长白山区4种针叶林有效叶面积指数遥感精细反演及空间分布规律  

Remote Sensing Inversion of Effective Leaf Area Index of Four Coniferous Forest Types and Their Spatial Distribution Rule in Changbai Mountain

作　　者：包广道[1] 刘婷 张忠辉[1] 任志彬[2] 翟畅 丁铭铭 姜雪菲 Bao Guangdao;Liu Ting;Zhang Zhonghui;Ren Zhibin;Zhai Chang;Ding Mingming;Jiang Xuefei(Jilin Provincial Academy of Forestry Sciences,Changchun 130033;Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences,Changchun 130102;Changchun University,Changchun 130022;Beijing Forestry University,Beijing 100083)

机构地区：[1]吉林省林业科学研究院,长春130033 [2]中国科学院东北地理与农业生态研究所,长春130102 [3]长春大学,长春130022 [4]北京林业大学,北京100083

出　　处：《林业科学》2024年第5期127-138,共12页Scientia Silvae Sinicae

基　　金：吉林省科技发展计划重点研发项目(20230202098NC);吉林省科技发展计划自然科学基金项目(YDZJ202201ZYTS446);吉林省重大科技专项(20230303006SF)。

摘　　要：[目的]研究快速、准确、宏观获取不同森林类型有效叶面积指数(LAI_(e))的方法,探讨其空间分布规律,为中小尺度森林LAI_(e)遥感产品的开发提供新思路,为林业精细化监测和森林生态系统碳水循环模拟提供科学可靠的技术手段。[方法]以长白山为研究区,基于Sentinel-2A多光谱影像,运用三维卷积神经网络提取研究区4种针叶林型(长白落叶松、樟子松、红松和红皮云杉)的空间分布;采用区分林型和全样本2种方案,分析样地实测LAI_(e)与7种植被指数(增强植被指数、反红边叶绿素指数、改进简单植被指数、归一化水体指数、归一化植被指数、土壤调节植被指数、简单植被指数)的相关关系;利用各林型对应的最优植被指数,构建区分林型和全样本LAI_(e)与植被指数的回归模型,并基于验证样本数据对比区分林型模型、全样本模型和PROSAIL模型在LAI_(e)反演中的精度表现;结合地理因子分析4种针叶林型LAI_(e)空间格局及变化规律。[结果]所有样本组中7种植被指数与相对的LAI_(e)均存在极显著相关关系(P<0.01),除增强植被指数(EVI)与红松LAI_(e)、简单植被指数(SR)与红皮云杉LAI_(e)外,相关系数均大于0.6,但组间LAI_(e)与不同植被指数相关性具有较大差异;红松、长白落叶松和樟子松LAI_(e)与反红边叶绿素指数(IRECI)相关性最高,红皮云杉、红松LAI_(e)分别与EVI、改进简单植被指数(MSR)相关性最高;4种不同林型模型比全样本模型的R2提高12.7%以上,RMSE降低34.5%;研究区内4种林型LAI_(e)范围在0.37~5.86之间,平均LAI_(e)由高至低依次为红松、长白落叶松、樟子松、红皮云杉。红松对海拔、坡度、坡向的变化最为敏感,红皮云杉、樟子松次之,长白落叶松最小。[结论]不同林型LAI_(e)与遥感植被指数的相关程度存在明显差异,区分林型构建回归模型能够提高LAI_(e)反演精度;区分林型后拟合的线性模型精�【Objective】The aim of this study was to study develop the rapid,accurate and macroscopic methods for obtaining effective leaf area index(LAI_(e))of different forest types,and to explore their spatial distribution,so as to provide new ideas for the development of medium and small-scale forest LAI_(e) remote sensing products,and offer scientific and reliable technological means for precision forestry monitoring and simulation of forest ecosystem carbon and water cycles.【Method】Using Changbai Mountain as the research area,this study extracts the spatial distribution of four coniferous forest types(Larix olgensis,Pinus sylvestris var.mongolica,Pinus koraiensis and Picea koraiensis)based on Sentinel-2A multispectral images through a three-dimensional convolutional neural network.It adopts 2 schemes,differentiated forest types and full sample,to analyze the correlation between field-measured LAI_(e) and 7 vegetation indices[enhanced vegetation index(EVI),inverted red-edge chlorophyll index(IRECI),modified simple ratio(MSR),normalized difference water index(NDWI),normalized difference vegetation index(NDVI),soil adjusted vegetation index(SAVI),simple ratio(SR)].The optimal vegetation index corresponding to each tree species was used to construct regression models for LAI_(e) and vegetation index of differentiated forest type and full sample,and the accuracy performance of differentiated forest type model,full sample model and PROSAIL model in LAI_(e) inversion was compared based on validation sample data.Subsequently,spatial pattern and change rule of LAI_(e) of 4 tree species were analyzed by combining geographical factors.【Result】7 vegetation indices in all sample groups were significantly correlated with relative LAI_(e) values(P<0.01),except for EVI and LAI_(e) of Pinus koraiensis,SR and LAI of Picea koraiensis,the correlation coefficients were all greater than 0.6,while the correlation between LAI_(e) and different vegetation indexes was significantly different among groups.LAI_(e) of Pinus koraiensis,L

关 键 词：有效叶面积指数 针叶树种 森林类型 卫星遥感 空间分布规律 

分 类 号：Q958.15[生物学—动物学] S154.5[农业科学—土壤学]