Integration of NOAA-AVHRR data and geographical factors for China vegetation classification

Author

Gao, Yanchun ; Jiang, Xiaoguang ; Dang, Anrong ; Niu, Zheng ; Wang, Changyao

Author_Institution

Inst. of Geogr. Sci. & Natural Resources Res., Acad. Sinica, Beijing, China

Volume

4

fYear

2001

fDate

2001

Firstpage

1933

Abstract

Integration of remotely-sensed and non-remotely-sensed information becomes an effective approach for vegetation classification. In this paper, vegetation in China is comprehensively classified by integration of NOAA-AVHRR data and geographical factors, such as temperature, precipitation and DEM. The procedure of comprehensive vegetation classification is chiefly composed of four steps: feature selection of NOAA data, creation of geographic information image, data integration and image comprehensive classification. Precision test and error analysis indicate a higher precision of the classification result

Keywords

image classification; maximum likelihood estimation; vegetation mapping; China vegetation classification; DEM; NOAA-AVHRR data; data integration; error analysis; feature selection; geographic information image; geographical factors; image comprehensive classification; precipitation; remote-sensed information; temperature; test analysis; Content addressable storage; Data mining; Geology; Humans; Hydrology; Information analysis; Principal component analysis; Remote sensing; Surfaces; Vegetation mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International

Conference_Location

Sydney, NSW

Print_ISBN

0-7803-7031-7

Type

conf

DOI

10.1109/IGARSS.2001.977120

Filename

977120