Perera, Kithsiri and Herath, Srikantha and Apan, Armando and Samarakoon, Lal (2010) Application of Modis 250m images without in situ observations for mapping Mekong River Basin land cover. In: ISPRS Technical Commission VIII Symposium (ISPRS 2010): Networking the World with Remote Sensing, 9-12 Aug 2010, Kyoto, Japan.
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Mekong River runs from Hengduan Mountains in central-west China to Vietnam covering 805,604 sq km of land by its basin. The Lower Mekong Basin (LMB), the region mapped in this study, covers nearly 3/4 of the entire basin. About 90% of the population and agricultural activities of the Mekong River basin is located in this fertile LMB which faces disastrous floods almost annually. Mapping LMB at moderate resolution gives number of advantages for studies of flood mitigation and land utilization. However, compiling a cloud free mosaic and collecting ground truth data for training samples and map validation make map production process a challenging task. This study utilized MODIS 250m image data of the region obtained in 2005 February. Dry weather in Jan-Apr makes the sky relatively free of clouds and 2005 February also had fewer disturbances coming from smoke of biomass burning. The methodology of the study substantially relied on high resolution images in Google Earth for collection of training sample for supervised classification and accuracy assessment. Arc GIS generated KMZ file of unclassified and classified maps used to overlay image and map on Google Earth for identifying training site and field information extraction for accuracy assessment. Also ground information collected by a different research projects in 2008 were combined with information gathers from Google Earth images. The classified map showed 29.2% of the LMB under forest, 36.5% under Scrubland, when combined its Highland and Lowland subcategories. Three subcategories of paddy cultivated area covered 27.9% of LMB. Accuracy assessment conducted with randomly selected 200 points against high resolution images gave an overall accuracy of 80.7% in major land cover classes. According to the 250m resolution, urban features have not clearly separated though large urban areas like Phnom Penh and Can Tao have accurately classified. The methodology of this study produced a noteworthy success in classifying land cover of large areas like LMB, without expensive data sources and difficult and costly field investigations.
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|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
|Item Status:||Live Archive|
|Additional Information:||This publication is copyright. It may be reproduced in whole or in part for the purposes of study, research, or review, but is subject to the inclusion of an acknowledgment of the source. Since Volume XXXII-3/W14, 1999, the Archives are open accees publications, they are published under the Creative Common Attribution 3.0 License, see publications.copernicus.org/for_authors/license_and_copyright.html for details.|
|Depositing User:||Dr Kithsiri Perera|
|Faculty / Department / School:||Historic - Faculty of Engineering and Surveying - Department of Surveying and Land Information|
|Date Deposited:||17 Feb 2011 12:05|
|Last Modified:||01 Dec 2014 02:45|
|Uncontrolled Keywords:||MODIS; Lower Mekong River Basin; land cover; maximum likelihood classification; Mekong Delta|
|Fields of Research :||09 Engineering > 0909 Geomatic Engineering > 090901 Cartography
09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing
05 Environmental Sciences > 0502 Environmental Science and Management > 050205 Environmental Management
|Socio-Economic Objective:||D Environment > 96 Environment > 9608 Flora, Fauna and Biodiversity > 960899 Flora, Fauna and Biodiversity of Environments not elsewhere classified
D Environment > 96 Environment > 9606 Environmental and Natural Resource Evaluation > 960699 Environmental and Natural Resource Evaluation not elsewhere classified
D Environment > 96 Environment > 9609 Land and Water Management > 960999 Land and Water Management of Environments not elsewhere classified
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