Location within country: Tonle Sap watershed
The objectives of the project were: 1) To develop an appropriate methodology and identify the data/resource requirements for preparing the landuse and landcover maps, 2) To develop a landuse/landcover change map by using high-resolution satellite images, existing maps as well as other available information.
Thirty scenes of ALOS/AVNIR-2 from 2007 to 2009 and 8 scenes of Landsat-TM from 1990 to 1991 were used for the study in Tonle Sap watershed. The catchment area was delineated by performing hydrological analysis on 30m ASTER-GDEM data.
Location within country: Nha Trang Bay and the surrounding area
The objectives of this project were: 1) To detect the landcover change including the land and water parts of the study area, 2) To detect the changes in water turbidity and its relationship to the total suspended solids.
The study area, Nha Trang is a coastal city and the capital city of Khanh Hoa province in Vietnam. Landsat-TM image of 1989 and ALOS/AVNIR-2 of 2008 were selected for multi-temporal environmental change detection of the study area, especially in the land area. The images were masked out on the land and sea parts and analyzed for the changes taken place. In the land part, the classification and analysis of the landcover change were carried out.
Location within country: Mondul Seima district, Koh Kong province
The objectives of this project were: 1) To assess the feasibility of combining RS & GIS technique, as a tool for identifying and monitoring landuse/landcover both in terms of spatial and temporal resolutions, 2) To provide useful information and indicate the trend of changes in landuse/landcover to land management supervisor.
The study area, Mondul Seima district is located in Koh Kong province of Cambodia. The exploration of JERS and Landsat TM satellite data in this project was directed towards the detection of land use type at district level, both in terms of temporal and spatial resolutions. Use of remotely sensed data particularly JERS and Landsat TM data was considered a major tool for this study. Unsupervised classification method was used to extract landcover classes from the digital data sets.