AiFRES: AI-Powered Forest Restoration for Climate Resilience
Building climate-resilient landscapes and livelihoods in the Himalayas using a community-first, AI-driven approach to ecosystem restoration.
The Problem
In the Indian Himalayas, communities face urgent climate risks like landslides, floods, and droughts. For decades, forest restoration programs have had disappointing results, often replacing biodiverse ecosystems with fire-prone forests that lack value for local livelihoods. These efforts fail because they lack the tools to adapt global goals to local needs and ecological conditions.
Our Solution
AiFRES (AI-enabled Forest Restoration and Evaluation System) is our answer. It is an AI-powered, community-governed system that provides data-driven recommendations for plantation site selection, species choice, and land improvement activities. By integrating satellite data, advanced machine learning, and crucial community feedback, we empower local women and youth-led groups to execute effective, sustainable restoration that enhances both ecosystems and livelihoods.
Active Project: The WhereToPlant Bot
The foundational component of AiFRES is the WhereToPlant Bot, a decision support tool that is live and in active beta. It uses geospatial, social, and ecological data to help communities and forest officials identify optimal planting sites, reducing wasteful expenditure and improving survival rates.
Currently Used in Field
Active deployment for site selection and verification
~80% Prediction Accuracy
Continuously improving through user feedback
• 🎯 ~80% success probability
• 🌱 Excellent soil quality
• ☀️ Optimal climate conditions
• ⚠️ Low risk factors detected
• 🌳 Recommended: Pine & Deodar species
The AiFRES Vision: A Complete Restoration Toolkit
AI-Powered Species Recommendation
We are expanding beyond site selection to recommend the right species for the right place. Using a comprehensive database of where different tree species are thriving, our AI engine will provide tailored recommendations to optimize for livelihoods, nutrition, and carbon sequestration. This species suggestion module will be piloted in the Palampur and Kangra regions of Himachal Pradesh.
Holistic Restoration Guidance
Effective restoration is more than just planting trees. AiFRES will provide site-specific recommendations for non-planting measures, including invasive weed removal, assisted natural regeneration, erosion control, and water conservation works. This ensures a comprehensive approach to strengthening the resilience of forest sites.
Community-Driven Learning
AiFRES is designed to be a living system. The platform incorporates both top-down, model-based data and bottom-up feedback from community members and forest staff. This user feedback is systematically collected to retrain our machine learning models, ensuring our recommendations become progressively more accurate and locally informed.
Our Collaborative Network
Himachal Pradesh Forest Department (RGVSY Program)
University of Minnesota
Indian Institute of Technology, Delhi (IIT Delhi)
Trestle Management Advisors
CoRE stack network
Nature Conservation Foundation (NCF)
Swedish University of Agricultural Sciences (SLU)
Project Roadmap
The project is in an advanced design and pre-implementation stage with foundational components already in place. Our partnerships, government alignment, and initial tools like the Plantation Bot are pre-positioned for immediate launch and success upon grant award.