摘要
近年来,我国公路对自然生态环境的影响随着通车里程的增加而日益显著。在公路选线的过程中,全面分析拟建公路走廊带内景观破碎度的空间分异特征,可有效减少公路修建对生态环境的不利影响。本文利用遥感分类技术和GIS(地理信息系统)空间分析方法,以湖南长湘公路走廊带某段土地斑块和景观分类为基础,借助斑块密度指数、最大斑块指数和相似邻近比指数,在斑块类型水平和景观水平两个层面全面分析公路走廊带内自然林地景观、农业景观和城乡建设景观破碎度的空间分异特征。结果表明:长湘公路走廊带内景观破碎度高的地区主要分布在大型斑块边缘、平原地区以及公路沿线;自然林地景观和农业景观的破碎度较低,城乡建设景观的破碎度较高;城乡建设景观的破碎度与公路相关性最强,表现出顺应公路走向的条带状蔓延特征。该研究成果可为公路选线方案的制订和完善提供环境影响方面的参考。
Recently, impacts of road construction on the ecological environment are growingly significant as the traffic mileage increases.To reduce these adverse impacts, it is increasingly critical to fully recognize the spatial heterogeneity of the landscapes of a road corridor in the routes planning.Utilizing remote sensing technology and spatial analysis of geographic information system, this study firstly reclassified the land use/cover as natural forest landscape, agricultural landscape and urban-rural built up landscape in the Chang-Xiang road corridor in Hunan province.Then, indices including patch density (PD), largest patch index (LPI), and percentage of like adjacencies (PLADJ) were employed to assess the fragmentation of various landscapes and their associated spatial heterogeneity at patch and landscape levels.Results show that areas of high fragmentation in Chang-Xiang road corridor were mainly located in the edge of large patches and the plain areas, as well as alongside of the road.While the fragmentation degree of natural forest landscape and agricultural landscape were lower, it is lower for urban and rural built up landscape.The high fragmentation of urban and rural built up landscape in this case area were strongly correlated to the road distribution, demonstrating the strip spread characteristic along the road.These results could be helpful for the scheme programming in routes planning by providing the environmental impact assessment reference.
出处
《测绘与空间地理信息》
2017年第8期106-109,共4页
Geomatics & Spatial Information Technology
关键词
公路选线
走廊带
景观破碎度
空间异质性
景观指数
遥感影像
routes planning
corridor belt
landscape fragmentation
spatial heterogeneity
landscape index
remote sensing image