Land Cover Classification Using Band RatioingforHigher Accuracy inHilly Terrain of Mandakini Valley, Central Himalaya
Land Cover Classification Using Band RatioingforHigher Accuracy inHilly Terrain of Mandakini Valley, Central Himalaya
Keywords:
Land Use Land Cover, Maximum Likelihood Classifier, LISS IV, NDVI, DEMAbstract
This paper describes Remote Sensing as an advance
cover (LULC) classification with particular emphasis on Image Statistic for the rugged terrain
of the central Himalaya. Digital image classification
maps from remote sensing data at p
utilizes only the remote sensing data often deteriorates, due to the presence of shadows of
high peaks, especially in mountainous regions. In this study, a multi
classification approach has been used to map land cover in the Himalayan region of
Rudraprayag District with high mountain peaks having elevations up to 6654 m above mean
sea level has. Remote sensing data from IRS LISS IV image along with Normalized
Difference Vegetation Index (NDVI
were used to perform multi-source image classification using supervised maximum likelihood
classifier method. The results exhibit a notable improvement in the accuracy of classification
from 71.25% to 89.33% on integrating of NDVI and DEM as ancillary data with the
data of satellite image
