Forestry and Other Land Use: Difference between revisions

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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.''
 
 
 
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
   
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*[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

Revision as of 14:36, 12 July 2021

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[1] (and this may be an underestimate[2]), 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

Major journals

Major societies and organizations

Major initiatives

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,

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

References

  1. 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.
  2. Mahowald, Natalie (2017). "Are the impacts of land use on warming underestimated in climate policy?". Cite journal requires |journal= (help)