The Power of AI in Environmental Conservation

Introduction

As climate change intensifies, extreme weather events like droughts, wildfires, and floods are becoming more frequent and severe, threatening communities worldwide. Artificial intelligence (AI) is increasingly being utilized to improve our understanding of climate-related threats and enhance efforts to protect ecosystems and biodiversity. This post will examine various applications of AI in monitoring and conserving natural environments.

Mapping and protecting fragile ecosystems

Scientists around the globe are harnessing the power of AI for ecosystem protection and monitoring. Image and sound recognition technologies, for instance, are used to map and predict the impacts of deforestation on the climate. Companies like Space Intelligence have used satellite data to map over one million hectares of land, focusing on vital metrics such as deforestation rates and carbon storage levels in forests.

On another note, in January 2024, NTT Group announced a partnership with ClimateForce to create “the world’s first Smart Rainforest” using NTT’s Smart Management Platform technology. This initiative aims to regenerate a section of Australia’s Daintree Rainforest that was cleared for agriculture in the past and has since been overrun by invasive plant species. With the help of AI, data analytics, and predictive technologies, the project will assess organic reforestation techniques and create sustainable models for ecosystem restoration.

AI is also deployed in innovative ways to combat illegal deforestation. Rainforest Connection, for example, uses sound-capturing devices on trees inside of rainforests to capture different forest sounds. These devices transmit continuous recordings of the rainforest’s soundscape to the cloud, where an AI algorithm analyzes the audio in real-time. By identifying sounds linked to illegal activities, such as chainsaws or trucks, this technology sends immediate alerts to local rangers, enabling swift intervention to prevent further deforestation.

Beyond forests, AI’s role in environmental conservation also extends to the oceans. In Australia, marine scientists use AI-powered underwater drones like Hydrus from Advanced Navigation to tackle the growing issue of plastic waste. These drones not only track the accumulation of plastic debris but also predict its movement and concentration. By optimizing cleanup efforts and aiding coral reef restoration, AI helps mitigate the environmental impact of plastic waste.

Biodiversity and wildlife

AI is transforming wildlife monitoring on a broader scale, offering unprecedented insight into species conservation. One notable example is the Zoological Society of London; in collaboration with Network Rail and Google Cloud, it uses camera traps and acoustic monitoring to protect wildlife along key railway lines that intersect critical habitats. In 2022, this project collected 3,000 hours of audio and 40,000 images across three pilot sites in London. Processing such vast amounts of data manually would have been very time-consuming. However, with the help of machine learning algorithms, the project quickly identified over 30 bird species as well as foxes and deer inhabiting these green spaces. This level of efficiency and accuracy is invaluable for conservationists seeking to protect endangered species and foster biodiversity.

Further illustrating the role AI can play in wildlife conservation, in August 2024, IBM and WWF announced a joint initiative to monitor the critically endangered African forest elephants. Utilizing IBM’s AI-powered visual inspection software, this solution enhances the identification of individual elephants from camera trap images, thereby making it easier for conservationists to track and protect these animals.

AI’s role in biodiversity tracking doesn’t stop with large mammals. Naturalis, the Dutch research institute for biodiversity, has partnered with AWS and Capgemini to address the growing challenge of tracking insect populations, which are often difficult to monitor due to their size and diversity. By leveraging AI for automatic species recognition, this initiative aims to identify insects based on the sounds they produce, much like how speech recognition technology works for human languages. The ultimate goal of the project is to gather comprehensive biodiversity data for all species across the Netherlands by 2030, creating a centralized database that will make tracking insect populations far more accessible to researchers and conservationists.

Conclusion

As climate change continues to accelerate, there is an urgent need for advanced solutions to mitigate its impact on ecosystems and biodiversity. AI has emerged as a key tool in this effort, helping to monitor and protect fragile ecosystems, track wildlife, and improve conservation initiatives. However, AI’s growing energy consumption poses a significant challenge. As AI models, especially generative AI, become more complex, the demand for processing power is increasing rapidly. The task ahead will be to strike a balance where the benefits of AI in reducing environmental impacts outweigh the carbon footprint of AI itself.

Further reading: This report examines the potential and challenges of AI technology regarding CO2 emissions as well as the different approaches to meeting these challenges.