Recently, Heilongjiang Province officially completed the development of its first Smart Monitoring and Early Warning Platform for Human-Tiger Conflict. This province-wide intelligent system enables, for the first time, a comprehensive understanding of the population size and distribution patterns of Amur tigers. The platform provides accurate data and advanced technological tools to support scientific prevention and management of human-tiger conflicts. It was officially unveiled on October 17th during the Technical Training Workshop for Phase II of the Heilongjiang Province Amur Tiger Monitoring, Active Early Warning, and Risk Prevention Project, held at Northeast Forestry University (NEFU).

A Province-Wide Network: AI Identifies Each Amur Tiger by Its Unique Pattern

Heilongjiang Province, which hosts over 70% of China’s Amur tiger habitat, has the widest range of this endangered species in the country. In the past five years, monitoring efforts have documented more than 400 sightings of wild Amur tigers, resulting in the identification of at least 38 individual tigers.

“In the past, we relied on manual patrols; now, technology drives our conservation efforts,” said Professor Jiang Guangshun, Executive Deputy Director of the Felid Research Center under the National Forestry and Grassland Administration and a professor at the College of Wildlife and Protected Area NEFU. The newly established intelligent monitoring and early-warning platform integrates 165 standardized survey units and 8,085 infrared cameras, covering 32,000 square kilometers of forest. It enables systematic monitoring and AI-based identification of 13 species of large and medium-sized mammals, including the Amur tiger, Amur leopard, Asiatic black bear, and brown bear.

The system’s most innovative feature is its ability to identify each Amur tiger by its unique stripe pattern, similar to facial recognition technology for humans. “Each tiger’s stripes are one of a kind. With our AI-based recognition software, individual identification can be performed quickly and accurately,” said Professor Jiang.

Three-Tier Early Warning System: Establishing a “Safety Perimeter” Around Villages

To decrease the growing risk of human-tiger conflicts, the platform features an innovative three-layer early warning and prevention system, with pilot projects launched in three high-risk villages: Muling Huashuhe Forestry Farm, Qiyuan in Dongfanghong Town, and Baomashan in Yingchun Town. Centered around the village areas, the system establishes three defense zones within a 5-kilometer radius. When an Amur tiger is detected 2-5 km away, Level III alerts are triggered. At 1-2 km, Level II warnings are activated, and deterrent measures are put into effect. Within 1 km, Level I alerts are issued, with immediate emergency response mechanisms on standby. Each alert level initiates corresponding monitoring, warning, and response actions to safeguard human safety and reduce potential conflicts.

The platform integrates real-time monitoring cameras, intelligent drones, and various deterrence technologies, such as gas, laser, and ultrasonic systems, creating a comprehensive “monitoring-warning-deterrence-rescue” system for prevention and control. Equipped with 4G and BeiDou dual-mode communication, the monitoring cameras stay fully operational even in extreme cold, ensuring reliable, continuous data transmission across different terrains and weather conditions.

Innovative Technologies Unveiled: Drone Nests Enable “Rapid Response”

The platform incorporates advanced technological equipment, showcasing the latest innovations in wildlife protection. Intelligent tracking drones are fitted with automated nests for 24/7 patrols; laser deterrent systems use controlled laser beams to safely repel wildlife; and anesthetic drones provide a dependable, efficient solution for emergency management.

All these technologies share essential features of intelligence, automation, and precision, ensuring effective prevention and control while minimizing disruption to both humans and wildlife. Even under extreme weather conditions, the equipment remains stable and reliable, fully adapted to the complex natural environments of Heilongjiang Province.

The project team highlighted that all technologies had undergone extensive testing to confirm they effectively deter wildlife without harm, supporting the vision of peaceful coexistence between humans and tigers.

Data-Driven Decisions: Empowering Scientific Management with a “Smart Brain”

The platform’s main strength is its smooth integration of data collection, analysis, and application. For the first time, standardized monitoring across Heilongjiang Province enables the gathering of comprehensive data on the population density, distribution, and genetic traits of Amur tigers and other mammals.

“This system not only helps resolve immediate human-tiger conflicts but, more importantly, provides a solid scientific foundation for long-term conservation planning,” said Professor Jiang. The platform’s real-time reporting module allows local teams to quickly upload tiger-sighting information, send genetic samples for analysis, and access feedback reports, thereby enhancing the efficiency and effectiveness of data collection and utilization.

The platform also includes a data visualization module that creates spatiotemporal heat maps of wildlife activity, offering clear, data-driven support for management and policy decisions. These visual datasets are essential for identifying ecological barriers to Amur tiger migration and dispersal, as well as for planning ecological corridors and boosting regional biodiversity connectivity.

The establishment of this Smart Monitoring and Early Warning Platform not only offers strong technological support for Amur tiger conservation in Heilongjiang but also provides a replicable and scalable “Heilongjiang Model” for managing human-wildlife conflicts both in China and worldwide.

Looking ahead, the project team will gradually expand the platform’s coverage beyond the current three pilot villages to include more high-risk areas. Simultaneously, they will enhance data mining and analysis, concentrating on cross-border migration patterns and habitat preferences of Amur tigers, to offer more precise scientific support for ecological corridor planning and long-term conservation efforts.