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]

Remote sensing of emissions[edit | edit source]

Monitoring peatlands[edit | edit source]

Managing forests[edit | edit source]

  • Estimating carbon stock
  • Automating afforestation
  • Managing forest fires
  • Reducing deforestation

Background Readings[edit | edit source]

Other[edit | edit source]

  • "Monitoring tropical forest carbon stocks and emissions using Planet satellite data" (2019)[6]: An example of carbon stock estimation from satellite imagery.
  • "Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices" (2018)[7]: A study of drought phenomena using remote sensing data.
  • "Remote Sensing Northern Lake Methane Ebullition" (2020)[8]: A study relating methane emissions estimates from airplanes and satellite imagery.

Online Courses and Course Materials[edit | edit source]

Community[edit | edit source]

Major conferences[edit | edit source]

  • EARTHVISION: A workshop regularly held at computer vision conferences. Website here.
  • Space and AI: A conference organized by the ESA-CLAIRE AI Special Interest Group on Space. Website here.

Major journals[edit | edit source]

Major societies and organizations[edit | edit source]

Libraries and Tools[edit | edit source]

Some packages for working with remote sensing data are,

  • eo-learn: A python package maintained by the European Space Agency, giving easy access to imagery from Sentinel satellites, as well as utilities for data processing, available here.
  • robosat: A package maintained by mapbox, available here.
  • solaris: A package from CosmiQ Works (SpaceNet Challenge), available here.

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.
  6. Csillik, Ovidiu; Kumar, Pramukta; Mascaro, Joseph; O’Shea, Tara; Asner, Gregory P. (2019-11-28). "Monitoring tropical forest carbon stocks and emissions using Planet satellite data". Scientific Reports. 9 (1). doi:10.1038/s41598-019-54386-6. ISSN 2045-2322.
  7. Nakalembe, Catherine (2018-02-10). "Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices". Natural Hazards. 91 (3): 837–862. doi:10.1007/s11069-017-3106-x. ISSN 0921-030X.
  8. Engram, M.; Walter Anthony, K. M.; Sachs, T.; Kohnert, K.; Serafimovich, A.; Grosse, G.; Meyer, F. J. (2020-05-11). "Remote sensing northern lake methane ebullition". Nature Climate Change. 10 (6): 511–517. doi:10.1038/s41558-020-0762-8. ISSN 1758-678X.