Forestry and Other Land Use

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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.

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

As described in the paper "Tackling Climate Change with Machine Learning"[1],

Plants, microbes, and other organisms have been drawing CO2 from the atmosphere for millions of years. Most of this carbon is continually broken down and recirculated through the carbon cycle, and some is stored deep underground as coal and oil, but a large amount of carbon is sequestered in the biomass of trees, peat bogs, and soil. Our current economy encourages practices that are freeing much of this sequestered carbon through deforestation and unsustainable agriculture. On top of these effects, cattle and rice farming generate methane, a greenhouse gas far more potent than CO2 itself. Overall, land use by humans is estimated to be responsible for about a quarter of global GHG emissions[2](and this may be an underestimate[3]). In addition to this direct release of carbon through human actions, the permafrost is now melting, peat bogs are drying, and forest fires are becoming more frequent as a consequence of climate change itself – all of which release yet more carbon[4]. The large scale of this problem allows for a similar scale of positive impact. According to one estimate[5], about a third of GHG emissions reductions could come from better land management and agriculture. ML can play an important role in some of these areas. 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.

Machine Learning Application Areas[edit | edit source]

Ecosystem monitoring[edit | edit source]

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[edit | edit source]

Main article: Land Use Management

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

Background Readings[edit | edit source]

Key documents[edit | edit source]

Special Report on Climate Change and Land — IPCC site

Online Courses and Course Materials[edit | edit source]

National Geographic Free Conservation Course

UC Davis Geospatial and Environmental Analysis

University of Geneva's Introduction to Ecosystem Services

Conferences, Journals, and Professional Organizations[edit | edit source]

Major conferences[edit | edit source]

Major journals[edit | edit source]

Major societies and organizations[edit | edit source]

Major initiatives[edit | edit source]

Libraries and Tools[edit | edit source]

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

  • 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[edit | edit source]

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

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

References[edit | edit source]

  1. Rolnick, David; Donti, Priya L.; Kaack, Lynn H.; Kochanski, Kelly; Lacoste, Alexandre; Sankaran, Kris; Ross, Andrew Slavin; Milojevic-Dupont, Nikola; Jaques, Natasha; Waldman-Brown, Anna; Luccioni, Alexandra (2019-11-05). "Tackling Climate Change with Machine Learning". arXiv:1906.05433 [cs, stat].
  2. Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 2014.
  3. Mahowald, Natalie (2017). "Are the impacts of land use on warming underestimated in climate policy?". Cite journal requires |journal= (help)
  4. "The study of Earth as an integrated system". Cite journal requires |journal= (help)
  5. Paul Hawken (2015). Drawdown: The most comprehensive plan ever proposed to reverse global warming.