Mechanism for Predicting Forest Land Cover from Remote Sensing

As humans intrude on forest areas, deforestation occurs. The main effect of this must be resolved by detecting and evaluating certain methods to protect the area of the forest. For example, deforestation has become a significant contributor to biodi versity loss and environmental problems. A model which may provide interpretable forecasts of deforestation is a significant tool for preventing the loss of forest area such as unlawful logging and grazing or planting. Advances in the study of satellite data and artificial neural networks have resulted in several ways to develop such a model. This article discusses the most commonly used machine learning methods. The primeval aim of this research is to develop a framework for comparing several evaluation metrics in order to enhance the existing solution and provide interpretable temporal predictions. This means that the image processing technique have tremendous promise for predicting deforestation in forests. The findings of this research will help to improve forest observation and policy initiatives for rehabilitation. This research deals with the vandalur forest area and detects the prediction of how much deforestation has been happened from 2008 to 2021 using satellite images. By comparing various models, the efficacy amongst the most frequently used assessment measures is investigated. The research finding suggests that different model for forecasting degradation may be used, as well as the areas lost throughout the research period. According to the data, 50% of the land cover area was deteriorated between 2008 and 2021. By 2030, it is predicted that 75% of the land cover area would have been damaged by constructions, condominiums, and parks.