MAPPING DEFORESTATION IN PERMANENT FOREST RESERVE OF PENINSULAR MALAYSIA WITH MULTI-TEMPORAL SAR IMAGERY AND U-NET BASED SEMANTIC SEGMENTATION

Abstract

Deforestation is the long-term or permanent conversion of forest land to other uses, such as agriculture, mining, and urban development. As a result, deforestation has catastrophic consequences for the environment, including the loss of biodiversity, disruption of clean water supplies, and the acceleration of climate change. According to statistics, the deforestation trend in developing countries is at an alarming rate including Malaysia where plantation activities are the primary cause of forest loss. Recent anecdotal studies have demonstrated the effectiveness of the deep learning-based (DL) approach in producing deforestation maps. However, there are limited studies concentrating on DL approach for synthetic aperture radar (SAR) imaging due to complexity of the computational concepts of the method. The SAR imagery can be challenging to interpret but its all-weather and all-day capability can be critical in forest monitoring compared to optical imagery. Thus, in this study, we propose to map deforestation areas in Permanent Forest Reserve (HSK) using multi-temporal Sentinel-1 SAR data. Deep learning-based U-Net was employed to classify the SAR imagery as forest and non-forest due to its semantic segmentation capabilities. The experiment results showed that the proposed deep learning-based technique successfully achieved 0.993 of intersection over union (IoU) and 0.980 of overall accuracy (OA). Also, we explained the entire procedure from beginning to end as simple as possible for beginners to comprehend. In brief, the findings of this study have the potential to improve monitoring of damaged HSK areas, prioritize the restoration of the affected forest areas and protecting the forest lands from illegal deforestation activities.