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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 [https://en.wikipedia.org/wiki/Land_use Wikipedia page] on this topic.''[[File:Agriculture.png|thumb|A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use.]]
AsThe described in the paper "Tackling Climate Change with Machine Learning"<ref>{{Cite journal|last=Rolnick|first=David|last2=Donti|first2=Priya L.|last3=Kaack|first3=Lynn H.|last4=Kochanski|first4=Kelly|last5=Lacoste|first5=Alexandre|last6=Sankaran|first6=Kris|last7=Ross|first7=Andrew Slavin|last8=Milojevic-Dupont|first8=Nikola|last9=Jaques|first9=Natasha|last10=Waldman-Brown|first10=Anna|last11=Luccioni|first11=Alexandra|date=2019-11-05|title=Tackling Climate Change with Machine Learning|url=http://arxiv.org/abs/1906.05433|journal=arXiv:1906.05433 [cs, stat]}}</ref>,<blockquote>Plants, microbes, and other organisms have been drawing CO2 from the atmosphere for millionsdeterioration of years. Most of this carbon is continually broken down and recirculated through the carbonnatural cycle, and someworld is storedunparalleled deepin undergroundhuman as coalhistory and oil, but a largekey amountdriver of carbon is sequestered in the biomassclimate of trees,crisis. peatSince bogs2000, andwe soil.have Ourlost current361 economymillion encourages practices that are freeing muchha of thisforest sequesteredcover carbon(roughly throughthe deforestation and unsustainable agriculture. On topsize 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 beEurope) responsibleaccounting for about a quarter of global GHGanthropogenic emissions<ref>{{Cite book|title=Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change|url=https://www.ipcc.ch/report/ar5/wg3/|date=2014|coeditors=|last=|first=|publisher=|year=|isbn=|location=|pages=}}</ref> (and this may be an underestimate<ref>{{Cite journal|title=Are the impacts of land use on warming underestimated in climate policy?|url=https://iopscience.iop.org/article/10.1088/1748-9326/aa836d|coauthors=|date=2017|last=Mahowald|first=Natalie|journal=|volume=|pages=|via=}}</ref>)., Inlargely additiondriven toby thisdeforestation directand releaseforest ofdegradation. Deforestation does not only release carbon (e.g., through slash-and-burn), humanbut actionsalso destroys a multitude of other forest ecosystem services: preserving biodiversity, counteracting flooding and soil erosion, filtering water, and offering a livelihood for the permafrostlocal 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 nowan meltingurgent need to understand the location, peathealth bogsand areecological value of nature and dryingbiodiversity, and forestensure firesthese metrics are becomingreflected morein frequentpolicy, asfinance, and decision-making. Machine learning (ML) can play a consequencesignificant ofrole climatein changeresponding itselfto –this allcritical ofcall whichfor releaseaction yetand morecan carbon<ref>{{Citeaccelerate journal|title=Thethe studyconservation and sustainable use of Earthforestry asand another integratedland system|url=https://climateuse.nasa.gov/nasa_science/science/# [[File:~:text=The%20Study%20of%20Earth%20as%20an%20Integrated%20System&text=Earth%20system%20science%20is%20theAgriculture.png|thumb|A schematic of the ways that machine learning can support carbon negative agriculture,whole%2C%20including%20its%20changing%20climate.}}</ref> forestry, and land use.]]
The large scale of this problem allows for a similar scale of positive impact. According to one estimate<ref>{{Cite book|title=Drawdown: The most comprehensive plan ever proposed to reverse global warming|author=Paul Hawken|date=2015}}</ref>, 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.</blockquote> ▼
== Machine Learning Application Areas ==
▲The large scale of this problem allows for a similar scale of positive impact. According to one estimate<ref>{{Cite book|title=Drawdown: The most comprehensive plan ever proposed to reverse global warming|author=Paul Hawken|date=2015}}</ref>, about a third of GHG emissions reductions could come from better land management and agriculture. ML can play an important role in somehelping ofus thesetackle areasclimate 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. </blockquote>
=== Ecosystem monitoring ===
Some packages and tools that support machine learning-based forestry work are
*[https://restor.eco Restor.eco], an open data platform for the global restoration movement
*[https://www.globalforestwatch.org/ GlobalForestWatch], is a dynamic online forest monitoring and alert system that empowers people everywhere to better manage forests
*[https://www.globalmangrovewatch.org/ GlobalMangroveWatch], monitors to catalyse the action needed to protect and restore mangroves
* [https://www.kaggle.com/c/planet-understanding-the-amazon-from-space Planet: Understanding the Amazon from space]
* [https://idtrees.org/ 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]
* [https://github.com/blutjens/awesome-forests Awesome-forests is a curated list of ground-truth/validation/in situ forest datasets for the forest-interested machine learning community]
== References ==
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