5 days ago
Inclusive & effective forest conservation, courtesy AI
Amid the climate crisis and loss of biodiversity, artificial intelligence (AI) can be a valuable ally for forest and wildlife conservation. From acoustic surveillance systems to AI-assisted reforestation planning, new technologies are being deployed across India. Recently, several states have started using AI-enabled alert systems and cameras for forest management, identifying poachers and trespassers, and tracking animals to prevent human-animal conflict. Tamil Nadu has used AI surveillance systems to avert forest fires. The Railways introduced an AI-enabled Distributed Acoustic Sensing (DAS) system to prevent elephants and other wildlife from being struck by trains.
Traditionally, forest governance has relied on satellite imagery and ground patrolling. While valuable, these are mostly reactive. AI enhances their utility by converting large datasets into real-time, actionable intelligence. To illustrate, Rainforest Connection employs solar-powered, old smartphones equipped with extra microphones to 'listen' to forests for sounds of chainsaws, gunshots, or even rare species' movements.
In India, the Forest Survey of India (FSI) is experimenting with AI-assisted satellite data to better estimate forest cover and detect seasonal changes. Platforms such as Global Forest Watch provide precise and timely information about forest management. Beyond monitoring, AI offers predictive capability, processing diverse datasets such as rainfall, soil quality, biodiversity indices, deforestation rates, encroachment patterns and even road proximity to forecast potential hotspots of ecological stress. Such predictive intelligence can prove invaluable for conserving biodiversity-rich but vulnerable zones, such as the Western Ghats.
In areas outside wildlife protected zones (around 5% of India's land), AI has begun aiding mitigation of human-wildlife conflict. In Tamil Nadu and Chhattisgarh, AI systems track elephant movements near settlements and send automated alerts via SMS and public display systems. AI can also protect forest patrollers from animal attacks (tigers and elephants) by identifying high-risk zones and times based on historical data. AI-enabled geofencing and aggressive animal behaviour tracking can issue real-time alerts, offering guards precious seconds to retreat or take safety measures.
Reforestation strategies too can be reshaped by AI. In the Aravallis, to combat desertification, AI is being used to design ecologically suitable replanting models based on species resilience and habitat compatibility. In Uttarakhand and Himachal Pradesh, AI tools can be tested for post-disaster recovery (landslides and forest fires), helping prioritise areas for replantation based on disaster intensity, topography, and native species presence. Another crucial role AI can play is in addressing forest encroachments and land disputes. By producing evidence-backed models of land use, encroachment patterns, and habitat changes, AI tools could support lawful enforcement while ensuring communities are not unfairly displaced.
Despite such promising examples, India lacks a national policy on AI for forest management. A framework is needed to identify focus areas, from biodiversity monitoring and deforestation prediction to poaching prevention, and to set and implement ethical and legal standards.
A key concern is the privacy of tribal and forest-dwelling communities. AI-based surveillance may impinge on their rights if implemented without safeguards. Its deployment must be consented to and be sensitive to rights of forest dwellers under the Forest Rights Act, 2006. It must not disregard their privacy and traditional knowledge systems, as they are effective custodians of local ecology. Community-informed deployment of AI, developed in collaboration with local stakeholders, is essential to prevent human alienation and build trust.
Currently, forest data in India is siloed across agencies. State forest departments maintain individual records. FSI releases broad and retrospective biennial forest cover reports, and the National Biodiversity Authority and Wildlife Institute of India hold datasets that lack interoperability. Creating a centralised forest data exchange can enable better monitoring. Though platforms like E-Green Watch and Isro's Bhuvan have made some headway, their potential remains underutilised.
Additionally, unlike other sectors, AI innovation in forest conservation has not received targeted state funding. Inclusion of forest-specific AI in national startup and innovation schemes could boost tailored solutions.
India is committed to targets under the Kunming-Montreal Global Biodiversity Framework. To meet these, it must align ecological priorities with technological potential. AI offers scalable, efficient tools for conservation. But without a robust policy, it may also become a tool of exclusion and overreach. What is required is convergence between innovation and tradition, technology and ethics, and central planning and community participation.
Mrinalini Naik and Ujjwal Bhardwaj are advocates practising in the Supreme Court. The views expressed are personal.
