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Proceedings Paper

Landscape heterogeneity and net primary productivity (NPP) of mountain-oasis-desert ecosystems in western China
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Paper Abstract

This study was conducted to develop an appropriate assessment technique to define impact of mountain-desert-oasis ecosystem on net primary productivity (NPP) in northern foothills of Tianshan Mountains. Geographic Information System (GIS) was used to estimate land use/land cover of the mountain, desert and oasis zones. An ecological process model was used to estimate NPP by using data entirely derived from satellite. The results show that landscape heterogeneity was important factor to affect NPP values in mountain-desert-oasis ecosystem. Simulated results indicated a total annual NPP of 1.5081×1014 g C for selected transect in 2002. There was 32.67% of total NPP which came from oasis areas, 28.16% from alpine meadows areas, 9.15% from forests area. Mean NPP values over the selected transect was 150.29 g C m-2 year-1 in 2002. However, NPP values varied greatly with different geography and season.

Paper Details

Date Published: 22 December 2003
PDF: 11 pages
Proc. SPIE 5153, Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture, (22 December 2003); doi: 10.1117/12.524183
Show Author Affiliations
Jie Zhang, Xinjiang Univ. (China)
International Ctr. for Desert Affairs (China)
Nanjing Univ. (China)
Xiaoling Pan, International Ctr. for Desert Affairs (China)
Xinjiang Univ. (China)
Zhiqiang Gao, Institute of Geographical Sciences and Natural Resources Research (China)
International Ctr. for Desert Affairs (China)
Wei Gao, Colorado State Univ. (United States)
International Ctr. for Desert Affairs (China)
Suling Zhao, RESOURCE21, LLC (United States)
International Ctr. for Desert Affairs (China)
Qingdong Shi, International Ctr. for Desert Affairs (China)
Xinjiang Univ. (China)
Guanghui Lu, International Ctr. for Desert Affairs (China)
Xinjiang Univ. (China)


Published in SPIE Proceedings Vol. 5153:
Ecosystems' Dynamics, Agricultural Remote Sensing and Modeling, and Site-Specific Agriculture
Wei Gao; David R. Shaw, Editor(s)

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