Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques
Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques
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DOI：
10.1080/01431160110114998
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发表时间：
2002-10-10
影响因子：
3.4
通讯作者：
Shyy, PT
中科院分区：
文献类型：
--
作者：
Phinn, S;Stanford, M;Shyy, PT
目前，世界上大多数人口居住在城市环境中，关于这些环境的内部构成和动态的信息对于维持某些生活标准至关重要。遥感数据，特别是大地卫星、印度资源卫星和地球观测系统等中等空间分辨率卫星的全球覆盖面，为绘制这些城市的构成图和研究其随时间的变化提供了非常有用的数据来源。如果有一个将城市环境与成像传感器的取样分辨率和处理程序联系起来的更适当的概念模型，就可以改进城市环境中遥感数据的效用和应用范围。因此，这项工作的目的是采取植被不透水的表面土壤（维斯）模型的城市组成，并将其与最合适的图像处理方法，以提供信息的维斯组成的城市环境。对利用大地卫星5号专题制图仪数据和1：5000航空照片绘制布里斯班市（澳大利亚南投昆士兰州）城市构成图的几种方法进行了评价。评价的方法是：图像分类;航空照片的解释;和约束线性混合分析。从航空照片中提取了四个样带的900多个参考样本点，并将其用作检查分类和混合分析输出的基础。独特的分区维斯相关的城市组成中发现的每像素分类和汇总的空气照片的解释，但是，显着的光谱混乱也导致类之间。相比之下，从混合物分析产生的维斯分数图像使城市内的商业，工业和住宅区的独特密度被清楚地定义，基于它们的相对数量的植被覆盖。土壤分数图像作为一个指标的地区正在（重新）开发。低（L）分辨率光谱混合分析方法与中等空间分辨率图像数据的逻辑匹配，确保了处理模型与Landsat专题成像仪数据尺度上城市环境的光谱异质性相匹配。
The majority of the world's population now resides in urban environments and information on the internal composition and dynamics of these environments is essential to enable preservation of certain standards of living. Remotely sensed data, especially the global coverage of moderate spatial resolution satellites such as Landsat, Indian Resource Satellite and Systeme Pour I'Observation de la Terre (SPOT), offer a highly useful data source for mapping the composition of these cities and examining their changes over time. The utility and range of applications for remotely sensed data in urban environments could be improved with a more appropriate conceptual model relating urban environments to the sampling resolutions of imaging sensors and processing routines. Hence, the aim of this work was to take the Vegetation-Impervious surface-Soil (VIS) model of urban composition and match it with the most appropriate image processing methodology to deliver information on VIS composition for urban environments. Several approaches were evaluated for mapping the urban composition of Brisbane city (south-cast Queensland, Australia) using Landsat 5 Thematic Mapper data and 1:5000 aerial photographs. The methods evaluated were: image classification; interpretation of aerial photographs; and constrained linear mixture analysis. Over 900 reference sample points on four transects were extracted from the aerial photographs and used as a basis to check output of the classification and mixture analysis. Distinctive zonations of VIS related to urban composition were found in the per-pixel classification and aggregated air-photo interpretation; however, significant spectral confusion also resulted between classes. In contrast, the VIS fraction images produced from the mixture analysis enabled distinctive densities of commercial, industrial and residential zones within the city to be clearly defined, based on their relative amount of vegetation cover. The soil fraction image served as an index for areas being (re)developed. The logical match of a low (L)-resolution, spectral mixture analysis approach with the moderate spatial resolution image data, ensured the processing model matched the spectrally heterogeneous nature of the urban environments at the scale of Landsat Thematic Mapper data.