Flood monitoring with satellite images is an effective method of detecting and tracking floods. This approach involves the use of satellite imagery to detect changes in water levels and identify flooded areas. To monitor floods using satellite images, the images are analyzed to detect changes in water levels over time. To detect changes in water levels and identify flooded areas based on a set of predefined criteria, we can train algorithms. Amazon SageMaker geospatial capabilities make it easier for data scientists and machine learning (ML) engineers to build, train, and deploy ML models using geospatial data.  These capabilities also provide pre-trained models. One of the pre-trained models is land cover segmentation model. This land cover segmentation model can be run with a simple API call and can be leverage to analyze changes in the water level.

This article demonstrates geospatial analysis techniques to visualize and quantify deforestation using Sentinel-2 satellite imagery. It leverages Amazon SageMaker and open datasets from the Amazon Sustainability Data Initiative (ASDI) to process time series imagery capturing landscape changes related to wildfires near Paradise, California. After configuring access to the Sentinel-2 data registry, bounding boxes and date ranges isolate relevant pre-treatment and post-treatment scenes bracketing major fire events. Comparative visualization highlights patterns of healthy forest persistence versus zones of more complete canopy removal post-fire. Suggestion is to move such analytical routines into automated pipeline to enable scalable automated deforestation mapping as new Sentinel-2 observations become available over time. Overall, this article demonstrates core techniques for leveraging cloud-based geospatial data and computing tools to derive actionable intelligence maps and indicators pertinent to sustainability challenges like wildfire impacts and climate adaptation.