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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Accuracy of the K-Distribution Regression Model for Forest Biomass Estimation by High-Resolution Polarimetric SAR: Comparison of Model Estimation and Field Data
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Accuracy of the K-Distribution Regression Model for Forest Biomass Estimation by High-Resolution Polarimetric SAR: Comparison of Model Estimation and Field Data

机译：高分辨率极化SAR估算森林生物量的K分布回归模型的准确性：模型估算与现场数据的比较

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摘要

In our previous regression model for estimating forest biomass, it was shown that non-Gaussian amplitude fluctuations in high-resolution polarimetric synthetic aperture radar (SAR) data of coniferous forests can accurately be described by the $K$-distribution and that the order parameter of the $K$-distribution can be useful in estimating the tree biomass of coniferous forests from L-band cross-polarization amplitude images in a wider range than the conventional method using the radar cross section alone. The result was based on the analysis of the “ground-truth” biomass data of 19 forest stands and airborne polarimetric interferometric SAR L-band data over the Tomakomai forests in Hokkaido, Japan. From this relation, an empirical regression model was developed to estimate forest biomass from SAR data. In this paper, we report the results on further analyses of this regression model. The validity of the $K$-distribution is first reconfirmed using the Akaike information criterion, followed by the description on the accuracy of the model. To examine model accuracy, we carried out further field measurements on 22 forest stands in 2005, and the ground survey was made in 2006 to find out the causes of several anomalous data. Based on a comparison of the model-based biomass and the ground-truth data, the accuracy of the model was found to be approximately 86%. The regression model was then updated for practical application in estimating the biomass of the Hokkaido forests by including the ground-truth data of all 41 forest stands.
机译：在我们先前用于估算森林生物量的回归模型中，表明针叶林的高分辨率极化合成孔径雷达（SAR）数据中的非高斯振幅波动可以通过$ K $分布准确地描述，并且阶参数与仅使用雷达横截面的常规方法相比，K分布中的K分布有助于从L波段交叉极化幅度图像中更广泛的范围内估计针叶林的树木生物量。该结果基于对日本北海道Tom小牧森林的19个林分的“地面真相”生物量数据和机载极化干涉SAR L波段数据的分析。根据这种关系，建立了经验回归模型，可从SAR数据估算森林生物量。在本文中，我们报告了对该回归模型进行进一步分析的结果。首先使用Akaike信息准则重新确认$ K $分布的有效性，然后再描述模型的准确性。为了检验模型的准确性，我们在2005年对22个林分进行了进一步的野外测量，并于2006年进行了地面调查，以找出一些异常数据的原因。根据基于模型的生物量和地面真实数据的比较，发现模型的准确性约为86％。然后，通过包括所有41个林分的地面实况数据，对回归模型进行更新，以在实际应用中估算北海道森林的生物量。

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