A Systematic Literature Review on AI-Based Forest Vegetation Loss Detection and Blockchain -Enabled Traceability System

The studies discussed in this paper show that satellite remote sensing is extensively utilized for monitoring and detection of deforestation and forest changes. The data from Landsat, Sentinel-1, Sentinel-2, LiDAR and other satellite observations are used in several studies to better understand forest changes [11], [18], [19]. In addition, different AI methods (e.g., CNN, Random Forest, U-Net, transformer-based model, and others) are applied for automation of the detection process [1], [8], [13], [15]. The review also demonstrates the utility of images of different dates for early detection of changes in forest cover. SAR images are very useful in areas where clouds or smoke make optical images inaccurate [12], [18], [19]. In addition, the current detection approaches are limited by some factors such as low spatial resolution, availability of relevant images, delay in detection and manual intervention. The majority of the approaches can only detect the existence of change in the forest but cannot clearly specify the cause of the change and how the change relates to the movement of forest products. The review on blockchain has shown that it is possible to record, verify and trace information on timber at different stages of the supply chain [4], [6], [7], [20]. It is to be noted that most of these approaches are independent of the satellite based forest detection approaches. Therefore, as discussed in this paper, it can be said that there is need to integrate satellite based forest detection, AI analysis, geospatial verification and blockchain based traceability.  

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