A Comprehensive Analysis and Detection of Wildfire Impact onVegetation and Wildlife and Biomass Emission

Wildfires pose severe threats to ecosystems, biodiversity, and public safety, necessitating precise and efficient detection systems. While existing algorithms like YOLO and Mask R-CNN are commonly used, they struggle with predicting complex fire zones and evaluating ecological impacts. This study introduces a novel wildfire detection application using a modified U-Net model, leveraging satellite imagery for accurate fire forecasting. It integrates synthetic datasets to assess wildlife impacts, such as habitat loss and species vulnerability, and estimates vegetation loss through NDVI analysis. Furthermore, it calculates CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions and equivalent daily outputs to quantify environmental impact, thus helping policymakers to come up with effective wildfire management strategies. The approach thus provides a holistic and innovative solution to the monitoring of wildfires by dealing with ecological disturbances along with fire detection.