AI Tools Revolutionize Forest Restoration, Says IFS Officer Pushpendra Rana

Artificial intelligence (AI) and machine learning hold immense potential for advancing forest restoration and governance, but their application must be carefully tailored to local ecological and social contexts, according to Indian Forest Service (IFS) officer Dr. Pushpendra Rana. In an interview with The Indian Express, Dr. Rana outlined both the opportunities and limitations of using technology in environmental conservation, drawing from his work in Himachal Pradesh.

Leveraging AI for Smarter Forest Restoration

Dr. Rana has developed innovative tools such as WhereToPlant and WhatToPlant, Telegram-based chatbots designed to assist forest rangers and farmers in identifying the most suitable locations and tree species for ecological restoration. These tools integrate machine learning models with data on climate, soil, terrain, vegetation, and other factors to make informed recommendations.

For example, WhereToPlant was used during the 2025 plantation season in Himachal Pradesh to guide site selection for over 500 hectares of plantations across 250 locations. The system’s success lies in its user-friendly interface, which eliminates the need for specialized GIS expertise and aligns with the workflows of field officers.

The forthcoming WhatToPlant module will enhance this system by recommending native tree species best suited to specific locations. This feature is powered by a database of over 226,000 geo-referenced field observations and environmental data, enabling habitat suitability predictions for 98 native tree species across Himachal Pradesh.

Challenges in Applying AI to Ecosystem Restoration

Despite the promise of these technologies, Dr. Rana emphasized the challenges inherent in using AI for ecosystem management. He noted that while AI can identify suitable habitats, it cannot account for factors like grazing, invasive species, or community involvement, which significantly influence restoration success. Moreover, models trained in one ecological zone often cannot be directly applied to another due to variations in environmental conditions.

Dr. Rana also highlighted the importance of designing tools that address practical, on-ground challenges faced by field staff. He stressed that AI should complement, not replace, local knowledge. Recommendations must be explainable and adaptable, allowing communities and field officers to modify or reject them based on ground realities. For instance, a site deemed suitable by AI might not account for issues like seasonal waterlogging or cultural uses of the land. Such feedback should be incorporated into the system for continuous improvement.

A Data-Driven Approach to Environmental Governance

Dr. Rana’s work exemplifies a shift toward evidence-based environmental governance. He noted that many environmental programmes focus on metrics like the number of seedlings planted or funds spent, often neglecting whether these efforts yield lasting ecological and social benefits. His initiatives aim to bridge this gap by integrating science, technology, and local expertise to create more accountable and effective restoration practices.

Broader Implications for Conservation Technology

Dr. Rana acknowledged that while AI and machine learning are transforming conservation, their success depends on robust data, institutional support, and long-term adoption. He cited examples of successful tech interventions in India, such as the Forest Survey of India’s satellite-based forest fire alert system and advances in cyclone forecasting. However, he also cautioned against over-reliance on technology that prioritizes administrative metrics over ecological outcomes or lacks follow-up mechanisms, such as drone-based tree planting.

Ultimately, Dr. Rana advocates for a balanced approach where technology supports better ecological decisions and empowers local communities, rather than merely increasing administrative efficiency. His work underscores the need for adaptive learning systems that integrate machine intelligence with human expertise to address the complex challenges of forest restoration.

Source: Indian Express