Integrated Assessment of Forest Ecosystem Health, Carbon Balance, and Climate Change Adaptation Strategies

Abstract

Forest ecosystem is important in terms of ecological balances, conservation of biodiversity, and global carbon storage; but growing deforestation, climatic changes, and environmental degradation have posed severe challenges to the sustainability and ecological stability of forest all over the world. Current observation and smart analysis of the health of forest ecosystems is hence the key to successful climate adaptation and sustainable management of the environment. This paper suggests a combined evaluation system of the health of forest ecosystems, balancing carbon, and adapting to climate changes based on Remote Sensing, Geographic Information Systems (GIS), and the Random Forest machine learning algorithm. Multi-source nature records were also integrated such as Landsat satellite imagery, vegetation indices, weather variables, and carbon-related factors to create a sound environmental intelligence framework that can detect healthy and unhealthy forest areas, estimates carbon-sequestration trends, and the climate-vulnerability of the area. Random Forest classification was combined with NDVI-based vegetation analysis and GIS-assisted spatial mapping in order to enhance the accuracy and dependability of the environmental prediction and ecosystem assessment. The experiments have shown good performance in classification and prediction with an Accuracy of 96.8%, Precision of 95.9%, Recall of 96.2%, F1-score of 96.0%, and ROC-AUC of 0.98, showing the strength and usability of the proposed framework with complex environmental data. Random Forest algorithm was effective in minimizing the problem of overfitting and a better representation of nonlinear ecological relationship than traditional methods used to assess. The suggested framework presents a high-quality and scalable system of intelligent forest monitoring, estimating a carbon balance, biodiversity preservation, and climate-resilient environmental regulation. The results demonstrate that AI-based environmental sustainability systems hold a great potential to assist in future forest conservation planning as well as handling climate change adaptation strategies.