Paper
12 December 2006 Changing landscapes: monitoring ecologically sensitive ecosystems in a dynamic semi-arid landscape using satellite imagery: a case study in Ejin Oasis, western China
Meng-Lung Lin, Yu Cao, Yi-Huang Tao, Jyh-yi Shih, Guo-Jing Yan, Yung-Tan Lee, Du-Ning Xiao, Shin Wang, Hsien-Hao Chiu
Author Affiliations +
Abstract
Land degradation has become an important issue in western China recently. Oasis ecosystem is sensitive to environmental disturbances, such as abnormal /extreme events of precipitations, water supply from upper watersheds, fluctuations of temperatures, etc. Satellite remote sensing of terrestrial ecosystems provides temporal dynamics and spatial distributions of landscape green covers over large areas. Seasonal green cover data are normally important in assessing landscape health (ex. desertification, rate of urban sprawl, natural disturbances) in arid and semi-arid regions. In this study, green cover data is derived from vegetation indices retrieved from MODIS sensors onboard Terra. The satellite images during the period April 2000 to December 2005 are analyzed to quantify the spatial distribution and temporal changes of Ejin Oasis. The results will help improving monitoring techniques to evaluate land degradation and to estimate the newest tendency of landscape green cover dynamics in the Ejin Oasis.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Meng-Lung Lin, Yu Cao, Yi-Huang Tao, Jyh-yi Shih, Guo-Jing Yan, Yung-Tan Lee, Du-Ning Xiao, Shin Wang, and Hsien-Hao Chiu "Changing landscapes: monitoring ecologically sensitive ecosystems in a dynamic semi-arid landscape using satellite imagery: a case study in Ejin Oasis, western China", Proc. SPIE 6411, Agriculture and Hydrology Applications of Remote Sensing, 641122 (12 December 2006); https://doi.org/10.1117/12.693774
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Cited by 4 scholarly publications.
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KEYWORDS
Vegetation

Ecosystems

MODIS

Satellites

Earth observing sensors

Satellite imaging

Remote sensing

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