海南省生态环境综合评价制图方法

The Cartographic Method of Synthetic Assessment for Eco-environment in Hainan

介绍以景观生态学理论思想为指导 ,以多因素综合评价为主要方法 ,以遥感与地理信息系统为手段 ,编绘海南生态环境评价图的方法。在生态环境制图的基础上 ,对海南省生态环境质量作了评价分析。

RS is at present the primary and effective means to measure ecological situation. GIS provides an efficient tool to analyze the status and the changes of eco environment. In this study, we introduce how to draw up the assessment map eco environment based on RS and GIS with the theory of eco landscape as a guidance, which takes the integrated assessment of the multi factors as a major method. Firstly, we overlap the topography, soil and vegetation types based on the vegetation type spot and divide it into different eco landscape types which are considered as the basic unit of cartography for assessing eco environment. Secondly, we put the TM Landsat data and others such as DEM into ERDAS to classify them with maximum likelihood supervised classification, and verify the supervised classification results by visual interpretation to extract the thematic information. Then, the topographic map, soil map and vegetation types map are made with ARC/INFO. Finally, we build the assessment index system of eco landscape factors and have the index value processed normalization to calculate the single factor assessment index and make the single factor assessment maps. Then, we overlap the single factor assessment maps to get the synthetic assessment map by ARC/INFO spatial overlapping analysis. During the overlapping, the single factor assessment indexes are processed with weight function to get the synthetic assessment index, then the eco landscape type units are assigned. According to the method mentioned above, the eco landscape type units are evaluated one by one. The eco environment assessment map of Hainan Island made by the method mentioned above shows that the eco environmental quality gradually declines from the central to the peripheral. And there are four counties in the ideal state, another four ones in the state of kilter, and the remaining in the relatively poor state. We can dynamically inspect and effectively predict the eco environmental change with the dynamic serial maps of the regional eco environment made by the method mentioned above.