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.''[[File:Agriculture.png|thumb|A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use.]]
''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.]]
As 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 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<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=O. Edenhofer, R. Pichs-Madruga, Y.
Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J.
Savolainen, S. Schlomer, C. von Stechow, T. Zwickel, J.C. Minx}}</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=Natalie M Mahowald, Daniel S Ward, Scott C Doney, Peter G Hess, and James T Randerson|date=2017}}</ref>). 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<ref>{{Cite journal|title=The study of Earth as an integrated system|url=https://climate.nasa.gov/nasa_science/science/#:~:text=The%20Study%20of%20Earth%20as%20an%20Integrated%20System&text=Earth%20system%20science%20is%20the,whole%2C%20including%20its%20changing%20climate.}}</ref>.

⚫
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 ==
== 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.


=== '''Remote sensing of emissions''' ===
=== 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.
=== '''Monitoring peatlands''' ===


* Biodiversity monitoring
=== '''Managing forests''' ===
* Peatland monitoring
* Forest health monitoring
*Reducing deforestation

==='''Land use management'''===
Main article: ''[[Land Use Management]]''


⚫
* Estimating carbon stock
* Automating afforestation
* Automating afforestation
* Managing forest fires
* Forecasting forest fires
* Reducing deforestation
* Reducing deforestation
*Empowering forest communities
⚫
*Estimating carbon stock


== Background Readings ==
== Background Readings ==


=== Other ===
=== Key documents ===
[https://www.ipcc.ch/srccl/ Special Report on Climate Change and Land — IPCC site]

*'''Monitoring tropical forest carbon stocks and emissions using Planet satellite data'''<ref>{{Cite journal|last=Csillik|first=Ovidiu|last2=Kumar|first2=Pramukta|last3=Mascaro|first3=Joseph|last4=O’Shea|first4=Tara|last5=Asner|first5=Gregory P.|date=2019-11-28|title=Monitoring tropical forest carbon stocks and emissions using Planet satellite data|url=http://dx.doi.org/10.1038/s41598-019-54386-6|journal=Scientific Reports|volume=9|issue=1|doi=10.1038/s41598-019-54386-6|issn=2045-2322}}</ref>: An example of carbon stock estimation from satellite imagery.
*'''Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices'''<ref>{{Cite journal|last=Nakalembe|first=Catherine|date=2018-02-10|title=Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices|url=http://dx.doi.org/10.1007/s11069-017-3106-x|journal=Natural Hazards|volume=91|issue=3|pages=837–862|doi=10.1007/s11069-017-3106-x|issn=0921-030X}}</ref>: A study of drought phenomena using remote sensing data.
*'''Remote Sensing Northern Lake Methane Ebullition'''<ref>{{Cite journal|last=Engram|first=M.|last2=Walter Anthony|first2=K. M.|last3=Sachs|first3=T.|last4=Kohnert|first4=K.|last5=Serafimovich|first5=A.|last6=Grosse|first6=G.|last7=Meyer|first7=F. J.|date=2020-05-11|title=Remote sensing northern lake methane ebullition|url=http://dx.doi.org/10.1038/s41558-020-0762-8|journal=Nature Climate Change|volume=10|issue=6|pages=511–517|doi=10.1038/s41558-020-0762-8|issn=1758-678X}}</ref>: A study relating methane emissions estimates from airplanes and satellite imagery.


== Online Courses and Course Materials ==
== Online Courses and Course Materials ==
[https://www.nationalgeographic.org/projects/exploring-conservation/ National Geographic Free Conservation Course]


[https://www.coursera.org/learn/spatial-analysis UC Davis Geospatial and Environmental Analysis]
== Community ==

[https://www.coursera.org/learn/ecosystem-services University of Geneva's Introduction to Ecosystem Services]

