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Forestry and Other Land Use: Difference between revisions
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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.
== Data ==▼
Satellite imagery are often useful for monitoring land use. Some widely accessed resources include,▼
== Machine Learning Application Areas ==
* [https://developers.google.com/earth-engine/datasets/catalog/landsat/ Landsat]▼
* [https://developers.google.com/earth-engine/datasets/catalog/sentinel/ Sentinel]▼
* [https://developers.google.com/earth-engine/datasets/catalog/ Earth Engine Data Catalog]▼
* [https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-methane?tab=overview Methane detection from satellite]▼
Forestry related data have also been the focus of machine learning competitions, including▼
*
* [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]▼
*
==
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▼
* [https://github.com/mapbox/robosat robosat]: A package mapbox.▼
* [https://solaris.readthedocs.io/en/latest/ solaris]: A package from CosmiQ Works (SpaceNet Challenge). ▼
▲== Recommended Readings ==
▲* [https://www.nature.com/articles/s41598-019-54386-6 ''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]''
▲* ''[https://www.nature.com/articles/s41558-020-0762-8 Remote Sensing Northern Lake Methane Ebullition]''
== Community ==
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=== Journals and conferences ===
*
*
=== Societies and organizations ===
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=== Past and upcoming events ===
==
▲Some packages for working with remote sensing data are,
▲*
▲*
▲== Data ==
▲Satellite imagery are often useful for monitoring land use. Some widely accessed resources include,
▲* [https://developers.google.com/earth-engine/datasets/catalog/landsat/ Landsat]
▲* [https://developers.google.com/earth-engine/datasets/catalog/sentinel/ Sentinel]
▲* [https://developers.google.com/earth-engine/datasets/catalog/ Earth Engine Data Catalog]
▲* [https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-methane?tab=overview Methane detection from satellite]
▲Forestry related data have also been the focus of machine learning competitions, including
▲* [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]
== References ==
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