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author:

Jiang, H. (Jiang, H..) [1] | Bo, W. (Bo, W..) [2] | Wang, X.Q. (Wang, X.Q..) [3]

Indexed by:

Scopus

Abstract:

Numerous vegetation indexes have been developed for assessing vegetation characteristics and are still of increasing importance in environmental application of remote sensing. However, few of them are targeted to reduce topographic effects in rugged area. A novel topography-adjusted vegetation index (TAVI) is presented in this paper by combination of near-infrared and red wavebands data with topography adjusting coefficient without digital elevation model (DEM) data. The validity of TAVI is illustrated with a case study in Changting county, a mountainous area in southeast of China. The experimental results show that the proposed algorithm can effectively eliminate topographic effects caused by rugged terrain. The slope of linear regression equation of TAVI - the solar incidence cosine, and the correlation coefficient between them are only around 0.01 respectively. It also indicates that this topography-adjusted vegetation index could be applied in other mountainous areas. © 2010 IEEE.

Keyword:

Rugged area; Topographic effect; Topography-adjusted vegetation index (TAVI)

Community:

  • [ 1 ] [Jiang, H.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350002, China
  • [ 2 ] [Jiang, H.]College of Public Administration, Fuzhou University, Fuzhou 350108, China
  • [ 3 ] [Bo, W.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350002, China
  • [ 4 ] [Wang, X.Q.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350002, China

Reprint 's Address:

  • [Jiang, H.]Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350002, China

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Source :

International Geoscience and Remote Sensing Symposium (IGARSS)

Year: 2010

Page: 2075-2078

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 6

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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