Classification of Carbon Sequestration pattern in Casuarina plantation using SVM and RBFNN
The gross primary production (GPP) of an ecosystem represents the gross uptake of carbon dioxide (CO2) by vegetation for photosynthesis. Two machine learning techniques viz support vector machine and Radial basis function neural network were used to classify the carbon sequestration pattern of casuarinas vegetation. The features which include global vegetation moisture index (GVMI), land surface temperature (LST), enhanced vegetation index (EVI), total ratio vegetation index(TRVI), albedo were derived from MODIS imagery and calculated GPP using IRS model as input data set. Among the two techniques SVM was identified as the best machine classification technique for studying the carbon sequestration pattern of casuarina plantation. It classifies the pattern of carbon sequestration of casuarina plantation with an accuracy of 93%.