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Proceedings Paper

Dynamic supervising methods in the north part of Shaanxi province
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Paper Abstract

In order to have an objective understanding of the status that vegetation is resumed and the progress that the ecology environment construction project makes in Shaanxi province, we utilized the year of 1997 as a benchmark in our investigation and did the dynamic supervising for the variations in the north part of Shaanxi province under the support of 3S technology. The major task of the dynamic supervising had been completed by the software of ERDAS IMAGINE. Three dynamic supervising methods are used: (1) Difference value or ratio value of TM images from two different periods was calculated and the area of variation was obtained. (2) TM remote sensing images from the two different periods are merged, based on which the corresponding relationship between the zone where the light spectrum property changed abruptly and the variation of practical use of land was found. (3) Light spectrum curves for different ground objects in different areas were established on the basis of the classified result of 1997. According to those curves, the present classified diagram was made and the variation diagrams of the land using were obtained after comparing these two classified diagrams.

Paper Details

Date Published: 14 July 2003
PDF: 5 pages
Proc. SPIE 4890, Ecosystems Dynamics, Ecosystem-Society Interactions, and Remote Sensing Applications for Semi-Arid and Arid Land, (14 July 2003); doi: 10.1117/12.466811
Show Author Affiliations
Jing Zhuo, Shaanxi Remote Sensing Information Ctr. for Agriculture (China)
Anlin Liu, Shaanxi Remote Sensing Information Ctr. for Agriculture (China)
Dengke Li, Shaanxi Remote Sensing Information Ctr. for Agriculture (China)
Fengdong Deng, Shaanxi Remote Sensing Information Ctr. for Agriculture (China)
Jinghong Zhang, Shaanxi Remote Sensing Information Ctr. for Agriculture (China)


Published in SPIE Proceedings Vol. 4890:
Ecosystems Dynamics, Ecosystem-Society Interactions, and Remote Sensing Applications for Semi-Arid and Arid Land
Xiaoling Pan; Wei Gao; Michael H. Glantz; Yoshiaki Honda, Editor(s)

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