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Proceedings Paper

Spatial scaling of net primary productivity using subpixel landcover information
Author(s): X. F. Chen; Jing M. Chen; Wei M. Ju; L. L. Ren
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Paper Abstract

Gridding the land surface into coarse homogeneous pixels may cause important biases on ecosystem model estimations of carbon budget components at local, regional and global scales. These biases result from overlooking subpixel variability of land surface characteristics. Vegetation heterogeneity is an important factor introducing biases in regional ecological modeling, especially when the modeling is made on large grids. This study suggests a simple algorithm that uses subpixel information on the spatial variability of land cover type to correct net primary productivity (NPP) estimates, made at coarse spatial resolutions where the land surface is considered as homogeneous within each pixel. The algorithm operates in such a way that NPP obtained from calculations made at coarse spatial resolutions are multiplied by simple functions that attempt to reproduce the effects of subpixel variability of land cover type on NPP. Its application to a carbon-hydrology coupled model(BEPS-TerrainLab model) estimates made at a 1-km resolution over a watershed (named Baohe River Basin) located in the southwestern part of Qinling Mountains, Shaanxi Province, China, improved estimates of average NPP as well as its spatial variability.

Paper Details

Date Published: 3 November 2008
PDF: 9 pages
Proc. SPIE 7145, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Monitoring and Assessment of Natural Resources and Environments, 71450S (3 November 2008); doi: 10.1117/12.813005
Show Author Affiliations
X. F. Chen, Hohai Univ. (China)
Nanjing Univ. (China)
Jing M. Chen, Univ. of Toronto (Canada)
Wei M. Ju, Nanjing Univ. (China)
L. L. Ren, Hohai Univ. (China)


Published in SPIE Proceedings Vol. 7145:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Monitoring and Assessment of Natural Resources and Environments
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang; Yong Lao, Editor(s)

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