Forestry and Other Land Use

This page is about the intersection of forestry and machine learning in the context of climate change mitigation. For an overview of land use as a whole, please see the Wikipedia page on this topic.

The deterioration of the natural world is unparalleled in human history and a key driver of the climate crisis. Since 2000, we have lost 361 million ha of forest cover (roughly the size of Europe) accounting for about a quarter of global anthropogenic emissions (and this may be an underestimate), largely driven by deforestation and forest degradation. Deforestation does not only release carbon (e.g., through slash-and-burn), but also destroys a multitude of other forest ecosystem services: preserving biodiversity, counteracting flooding and soil erosion, filtering water, and offering a livelihood for the local population. Major conservation and restoration efforts are underway to mitigate and safeguard against these losses, and to highlight the urgency of the issue, 2021-2030 has been declared the “UN Decade on Ecosystem Restoration”. However, we cannot preserve what we cannot measure. There is an urgent need to understand the location, health and ecological value of nature and biodiversity, and ensure these metrics are reflected in policy, finance, and decision-making. Machine learning (ML) can play a significant role in responding to this critical call for action and can accelerate the conservation and sustainable use of forestry and other land use.

A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use.

Machine Learning Application Areas

ML can play an important role in helping us tackle climate change through land use. Precision agriculture could reduce carbon release from the soil and improve crop yield, which in turn could reduce the need for deforestation. Satellite images make it possible to estimate the amount of carbon sequestered in a given area of land, as well as track GHG emissions from it. ML can help monitor the health of forests and peatlands, predict the risk of fire, and contribute to sustainable forestry. These areas represent highly impactful applications, in particular, of sophisticated computer vision tools, though care must be taken in some cases to avoid negative consequences via the Jevons paradox.

Ecosystem monitoring

Main article: Ecosystem Monitoring

Monitoring the nature's health is a key element in policies surrounding its protection. Advancements in machine learning and remote sensing allow us to scale measurement, reporting and verification processes at an unprecedented scale.

• Biodiversity monitoring
• Peatland monitoring
• Forest health monitoring
• Reducing deforestation

Land use management

Main article: Land Use Management

• Automating afforestation
• Forecasting forest fires
• Reducing deforestation
• Empowering forest communities
• Estimating carbon stock

Background Readings

Key documents

Special Report on Climate Change and Land — IPCC site

Online Courses and Course Materials

National Geographic Free Conservation Course

UC Davis Geospatial and Environmental Analysis

University of Geneva's Introduction to Ecosystem Services

Conferences, Journals, and Professional Organizations

Major conferences

• IUCN World Congress (every four years)
• Committee on Forestry (annual)
• AGU Fall Meeting (annual)
• Natural Capital Symposium (annual)
• Global Landscapes Forum (annual)

Major journals

• Nature (Climate Change, Ecology and Evolution)
• Science
• Remote Sensing of Environment
• Remote Sensing

Major societies and organizations

• The Food and Agriculture Organization of the United Nations (FAO) is a specialized agency of the United Nations that leads international efforts to defeat hunger and improve nutrition and food security
• The Global Environment Facility (GEF) was established on the eve of the 1992 Rio Earth Summit to help tackle our planet’s most pressing environmental problems.
• The Nature Conservancy is a non-profit environmental organisation that has over one million members, and has protected more than 119,000,000 acres (48,000,000 ha) of land and thousands of miles of rivers worldwide
• The International Union for Conservation of Nature (IUCN) is an international organization working in the field of nature conservation and sustainable use of natural resources.
• SouthPole, develops and finance projects around the world to reduce carbon emissions and protect biodiversity
• GoldStandard, is a third-party certification agency that ensures projects that reduced carbon emissions featured the highest levels of environmental integrity and also contributed to sustainable development
• WWF
• World Bank
• 1t.org
• Global Mangrove Alliance

Major initiatives

• UN Decade of Ecosystem Restoration

Libraries and Tools

Some packages and tools that support machine learning-based forestry work are

• Restor.eco, an open data platform for the global restoration movement
• GlobalForestWatch, is a dynamic online forest monitoring and alert system that empowers people everywhere to better manage forests
• GlobalMangroveWatch, monitors to catalyse the action needed to protect and restore mangroves
• Trase.Earth provides a dynamic supply chain visualisations for land change drivers and commodities
• Resourcetrade.Earth features Chatham House’s extensive and authoritative database of international trade in natural resources, developed from United Nations data
• Collect Earth is a free, open source, and user-friendly tool using Google Earth Engine to visualize and analyze plots of land in order to assess deforestation and other forms of land-use-change
• The Forest Atlases are online platforms that help countries better manage their forest resources by combining government data with the latest forest monitoring technology
• DeepForest, is a python package for tree crown detection in airborne RGB imagery
• BarkNet, is an open-source tree bark classification algorithm and dataset

Data

Satellite imagery are often useful for monitoring land use. Some widely accessed resources include,

• Landsat
• Sentinel
• Earth Engine Data Catalog
• Methane detection from satellite

Forestry related data have also been the focus of machine learning competitions, including

• Planet: Understanding the Amazon from space
• IDTReeS: Integrating Data science with Trees and Remote Sensing

Tree datasets:

• [hhttps://github.com/selva-lab-repo/TALLO TALLO: A global tree allometry and crown architecture database]
• Awesome-forests is a curated list of ground-truth/validation/in situ forest datasets for the forest-interested machine learning community