文献类型：期刊文献

中文题名：高分航空多波段极化SAR寒温带针叶林蓄积量估测能力评价

英文题名：Evaluation of forest stock estimation ability of high resolution airborne multi-band PolSAR in cold temperate coniferous forests

作者：范亚雄[1,2] 赵磊[1,2] 陈尔学[1,2] 徐昆鹏[1,2] 张王菲[3] 马云梅[1,2]

第一作者：范亚雄

机构：[1]中国林业科学研究院资源信息研究所,北京100091;[2]国家林业和草原局林业遥感与信息技术重点实验室,北京100091;[3]西南林业大学林学院,昆明650224

年份：2024

卷号：28

期号：10

起止页码：2525-2539

中文期刊名：遥感学报

外文期刊名：NATIONAL REMOTE SENSING BULLETIN

收录：CSTPCD;;北大核心:【北大核心2023】；CSCD:【CSCD2023_2024】；

基金：高分辨率对地观测重大专项(编号:21-Y20B01-9001-19/22);民用航天“十四五”技术预先研究第一批项目(编号:D010206);国家重点研发计划(编号:2022YFB3902600)。

语种：中文

中文关键词：遥感;多波段SAR;极化SAR;饱和点;森林蓄积量;水云模型

外文关键词：remote sensing;multi-band SAR;polarimetric SAR;saturation point;forest stock;water cloud model

分类号：S771.8;P2

摘要：以寒温带针叶林为研究对象,基于机载多维度SAR系统获取的多波段极化SAR数据,系统性分析不同波段极化特征对森林蓄积量的响应规律及敏感性,并评价单一波段及多波段极化SAR联合估测森林蓄积量的能力。首先,对多波段SAR数据进行地理编码和地形辐射校正处理,提取包含后向散射强度和极化分解分量的极化特征集。其次,基于水云模型和相关系数,分析不同波段极化特征对蓄积量的响应规律与敏感性。最后,采用机器学习算法进行特征筛选和建模,评价每个波段单独以及多波段联合估测森林蓄积量的能力。研究表明,不同波段的后向散射强度对森林蓄积量的响应均呈上升趋势,但饱和点因波长与极化方式而异。其中,P波段饱和点高于160 m^(3)/ha,其他波段均不超过110 m^(3)/ha。另外,P波段、L/S波段和C/X波段与蓄积量的相关性依次降低,分别在0.6以上、0.3—0.4以及0.3以下。基于单一波段估测蓄积量时,P波段精度为73.79%,其他波段不超过60%;多波段联合估测时,L或S波段和P波段联合的估测精度比单独使用P波段提升约2%。C或X波段的加入对精度提升的贡献很小。所有波段的联合可取得最佳的估测结果,精度达到77.25%。从信号动态范围、饱和点和相关性等指标综合考虑,P波段对蓄积量的敏感性最高,L/S波段次之,而C/X波段的敏感性最低。因此,在基于极化SAR估测森林蓄积量时,应首选P波段,同时在多波段联合时应首选P波段和L或S波段。
Research investigating the estimation ability of forest stock volume combining multiband polarimetric SAR(PolSAR)has hardly been explored,particularly the complementarity between long wavelengths,such as P-band and other shorter wavelengths.This study takes cold temperate coniferous forests in Inner Mongolia as the research object.Having available a multiband stack of airborne P-,L-,S-,C-,and X-band PolSAR data acquired by the high-resolution airborne multidimensional space joint-observation SAR(MSJosSAR)system,the aim is to analyze systematically the response and sensitivity of polarimetric characteristics in different bands to forest stock and evaluate the performance of forest stock retrieval using single and multiband PolSAR data.First,geocoding and terrain radiometric correction were performed on multiband PolSAR data,and then a polarimetric feature set containing backscatter intensity and polarization decomposition components was extracted.Second,on the basis of the water cloud model and correlation coefficient,the response law and sensitivity of polarimetric characteristics in different bands to forest stock was analyzed.Finally,machine learning algorithms were used to perform feature selection and modeling,and the ability of each band and jointly with multiband to estimate forest stock was evaluated.The response of backscatter intensity in different bands to forest stock shows a similar upward trend,but the saturation point varies depending on wavelength and polarimetric channel.Among them,the saturation point for the P-band is higher than 160 m^(3)/ha,whereas it does not exceed 110 m^(3)/ha for the other bands.In addition,the correlation between forest stock and the P-band,L/S-band,and C/X-band decreases in order,with values above 0.6,between 0.3 and 0.4,and below 0.3,respectively.When forest stock was estimated on the basis of a single band,the accuracy of the P-band was 73.79%,and the accuracy of other bands did not exceed 60%.When multiband joint estimation was used,the estimation accuracy of L-or S-band and P-band joint estimation was approximately 2%higher than using P-band alone.The contribution of adding the C-or X-band to the accuracy improvement was minimal.The best estimation performance was achieved through the combination of all bands with an accuracy of 77.25%.Considering various indicators,such as signal dynamic range,saturation point,and correlation,the P-band exhibits the highest sensitivity to forest stock,followed by the L/S-band,and the C/X-band,which is the least sensitive.Therefore,when estimating forest stock using PolSAR data,the P-band should be the first choice.Additionally,when using multiband joint estimation,the combination of P-and L-or S-band should be preferred.In recent years,long-wavelength SAR satellites are being vigorously developed from China and overseas,e.g.,China’s LT-1 satellite is already in orbit,ESA BIOMASS and NASA-ISRO NISAR missions are about to be launched,and China’s civil P-band SAR satellite has also entered the preliminary research stage.The above long-wavelength SAR satellites will greatly enhance the estimation ability of regional forest stock in our country and provide strong support for the refined and scientific management of forest resources.