AI's Role in the Future of Forest Conservation

Written by: Ryan Smith 


Forests cover about 31% of the world's land area (FRA 2020), playing a critical role in maintaining ecological balance, supporting biodiversity, and providing livelihoods for millions of people. Yet these vital ecosystems are under unprecedented threat. In the coming decades, the diminishing availability of productive land, competition from other land uses, and climate change will increasingly place global forests under threat (Kissinger, Herold, & De Sy, 2012).


Artificial Intelligence (AI) may serve as a powerful tool to reverse the adverse effects of human activity on forested areas. This technology's capacity to analyze vast datasets to make insightful predictions can significantly enhance our approach to forest management. In doing so, AI propels us towards a sustainable coexistence with these vital ecosystems, ensuring their preservation for future generations.



What is AI?

Artificial Intelligence (AI) is a branch of computer science dedicated to creating machines capable of performing tasks that typically require human intelligence. At its core, AI involves the development of algorithms and computational processes that enable computers to learn from data, make decisions, and solve problems. This technology spans several fields, including machine learning, natural language processing, robotics, and computer vision, each contributing to the vast capabilities of AI systems. In forest conservation solutions, machine learning, a subset of AI, has become instrumental in analyzing complex environmental data, enabling predictive modeling for monitoring deforestation and land use changes, predicting and managing wildfires, and species distribution mapping (Perry et al., 2022).


Monitoring Deforestation and Land Use Changes

The world has a total forest area of 4.06 billion hectares (ha) of forest as of 2020 (FRA 2020). Regulating activities that result in canopy loss such as logging, agricultural expansion and mining is essential for protecting forest ecosystems. Ensuring regulatory compliance is crucial but is often challenged by the high costs associated with manual monitoring. Employing AI solutions can help to reduce labour cost on compliance monitoring and improve accuracy. By processing vast amounts of satellite data, AI can pinpoint areas of deforestation, track the expansion of agricultural lands into forested territories, and detect signs of illegal logging activities. This real-time surveillance allows for immediate action, facilitating interventions before irreversible damage occurs.


Predicting and Managing Wildfires

The advent of AI has opened new horizons in predicting and managing wildfires, a threat that is becoming increasingly frequent and severe due to climate change (Flannigan et al., 2009). By harnessing the power of AI algorithms, we can now analyze vast datasets encompassing weather patterns, historical fire occurrences, and other environmental factors, providing predictive insight necessary to foresee wildfire outbreaks. This predictive capability is not just about foreseeing the occurrence of fires but also about understanding their potential severity, spread, and impact on ecosystems and human settlements.


Biodiversity Protection and Wildlife Conservation

It is widely accepted that the emergence of the human industrial era is responsible for the ongoing 6th global mass extinction event. Vertebrate species loss over the last century is up to 100 times higher than the background rate, indicating that a sixth mass extinction is already underway (Ceballos et al., 2015). Implementing conservation strategies around our forests is vital for the protection of our biodiversity. The identification of key habitat features to better understand species presence, abundance and distribution is essential to effectively implement targeted conservation strategies. Gathering this data at a landscape level is very costly.


AI plays a crucial role in mitigating these costs and enhancing the effectiveness of conservation strategies.  AI can analyze vast amounts of data from satellite imagery, drones, and sensor networks to identify key habitat features with unprecedented speed and accuracy. This capability enables conservationists to monitor wildlife populations, track changes in habitat quality, and predict potential threats to biodiversity with a level of detail that was previously unattainable.


Case Study

The Muskoka Integrated Watershed Management project located in Central Ontario was completed by Dougan & Associates in 2023. The project leveraged advanced AI technologies, particularly in GIS modeling and machine learning, to enhance watershed management and species conservation.  By employing deep learning and GIS modeling, powered by high-resolution LiDAR data and multispectral imagery, the project achieved detailed classification of terrestrial, wetland, and aquatic systems. This precise land cover mapping facilitated the identification of ecologically significant areas, including habitats for rare and endangered species. Machine learning models were employed to predict potential locations for select species based on their habitat preferences and known occurrences. This methodology not only refined the understanding of land cover types within the watershed but also enhanced habitat suitability models for wildlife conservation. The combined use of cutting-edge technologies ensured an accurate and comprehensive assessment of natural capital, crucial for integrated watershed management and ecological preservation.


Call to Action

The potential for AI to have a positive impact on forest conservation is profound, yet its application in this field is still in the early stages of adoption. To effectively utilize AI for forest conservation, a collaborative and holistic strategy is vital. Collaborative investment is needed from the private and public sector tailored for environmental preservation, involving interdisciplinary efforts that blend computer and ecological sciences. Policy frameworks must evolve to support AI's role in forest management, encouraging the open sharing of environmental data and enforcing AI-driven compliance monitoring.






Ceballos, G., Ehrlich, P. R., Barnosky, A. D., García, A., Pringle, R. M., & Palmer, T. M. (2015). Accelerated modern human-induced species losses: Entering the sixth mass extinction. Science Advances, 1(5), e1400253.


Flannigan, M., Krawchuk, M., Groot, W., Wotton, B., & Gowman, L. (2009). Implications of changing climate for global wildland fire. International Journal of Wildland Fire, 18, 483-507.


Food and Agriculture Organization of the United Nations. (2020). Global Forest Resources Assessment 2020: Key findings. FAO.


G. L. W. Perry, R. Seidl, A. M. Bellvé, W. Rammer, An outlook for deep learning in ecosystem science. Ecosystems 25, 1700–1718 (2022).


Kissinger, G., M. Herold, V. De Sy. Drivers of Deforestation and Forest Degradation: A Synthesis Report for REDD+ Policymakers. Lexeme Consulting, Vancouver Canada, August 2012.

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