Forest plays a pivotal role in maintaining the ecological balance. It is necessary to detect the changes in forest cover as the forests have a significant role in promoting carbon cycle. Remote sensing domain has shown a promising potential for monitoring forest degradation. However, the problem arising due to missing satellite images in temporal domain and problems due to artefacts such as clouds need to be addressed. To detect the changes in the forest area, an index for mapping forest cover known as Normalized Difference Fraction Index (NDFI) has been used. NDFI is calculated for three satellite images (Landsat7, Landsat8, and Sentinal2) and for the fusion of all these satellite images. Following this, the missing image is predicted by applying regression methods and the best regression method was identified. For change detection problem, optimal values for Convolution Neural Network (CNN) parameters were obtained using the Genetic Algorithm (GA). Later, various filters were applied for the optimal CNN and best filter was identified.

森林在维持生态平衡方面发挥着举足轻重的作用。由于森林在促进碳循环方面具有重要作用，因此有必要检测森林覆盖的变化。遥感领域已显示出监测森林退化的巨大潜力。然而，由于时域卫星图像丢失和云等伪影引起的问题需要解决。为了检测森林面积的变化，使用了一种称为归一化差异分数指数（NDFI）的森林覆盖指数。NDFI 是针对三个卫星图像（Landsat7、Landsat8 和 Sentinal2）以及所有这些卫星图像的融合计算的。在此之后，通过应用回归方法预测丢失的图像，并确定最佳回归方法。对于变化检测问题，卷积神经网络 (CNN) 参数的最佳值是使用遗传算法 (GA) 获得的。后来，各种过滤器被应用于最佳 CNN，并确定了最佳过滤器。