Soil erosion is one of the most implicit hazards as it degrades quality of water as well as soil in watershed. Lesser Himalayan region is highly susceptible to natural hazards particularly which are instigated by action and movement of water such as soil erosion, flood and mass movements. Hilly watersheds with diversified land uses and fragile ecosystem are responsible for accelerating soil erosion. Satellite based remote sensing data has been used in this study to demonstrate its utilisation in analysing soil erosion susceptible areas in Tons river watershed (India). In this study an attempt is made to understand interrelationship and role of morphometry, hypsometry and land cover together as coupled criteria in soil erosion. Remote sensing data and Multi-Criteria Analytical (MCA) framework has been used to estimate soil erosion susceptibility of sub watersheds of Tons river basin. The watershed was delineated using ArcGIS to generate the natural drainage network and stream orders were defined using Strahler’s method in hydrology tool. Land cover was classified using Forest Survey of India (FSI) forest cover map and ASTER Digital Elevation Model (30m) for calculating morphometric parameters and hypsometric integral for further analysing geological stage with the help of ArcGIS 10.3. Soil erosion susceptibility maps were generated for sub watersheds for each criterion and also for coupled criteria. Outcomes indicated that sub watershed with more vegetation cover and mature geological stage is least prone to erosion. It can be inferred from results that morphometry, land cover and hypsometry all together as coupled criteria can be better indicator for assessing soil susceptibility rather than any single criteria. Also, the study suggested that remote sensing can be one of the most competent tools with lots of scopes to study watershed management and can help efficiently in decision making for formulation of conservation strategies, it is implementation and monitoring.
Remote sensing technologies can provide accurate, cost-effective and real time information for sustainable forest management. Present study demonstrates the use of high resolution IRS1C data and Principal Component Analysis (PCA) with supervised classification to define different types of forest cover in protected and unprotected areas for growing forest stock assessment of Shorea robusta Gaertn. F. (Sal) dominated forest. Further, a map of the same was generated which was subsequently validated using phyto-sociological field data in Dehradun, India. Three forest canopy density classes, viz., 10-30%, 30-70% and <70% could be differentiated. Aim of this study was to test usefulness of LISS-III and test its efficacy for assessment of total growing stock and further in generalising the method for the whole area. The homogeneous forest strata were field inventoried for individual tree (≥10 cm dbh) diameter using sample quadrats at each of the study sites. The plot inventory data was analysed to arrive at image level growing stock estimates. An inspection review was carried out to be familiar with the study area using hard copy of LISS-III data to correlate the image. A scheme of forest classification was developed and the forests were stratified into moist Siwalik sal forest (3C/C2a), dry Siwalik sal forest (5B/C1a) and non-forest. High level of accuracy was achieved by ground truthing. The objective of the stratification was to categorize the forest into homogeneous strata. The biomass measurement from same sample plots was also integrated into the remote sensing technique for large spatial information on AGB distribution. The study revealed that protected sal forest has maximum volume and growing stock per hectare followed by unprotected sal forest. Same was also true for sal associates. The study also shows good scope of high resolution data for growing stock and forest carbon assessment, opening scope in estimating carbon sequestration potential of forest which may help researchers and policy makers to understand global and regional CO2 cycle.
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