森林工程2025，Vol.41Issue(3)：505-516,12.DOI:10.7525/j.issn.1006-8023.2025.03.007
融合哨兵2号时序特征与连续变化检测分类算法的优势树种识别
Integration of Sentinel-2 Temporal Features and Continuous Change Detection Classification Algorithm for Dominant Tree Species Identification
摘要
Abstract
The identification of dominant tree species is an important part of forestry resource surveys.Improving the ac-curacy of dominant tree species identification has significant practical implications for conducting forest resource surveys and related research.Using the Google Earth Engine(GEE)cloud platform,we obtained Sentinel-2 time series images for the Huodong mining area from January to December 2023.The annual growth trajectory features of dominant tree spe-cies were constructed based on the CCDC algorithm and the NDFI index.A dominant tree species hierarchical identifica-tion method combining"trajectory features+spectral features+texture features"of long-time series remote sensing im-ages was proposed.A control group of"spectral features+texture features"was set up,and hierarchical classification and random forest classification algorithms were used to identify 7 dominant tree species(Pinus tabuliformis,Quercus wutaishansea,Betula playphylla,Larix principis-rupprechtii,Platycladus orientalis,Populus davidiana,and poplars spp.)in the Huodong mining area.The results showed that:1)The NDFI index can effectively distinguish between de-ciduous forests and evergreen forests;2)The dominant tree species identification based on"trajectory features+spectral features+texture features"performed well,with an overall classification accuracy of 79.6%and a Kappa coefficient of 0.742 in the study area,which was 7.3%higher than the control group.关键词
优势树种识别/GEE/时序轨迹特征/归一化退化指数/CCDC算法/时间谐波分析Key words
Dominant tree species identification/GEE/temporal trajectory features/normalized disturbance index/CCDC algorithm/time series harmonic analysis分类
林学引用本文复制引用
陈丹,李晶,霍江润,马天跃,闫星光,李雨霏..融合哨兵2号时序特征与连续变化检测分类算法的优势树种识别[J].森林工程,2025,41(3):505-516,12.基金项目
国家重点研发计划项目(2022YFE0127700). （2022YFE0127700）
