作　　者： ; ;

机构地区： 广州大学地理科学学院

出　　处： 《国土资源遥感》 2006年第2期64-68,共5页

摘　　要： 运用归一化光谱混合分析(NSMA)方法,用ETM+数据调查广州市海珠区城市地表组成,采用亮度标准化方法减小亮度变化。通过标准化,使亮度差异在每个植被-非渗透性表面-土壤-水体(V-I-S-W)组成中减小或者消除,这样使得一个单一的端元能够代表一种地表组分。在此基础上,通过归一化影像,选择了植被、非渗透性表面、土壤和水体4种端元,运用一种约束光谱混合分析(SMA)模型,分解了不同种类的城市地表组成。通过与已有模型计算结果比较,认为本文所构建的模型较优,其对研究区非渗透性表面估计的均方根误差为12.6%。 With rapid urban growth in recent years, the understanding of urban biophysical composition and dynamics has become an important research topic. Remote sensing technologies constitute a potentially scientific basis for examining urban composition and monitoring its changes over the time. The vegetation - impervious surface - soil - water （V -I -S -W） model, in particular, provides a foundation for describing urban/suburban environments and also serves as a basis for further urban analyses comprising urban growth modeling, environmental impact analysis, and socioeconomic factor estimation. This paper developed a normalized spectral mixture analysis （NSMA） method for examining urban composition in Haizhu district using Landsat ETM^＋ data. In particular, a brightness normalization method was applied to reduce brightness variation. Through this normalization, brightness variability within each V- I- S- W component was reduced or eliminated, thus allowing a single end- member to represent each component. Furthermore, with the normalized image, four end - members, namely vegetation, impervious surface, soil, and water, were chosen to model heterogeneous urban composition using a constrained spectral mixture analysis （SMA） model. The accuracy of impervious surface estimation was assessed and compared with the other existing models. The results indicate that the proposed model is a better alternative to existing models, with a root mean square error （RMSE） of 12.6% for impervious surface estimation in the study area.

关 键 词： 归一化光谱混合分析 植被 非渗透性表面 土壤 水体模型 城市 数据

领　　域： [自动化与计算机技术] [自动化与计算机技术]