A case study of spatial analysis for environmental monitoring

摘要

Spatial analysis is an effective technique for environmental monitoring. Since river systems, road networks and land use patterns are closely related, establishing the spatial distribution of these patterns is of fundamental importance as basic information for environmental monitoring. In recent years, development of road networks has been rapidly accelerated in developing countries and its impact to natural environment such as forests and river systems are strong. Currently, remote sensing data and GIS technique are improved and they have become a key tool for providing information for environmental monitoring.The objective of this study is to determine spatial distribution of forests, river systems and road networks in 1989 and 2002 at the lowland watershed of Vam Co Dong river basin which flows from Cambodia to Vietnam. Digital Elevation Model (DEM) has recently become a powerful tool for detecting river systems at various scales because DEM enables direct detection of river systems. However, DEM analysis was not effective for flat plains and was considered difficult to apply in the past In this research, river networks were successfully detected by pit-filling method with Shuttle Radar Topographic Mission (SRTM) DEM which is available with 90 m mesh for the whole world. Forests and road networks were delineated by using ASTER and LANDSAT. Observation of spatial distribution and forest cover changes between 1989 and 2002 indicate that natural forest decreased to 66%, while rubber plantation areas have increased twofold. Distances between natural forests or rubber plantation areas from river systems have not changed significantly. However, maximum distance of natural forest from road networks halved to 5 km in 2002. This drastic change may reflects rapid road networks development, while geographical locations of natural forest and rubber plantation areas are stable similar to the distance from river systems if catchment area is larger than 10km~2.