Satellite estimation of aboveground biomass and impacts of forest stand structure
Satellite estimation of aboveground biomass and impacts of forest stand structure
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DOI：
10.14358/pers.71.8.967
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发表时间：
2005-08
影响因子：
1.3
通讯作者：
D. Lu;M. Batistella;E. Moran
中科院分区：
文献类型：
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作者：
D. Lu;M. Batistella;E. Moran
异质性的亚马逊河景观和复杂的林分结构往往使地上生物量（AGB）的估计困难。在这项研究中，光谱混合分析被用来转换成绿色植被，阴影，土壤部分图像的陆地卫星专题制图（TM）图像。利用熵值分析了森林林分结构的复杂性，并研究了不同林分结构对TM反射率数据的影响。分别以演替林和原始林为研究对象，研究了AGB与分形图像或TM光谱特征之间的关系，并建立了两种森林类型的AGB估算模型。我们的研究结果表明，使用分数图像的AGB估计模型进行演替森林生物量估计比使用TM光谱特征。然而，这两个模型的基础上TM光谱特征和分数提供了原始森林生物量估计性能差。复杂的林分结构和相关的冠层阴影大大降低了AGB和TM反射率或分数图像之间的关系。
Heterogeneous Amazonian landscapes and complex forest stand structure often make aboveground biomass (AGB) estimation difficult. In this study, spectral mixture analysis was used to convert a Landsat Thematic Mapper (TM) image into green vegetation, shade, and soil fraction images. Entropy was used to analyze the complexity of forest stand structure and to examine impacts of different stand structures on TM reflectance data. The relationships between AGB and fraction images or TM spectral signatures were investigated based on successional and primary forests, respectively, and AGB estimation models were developed for both types of forests. Our findings indicate that the AGB estimation models using fraction images perform better for successional forest biomass estimation than using TM spectral signatures. However, both models based on TM spectral signatures and fractions provided poor performance for primary forest biomass estimation. The complex stand structure and associated canopy shadow greatly reduced relationships between AGB and TM reflectance or fraction images.