== Conferences, Journals, and Professional Organizations ==


=== Major conferences ===
=== Major conferences ===


* [https://www.iucncongress2020.org/ IUCN World Congress] (every four years)
*'''EARTHVISION''': A workshop regularly held at computer vision conferences. Website [https://www.grss-ieee.org/earthvision2020/ here].
* [http://www.fao.org/about/meetings/cofo/en/ Committee on Forestry] (annual)
*'''Space and AI:''' A conference organized by the ESA-CLAIRE AI Special Interest Group on Space. Website [https://claire-ai.org/sig-space/?lang=fr here].
* [https://www.agu.org/fall-meeting AGU Fall Meeting] (annual)
* [https://naturalcapitalproject.stanford.edu/events/2019-natural-capital-symposium Natural Capital Symposium] (annual)
* [https://www.globallandscapesforum.org/ Global Landscapes Forum] (annual)


=== Major journals ===
=== Major journals ===

* [https://www.nature.com/ Nature] (Climate Change, Ecology and Evolution)
* [https://www.sciencemag.org/ Science]
* [https://www.journals.elsevier.com/remote-sensing-of-environment Remote Sensing of Environment]
* [https://www.mdpi.com/journal/remotesensing Remote Sensing]


=== Major societies and organizations ===
=== Major societies and organizations ===

* [http://www.fao.org/home/en/ The Food and Agriculture Organization of the United Nations] (FAO) is a specialized agency of the United Nations that leads international efforts to defeat hunger and improve nutrition and food security
* [https://www.thegef.org/ The Global Environment Facility] (GEF) was established on the eve of the 1992 Rio Earth Summit to help tackle our planet’s most pressing environmental problems.
* [https://www.nature.org/en-us/ The Nature Conservancy] is a non-profit environmental organisation that has over one million members, and has protected more than 119,000,000 acres (48,000,000 ha) of land and thousands of miles of rivers worldwide
* [https://www.iucn.org/ The International Union for Conservation of Nature] (IUCN) is an international organization working in the field of nature conservation and sustainable use of natural resources.
*[https://www.southpole.com/ SouthPole], develops and finance projects around the world to reduce carbon emissions and protect biodiversity
*[https://www.goldstandard.org/ GoldStandard], is a third-party certification agency that ensures projects that reduced carbon emissions featured the highest levels of environmental integrity and also contributed to sustainable development
*[https://www.worldwildlife.org/ WWF]
*[https://www.worldbank.org/ World Bank]
*[https://www.1t.org/ 1t.org]
*[http://www.mangrovealliance.org/ Global Mangrove Alliance]

=== Major initiatives ===

* [https://www.unenvironment.org/news-and-stories/press-release/new-un-decade-ecosystem-restoration-offers-unparalleled-opportunity UN Decade of Ecosystem Restoration]


== Libraries and Tools ==
== Libraries and Tools ==
Some packages for working with remote sensing data 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
*'''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 [https://github.com/sentinel-hub/eo-learn here].
*[https://www.globalforestwatch.org/ GlobalForestWatch], is a dynamic online forest monitoring and alert system that empowers people everywhere to better manage forests
*'''robosat''': A package maintained by mapbox, available [https://github.com/mapbox/robosat here].
*[https://www.globalmangrovewatch.org/ GlobalMangroveWatch], monitors to catalyse the action needed to protect and restore mangroves
*'''solaris''': A package from CosmiQ Works (SpaceNet Challenge), available [https://solaris.readthedocs.io/en/latest/ here].
*[https://trase.earth/ Trase.Earth] provides a dynamic supply chain visualisations for land change drivers and commodities
*[https://resourcetrade.earth/ Resourcetrade.Earth] features Chatham House’s extensive and authoritative database of international trade in natural resources, developed from United Nations data
*[http://www.openforis.org/tools/collect-earth.html 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
*[https://www.wri.org/our-work/project/forest-atlases/open-data-portals The Forest Atlases] are online platforms that help countries better manage their forest resources by combining government data with the latest forest monitoring technology
*[https://github.com/weecology/DeepForest DeepForest], is a python package for tree crown detection in airborne RGB imagery
*[https://github.com/ulaval-damas/tree-bark-classification BarkNet], is an open-source tree bark classification algorithm and dataset


== Data ==
== Data ==
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* [https://www.kaggle.com/c/planet-understanding-the-amazon-from-space Planet: Understanding the Amazon from space]
* [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]
* [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 ==
== References ==

Latest revision as of 20:03, 20 June 2022

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[edit | edit source]

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[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

  • 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[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

Tree datasets:

References[edit | edit source]

  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)
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