文献类型：期刊文献

中文题名：基于人工合成样本和随机森林回归模型的长时序中国寒温带森林类型和树种覆盖度反演

英文题名：Mapping forest type and tree species fractions in China’s cold-temperate forests based on synthetically mixed data and random forest regression

作者：王梦雨[1,2] 赵峰[2] 庞勇[3,4] 孟冉[5] 荚文[3,4] 岳超[1,6]

第一作者：王梦雨

机构：[1]西北农林科技大学资源环境学院,杨凌712100;[2]华中师范大学城市与环境科学学院,武汉430079;[3]中国林业科学研究院资源信息研究所,北京100091;[4]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;[5]华中农业大学资源与环境学院,武汉430070;[6]中国科学院水利部水土保持研究所,杨凌712100

年份：2025

卷号：29

期号：1

起止页码：118-133

中文期刊名：遥感学报

外文期刊名：NATIONAL REMOTE SENSING BULLETIN

收录：;EI(收录号：20250517770520);北大核心:【北大核心2023】；

基金：国家自然科学基金(编号:41901382,42101403);遥感国家重点实验室开放基金(编号:OFSLRSS201917);国家重点研发计划(编号:2017YFD0600404)。

语种：中文

中文关键词：遥感;森林类型覆盖度;树种覆盖度;人工合成样本;长时间序列;机器学习

外文关键词：remote sensing;forest type coverage;tree species coverage;synthetically training data;long time series;machine learning

分类号：TP79;P2

摘要：寒温带森林是陆地上分布面积最广的森林生态系统,具有重要的生态和社会经济价值。定量描述长时序寒温带森林类型和树种覆盖信息对于量化其生态系统服务功能以及制定森林管理政策具有重要意义。然而受实测覆盖度数据缺乏和多光谱影像光谱信息有限的限制,现有研究较少探究中分辨率多光谱星载数据(如Landsat卫星)对中国寒温带森林类型覆盖度和树种覆盖度进行长时序反演的可行性,并且对于遥感影像获取时间频率(单时相、多时相)对反演精度的影响仍缺乏定量评估。为此,本研究利用人工合成样本和随机森林回归模型对黑龙江省孟家岗林场的森林类型和树种覆盖度分别进行了反演,并将模型应用于1986年—2020年的Landsat影像,估算孟家岗林场阔叶林和针叶林35年的覆盖度。结果表明:(1)对于森林类型覆盖度反演,基于生长季Landsat波段和植被指数(归一化耕作指数以及缨帽变换系数)的中值特征估算的精度最高,阔叶林覆盖度估算R^(2)=0.76,针叶林覆盖度估算R^(2)=0.71;(2)对于树种覆盖度反演,基于多时相Landsat波段和植被指数的精度最高,落叶松覆盖度估算R^(2)=0.40,红松覆盖度估算R^(2)=0.23,樟子松覆盖度估算R^(2)=0.61;(3)增加影像获取时间密度对于森林类型覆盖度反演精度的提高没有显著贡献(阔叶林ΔR^(2)=0.01,针叶林ΔR^(2)=-0.03),但对提高树种覆盖度的反演精度帮助较大(落叶松ΔR^(2)=0.04,红松ΔR^(2)=0.07,樟子松ΔR^(2)=0.27)。本研究可为中国北方森林以及全球寒温带森林类型和树种覆盖度的大尺度长时序估算提供了思路。
Cold-temperate forests,recognized as the most extensive terrestrial ecosystems,cover vast areas around the globe and hold important ecological and social values.Accurate mapping of forest type and tree species cover fraction in these forests across space and time is crucial for quantifying ecosystem services and formulating effective forest management policies to ensure their sustainable conservation.However,despite the increasing development of remote sensing technologies,studies exploring the feasibility of inverting forest type and tree species cover fraction using medium-resolution multispectral satellite-based data,such as Landsat,in China’s cold-temperate forests are limited.This limitation is primarily attributed to the scarcity of reference data and the restricted spectral information available in multispectral images.Moreover,the quantitative impact of the temporal frequency of data acquisitions(e.g.,single-date,multidate)on mapping forest type and tree species cover fraction remains largely unexplored.The timing and frequency of satellite data acquisition can significantly influence the detection and characterization of dynamic changes in forests,which in turn affects the accuracy of mapping forest attributes.To address these gaps,our study aims to map the forest type and tree species cover fraction in Mengjiagang Forest,Heilongjiang Province by employing synthetically mixed data and a random forest regression model.We extend our analysis to three decades(from 1986 to 2020)of Landsat data,mapping the cover fractions of broadleaf and needleleaf forests in Mengjiagang Forest by using an optimal broadleaf and needleleaf random forest regression model.The results of our study reveal the following key findings:(1)For forest type cover fraction inversion,the random forest regression model based on the growing season median index(including spectral bands,NDTI,and TCT indices)is the optimal model(achieving R^(2)=0.76 for broadleaf and R^(2)=0.71 for needleleaf).(2)For tree species cover fraction inversion,the random forest regression model based on multidate spectral features(including the spectral bands,NDTI,and TCT indices of growth and leaf off seasons)is the optimal model(achieving R^(2)=0.40 for Larch,R^(2)=0.23 for Korean pine,and R^(2)=0.61 for Mongolian pine).(3)Increasing the temporal frequency of data acquisition can enhance tree species cover fraction inversion accuracy(achievingΔR^(2)=0.04 for Larch,ΔR^(2)=0.07 for Korean pine,andΔR^(2)=0.27 for Mongolian pine),whereas its effect on improving forest type cover fraction inversion accuracy is limited.By effectively combining the advantages of synthetically trained data and random forest regression,we have successfully mapped the forest type and tree species cover fraction of Mengjiagang Forest.Moreover,our study provides a comprehensive analysis that accurately quantifies the influence of temporal data acquisition frequency on mapping forest type and tree species cover fraction.This study offers valuable insights into the future mapping of forest type and tree species cover fraction across space and time,particularly for regions with similar species composition.The outcomes of this research will make a significant contribution to the understanding and management of cold-temperate forests,thereby supporting their conservation and sustainable use.