文献类型：期刊文献

中文题名：基于GEE与Sentinel-2影像的落叶针叶林提取

英文题名：Extraction of deciduous coniferous forest based on Google earth engine(GEE)and Sentinel-2 image

作者：王春玲[1,2] 樊怡琳[1,2] 庞勇[3] 荚文[3]

第一作者：王春玲

机构：[1]北京林业大学信息学院,北京100083;[2]国家林业草原林业智能信息处理工程技术研究中心,北京100083;[3]中国林业科学研究院资源信息研究所,北京100091

年份：2023

卷号：45

期号：8

起止页码：1-15

中文期刊名：北京林业大学学报

外文期刊名：Journal of Beijing Forestry University

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD2023_2024】；

基金：“十三五”国家重点研发计划(2017YFD0600404)。

语种：中文

中文关键词：落叶针叶林;Sentinel-2影像;物候特征;随机森林;GEE平台

外文关键词：deciduous coniferous forest;Sentinel-2 image;phenological characteristics;random forest;Google earth engine(GEE)platform

分类号：S757.3

摘要：【目的】针对森林资源精细监测评价的需求,探索多时相、多特征的Sentinel-2影像在落叶针叶林识别中的应用潜力,根据落叶针叶林的物候特征构建分类模型,为大范围落叶针叶林识别提供方法参考。【方法】基于GEE平台,以黑龙江省孟家岗林场为研究区,分析不同季节落叶针叶林与其他森林之间的差异。研究使用2020年春季(5月7日和5月27日)、夏季(8月9日)和秋季(10月19日)的4景Sentinel-2影像,提取光谱特征、纹理特征和地形特征构建多特征数据集,根据特征重要性得分进行特征优选,最后使用随机森林分类器得到落叶针叶林识别的最佳模型,实现孟家岗林场落叶针叶林的精确提取。【结果】试验结果表明落叶针叶林具有明显的植被光谱特征和季相特性,多时相影像数据包含落叶针叶林更多物候期,春季和秋季的影像更有利于区分落叶针叶林与其他森林。此外,近红外、短波红外波段的光谱信息对识别落叶针叶林有较大帮助。利用GEE平台和多时相Sentinel-2影像可以高效快速地提取植被信息,落叶针叶林提取总体精度与Kappa系数分别达到91.20%,0.82。【结论】基于GEE平台和Sentinel-2影像构建的分类模型对落叶针叶林信息的快速提取有一定的可行性和适用性,研究结果对大面积落叶针叶林的空间位置分布提取具有一定的参考价值。
[Objective]In view of fine monitoring and evaluation needs of forest resources,the application potential of multi-temporal and multi-feature Sentinel-2 images in the identification of deciduous coniferous forests was exploratively studied,and a classification model was built according to the phenological characteristics of deciduous coniferous forests to provide method reference for identifying deciduous coniferous forests on a large scale.[Method]Based on the GEE platform,the difference between deciduous coniferous forests and other forests in different seasons was analyzed by taking Mengjiagang Forest Farm in Heilongjiang Province of northeastern China as the research area.In this study,four seasonal Sentinel-2 images of spring(May 7 and May 27),summer(August 9),and autumn(October 19)in 2020 were used to construct a multi-feature dataset by extracting spectral features,texture features,and topographic features.Feature optimization was carried out according to feature importance scores.Finally,the optimal model for identifying deciduous coniferous forests was established by a random forest classifier to achieve rapid extraction of deciduous coniferous forest in Mengjiagang Forest Farm.[Result]The experimental results showed that deciduous coniferous forests displayed obvious vegetation spectral features and seasonal characteristics.The multi-temporal image data contained more phenological period information on deciduous coniferous forests,and the images in spring and autumn can enable better differentiation between deciduous coniferous forests and other forests.In addition,near-infrared and short-wave infrared spectral information can greatly help identify deciduous coniferous forests.By the GEE platform and multi-temporal Sentinel-2 images,it is possible to extract vegetation information efficiently and quickly.The overall extraction accuracy and Kappa coefficient of deciduous coniferous forest reached 91.20%and 0.82,respectively.[Conclusion]The classification model constructed based on the GEE platform and Sentinel-2 image has certain feasibility and applicability for the rapid extraction of deciduous coniferous forest information,and the research results provide a certain reference value for the large-scale extraction of spatial location distribution information of deciduous coniferous forest.