Optimum strategies for mapping vegetation using multiple-endmember spectral mixture models
Optimum strategies for mapping vegetation using multiple-endmember spectral mixture models
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DOI：
10.1117/12.278930
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发表时间：
1997-10
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影响因子：
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通讯作者：
D. Roberts;Margaret E. Gardner;R. Church;S. Ustin;R. Green
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文献类型：
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作者：
D. Roberts;Margaret E. Gardner;R. Church;S. Ustin;R. Green
火灾管理和生物多样性评估需要改进植被图，这些植被图来自水文和地球化学模型的关键投入，是扩大点测量的一种手段。在大于10米的尺度上，植被群落通常由树叶、树枝、裸露的土壤和阴影组成。为了绘制混合植被图，许多研究人员采用光谱混合分析（SMA）。在大多数SMA应用中，使用由绿色植被、土壤、非光合植被和阴影组成的单组光谱来“解混”图像。然而，由于大多数场景包含四个以上的组件，这种简单的方法会导致分数误差，并可能无法区分许多植被类型。在这项工作中，我们采用了一种新的方法，称为多个端元光谱混合分析，其中端元的数量和类型不同的每像素。使用这种方法，产生了数百个独特的模型，这些模型解释了植物化学，物理属性和物候学方面的社区特定差异。此外，我们描述了一个新的战略，开发和组织区域特定的光谱库。我们目前的研究结果在圣莫尼卡山脉使用AVIRIS数据，在其中我们映射草原和查帕拉尔社区，映射物种优势在某些情况下，以高度的准确性。
Improved vegetation maps are required for fire management and biodiversity assessment, from critical inputs for hydrological and biogeochemical models and represent a means for scaling-up point measurements. At scales greater than 10 meters, vegetation communities are typically mixed consisting of leaves, branches, exposed soil and shadows. To map mixed vegetation, many researchers employ spectral mixture analysis (SMA). In most SMA applications, a single set of spectra consisting of green vegetation, soil, non- photosynthetic vegetation and shade are used to 'unmix' images. However, because most scenes contain more than four components, this simple approach leads to fraction errors and may fail to differentiate many vegetation types. In this work, we apply a new approach called multiple endmember spectral mixture analysis, in which the number and types of endmembers vary per-pixel. Using this approach, hundreds of unique models are generated that account for community specific differences in plant chemistry, physical attributes and phenology. Additionally, we describe a new strategy for developing and organizing regionally specific spectral libraries. We present result from a study in the Santa Monica Mountains using AVIRIS data, in which we map grassland and chaparral communities, mapping species dominance in some cases to a high degree of accuracy.