基于混合光谱分解的城市不透水面分布估算

Urban Impervious Surface Distribution Estimation by Spectral Mixture Analysis

城市化的一个重要表现就是不透水面分布比率的上升,城市内部不透水面分布是城市生态环境的一个重要指标。对于规模较大的大城市,采用高性价比的中等分辨率影像,获取不透水面的分布,是当前国际研究的一个热点。本研究利用Landsat 7的ETM+影像,在线性光谱分解的技术上,提取了上海市的不透水面分布并对其空间特征进行了分析。研究揭示,ETM+影像对于城市尺度的信息提取,其成本是较低的;对于城市地域来说,利用植被、高反照度、低反照度和裸露的土壤四种最终光谱端元的线性组合,可以较好地模拟ETM+波谱特征,而除了水面以外的高反照度、低反照度两种最终光谱端元,可以较好地表达城市不透水表面信息。结果显示,利用中等分辨率影像对上海中心城区不透水面分布提取的精度还是令人满意的,总体上,上海市不透水面分布比率较高,不透水面分布的空间差异进一步揭示了城市土地覆被空间结构以及城市空间扩展的差异性。

In urbanization process, greater consideration of the manner in which rural lands are developed to urban lands will become progressively more important. Removal of rural land cover types such as soil, water, and vegetation and their replacement with common urban impervious surface materials such as asphalt, concrete, and metal have significant environmental implications. Impervious surfaces are anthropogenic features through which water cannot infiltrate into the soil, existed in roads, driveways, sidewalks, parking lots, rooftops, and so on. To estimate urban impervious surface distribution, a major component of the vegetation- impervious surface-soil（V-I-S） model, is important in monitoring urban eco-environment, such as reduction in evapotranspiration, promotion of more rapid surface run-off, increased storage and transfer of sensible heat, and reduction of air and water quality. The conceptual V-I-S model may be implemented by using the technique of linear spectral mixture analysis （ LSMA）, which decomposes the spectral reflectance of a pixel into different proportions. LSMA is regarded as a physically-based image processing tool that supports repeatable and accurate extraction of quantitative subpixel information. In this paper, impervious surface distribution, together with vegetation and soil cover, is estimated through a constrained linear spectral mixture model using Landsat ETM ＋ data within the metropolitan area of Shanghai city in China. Four endmembers, low albedo, high albedo, vegetion, and soil are selected to model complicated urban land cover. Impervious surface fraction is obtained by adding low and high albedo endmembers fraction. Estimation accuracy is assessed using root mean square （RMS） error and color aerial photography. The overall root mean square error is 0.71%. Results indicate that impervious surface distribution can be derived from remotely sensed imagery with promising accuracy. Then the spatial pattern of impervious surface fraction in central area of Shanghai is analyzed. The spatial pattern of impervious surface discloses urban framework and the characters of urban sprawling.