Forests play a crucial role in our global ecosystem, absorbing and storing carbon dioxide, regulating rainfall and moderating the climate. The United Nations says that forests cover 31 percent of the world and are home to more than 80 percent of all land animals, plants and insects. Large-scale forest monitoring can help us to understand how climate change is impacting these important ecosystems.

Cambridge researchers are harnessing artificial intelligence to improve how forests are monitored. Associate Professor Dr Emily Lines and Research Associate Dr Harry Owen are using billions of laser-captured data points to measure biodiversity and make carbon accounting more accurate.

Dr Emily Lines, Co-Director of the Cambridge Centre for Earth Observation, and her team have been monitoring forests across Europe to collect data from ground-based instruments such as Terrestrial Laser Scanning, drones and even traditional tape measures.

From this, the team hopes to directly monitor Essential Biodiversity Variables (EBVs) with the help of AI. EBVs are a set of core variables that will collectively show the effect of anthropogenic change on biodiversity.

An initial list of EBVs has been refined through extensive consultation and discussion with biodiversity scientists, and are grouped into six classes:

• Genetic diversity
• Population abundance
• Functional diversity
• Community composition
• Ecological structure
• Ecological function

The ability to measure these EBVs will improve carbon accounting and biodiversity credits.

Lines and Owen are now using AI and drones to speed up this process to take just a few minutes from hours and days. Owen has been building an AI model to automatically classify data points into individuals trees and break them down into 3D components such as wood and leaves. This will reveal how carbon is stored in each tree and where microhabitats are formed.

Data collected by Lines and her team from across Europe, along with other public forest data, is being used to train the AI model. Owen has undergone the lengthy process of manually labelling more than 50 million data points in a single plot – separating them into individual trees ‘leaf’ and ‘wood’ elements.

The pair hope that their 3D-model algorithm will speed up the forest monitoring pipeline, going from raw data to 3D models to key ecological measures without needing much expertise.

Owen’s 3D algorithm can identify individual trees, which are shown in different colours, revealing how each tree is growing, its structure and biomass.

The team can use this data to determine essential biodiversity variables (EBVs) which act as indicators for biodiversity success in certain areas.

Owen is then using this 3D data to understand how light is scattered throughout the forest’s ecosystem. He has developed a further algorithm which highlights areas of direct sunlight and shade within the forest.

The intercepted light within the forest creates microhabitats which are essential for species biodiversity within the forest. The team hopes that understanding where they are located will help to inform how they can be better protected.

In order to use AI to accurately process the large amount of complex and dense data for modelling individual trees, Owen had to build his own algorithm from scratch.

The intricacies of nature require an AI model that is trained on rich high-quality data. By using fewer samples, but of higher resolution data they are reducing unnecessary computer-processing power and cutting the environmental impact of training the model.

Owen is also accounting for different types of forests, not just in European regions. He hopes that users across the world can harness the AI model to speed up the processing of data, from tropical to boreal forests.

The pair hope this AI model will accelerate forestry data processing, providing stronger evidence for improved policies and programmes for carbon accounting and biodiversity credits.

Lines and her team are also using drones with laser sensors to cover a much greater area than possible with ground-based monitoring instruments. The detailed 3D scans offer a wealth of information for forest managers and conservationists, but turning the data into useful insights is a time-consuming process.

Images: Emily Lines

This project is one of several projects under a new Cambridge focus on AI for Climate and Nature, which has received seed funding from ai@cam, the University’s flagship AI mission. AI for Climate and Nature is an interdisciplinary collaboration between Cambridge Zero, Cambridge Conservation Research Institute, Conservation Evidence, Institute of Computing for Climate Science, Centre for Landscape Regeneration, Cambridge Centre for Carbon Credits (4C), Cambridge Conservation Initiative, Cambridge Centre for Earth Observation and the Dawn supercomputer.