Difference between revisions of "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 [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.''[[File:Agriculture.png|thumb|A schematic of the ways that machine learning can support carbon negative agriculture, forestry, and land use.]]
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
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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.
 
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.
 
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>.
 
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.
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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 ==
  +
'''Remote sensing of emissions'''
   
  +
'''Monitoring peatlands'''
  +
  +
'''Managing forests'''
  +
  +
* Estimating carbon stock
  +
* Automating afforestation
  +
* Managing forest fires
  +
* Reducing deforestation
  +
  +
<br />
 
== Background Readings ==
 
== Background Readings ==
   
*[https://www.nature.com/articles/s41598-019-54386-6 ''Monitoring tropical forest carbon stocks and emissions using Planet satellite data'']
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*'''Monitoring tropical forest carbon stocks and emissions using Planet satellite data'''
*''[https://link.springer.com/article/10.1007/s11069-017-3106-x Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices]''
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*'''Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices'''
*''[https://www.nature.com/articles/s41558-020-0762-8 Remote Sensing Northern Lake Methane Ebullition]''
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*'''Remote Sensing Northern Lake Methane Ebullition'''
   
 
== Online Courses and Course Materials ==
 
== Online Courses and Course Materials ==
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== Community ==
 
== Community ==
   
=== Journals and conferences ===
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=== Major conferences ===
   
*[https://www.grss-ieee.org/earthvision2020/ EARTHVISION] is regularly held at computer vision conferences.
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*'''EARTHVISION''': A workshop regularly held at computer vision conferences.
*[https://groups.google.com/forum/#!category-topic/ml-news/b56zx6rIw2Q Space and AI] is organized by the ESA-CLAIRE AI Special Interest Group on Space.
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*'''Space and AI:''' A conference organized by the ESA-CLAIRE AI Special Interest Group on Space.
   
=== Societies and organizations ===
+
=== Major journals ===
   
=== Past and upcoming events ===
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=== Major societies and organizations ===
   
 
== Libraries and Tools ==
 
== Libraries and Tools ==
 
Some packages for working with remote sensing data are,
 
Some packages for working with remote sensing data are,
   
*[https://medium.com/sentinel-hub/introducing-eo-learn-ab37f2869f5c 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
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*'''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
*[https://github.com/mapbox/robosat robosat]: A package mapbox.
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*'''robosat''': A package mapbox.
*[https://solaris.readthedocs.io/en/latest/ solaris]: A package from CosmiQ Works (SpaceNet Challenge).
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*'''solaris''': A package from CosmiQ Works (SpaceNet Challenge).
   
 
== Data ==
 
== Data ==

Revision as of 03:11, 24 September 2020

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

Remote sensing of emissions

Monitoring peatlands

Managing forests

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


Background Readings

  • Monitoring tropical forest carbon stocks and emissions using Planet satellite data
  • Characterizing agricultural drought in the Karamoja subregion of Uganda with meteorological and satellite-based indices
  • Remote Sensing Northern Lake Methane Ebullition

Online Courses and Course Materials

Community

Major conferences

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

Major journals

Major societies and organizations

Libraries and Tools

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
  • robosat: A package mapbox.
  • solaris: A package from CosmiQ Works (SpaceNet Challenge).

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. 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. Unknown parameter |coeditors= ignored (help); line feed character in |title= at position 98 (help)
  3. "Are the impacts of land use on warming underestimated in climate policy?". 2017. Unknown parameter |coauthors= ignored (|author= suggested) (help); 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.