崎岖地形动植物栖息地生态环境遥感制图与应用

Habitat Mapping in Rugged Terrain using IKONOS Satellite Images

传统制图方法周期长、成本较高,影响了大面积精细动植物栖息地生态环境制图的生产。鉴此,本文采用IKONOS(VHR)图像对香港郊野公园崎岖地形的动植物栖息地生态环境制图进行了研究。由于香港植被的多样性,而且观察的动植物相互作用出现于结构的层面,所以对动植物栖息地的分类应以结构为基础而非以植物为基础。本文利用图像处理技术,运用决策树之多层次地物导向分割分类(MOOSC)方法绘制九种动植物栖息地类型图。MOOSC方法和其他现有的几种分类方法相比,其分类精度高、成本低。

Ecological mapping in the tropics is difficult due to the heterogeneity of the vegetation, the nature of the terrain which is often highly dissected, and general problem of determining ecological boundaries which may be indistinct, even to a field observer. There are no studies in the literature discussing the successful mapping of vegetation or habitats over large areas. In the last 20 years, two habitat surveys in the form of vegetation maps have been completed by Hong Kong government departments and private consultants, with inadequate accuracy and poor results. Since these previous projects used only medium spatial resolution sensors： Landsat and Satellite pour 1Observation de la Terre （SPOT） , it may be possible to produce more accurate ecological maps using the new generation of Very High Resolution （VHR） satellite sensor images.Traditionally, habitat mapping has used Aerial Photographic Interpretation （API）. However, 45 air photos are required to cover the study area, Shing Mun and Tai Mo Shan country parks in Hong Kong, compared with a single IKONOS scene. Additional advantages of IKONOS include spatial, spectral and temporal consistency. Therefore, if a suitable methodology for automatic habitat mapping can be developed, reduced costs and less processing time would be required. This study attempts to develop a methodology for detailed ecological mapping based on a suite of integrated image processing techniques, and with stated accuracy levels, for IKONOS images - ＂Multi-scale object-oriented segmentation with decision tree classification＂ （MOOSC）. The results show that 95% overall accuracy was achieved using API and 94% was achieved using MOOSC method when the results were referenced to GPS field data. These findings support the applicability and feasibility of MOOSC method, and it was only one third of the cost comparing with API.