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Development of Forest Information Measurement System by Multi-Wavelength and Multi-Polarization High-Resolution Synthetic Aperture Radar

多波长多偏振高分辨率合成孔径雷达森林信息测量系统研制

基本信息

批准号：
17360194
负责人：
TAKAGI Masataka
金额：
$ 6.46万
依托单位：
Kochi University of Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2006
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17360194/
关键词：
measurement engineering remote sensing forestry imaging radar algorithm

项目摘要

The purpose of this research is to develop techniques to extract forest information from the high-resolution multi-wavelength and multi-polarization synthetic aperture radar, Pi-SAR, developed jointly by the Japan Aerospace Exploration Agency (JAXA) and the National Institute of Information and Communications Technology (NICT). The test site is the coniferous forests in Tomakomai, Hokkaido. First, regression analyses are carried out between the Pi-SAR images acquired in November 2002, and the forest data measured simultaneously on the ground and the forest data measured in August 2003. Using the conventional technique that utilizes the radar cross section (RCS), it is found that there is no significant correlation between the X-band RCS and forest parameters at all polarizations. The L-band RCS, however, is found to increase with increasing forest biomass up to approximately 40 tons/ha. These trends are similar to those already reported by several researchers. Next, because the high-re … More solution SAR images appear to show the structures of the forests, the relation between the image texture and forest information is sought. As a result, the image amplitudes are found to obey the K-distributed probability density function; and that strong correlation exists between the order parameter of the K-distribution in the cross-polarized images and the forest biomass. Further, it is found that the order parameter increases with increasing biomass up to around 100 tons/ha which is well beyond the saturation limit of the conventional RCS method. From the regression curve, the biomass values of unknown forests is estimated and compared with those measured on the ground in 2005-2006. The comparison yields the model accuracy of 86%. Finally, the regression model is updated using all biomass data measured on the ground. This model is considered to be effective for estimating the biomass of coniferous forests on flat ground in the entire areas of Hokkaido; and the accuracy of estimating the forest biomass can be improved to much higher levels by combining the conventional RCS technique and the texture analysis developed in this study. Less

这项研究的目的是开发从高分辨率多波长和多极化合成孔径雷达（Pi-SAR）提取森林信息的技术，Pi-SAR是由日本宇宙航空研究开发机构（JAXA）和国家信息和通信技术研究所（NICT）联合开发的。试验地点是北海道的驹牧市的针叶林。首先，在2002年11月获得的Pi-SAR图像之间进行回归分析，并在地面上同时测量的森林数据和2003年8月测量的森林数据。使用传统的技术，利用雷达散射截面（RCS），它被发现，有没有显着的相关性之间的X波段RCS和森林参数在所有极化。然而，L-波段雷达散射截面随着森林生物量的增加而增加，最高可达约40吨/公顷。这些趋势与一些研究人员已经报告的趋势相似。其次，由于高分辨率 ...更多信息解决SAR图像呈现森林结构的问题，寻求图像纹理与森林信息之间的关系。结果表明，图像幅值服从K分布概率密度函数，交叉偏振图像中K分布的序参量与森林生物量之间存在较强的相关性。此外，它被发现，顺序参数增加，增加生物量高达约100吨/公顷，这是远远超出了传统的RCS方法的饱和极限。根据回归曲线，对未知森林的生物量值进行了估计，并与2005-2006年在地面测量的生物量值进行了比较。比较得出的模型准确度为86%。最后，使用在地面测量的所有生物量数据更新回归模型。该模型被认为是有效的，估计针叶林的生物量在整个地区的北海道的平地上，并估计森林生物量的精度可以提高到更高的水平，通过结合传统的RCS技术和纹理分析在这项研究中开发的。少

项目成果

期刊论文数量（0）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

On the accuracy of empirical relation between forest biomass and order parameter of K-distribution in Pi-SAR images

论Pi-SAR图像中森林生物量与K分布序参数经验关系的准确性

DOI：
发表时间：
2006
期刊：
IEICE Tech. Rep. (WSANE 2006, X' an, China) 2006-21
影响因子：
0
作者：
H.Wang;et al.
通讯作者：
et al.

On the accuracy of the empirical model for estimating forest biomass from K-distributed SAR images

K分布SAR影像估算森林生物量经验模型的准确性研究

DOI：
发表时间：
2006
期刊：
技術研究報告(電子情報通信学会) SANE2006-57
影响因子：
0
作者：
H.Wang;K.Ouchi;M.Watanabe;M.Shimada
通讯作者：
M.Shimada

In Search of the Statistical Properties of High-Resolution Polarimetric SAR Data for the Measurements of Forest Biomass Beyond the RCS Saturation Limits

DOI：
10.1109/lgrs.2006.878299
发表时间：
2006-10
期刊：
IEEE Geoscience and Remote Sensing Letters
影响因子：
4.8
作者：
Haipeng Wang;K. Ouchi;Manabu Watanabe;M. Shimada;T. Tadono;A. Rosenqvist;S. Romshoo;M. Matsuoka;T. Moriyama;S. Uratsuka
通讯作者：
Haipeng Wang;K. Ouchi;Manabu Watanabe;M. Shimada;T. Tadono;A. Rosenqvist;S. Romshoo;M. Matsuoka;T. Moriyama;S. Uratsuka

Evaluating the biomass estimation algorithm of coniferous forests based on statistical texture analysis approach hi-resolution polarimetric SAR data

基于统计纹理分析方法高分辨率极化SAR数据评估针叶林生物量估计算法