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An approach to the assessment of carbon reservesin KHMAO-Yugra using carbon maps
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Khanty-Mansi Autonomous Okrug-Yugra has a large area of forest territories. And forest vegetation, like any vegetation, naturally dies sooner or later, as a result of which carbon dioxide is released into the atmosphere from organic matter. This fact leads to an increase in the greenhouse effect and an increase in global warming.
In order to prevent an increase in global temperature, it is necessary to estimate the carbon stock in the form of the amount of plant biomass, since more than 90% of the territory of the Khanty-Mansi Autonomous Okrug-Yugra (KhMAO-Yugra) is covered with forests.
One of the ways to assess plant biomass is to create so-called carbon maps using remote sensing of the Earth (remote sensing) and machine learning methods.
This paper provides an overview of existing solutions in the field of remote sensing and machine learning aimed at creating carbon maps. Based on this review, a research program has been proposed that will allow us to develop an approach that allows us to obtain a digital carbon map of the KhMAO with a given accuracy.
Title: An approach to the assessment of carbon reservesin KHMAO-Yugra using carbon maps
Description:
Khanty-Mansi Autonomous Okrug-Yugra has a large area of forest territories.
And forest vegetation, like any vegetation, naturally dies sooner or later, as a result of which carbon dioxide is released into the atmosphere from organic matter.
This fact leads to an increase in the greenhouse effect and an increase in global warming.
In order to prevent an increase in global temperature, it is necessary to estimate the carbon stock in the form of the amount of plant biomass, since more than 90% of the territory of the Khanty-Mansi Autonomous Okrug-Yugra (KhMAO-Yugra) is covered with forests.
One of the ways to assess plant biomass is to create so-called carbon maps using remote sensing of the Earth (remote sensing) and machine learning methods.
This paper provides an overview of existing solutions in the field of remote sensing and machine learning aimed at creating carbon maps.
Based on this review, a research program has been proposed that will allow us to develop an approach that allows us to obtain a digital carbon map of the KhMAO with a given accuracy.
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