Abstract
Forest canopy density is an important parameter to assess the ecological conditionsviz, light penetration through canopy, undergrowth, surface reflectance, rainfall interception, etc. in a forest landscape. The rate of change in the cover and density has increased due to human need for fuel and fodder. Hence, quick, repetitive and accurate information about forest density is required at the local, regional, state and national levels for sustainable forest management. Satellite remote sensing has the potential to provide information on the forest canopy closure. The present study aims at forest canopy density mapping using satellite remote sensing data using three techniques: visual interpretation (VI), object oriented image segmentation (OOIS) and biophysical modeling (BM). On comparing the techniques, the BM has been found to be the better density mapping technique than other two in terms of accuracy, efficiency and high correlation with ground estimates.
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