基于多端元混合光谱模型与Landsat影像的北京不透水层动态研究

Impervious Surface Dynamic Quantification based on Multiple Endmember Spectral Mixture Analysis(MESMA) and Landsat Imagery Data:A Case Study in Beijing

中国正在经历快速地城市化过程,及时又准确地掌握城市化过程对我国社会经济发展具有重要的实际意义。以Landsat-TM和ETM+为主要数据源,通过多端元光谱混合分析法(MESMA)提取北京建成区不透水层的时空演变信息。在Ridd的V-I-S(植被—不透水层—土壤)概念模型框架下,基于最小噪音变换(MNF)将TM或ETM+的6个光谱波段转换成MNF空间,并定义4种端元光谱分别代表植被、高反射率地表、低反射率地表和土壤,同时构建北京建成区端元光谱数据库。然后在MATLAB软件包中实现MESMA模型程序,依次提取北京市6个时段的不透水层信息。研究结果表明:MESMA方法能够提高植被、土壤和不透水层提取精度,相对误差分别为14.6%、17.3%和11.9%。研究结论充分说明MESMA方法应用到一个时间序列的中分辨率多光谱遥感影像是非常有效的。MESMA光谱分解方法能高效实现北京城市动态变化和城市扩张的监测。

China is experiencing rapid urbanization process,timely and accurate quantification of the urbanization process is pivotal for the currently social and economic development in China.This study used Multiple Endmember Spectral Mixture Analysis(MESMA)model to extract impervious surface information from a time series of Landsat TM and ETM+images data under the framework of Ridd's Vegetation-Impervious Surface-soil(V-I-S)model.For MESMA implementation,minimum noise fraction transform(MNF)was applied to transform the TM or ETM six spectral bands into the MNF space and four endmembers representing vegetation,high-albedo surface,low-albedo surface and soil were determined for images acquired over the Beijing City.The results show that MESMA yielded relative accurate estimate vegetation,soil and impervious surface for the Beijing city.Accuracy assessment indicates that MESMA resulted in the lowest RMSEs for impervious surface,vegetation and soil are 14.6%,17.3% and 11.9%,respectively.Further,the MESMA model generated the low Mean Absolute Error(MAE)value.This work demonstrates that applied MESMA to a time series of the moderate-resolution multispectral remote sensing image can be an effective way to monitor the dynamics of urban environment variables dynamics and urban expansion,which has great potential for urbanization monitoring with MESMA modeling under the V-I-S framework.