Please use this identifier to cite or link to this item:
|Title:||Integrating Multi-Sensor Remote Sensing Data for Land Use/Cover Mapping in a Tropical Mountainous Area in Northern Thailand|
Vu Duc, H.
|Citation:||Wang, Y.-C., Feng, C.-C., Vu Duc, H. (2012-08). Integrating Multi-Sensor Remote Sensing Data for Land Use/Cover Mapping in a Tropical Mountainous Area in Northern Thailand. Geographical Research 50 (3) : 320-331. ScholarBank@NUS Repository. https://doi.org/10.1111/j.1745-5871.2011.00732.x|
|Abstract:||Accurate mapping of land use/cover conditions provides essential information for managing natural resources and is critical for further examination of land use/cover change and its subsequent impacts on the environment. Remote sensing offers a means of acquiring land use/cover data in a timely manner, with optical remote sensing images commonly being used in land use/cover related studies. The persistent cloud cover during the rainy season in Southeast Asia, however, presents a challenge for using optical images in land use/cover mapping. Integrating multi-sensor images of different spectral domains is thus desirable because more information can be extracted to improve the mapping accuracy. The purpose of this study is to assess the potential of using multi-sensor data sets for land use/cover mapping in a tropical mountainous area in northern Thailand. Optical data from Landsat Thematic Mapper, radar images from Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (PALSAR), and topographical data were used, providing complementary information on land use/cover. Classification and accuracy assessment were conducted for 12 different combinations of the data sets. The results suggested that short crop mapping using multi-temporal Phased Array type L-band Synthetic Aperture Radar images offered insights into the distributions of crop and paddy fields. Because of the mountainous environment of the study area, combining topographic data of elevation and slope into the classification greatly reduced the confusion between different land use/cover types. Improvement of classification accuracy was evident especially in separating evergreen and deciduous forests from other vegetation types and discriminating urban village and the fallow field classes. © 2011 The Authors. Geographical Research © 2011 Institute of Australian Geographers.|
|Source Title:||Geographical Research|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Sep 21, 2018
WEB OF SCIENCETM
checked on Sep 11, 2018
checked on Jul 27, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.