DocumentCode :
1886117
Title :
The integration of spectral analyses of Landsat ETM+ with the DEM data for mapping mangrove forests
Author :
Alsaaideh, Bayan ; Al-Hanbali, Ahmad ; Tateishi, Ryutaro ; Thanh, Hoan Nguyen
Author_Institution :
Center for Environ. Remote Sensing (CEReS), Chiba Univ., Chiba, Japan
fYear :
2011
fDate :
24-29 July 2011
Firstpage :
1914
Lastpage :
1917
Abstract :
This study aims to develop an efficient method to extract mangrove forests at a regional scale using remote sensing data and Geographic Information Systems (GIS) technique. A regional mangrove forests mapping method was developed based on the spectral characteristics and the topographic existence condition of mangrove forests. Landsat Enhanced Thematic Mapper plus (ETM+) and digital elevation model (DEM) were used to enhance the discrimination between mangrove forests and other different kinds of forests and vegetations to achieve the purpose of this study in the southern part of Japan. The maximum likelihood supervised classification technique was applied to map mangrove forests. The resultant mangrove forests map were further validated by using aerial photographs, topographic maps and other local vegetation maps. This study revealed that mangrove forests in the study area could be accurately mapped from integration of spectral data derived from Landsat ETM+ together with topographic information.
Keywords :
data assimilation; digital elevation models; geographic information systems; geophysical signal processing; maximum likelihood estimation; signal classification; topography (Earth); vegetation mapping; DEM data; GIS; Japan; Landsat ETM+ data; Landsat Enhanced Thematic Mapper plus; digital elevation model; geographic information systems; mangrove forest spectral characteristics; mangrove forest topographic existence condition; maximum likelihood supervised classification technique; regional mangrove forest mapping method; remote sensing data; spectral analysis integration; Accuracy; Earth; Geographic Information Systems; Indexes; Remote sensing; Satellites; Vegetation mapping; DEM; Mangrove forests; band ratio; vegetation indices;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International
Conference_Location :
Vancouver, BC
ISSN :
2153-6996
Print_ISBN :
978-1-4577-1003-2
Type :
conf
DOI :
10.1109/IGARSS.2011.6049499
Filename :
6049499
Link To Document :
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