India. The mapping of degraded and wasteland, its distribution and district wise statistics is very important for land resource management. This paper deals of kind of soils and status of land degradation with management options. The study further illustrated with regional example of Bhilwara district. The pressure on land resources has increased manifold with the increasing human and animal population. Geologically the state can be broadly divided into three regions: Aeolian sands, Alluvium and Aravallis. The Aeolian deposits belong to Pleistocene and recent times. There is distinct temperature range with diurnal variations in state, revealing the most typical phenomenon of the warm-dry continental climate. Twelve districts of Rajasthan have already been decertified. Desertification ranks among the greatest environmental challenges of ecosystem in this region. Wind erosion is the major cause of soil degradation in western Rajasthan whereas water erosion in south and eastern Rajasthan

Irrigated Area Maps and Statistics of India Using Remote Sensing and National Statistics

The goal of this research was to compare the remote-sensing derived irrigated areas with census-derived statistics reported in the national system. India, which has nearly 30% of global annualized irrigated areas (AIAs), and is the leading irrigated area country in the World, along with China, was chosen for the study. Irrigated areas were derived for nominal year 2000 using time-series remote sensing at two spatial resolutions: (a) 10-km Advanced Very High Resolution Radiometer (AVHRR) and (b) 500-m Moderate Resolution Imaging Spectroradiometer (MODIS). These areas were compared with the Indian National Statistical Data on irrigated areas reported by the: (a) Directorate of Economics and Statistics (DES) of the Ministry of Agriculture (MOA), and (b) Ministry of Water Resources (MoWR). A state-by-state comparison of remote sensing derived irrigated areas when compared with MoWR derived irrigation potential utilized (IPU), an equivalent of AIA, provided a high degree of correlation with R2 values of: (a) 0.79 with 10-km, and (b) 0.85 with MODIS 500-m. However, the remote sensing derived irrigated area estimates for India were consistently higher than the irrigated areas reported by the national statistics. The remote sensing derived total area available for irrigation (TAAI), which does not consider intensity of irrigation, was 101 million hectares (Mha) using 10-km and 113 Mha using 500-m. The AIAs, which considers intensity of irrigation, was 132 Mha using 10-km and 146 Mha using 500-m. In contrast the IPU, an equivalent of AIAs, as reported by MoWR was 83 Mha. There are “large variations” in irrigated area statistics reported, even between two ministries (e.g., Directorate of Statistics of Ministry of Agriculture and Ministry of Water Resources) of the same national system. The causes include: (a) reluctance on part of the states to furnish irrigated area data in view of their vested interests in sharing of water, and (b) reporting of large volumes of data with inadequate statistical analysis. Overall, the factors that influenced uncertainty in irrigated areas in remote sensing and national statistics were: (a) inadequate accounting of irrigated areas, especially minor irrigation from groundwater, in the national statistics, (b) definition issues involved in mapping using remote sensing as well as national statistics, (c) difficulties in arriving at precise estimates of irrigated area fractions (IAFs) using remote sensing, and (d) imagery resolution in remote sensing. The study clearly established the existing uncertainties in irrigated area estimates and indicates that both remote sensing and national statistical approaches require further refinement. The need for accurate estimates of irrigated areas are crucial for water use assessments and food security studies and requires high emphasis

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Modeling and Assessment of Land Degradation Vulnerability in Arid Ecosystem of Rajasthan Using Analytical Hierarchy Process and Geospatial Techniques

Wind erosion is a major natural disaster worldwide, and it is a key problem in western Rajasthan in India. The Analytical Hierarchy Process (AHP), the Geographic Information System (GIS), and remote sensing satellite images are effective tools for modeling and risk assessment of land degradation. The present study aimed to assess and model the land degradation vulnerable (LDV) zones based on the AHP and geospatial techniques in the Luni River basin in Rajasthan, India. This study was carried out by examining important thematic layers, such as vegetation parameters (normalized difference vegetation index and land use/land cover), a terrain parameter (slope), climatic parameters (mean annual rainfall and land surface temperature), and soil parameters (soil organic carbon, soil erosion, soil texture, and soil depth), using the Analytical Hierarchical Process (AHP) and geospatial techniques in the Luni River basin in Rajasthan, India. The weights derived for the thematic layers using AHP were as follows: NDVI (0.27) > MAR (0.22) > LST (0.15) > soil erosion (0.12) > slope (0.08) > LULC (0.06) > SOC (0.04) > soil texture (0.03) > soil depth (0.02). The result indicates that nearly 21.4 % of the total area is prone to very high degradation risks; 12.3% is prone to high risks; and 16%, 24.3%, and 26% are prone to moderate, low, and very low risks, respectively. The validation of LDV was carried out using high-resolution Google Earth images and field photographs. Additionally, the Receiver Operating Characteristic (ROC) curve found an area under the curve (AUC) value of 82%, approving the prediction accuracy of the AHP technique in the study area. This study contributes by providing a better understanding of land degradation neutrality and sustainable soil and water management practices in the river basin