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首页> 外文期刊>Remote Sensing >The Potential of Forest Biomass Inversion Based on Vegetation Indices Using Multi-Angle CHRIS/PROBA Data
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The Potential of Forest Biomass Inversion Based on Vegetation Indices Using Multi-Angle CHRIS/PROBA Data

机译：基于多角度CHRIS / PROBA数据的基于植被指数的森林生物量转化潜力

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Multi-angle remote sensing can either be regarded as an added source of uncertainty for variable retrieval, or as a source of additional information, which enhances variable retrieval compared to traditional single-angle observation. However, the magnitude of these angular and band effects for forest structure parameters is difficult to quantify. We used the Discrete Anisotropic Radiative Transfer (DART) model and the Zelig model to simulate the forest canopy Bidirectional Reflectance Distribution Factor (BRDF) in order to build a look-up table, and eight vegetation indices were used to assess the relationship between BRDF and forest biomass in order to find the sensitive angles and bands. Further, the European Space Agency (ESA) mission, Compact High Resolution Imaging Spectrometer onboard the Project for On-board Autonomy (CHRIS-PROBA) and field sample measurements, were selected to test the angular and band effects on forest biomass retrieval. The results showed that the off-nadir vegetation indices could predict the forest biomass more accurately than the nadir. Additionally, we found that the viewing angle effect is more important, but the band effect could not be ignored, and the sensitive angles for extracting forest biomass are greater viewing angles, especially around the hot and dark spot directions. This work highlighted the combination of angles and bands, and found a new index based on the traditional vegetation index, Atmospherically Resistant Vegetation Index (ARVI), which is calculated by combining sensitive angles and sensitive bands, such as blue band 490 nm/?55°, green band 530 nm/55°, and the red band 697 nm/55°, and the new index was tested to improve the accuracy of forest biomass retrieval. This is a step forward in multi-angle remote sensing applications for mining the hidden relationship between BRDF and forest structure information, in order to increase the utilization efficiency of remote sensing data.
机译：多角度遥感可以被视为变量检索的不确定性的附加来源，也可以被视为附加信息的来源，与传统的单角度观测相比，它可以增强变量检索的准确性。但是，这些角度和波段效应对于森林结构参数的大小很难量化。我们使用离散各向异性辐射传输（DART）模型和Zelig模型来模拟森林冠层双向反射分布因子（BRDF），以建立查找表，并使用八个植被指数来评估BRDF与植被之间的关系。森林生物量以寻找敏感的角度和频带。此外，还选择了欧洲航天局（ESA）的任务，机载自治项目（CHRIS-PROBA）上的紧凑型高分辨率成像光谱仪和野外样品测量，以测试对森林生物量获取的角度和波段影响。结果表明，离天底植被指数比天底能更准确地预测森林生物量。此外，我们发现视角效应更为重要，但带效应不可忽略，并且提取森林生物量的敏感角是较大的视角，尤其是在热点和暗点方向。这项工作强调了角度和波段的组合，并找到了一个基于传统植被指数的新指标，即抗大气植被指数（ARVI），该指数是通过组合敏感角度和敏感波段（例如490 nm /？55的蓝色波段）来计算的°，绿带530 nm / 55°和红带697 nm / 55°，并测试了新指标以提高森林生物量检索的准确性。这是在多角度遥感应用中迈出的一步，以挖掘BRDF与森林结构信息之间的隐藏关系，以提高遥感数据的利用效率。

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