利用碳图评价KHMAO-Yugra地区碳储量的方法

Arsenty I. Bredihin
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摘要

汉特-曼西自治区有大片的森林领土。森林植被,像任何植被一样,迟早会自然死亡,其结果是二氧化碳从有机物中释放到大气中。这一事实导致了温室效应的加剧和全球变暖的加剧。为了防止全球气温上升,有必要以植物生物量的形式估计碳储量,因为汉特-曼西自治区(KhMAO-Yugra) 90%以上的领土被森林覆盖。评估植物生物量的方法之一是利用地球遥感(遥感)和机器学习方法创建所谓的碳图。本文概述了旨在创建碳地图的遥感和机器学习领域的现有解决方案。在此综述的基础上,提出了一个研究计划,该计划将使我们能够开发一种方法,使我们能够以给定的精度获得KhMAO的数字碳图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An approach to the assessment of carbon reservesin KHMAO-Yugra using carbon maps
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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