基于Sentinel-2遥感影像的崇礼区地上生物量反演  

Aboveground biomass inversion based on Sentinel-2 remote sensing images in Chongli district

作　　者：颜辉 蒋湘涛[1] 王汶珮 伍振宇 刘帆 魏英杰 YAN Hui;JIANG Xiangtao;WANG Wenpei;WU Zhenyu;LIU Fan;WEI Yingjie(College of Computer and Information Engineering,Central South University of Forestry&Technology,Changsha 410004,Hunan,China;College of Geosciences and Info-physics,Central South University,Changsha 410006,Hunan,China)

机构地区：[1]中南林业科技大学计算机与信息工程学院,湖南长沙410004 [2]中南大学地球科学与信息物理学院,湖南长沙410006

出　　处：《中南林业科技大学学报》2024年第2期53-61,共9页Journal of Central South University of Forestry & Technology

基　　金：国家重点研发计划项目(2022YFD2200505)。

摘　　要：【目的】以Sentinel-2遥感影像数据为基础,结合森林实测样地数据,以崇礼区森林地上生物量反演为例提出反演新思路。【方法】基于2021年7月河北省崇礼区Sentinel-2遥感影像数据、2021年6—8月的71块崇礼区森林样地实测数据,利用实测数据中的胸径、树高,根据河北省森林生物量计算公式计算各样地实测生物量,通过SNAP、ENVI等软件对遥感数据进行重采样、裁剪等预处理,提取影像原始波段,并计算植被指数、纹理因子、缨帽指数等遥感因子,对遥感因子进行皮尔逊相关性分析筛选,并以匹配最佳纹理窗口大小优化纹理因子的选择,分别采用多元线性回归、BP神经网络以及随机森林3种算法进行崇礼区AGB建模,利用R2以及RMSE评价其模型精度,并选取最优模型进行生物量反演并绘制生物量空间分布图。【结果】1)在遥感因子选择中,除了常规的绿波段、红波段和2个植被红边波段与植被指数DVI、SAVI、EVI,纹理因子的均值和缨帽指数的亮度与绿度在生物量反演模型的建立中也起到了重要的作用,且纹理因子窗口大小的选择也会对最终模型的精度造成影响;2)3种模型的精度均满足反演生物量的要求,以随机森林模型效果最好、多元线性回归模型次之、BP神经网络模型精度最低,但经过十则交叉验证法的BP模型精度有所提升,最优的随机森林模型R2达到了0.843;3)经过最优模型的反演,崇礼区AGB分布主要在50~200 mg·hm^(-2),集中在西部环山地带,存在明显的空间异质性。【结论】利用Sentinel-2遥感影像反演森林生物量具有较高的精度,随着植被指数、缨帽指数、纹理因子的加入,模型效果呈递增趋势,并且纹理因子的窗口大小选择在森林生物量遥感反演中有着重要的影响。【Objective】Based on Sentinel-2 remote sensing image data,combined with the actual forest sample plot data,we proposed a new idea of inversion of forest above-ground biomass in Chongli district as an example.【Method】Based on Sentinel-2 remote sensing image data of Chongli district,Hebei province,in July 2021,and 71 forest sample plots of Chongli district in June and August 2021,the measured data were used.Using the diameter at breast height and tree height from the measured data,the measured biomass of each plot was calculated according to the formula for calculating forest biomass in Hebei province.The remote sensing data were pre-processed with SNAP and ENVI software for resampling and cropping to extract the original image bands and calculate remote sensing factors such as vegetation index,texture factor and tassel cap index.Pearson correlation analysis was performed to filter the remote sensing factors and optimize the selection of texture factors by matching the best texture window size.Three algorithms of multiple linear regression,BP neural network and random forest were used to model the AGB of Chongli district,respectively.The model accuracy was evaluated using R2 and RMSE,and the optimal model was selected for biomass inversion and biomass spatial distribution mapping.【Result】1)In the selection of remote sensing factors,in addition to the conventional green band,red band and two vegetation red edge bands and vegetation indices DVI,SAVI and EVI,the mean value of texture factor and the brightness and greenness of tassel hat index also played an important role in the establishment of biomass inversion model,and the selection of texture factor window size also affected the accuracy of the final model;2)The accuracy of all three models met the requirements of biomass inversion,with the best random forest model,the second best multiple linear regression model,and the lowest accuracy of BP neural network model,but the 2 accuracy of the BP model improved after the ten-rule cross-validation method,and

关 键 词：Sentinel-2 AGB 纹理因子 窗口选择 随机森林 

分 类 号：S771.8[农业科学—森林工程]