The health of our forests is vital for the environmental and economic health of our planet. An ESA Demonstration Project has enabled sophisticated development of a forest analysis tool based on satellite data and self-learning AI, to support landowners in their duty to improve the health of Earth’s forests. 

According to the World Wildlife Fund, forests are vital to life on Earth. They purify the air, filter the water we drink, and prevent erosion. Forests offer a home to a diverse array of plant and animal life. They also provide timber, food, and medicinal plants. But the world’s forests are under threat from illegal and irresponsible logging, poor management, extreme weather, and climate change. 

Keeping track of forest health is challenging due to the size and inaccessibility of the landscapes. Traditional monitoring methods, based on field measurements and laser scanning, can be slow; by the time a forest owner can identify concerning data, it is mostly out-of-date. 

New goals, new data

Today’s landowners are increasingly interested in more than just the economic profit that can be generated from the forests in their care. Nurturing biodiversity, provision of clean water and protecting the beauty of landscapes are all high on the agenda. Carbon storage is also a priority because forests are a key buffer against climate change. 

To achieve all these goals requires new kinds of information, rapidly and regularly delivered. That’s where EnviNavigator comes in – a forest analysis tool combining traditional measures with AI and satellite data to provide information like never before. 

Self-learning for greater accuracy

At the core of the EnviNavigator system – developed by Finnish company Bitcomp – is a self-learning AI engine, which helps to achieve what previous calculation methods could not. 

The engine trawls satellite images for changes to the forest canopy. When it finds an area of change it assigns a likely cause – say, storm damage. The algorithm then cross-checks against other inputs, such as user feedback and field observations. If these back-up the engine’s deduction – e.g. the forester inputs that the area has, indeed, been damaged in a storm – it learns that the same observations in other locations are also likely to indicate storm damage. However, if the deduction is not backed up by other sources, the engine will learn this is a less likely conclusion from the signals. 

Transparency and communication

High quality insights are only useful if they rapidly reach the people who can make change. A key part of the EnviNavigator project was to integrate the service into applications used by foresters and forest owners, so that information can be easily accessed. 

Through these applications, EnviNavigator automatically alerts sites that need attention. Both foresters and forest owners can have access to the data, enabling greater transparency, agency and understanding of the forest health in both parties. 

Sanna Härkönen, the R&D Director of Bitcomp Oy, said: “We do not analyse satellite data and develop artificial intelligence algorithms because it is trendy. We develop services that concretely make forest professionals’ work easier and forest owners’ services better. The EnviNavigator project is not only about satellite data but also about enabling its advantages in field work.”

Project development

EnviNavigator began as a forest change detection service in 2019, analysing physical changes in the environment. The Finnish Forest Centre began using Bitcomp’s service for real-time monitoring of illegal thinning and felling. 

Further development of the algorithm was possible with support from an ESA Demonstration Project. Some of the variables added include storm and insect damage, and the system is now able to provide better carbon storage estimations as well as predictions of future forest health based on detection of health risk status. 

“The EnviNavigator service is a great example of digital transformation arriving in forestry and using satellite Earth observation data to visualise forest management needs and in this way empowering and involving forest owners directly,” said Volker Schumacher, Business Applications Engineer at ESA.

In Finland, the EnviNavigator AI now covers the whole country. It is set to be made available for all Finnish forest owner associations and their customers through the platform LeafPoint, thanks to a contract with the head organisation of forest owner associations (MHYP). 

In Germany, EnviNavigator so far covers the whole country for change detection and vitality mapping services, with deeper insights planned. Here, it will be available as part of the Woodsapp.de solution. 

Organic cotton is becoming increasingly popular as the fashion industry and consumers seek to reduce pesticide, water, and energy use in garment production. As part of an ESA Kick-Start activity, Marple has developed new technology which uses satellite imagery and machine learning to identify organic cotton fields and speed up the monitoring and certification processes for the crops. 

The most widely used fibre in clothing, cotton, is also one of the most chemical-intensive crops to produce, accounting for only 2.5% of the world’s arable land but consuming 16% of the world’s pesticides. Growing organic cotton helps redress this imbalance, using 88% less water, 62% less energy and zero pesticides than conventional cotton-growing methods. The current challenge is that only around 1% of cotton production is organic.

The organic cotton industry is expanding rapidly, tripling in size in the last three years as a result of its growing popularity in fast fashion retailing and an increased awareness of environmental sustainability among consumers. This means rapid and reliable certification of organic cotton production is increasingly urgent if the industry is to meet market demand and ensure a positive impact.

Kick-Start activities assess the economic and technical feasibility of new projects. In 2021 ESA announced a Kick-Start activity to encourage circular economies. These are economic systems that minimise the use of resources, carbon emissions and waste. German start-up Marple rose to the challenge. Aware that the fashion and textile industry is responsible for a tenth of all the world’s carbon dioxide emissions, Marple realised that identifying the growing number of organic cotton producers quickly and easily, and speeding up the certification process for their crops, would bring benefits to both the fashion industry and the planet.

Growers, buyers, project managers and crop certifiers are located in many different areas of the world, making monitoring a time-consuming and expensive business. To solve this problem, Marple has developed an AI-based technology called CoCuRA (Cotton Cultivation Remote Assessment) which, using satellite images and machine learning (computer systems that learn and adapt using algorithms and statistical models to analyse patterns in data), is not only able to detect cotton fields but can also differentiate between the type of cotton being grown i.e. organic or conventional.

Marple initially trained a land use classification model to separate cropland from water, forests, or buildings on satellite imagery. It then trained CoCuRA to identify cotton fields and other produce using data from Uzbekistan, one of the world’s major cotton producers. Finally, CoCuRA was trained to identify, via remote sensing, differing indices between conventional and organic cotton crops. The data has shown the system to be 98% accurate and Marple is now in consultation with numerous certification and development organisations to apply CoCuRA to their markets and requirements.

“We have shown that it is possible to detect cotton fields on satellite imagery and remotely classify them according to their cultivation standard,” said Dr David Scherf, Marple’s co-founder. “We are also confident we can apply the technology to other crops such as corn or wheat.”

“This is the kind of innovation that ESA want to support that uses space solutions to create shared value and a positive impact on the world,” added Guillaume Prigent, ESA’s Business Development and Partnerships Officer. “It can help governments and NGOs track ecological changes over entire regions and benefit small farmers previously excluded from organic certification because of high costs.”