Evaluation of the structure and composition of forests in Moscow region based on field and remote sensing data

Q1 Social Sciences
T. V. Chernen’kova, Т.В. Черненькова, M. Puzachenko, М Ю Пузаченко, N. Belyaeva, Н.Г. Беляева, O. Morozova, Ольга Владимировна Морозова
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引用次数: 1

Abstract

The sequence and content of the main stages of determining the indicators of the structure and composition of forest communities based on satellite imagery of the Landsat system are described. It is shown that the application of quantitative processing methods for the interpolation of point data from field research, in particular canonical discriminant analysis, allows obtaining characteristics of the vegetation cover and investigating the factors of its biodiversity formation. The presented methods and results of the assessment of various parameters of the state of forests, their structure and typological composition can be integrated into the international network of the National Forest Inventory. Despite the difference in methodological approaches, there is a principal possibility of harmonizing the data obtained with the data of the Global Earth Observation System of Systems. For the test territory in the central partof the Russian plain (western sector of the Moscow oblast), the results of a joint analysis of field research data, remote sensing dataand a digital elevation model are presented. A series of maps of the medium scale characterizing the spatial structure and composition of the forest cover of the study area was obtained.
基于野外和遥感数据的莫斯科地区森林结构和组成评价
介绍了基于Landsat系统卫星图像确定森林群落结构和组成指标的主要阶段的顺序和内容。结果表明,应用定量处理方法对野外调查点数据进行插值,特别是典型判别分析,可以获得植被覆盖特征,研究其生物多样性形成的影响因素。所提出的评估森林状况、结构和类型组成的各种参数的方法和结果可以纳入国家森林清查的国际网络。尽管方法方法不同,但主要有可能使所获得的数据与全球综合地球观测系统的数据协调一致。对于位于俄罗斯平原中部(莫斯科州西部)的试验区,介绍了实地研究数据、遥感数据和数字高程模型的联合分析结果。获得了一系列表征研究区森林覆盖空间结构和组成的中比例尺图。
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来源期刊
Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya
Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya Social Sciences-Geography, Planning and Development
CiteScore
1.10
自引率
0.00%
发文量
11
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