Keeping an eye on forest degradation is important for keeping the climate stable, protecting biodiversity, and planning land use in a way that is good for the environment. Satellite images give us a lot of information about the environment, but it's still hard to turn that information into useful, actionable insights. A lot of current systems either only care about how accurate the algorithms are or give you visualisation tools without any built-in analytical intelligence.

This work presents a comprehensive AI-enabled geospatial monitoring platform developed as a deployable decision-support dashboard. Users can draw a region of interest (ROI) on an interactive satellite map and get multi-temporal Sentinel-2 images automatically. It figures out vegetation indices, finds changes in forests over time, guesses how much carbon is lost, and rates the level of environmental risk.

A lightweight segmentation module is added to make it possible to do spatial analysis at the stand level, and instance extraction based on watersheds is used to draw the boundaries of objects. Wildfire and air-quality data feeds improve the environmental context. Also, an urban construction impact estimator measures the stress on the environment that will happen because of planned built-up expansion.

A validation gate that isn't in a forest is added to stop wrong estimates of forest loss in oceans or barren areas. Using Streamlit, Rasterio, Shapely, Plotly, and STAC-based satellite data ingestion, the system is built in Python. The resulting framework connects remote sensing analytics with real-world decision support, making it useful for academic evaluation and future research.

Forest monitoring plays a key role in climate regulation, biodiversity preservation, and sustainable land use. However, traditional field-based monitoring can be slow, costly, and difficult to scale up to large areas. This paper presents an AI-Based Forest Monitoring & Impact Assessment System that uses satellite imagery, NDVI vegetation analysis, & deep learning segmentation models to monitor the state of a forest & find changes in vegetation.

The AI system allows users to select a Region Of Interest (ROI) on an interactive satellite map & then carries out a multi-step analysis of that area. The complete workflow consists of computing NDVI, segmenting forests into stands, estimating lost forests, & classifying land cover. Additionally, this system includes advanced metrics for estimating carbon loss & assessing other environmental risks to create a comprehensive picture of ecosystem health.

 

To improve the understanding of processes and support decision making, the system includes contextual environmental indicators such as wildfire history and air quality. A comprehensive dashboard-based interface is developed for visualizing, interpreting, & generating reports. This framework illustrates how geospatial AI can be leveraged to create an interpretable & workable forest monitoring solution that can be useful for academic research, environmental agencies, and conservation.