The foregoing visualization depicts a classified map delineating intact dense forest areas (dark green) and zones of dead or removed forest cover (lighter tones) within the study region. Building on this initial exploration, analogous image processing and spectral index derivation methods can be encoded into serverless Lambda functions as constructed in prior sections. Deploying the algorithms for on-demand execution facilitates scalable automated analysis of new Sentinel-2 scenes as they become available from the geospatial archive. With appropriately tuned categorization thresholds, the Lambda functions will output classified deforestation maps highlighting areas of forest loss over time. These discrete change maps can subsequently inform downstream sustainability policy decisions or conservation actions targeting documented zones of concern. More broadly, wrapping reusable image analysis scripts into cloud-based functions promotes not only scalability but also facilitates standardized products, sharing of best practices across projects, and accessibility to audiences beyond individual researchers.
Sample code available on
githubReferences: