GLIBERTY-DSAIL耦合模型反演南方混交林植被LAI

doi:10.13474/j.cnki.11-2246.2020.0351

测绘通报 ›› 2020, Vol. 0 ›› Issue (11): 39-42,75.doi: 10.13474/j.cnki.11-2246.2020.0351

GLIBERTY-DSAIL耦合模型反演南方混交林植被LAI

郭云开^1,2, 刘建琴^1,2, 郭燕青^1,2, 曹骁^1,2, 谢琼^3,4

1. 长沙理工大学交通运输工程学院, 湖南长沙 410076;
2. 长沙理工大学测绘遥感应用技术研究所, 湖南长沙 410076;
3. 湖南工程职业技术学院测绘地理学院, 湖南长沙 410151;
4. 长沙理工大学公路地质灾变预警空间信息技术湖南省工程实验室, 湖南长沙 410114

收稿日期:2020-06-04 修回日期:2020-07-07 出版日期:2020-11-25 发布日期:2020-11-30
通讯作者: 刘建琴。E-mail:2510025386@qq.com E-mail:2510025386@qq.com
作者简介:郭云开(1958-),男,博士,教授,主要从事路域植被生态环境遥感研究。E-mail:guoyunkai226@163.com
基金资助:
国家自然科学基金（41471421；41671498）；长沙理工大学公路地质灾变预警空间信息技术湖南省工程实验室开放基金（kfj190603）

GLIBERTY-DSAIL coupled model inversion of vegetation LAI in southern mixed forest

GUO Yunkai^1,2, LIU Jianqin^1,2, GUO Yanqing^1,2, CAO Xiao^1,2, XIE Qiong^3,4

1. School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410076, China;
2. Institute of Surveying and Mapping Remote Sensing Applied Technology, Changsha University of Science and Technology, Changsha 410076, China;
3. Department of Surveying and Mapping Geography, Hunan Vocational College Engineering, Changsha 410151, China;
4. Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning, Changsha University of Science and Technology, Changsha 410114, China

Received:2020-06-04 Revised:2020-07-07 Online:2020-11-25 Published:2020-11-30

摘要/Abstract

摘要： 针对南方丘陵地区针叶-阔叶混交林植被叶面积指数（leaf area index，LAI）反演精度低且研究较少的问题，本文提出了一种GLIBERTY-DSAIL耦合模型组合多元线性回归反演LAI的方法。本研究以GLIBERTY-DSAIL模型模拟光谱和植被实测高光谱为数据源，通过相关性分析，选取与LAI相关性高的植被指数作为反演因子，构建多元线性回归模型定量反演植被LAI并进行精度评定。结果表明：与LAI显著相关的RVI、DVI、GNDVI、MSAVI这4种植被指数作为反演因子，结合本文提出的组合模型反演LAI，模型预测决定系数R²为0.708 6，均方根误差RMSE为0.302 1，精度整体较高。该组合方法可较好地用于反演针叶-阔叶混交林植被LAI，为南方地区混交林LAI的研究提供新思路。

关键词: GLIBERTY-DSAIL模型, 多元线性回归, 混交林, 叶面积指数, 定量反演

Abstract: In order to solve the problem of low leaf area index (LAI) inversion accuracy and little research in the coniferous-broadleaved mixed forest in the southern hilly areas, this paper proposes a GLIBERTY-DSAIL coupling model combined with multiple linear regression inversion LAI method. In this study, the GLIBERTY-DSAIL model selects simulated spectrum and vegetation measured hyperspectral as data sources. Through the correlation analysis, the vegetation index with high correlation about LAI is selected as the inversion factor, a multiple linear regression model is constructed to quantitatively invert vegetation LAI. The paper evaluates the accuracy. The results show that: RVI, DVI, GNDVI, and MSAVI vegetation indexes that are significantly related to LAI are used as inversion factors, combined with the model proposed in this paper to invert LAI, the model prediction coefficient R² is 0.708 6, and the root mean square error RMSE is 0.302 1. The accuracy is higher overall. This combined method can be used to invert vegetation LAI of coniferous-broadleaved mixed forests, and provide new ideas for the study of mixed forest LAI in southern areas.

Key words: GLIBERTY-DSAIL model, multiple linear regression, mixed forest, LAI, inversion

中图分类号:

郭云开, 刘建琴, 郭燕青, 曹骁, 谢琼. GLIBERTY-DSAIL耦合模型反演南方混交林植被LAI[J]. 测绘通报, 2020, 0(11): 39-42,75.

GUO Yunkai, LIU Jianqin, GUO Yanqing, CAO Xiao, XIE Qiong. GLIBERTY-DSAIL coupled model inversion of vegetation LAI in southern mixed forest[J]. Bulletin of Surveying and Mapping, 2020, 0(11): 39-42,75.

参考文献

[1] CHEN J M, BLACK T A. Defining leaf area index for non-flat leaves[J]. Plant Cell Environment, 1992,15(4):421-429.
[2] 朱佳明,郭云开,刘海洋,等.一种组合反演叶面积指数的方法[J].测绘科学, 2019,44(1):60-65.
[3] 刘俊,孟庆岩,葛小三,等.基于BP神经网络的夏玉米多生育期叶面积指数反演研究[J].遥感技术与应用,2020,35(1):174-184.
[4] 尤号田,邢艳秋,彭涛,等.LiDAR不同强度校正法对樟子松叶面积指数估测的影响[J].测绘学报, 2018,47(2):170-179.
[5] 姜海玲,赵艺源,李耀,等.扬花期冬小麦叶面积指数高光谱遥感反演研究[J].吉林师范大学学报(自然科学版), 2020,41(1):135-140.
[6] 王博,周志勇,张欢,等.针阔混交林中兴安落叶松比例对土壤化学性质和酶化学计量比的影响[J].浙江农林大学学报,2020,37(4):611-622.
[7] 李雪玲,董莹莹,朱溢佞,等.基于EnMAP卫星和深度神经网络的LAI遥感反演方法[J].红外与毫米波学报,2020,39(1):111-119.
[8] 雷宇斌,朱善宽,郭云开,等.极限学习机辅助下路域植被叶面积指数的反演[J].测绘通报, 2018(9):82-86.
[9] 李海洋,范文义,于颖,等.基于Prospect, Liberty和Geosail模型的森林叶面积指数的反演[J].林业科学, 2011,47(9):75-81.
[10] DAWSON T P, CURRAN P J, PLUMMER S E. LIBERTY:modeling the effects of leaf biochemistry on reflectance spectra[J]. Remote Sensing of Environment, 1998,65(1):50-60.
[11] GUO Y K, AN G X, FENG C, et al. Gliberty-generalized model for spectral characteristic of coniferous leaves and broad-leaved leaves[J]. Photogrammetry, Remote Sensing and Spatial Information Sciences. 2018,22(1):173-179.
[12] VERHOEF W. Light scattering by leaf layers with application to canopy reflectance modeling:the SAIL model[J]. Remote Sensing of Environment. 1984,16(2):125-141.
[13] JACQUEMOUD S, BARET F. Prospect:a model of leaf optical properties spectra[J]. Remote Sensing of Environment, 1990,34(2):75-91.
[14] 郭云开,安冠星,谢琼,等.针对SAIL冠层模型的土壤背景反射率修正[J]. 测绘工程, 2017,26(8):1-4.
[15] 郭云开,冯超.路域植被覆盖度非密度模型方法探讨[J].测绘科学, 2016,41(1):103-108.
[16] 郭云开,刘雨玲,张晓炯,等.利用辐射传输模型和随机森林回归反演LAI[J].测绘工程, 2019,28(6):17-21.
[17] 郭云开,许敏,张晓炯,等.结合PRO-4SAIL和BP神经网络的叶绿素含量高光谱反演[J].测绘通报,2020(3):21-24.

GLIBERTY-DSAIL耦合模型反演南方混交林植被LAI

GLIBERTY-DSAIL coupled model inversion of vegetation LAI in southern mixed forest

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics

本文评价

[1]	李广超, 赵海盟, 伍毅, 杨彬, 李丽和, 张洪, 杨泉光, 陈伟. 2000-2016年广西防城金花茶自然保护区关键生态环境参数时空变化[J]. 测绘通报, 2021, 0(2): 13-17.
[2]	朱小灵, 王剑辉, 谢荣安. 基于GPS-IR的地表土壤湿度估算方法[J]. 测绘通报, 2020, 0(6): 128-133.
[3]	赵晓旭. 融合多源时空数据的城市火灾危险性评估[J]. 测绘通报, 2020, 0(5): 101-106.
[4]	郭云开, 刘海洋, 蒋明, 朱佳明. 南方典型植被遥感丰度信息提取[J]. 测绘通报, 2019, 0(6): 16-18,28.
[5]	雷宇斌, 朱善宽, 郭云开, 李丹娜, 刘磊, 刘宁. 极限学习机辅助下路域植被叶面积指数的反演[J]. 测绘通报, 2018, 0(9): 82-86.
[6]	欧盛华, 邵杉杉, 杨涛, 裔传祥. 遥感尺度效应的数值模拟——以叶面积指数为例[J]. 测绘通报, 2018, 0(8): 106-110.