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Proceedings Paper

Mapping grass communities based on multi-temporal Landsat TM imagery and environmental variables
Author(s): Yuandi Zeng; Yanfang Liu; Yaolin Liu; Jan de Leeuw
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Paper Abstract

Information on the spatial distribution of grass communities in wetland is increasingly recognized as important for effective wetland management and biological conservation. Remote sensing techniques has been proved to be an effective alternative to intensive and costly ground surveys for mapping grass community. However, the mapping accuracy of grass communities in wetland is still not preferable. The aim of this paper is to develop an effective method to map grass communities in Poyang Lake Natural Reserve. Through statistic analysis, elevation is selected as an environmental variable for its high relationship with the distribution of grass communities; NDVI stacked from images of different months was used to generate Carex community map; the image in October was used to discriminate Miscanthus and Cynodon communities. Classifications were firstly performed with maximum likelihood classifier using single date satellite image with and without elevation; then layered classifications were performed using multi-temporal satellite imagery and elevation with maximum likelihood classifier, decision tree and artificial neural network separately. The results show that environmental variables can improve the mapping accuracy; and the classification with multitemporal imagery and elevation is significantly better than that with single date image and elevation (p=0.001). Besides, maximum likelihood (a=92.71%, k=0.90) and artificial neural network (a=94.79%, k=0.93) perform significantly better than decision tree (a=86.46%, k=0.83).

Paper Details

Date Published: 26 July 2007
PDF: 12 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67521K (26 July 2007); doi: 10.1117/12.760497
Show Author Affiliations
Yuandi Zeng, Wuhan Univ. (China)
Yanfang Liu, Wuhan Univ. (China)
Yaolin Liu, Wuhan Univ. (China)
Jan de Leeuw, International Institute for Geo-Information Science and Earth Observation (Netherlands)

Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information

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