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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.''
 
''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.''
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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<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>), 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. [[File:Agriculture.png|thumb|A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use.]]
 
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<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>), 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. [[File:Agriculture.png|thumb|A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use.]]
 
== Machine Learning Application Areas ==
 
== Machine Learning Application Areas ==
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Some packages and tools that support machine learning-based forestry work are
 
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.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.globalmangrovewatch.org/ GlobalMangroveWatch], monitors to catalyse the action needed to protect and restore mangroves
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