Land-use and land-cover sceneries in China: an application of Dinamica EGO model

Wei Yi; Zhiqiang Gao; Zhihua Li; Maosi Chen

doi:10.1117/12.927782

24 October 2012 Land-use and land-cover sceneries in China: an application of Dinamica EGO model

Wei Yi, Zhiqiang Gao, Zhihua Li, Maosi Chen

Author Affiliations +

Proceedings Volume 8513, Remote Sensing and Modeling of Ecosystems for Sustainability IX; 85130I (2012) https://doi.org/10.1117/12.927782
Event: SPIE Optical Engineering + Applications, 2012, San Diego, California, United States

Abstract

Land is an indispensable natural resource for human, without which we cannot survive and develop. Land-use change, influenced by both natural environment and human activity, has a close relationship with food security, resource utilization, biodiversity and climate change. In order to understand the process and driving mechanism of land-use change, dynamic models were developed in these years, among which Dinamica EGO is a practical one and has been widely used in the world. In this paper, we aim to use Dinamica EGO to simulate the land-use of China in 2005 with data extracted from SPOT VGT NDVI. The real land-use map was compared with the simulation result so as to verify the feasibility of Dinamica EGO. Then we supposed three sceneries under which we could analyze the land-use change of China in 2020. Results indicated that: on the basis of no extreme natural disasters or exceptional policy fluctuation, the grassland area would reduce by 22.21 million hectares averagely. However forest would increase by 19.81 billion hectares on average. Water and unused land would probably remain stable as there was little change in three sceneries. Farmland areas showed a good agreement under these sceneries whereas the greatest difference in land-use area estimations lies in built-up with an uncertainty accounting for 1.67%.

Citation Download Citation

Wei Yi, Zhiqiang Gao, Zhihua Li, and Maosi Chen "Land-use and land-cover sceneries in China: an application of Dinamica EGO model", Proc. SPIE 8513, Remote Sensing and Modeling of Ecosystems for Sustainability IX, 85130I (24 October 2012); https://doi.org/10.1117/12.927782

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