Semantic spatial weighted regression for realizing spatial correlation of deforestation effect on soil degradation

Tackling Deforestation activity is not an easy task. Many approached on mapping and monitoring the change of forest cover has been actively introduced and yet the deforestation activity is still largely happens. In order to observe the deforestation activity and its natural impact on environment, a new way to serve knowledge is good approach to make more understandable information regarding on how deforestation activity effects on our environment. We proposed semantic spatial-weighted regression to create a system that able to presenting the distribution of deforestation effect on soil degradation based on human language regression. Our system is able to visualize the desire observed are based on the location given by user impression. We use Landsat satellite images as our input data. Our system calculates the band parameters value using semantic orthogonality for producing a new semantic regression model of deforestation area effect to capturing user intention.