How we’re working with WWF to save our vital forests

May the forest be with us

Forests cover one third of the land area on our planet. They’re home to much of the world’s biodiversity, they purify our water and air, and absorb carbon dioxide. But forests around the world are under threat from deforestation. Millions of acres are lost every year – and are gone forever.

Various monitoring systems can detect deforestation once it’s happened. What if we could predict where forests will be cut down, so we can intervene in time?

The World Wildlife Fund (WWF) has been working to protect forests for more than 50 years. They’ve recently developed an artificial intelligence solution that can predict illegal deforestation before trees are even felled – and we’re helping them bring it to life.

The idea behind WWF’s ‘Early Warning System’ is to connect satellite images and other geographical data with illegal human activity. For example, images of road works near a forest could indicate plans to create access for tree cutting equipment. This helps local governments take action before it’s too late.

When WWF were looking for a technology partner to improve and scale their solution, we knew we wanted to be part of it. Together, we’ve been working on turning the initial prototype into a fully customisable and scalable solution – all thanks to data science and the power of the cloud. Many of our own experts got to work on the project, through our Deloitte Impact Foundation, which enables our people to apply their knowledge and skills to benefit society. We also set up a consortium with Jheronimus Academy of Data Science and Utrecht University, giving us access to leading researchers, as well as Amazon Web Services (AWS), who provide the cloud platform.

Working with our consortium partners, we developed a solution for users on the island of Borneo that can predict illegal deforestation six months in advance.

How the warning system works

The main challenge is monitoring millions of hectares of rainforest, to spot varying activities. Once we’ve collected all this raw data, we need to transform it into information-rich data. For example, where the roads are that may provide easy logging transportation. This is where the cloud comes in. The amount of data we work with is huge, so we work with AWS to process the datasets much faster, as well as scale to larger landscapes.

Next, we need to share this information in the most effective way. The predictions are displayed in an interactive interface, to help local users identify which ones are a priority. For example, in Kalimantan (Indonesia) we can prioritise predictions based on whether certain species live in the area – such as orangutans or clouded leopards – and how much carbon is stored in the trees.

We’ve now expanded our scope from Central Kalimantan to an area of Borneo over three times as large, including Sarawak (Malaysia). Using the cloud means the technology can easily be expanded to other areas at risk. The project was put on hold temporarily due to the COVID-19 outbreak, but our team are looking forward to restarting it soon.

We’re proud to work closely with WWF and the much wider ecosystem of governments and local actors to address one of the most pressing issues facing our planet.

 

View the full report here and discover the many other ways we’ve made an impact in 2020.

What impact will you make with us? Explore careers at Deloitte here.

Comments

Verify your Comment

Previewing your Comment

This is only a preview. Your comment has not yet been posted.

Working...
Your comment could not be posted. Error type:
Your comment has been saved. Comments are moderated and will not appear until approved by the author. Post another comment

The letters and numbers you entered did not match the image. Please try again.

As a final step before posting your comment, enter the letters and numbers you see in the image below. This prevents automated programs from posting comments.

Having trouble reading this image? View an alternate.

Working...

Post a comment

Comments are moderated, and will not appear until the author has approved them.