ML-Driven Forest Ecosystem Intelligence Platform
WebGIS powered by AI and remote sensing for automated forest analysis quantifying biomass, carbon storage, and supporting climate-smart decisions.
Project Overview
Project Impact
Key results and achievements from this initiative
Forests are Kenya’s natural carbon banks, yet monitoring remains manual and fragmented. This platform integrates AI, GIS, and remote sensing to automate forest ecosystem analysis, biomass estimation, and carbon sequestration modelling.
Tech Stack & Innovation
- Data: Landsat, GEDI LiDAR, DEM, ESA World Cover
- Backend: Python (Django) + PostgreSQL/PostGIS
- ML alg: Random Forest for biomass & carbon estimation
- Frontend: Interactive WebGIS (NextJS)
Key Features
- Upload or draw areas of interest for instant analysis
- Compute NDVI, EVI, SAVI, NDMI automatically
- Estimate biomass & carbon storage in real-time
- Visualize results on a browser-based forest dashboard
Impact
This project demonstrates a scalable, data-driven approach to forest management enabling faster response to deforestation, accurate carbon accounting, and new opportunities for green investment in Africa.
Developed By
Clement Ndome — Lead AI-ML & GIS Developer
Project Partners
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Let's discuss how we can apply our geospatial intelligence expertise to your environmental monitoring and climate resilience challenges.