Abstract
The work proposes a methodology for studying the productivity dynamics of forest ecosystems based on remote sensing data. The possibility of using the NDVI vegetation index to study the interannual variability in productivity of regional ecosystems is shown. The long-term productivity dynamics of oak forests of the Crimean Peninsula is analyzed. On the basis of the data series obtained, the periods characterized by peculiarities of the productivity dynamics of forest ecosystems are identified.
Highlights
The history of GIS-modeling of ecosystem functioning and dynamics is extremely extensive
NDVI (Normalized Difference Vegetation Index) is often used as an indicator of ecosystem productivity, which is one of the many indexes developed at present to determine the biological ecosystems productivity based on the calculation of multispectral space images channels ratio [1,2,3,4,5,6]
It should only be noted that in criticizing the practice of using this index the question is raised concerning the necessity of comparing data with test sites to determine the actual biomass value, since the index is relative
Summary
The history of GIS-modeling of ecosystem functioning and dynamics is extremely extensive. NDVI index among the whole variety of vegetation indexes is one of the basic ones and has a great practice in determination, well-established methodology of interpretation data obtained and is already built as automatic scripts into modern geoinformation software packages. It should only be noted that in criticizing the practice of using this index the question is raised concerning the necessity of comparing data with test sites to determine the actual biomass value, since the index is relative. In this study we are more interested in the productivity dynamics in relative values when climatic parameters change rather than actual biomass. It should be noted that for the territory of Crimea, including natural plant communities, there is wide field experience in determining the communities’ productivity, and the index interpretation is possible, but in our studies is not necessary
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