Paper
14 July 2003 Ningxia land cover change and its driving factors during past decade
Yuan Qi, Yimou Wang, Jianhua Wang
Author Affiliations +
Proceedings Volume 4890, Ecosystems Dynamics, Ecosystem-Society Interactions, and Remote Sensing Applications for Semi-Arid and Arid Land; (2003) https://doi.org/10.1117/12.465519
Event: Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 2002, Hangzhou, China
Abstract
Ningxia Hui Autonomous Region locates in north west of China. Recent 10 years, the economic and social level of Ningxia improved much. Therefore, land cover influenced by the natural and human activities changed greatly. Remote sensing united GIS is an effective method to observing and overseeing the long-term and insignificant change of the environment or the ecosystem. TM images are the essential information sources of land-cover. In semi-arid and arid, especially in the transition belt of the physical geography, the method would be more useful to discover the change mechanism of land-cover. During the 10 years, farmland had great change, and about 86% was compensated by grassland. Rapidly increased population is the most important driver in the land cover change. So construction area also increased quickly. Comparing land cover change with socioeconomic variables, we found that the land cover changes with the little human influence were impacted by the climate alternation. Also climate alternation is another important driver. Among main land cover types, the changes are consistent with the climate change.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuan Qi, Yimou Wang, and Jianhua Wang "Ningxia land cover change and its driving factors during past decade", Proc. SPIE 4890, Ecosystems Dynamics, Ecosystem-Society Interactions, and Remote Sensing Applications for Semi-Arid and Arid Land, (14 July 2003); https://doi.org/10.1117/12.465519
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KEYWORDS
Climatology

Climate change

Vegetation

Remote sensing

Ecosystems

Agriculture

Data modeling

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