Enhancing Land Management through U-Net Deep Learning: A Case Study on Climate-Related Land Degradation in Berembun Forest Reserve in Malaysia - Chew, Yee Jian; Ooi, Shih Yin; Mohd-Razali, Sheriza; Pang, Ying Han; You Lim, Zheng (Society of Visual Informatics, 2024)
In the face of accelerating climate change, effective management of land resources needs innovative technological approaches. This study, conducted in the Berembun Forest Reserve, Jelebu, Malaysia, leverages advancements in geospatial technology and machine learning to address the pressing issue of land degradation, focusing on forested areas vulnerable to landslides. Utilizing high-resolution Unmanned Aerial Vehicle (UAV) imagery, the U-Net convolutional neural network model is employed for the precise classification and early detection of landslide-induced land degradation. Through a systematic analysis of 15 high-quality UAV images of 5472 x 3647 pixels, segmented into 256 x 256-pixel patches, the U-Net model demonstrated remarkable accuracy, achieving a mean Intersection-over-Union (IoU) of 0.9466. These findings underscore the model's potential to significantly enhance land management practices by providing timely and cost-effective landslide detection. Adopting such deep learning techniques is a pivotal shift towards more sustainable and resilient land management strategies in the era of climate change. This research showcases the practical application of machine learning in environmental monitoring and paves the way for future innovations. Implications for further research include integrating additional spectral bands, addressing environmental variability, and expanding applications across diverse landscapes to improve environmental monitoring, conservation efforts, and resilience strategies. Developing automated frameworks for real-time data processing and model deployment could further revolutionize the field, enabling more responsive and efficient land management practices.