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.

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.

Forests are often described as the ‘lungs of the Earth’, absorbing twice as much carbon as they emit, acting as carbon sinks (storing CO2 in their branches, roots and leaves). Carbon sinks are essential to climate mitigation and limiting further global temperature rises.

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.

The Natural History Museum likens EBVs to a share price in a biodiversity stock market that measures the value of many different aspects of biological diversity. The Essential Biodiversity Variables are what varies over time.

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

1. genetic diversity
2. population abundance
3. functional diversity
4. community composition
5. ecological structure
6. ecological function

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

Processing this substantial amount of raw data into meaningful metrics is a significant challenge.

It requires a lot of human effort and limits the number of trees a single researcher can process. Dr Harry Owen recalls in the early days of his PhD spending a vast amount of time extracting key measurements such as crown size to understand the forests from big datasets.

“In a 30x30 metre area, you were dealing with millions and millions of data points”, Owen said.

Lines and Owen are now using AI and drones to speed up this process to just a few minutes from hours and days. Owen has been building an AI model, using ‘deep learning’, to automatically classify data points into individual 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.

“It’s pretty straightforward to fly a lot of these smaller drones, they have a huge potential for being an important part of the conservation toolkit and monitoring toolkit – and AI is taking the manual labour bottleneck and accelerating it,” Lines said.

This is one of the several ground-breaking 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.

Sourse: https://www.cam.ac.uk/node/247151