| Pub. Date | : Feb' 2024 |
|---|---|
| Product Name | : The IUP Journal of Telecommunications |
| Product Type | : Article |
| Product Code | : IJTC030224 |
| Author Name | : A I Hewarathna and Vigneshwaran Palanisamy |
| Availability | : YES |
| Subject/Domain | : Arts & Humanities |
| Download Format | : PDF Format |
| No. of Pages | : 16 |
Effective monitoring and detection of changes in forest cover are paramount for environmental stewardship and sustainable management practices. However, existing methodologies face challenges in obtaining consistent and high-quality satellite imagery, predicting deforestation-prone areas, optimizing deep learning architectures for change detection, and establishing robust evaluation metrics. This paper makes a comprehensive literature review to examine the progress in deep learning segmentation techniques and their application in change detection using satellite imagery. The key questions guide the investigation, focusing on optimization of satellite image collection, development of predictive models for deforestation risk assessment, enhancement of U-Net architectures for change detection, and establishment of evaluation metrics. Through the review, significant advancements are identified, particularly in integration of attention mechanisms and modified U-Net architectures. Contrastingly, traditional image differencing methods are surpassed by convolutional neural network (CNN) approaches, showcasing superior feature extraction and change detection accuracy. Noteworthy innovations include residual learning, attention gates and spatiotemporal encoders, which enhance segmentation performance. The findings underscore the potential of advanced methodologies to enhance detection and monitoring of changes in forest cover. By drawing insights from related fields such as urban planning and disaster monitoring, the study advocates for a holistic approach to environmental management. Ultimately, this review contributes to the evolution of deep learning techniques for change detection, with a specific focus on advancing forest cover monitoring practices.
Monitoring and detecting changes in forest cover are of paramount importance for environmental conservation and sustainable resource management. Forests play a critical role in maintaining ecological balance, supporting biodiversity and mitigating climate change by acting as significant carbon sinks (Bilinski and Prisacariu, 2018; and Caye et al., 2018). However, the alarming rate of deforestation necessitates advanced techniques for accurate and timely detection of changes in forest cover (Hussain et al., 2013). Traditional methods of change detection have primarily relied on image differencing and other manual techniques, which often fail to capture the complex and dynamic nature of forest ecosystems (Zhan et al., 2017).
Forest cover change detection, U-net, Attention gates, Siam networks, Deep learning, Image segmentation, Supervised learning