Spectral mixture analysis for bi-sensor wetland mapping using Landsat TM and Terra MODIS data
Spectral mixture analysis for bi-sensor wetland mapping using Landsat TM and Terra MODIS data
复制标题
使用 Landsat TM 和 Terra MODIS 数据进行双传感器湿地测绘的光谱混合分析
DOI：
10.1080/01431161.2011.611185
复制
发表时间：
2012-01-01
影响因子：
3.4
通讯作者：
Xu, Bing
中科院分区：
文献类型：
--
作者：
Michishita, Ryo;Gong, Peng;Xu, Bing
湿地生态系统的时空分辨率是了解湿地生态系统时空特征和动态的基础。然而，目前还没有既具有高空间分辨率又具有高时间频率的单一卫星图像。相反，开发一种利用中高分辨率数据的空间细节和粗空间分辨率数据的时间细节的双传感器监测技术是非常可取的。对于我们的时间序列双传感器湿地制图的初步工作，多个端元光谱混合分析（MESMA）使用单日期双传感器图像与不同的轨道周期的适用性进行了研究。利用中国鄱阳湖地区和美国大盐湖地区的Landsat-5专题制图仪（TM）和Terra中分辨率图像光谱仪（MODIS）数据，研究了利用MESMA的三个决定性因素：（1）最佳端元选择方法;（2）二端元和三端元模型之间的阈值;（3）阴影分数的处理。结果表明：（1）满足最大、最小土地覆盖率和均方根误差（RMSE）建模约束的端元谱相似谱的类内谱数（In_CoB），满足类外建模约束的端元谱相似谱的类外谱数（Out_CoB），InCoB与OutCoB之比乘以类内光谱倒数（CoBI）和端元平均RMSE（RMSE）是TM图的最优端元选择方法，而CoBI、RMSE和最小平均光谱角（MASA）是MODIS图的最优端元选择方法;（2）与TM图相比，MODIS图对二端元和三端元模拟阈值的变化更敏感;（3）在暗水组分中加入阴影组分是一种合适的阴影处理。本研究展示了如何MESMA可以应用于湿地生态系统的多尺度映射，如何在TM和MODIS数据之间的观测日期的差异影响的协议，在土地覆盖的分数和如何黑暗的水和阴影之间的光谱相似性影响的协议，在土地覆盖的分数。
Spatial and temporal resolution is essential for understanding the spatial and temporal characteristics and dynamics of wetland ecosystems. However, single satellite imagery with both high spatial resolution and high temporal frequency is currently unavailable. Instead, the development of a bi-sensor monitoring technique utilizing spatial details of middle-to-high resolution data and temporal details of coarse spatial resolution data is highly desirable. For the initial work on our time-series bi-sensor wetland mapping, the applicability of multiple endmember spectral mixture analysis (MESMA) using single-date bi-sensor imagery with different orbiting periods was investigated. Landsat-5 Thematic Mapper (TM) and Terra Moderate Resolution Image Spectrometer (MODIS) data were utilized in the Poyang Lake area in China and the Great Salt Lake area in the USA to examine three decisive elements in utilizing MESMA: (1) the method of optimal endmember selection; (2) the threshold between two- and three-endmember models; and (3) the treatment of shade fractions. As a result, we found that (1) the number of spectra for an endmember spectrum similar to other endmember spectra meeting the modelling restrictions of maximum and minimum land-cover fractions and root mean square error (RMSE) within a class (In_CoB), the number of spectra for an endmember spectrum similar to other endmember spectra meeting the modelling restrictions outside of a class (Out_CoB), the ratio of In_CoB to Out_CoB multiplied by the inverse number of spectra within the class (CoBI) and the endmember average RMSE (EAR) were optimal endmember selection methods for the TM maps, whereas CoBI, EAR and minimum average spectral angle (MASA) were optimal endmember selection methods for the MODIS maps; (2) the MODIS maps were more sensitive to change in the two- and three-endmember modelling thresholds than the TM maps; and (3) the addition of shade fractions to dark water fractions were an appropriate shade treatment. This research demonstrated how MESMA can be applied for multi-scale mapping of wetland ecosystems, how the difference in observation dates between the TM and MODIS data affects the agreement in land-cover fractions and how spectral similarity between dark water and shade affects the agreement in land-cover fractions.