Abstract
Remote sensing is widely used in the prediction of forest cover. Forest plays an important role in the balance of the ecosystem. It helps to maintain the balance between climate. We depend a lot on forests for wood, oxygen, and also for the control of soil erosion. Hence it is our duty to maintain the forest cover on earth. Remote sensing images provide us with lots of information regarding the different landforms and materials. It is also used to predict natural disasters like forest fires, floods, etc. The normalized difference vegetation index is a simple graphical indicator that is used to analyze remote sensing measurements,(eg. space platform) predicting whether the target is live green vegetation or not. However, we have found out that it cannot be used for accurate prediction of forest land cover. With the help of time series data on the Amazon forest, it has been observed that the NDVI index fails to determine some of the important changes in the landform. To rectify this problem, the deep learning model was used to give an accuracy of 100 percent. The deep learning model gives similar results as observed results, hence making it the best-preferred method for predicting forest cover. With the help of multispectral analysis of the data, the deep learning model gives the best results for the red band, and near-infrared bands.
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