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PubMed · 12924111

[Ecosystem productivity process model for landscape based on remote sensing and surface data].

Abstract

In this paper, the a ecosystem productivity process model for landscape (EPPML) reflecting carbon and water cycles of system was described and discussed. In EPPML, leaf area index (LAI) that greatly influenced vegetation productivity was received from remote sensing images, the spatial distribution and temporal dynamics of net primary productivity (NPP) and evapotranspiration were simulated. Geographical information system (GIS) was used to process, analyze, and display spatial data. Thus, the studies on physiological ecology could be extended, and converted from small scale to larger scale. Using EPPML, the vegetation productivity in Changbai Mountain Natural Reserve in 1995 was simulated. EPPML could well and truly simulate the NPP of main vegetations in the natural reserve. NPP was estimated to be 0.680 kg Cm.-2.yr.-1, mostly ranging from 0.105 to 1.241 kg C.m-2.yr.-1, accounting for 82.1%. The highest NPP (1.084 kg C.m-2.yr-1) appeared in mixed broad-leaved and Korean pine forests. The total NPP was estimated to be 1.332 x 10(6) t C.yr-1. The highest total NPP appeared in mixed broad-leaved and Korean pine forests and spruce-fir forest, which were 0.540 x 10(6) t C.yr.-1 and 0.428 x 10(6) t C.yr.-1, respectively. The seasonal variation of NPP appeared obvious single peak with peak value in July (6.13 g C.m-2.d-1). The most NPP accumulated in the summer, which was 0.465 kg C.m-2, secondly in the spring, and least in the winter.

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BibTeXRIS

Na Zhang, Guirui Yu, Shidong Zhao, Zhenliang Yu. 2003. [Ecosystem productivity process model for landscape based on remote sensing and surface data].. https://pubmed.ncbi.nlm.nih.gov/12924111/

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