文献类型：期刊文献

中文题名：主被动遥感特征优化的天然林森林蓄积量估测——以新疆巩留县为例

英文题名：Estimation of Forest Stem Volume of Natural Forests based on the Optimization of Active and Passive Remote Sensing Features——A Case Study over Gongliu County of Xinjiang

作者：王鹏杰[1] 张绘芳[2] 田昕[1] 张景路[2] 朱雅丽[2]

第一作者：王鹏杰

机构：[1]中国林业科学研究院资源信息研究所,北京100091;[2]新疆林业科学院现代林业研究所,新疆乌鲁木齐830000

年份：2022

卷号：37

期号：3

起止页码：672-680

中文期刊名：遥感技术与应用

外文期刊名：Remote Sensing Technology and Application

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD2021_2022】；

基金：2019年新疆维吾尔自治区公益性科研院所基本科研业务费专项资金(KY2019043);中国林业科学研究院中央级公益性科研院所基本科研业务费专项资金项目“森林资源出数关键技术研究”(CAFYBB2021SY006)。

语种：中文

中文关键词：森林蓄积量;国产主被动遥感;特征优化;天然林

外文关键词：Forest stem volume;Active and passive remote sensing;Feature optimization;Natural forests

分类号：TP79

摘要：结合国产主被动遥感数据高分六号(GF-6)PMS和高分三号(GF-3)双极化PolSAR估测森林蓄积量,并针对多源遥感数据的冗余问题进行特征组合优化。以新疆巩留县天然林地为研究区,提取GF-6 PMS数据的光谱信息、植被指数、纹理以及植被覆盖度信息和GF-3 PolSAR数据的后向散射系数、极化分解参数,结合地形因子,在森林样地调查数据的基础上,利用快速迭代特征选择的K最近邻法(K-Nearest Neighbor with Fast Iterative Features Selection,KNN-FIFS)估测研究区的森林蓄积量。对比国产主被动遥感数据和单一遥感数据源时的估测结果,基于最优特征组合反演研究区的森林蓄积量,结果表明:联合GF-3 PolSAR和GF-6 PMS数据估测研究区森林蓄积量的精度为R2=0.72,RMSE=92.48 m3/hm2,相比于仅使用GF-6 PMS数据估测的精度(R2=0.56,RMSE=118.8 m3/hm2),R2提高了0.16,提高了28.6%,RMSE降低了26.32 m3/hm2,降低22.2%。说明主被动遥感数据协同反演可以提高森林蓄积量估测精度,KNN-FIFS方法可以有效地估测天然林森林蓄积量。
We estimated forest stem volume using domestic active and passive remote sensing data GF-3 PolSAR and GF-6 PMS. And in order to find a way out of the redundancy problem of multi-source remote sensing data,feature combination is optimized. The research area is the natural forest land in Gongliu County,Xinjiang.We extracted spectral information,vegetation index,texture,vegetation coverage from GF-6 PMS data and then extracted backscattering coefficient and polarization decomposition parameters from GF-3 PolSAR data.Combining the extracted parameters,terrain factor and forest sample survey data,we estimated forest stem volume in the study area using K-Nearest Neighbor with Fast Iterative Features Selection(KNN-FIFS)method.Comparing and validating the estimation results when combined active and passive remote sensing data and a single remote sensing data source,we inverted the forest stem volume in the study area based on the optimal feature combination. The results show that the accuracy of combining GF-3 PolSAR data and GF-6 PMS data to estimate the forest stem volume in the study area is R2=0.72 and RMSE=92.48 m3/hm2,which is compared with the accuracy estimated using only GF-6 PMS data(R2=0.56,RMSE=118.8 m3/hm2),R2 increased by0.16 with an increase rate of 28.6% and RMSE decreased by 26.32 m3/hm2 with a decrease rate of 22.2%. It indicated that the cooperative inversion of active and passive remote sensing data can improve the estimation accuracy of forest stem volume,and the KNN-FIFS method can effectively estimate the forest stem volume of natural forests.