Quantifying Habitat Loss and Restoration Success in the Amazon Rainforest and Coral Triangle Using Remote Sensing and AI

Abstract

Amazon Rainforest and Coral Triangle are among the most biodiverse ecosystems in the world, and they are significant in regulating climate on the globe as well as marine biodiversity. Nevertheless, the two areas are prone to rapid habitat destruction by way of deforestation, land use activities, and climate change. The proposed study will measure the extent to which the habitat has been lost and the rate of success of restoration in these locations using remote sensing and artificial intelligence (AI) technologies. It measured the degree of loss in habitats in the two regions and the effectiveness of restoration in both areas within a twenty-year time frame by comparing satellite data on systems like Landsat and Sentinel and AI models like Convolutional Neural Networks (CNN), Random Forests (RF), and Support Vector Machines (SVM). The significant observations reveal that the Amazon has been experiencing high levels of deforestation due to the spread of agriculture and illegal logging, and that there has not been much success in the process of reforestation. Habitat loss through coral bleaching and overfishing was also apparent in the Coral Triangle, but the process of habitat restoration, such as coral transplantation and marine protected areas, had uneven success. These findings indicate that remote sensing in conjunction with AI can be used as an effective instrument with regard to monitoring the environment on a large scale, as it will be possible to evaluate habitat degradation and restoration processes more accurately. These lessons play a significant role in the provision of conservation policies and management measures, especially of these crucial ecosystems that are at risk.