摘要
针对如何快速准确地估测桉树林分平均高以进一步评价桉树生长过程和质量的问题,该文研究利用ZY-3号立体像对数据,提取了研究区冠层高度模型(NF_CHM、NB_CHM和3S_CHM)。在此基础上,利用冠层高度模型(CHM)、ZY-3号多光谱数据提取的遥感因子,结合多种线性和非线性模型协同反演了桉树的林分平均高。研究结果表明,使用该方法估测精度(RMSE=2.15 m,RRMSE=14.5%,RE=0.11),优于单独使用冠层高度模型(CHM)估测桉树林分平均高的精度。研究结果为进一步利用ZY-3立体像对数据对桉树林分平均高估测提供了新的思路。
In order to evaluate the growth process and quality of eucalyptus by estimating the mean height of eucalyptus stands quickly and accurately,the canopy height models(NF_CHM,NB_CHM and 3 S_CHM)were extracted by using ZY-3 stereo image pair data.On this basis,the canopy height model(CHM)and ZY-3 multi-spectral data were used to extract remote sensing factors,and a variety of linear and nonlinear models were combined to invert the mean stand height of eucalyptus.The results showed that the estimation accuracy(RMSE=2.15 m,RRMSE=14.5%,RE=0.11)was better than that of the canopy height model(CHM)alone.It can be seen that the results of this study provide a new idea for further using ZY-3 stereoscopic data to estimate the mean height of eucalyptus stand.
作者
张廷琛
林辉
刘兆华
龙江平
ZHANG Tingchen;LIN Hui;LIU Zhaohua;LONG Jiangping(Engineering Research Center of Forest Remote Sensing Information,Central South University of Forestry and Technology,Changsha 410004,China;Hunan Key Laboratory of Forestry Remote Sensing Big Data and Security,Changsha 410004,China;Key Laboratory of Management and Testing of Forest Resources in Southern China,State Forestry and Grassland Administration,Changsha 410004,China)
出处
《测绘科学》
CSCD
北大核心
2022年第2期117-125,共9页
Science of Surveying and Mapping
基金
国家“十三五”重点研发计划项目(2017YFD0600900)。
关键词
资源三号
立体像对
林分平均高
机器学习模型
桉树
ZY-3
mean tree height of stand
stereo pair
machine learning models
eucalyptus
