| 植被叶面积指数估算的红边指数建模分析 |
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| 引用本文： | 郭云开,刘雨玲,许敏,张晓炯.植被叶面积指数估算的红边指数建模分析[J].测绘科学,2021,46(1):93-98. |
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| 作者姓名： | 郭云开 刘雨玲 许敏 张晓炯 |
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| 作者单位： | 长沙理工大学交通运输工程学院,长沙410076;长沙理工大学测绘遥感应用技术研究所,长沙410076;长沙理工大学交通运输工程学院,长沙410076;长沙理工大学测绘遥感应用技术研究所,长沙410076;长沙理工大学交通运输工程学院,长沙410076;长沙理工大学测绘遥感应用技术研究所,长沙410076;长沙理工大学交通运输工程学院,长沙410076;长沙理工大学测绘遥感应用技术研究所,长沙410076 |
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| 基金项目： | 国家自然科学基金面上项目 |
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| 摘 要： | 针对在路域环境监测中,如何精确估算叶面积指数问题,该文提出以长韶娄高速路域为研究区,筛选出4种常用植被指数和4种红边指数两类指数,分别构建了经验模型和机器学习的反演模型,利用Sentinel-2影像数据和同步的LAI-2000地面实测数据完成路域植被叶面积指数反演。结果表明,红边波段参与运算的植被指数与植被叶面积指数敏感性是显著相关,红边指数在反演精度上更优。由此可知,相较于常见植被指数,红边指数增强了其与叶面积指数的敏感性,提高了叶面积指数估算模型精度。
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| 关 键 词： | Sentinel-2 叶面积指数 反演模型 红边指数 |
Modeling and analysis of red edge index estimated by leaf area index in road vegetation |
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| GUO Yunkai,LIU Yuling,XU Min,ZHANG Xiaojiong.Modeling and analysis of red edge index estimated by leaf area index in road vegetation[J].Science of Surveying and Mapping,2021,46(1):93-98. |
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| Authors: | GUO Yunkai LIU Yuling XU Min ZHANG Xiaojiong |
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| Institution: | (Transportation Engineering College,Changsha University of Science and Technology,Changsha 410076,China;Institute of Surveying and Mapping and Remote Sensing Applied Technology,Changsha University of Science&Technology,Changsha 410076,China) |
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| Abstract: | Leaf area index is an important biochemical parameter to characterize vegetation health.It is of great significance to accurately estimate LAI in road environmental monitoring.In this paper,taking the Changshaolou expressway as the research area,four kinds of vegetation index and four kinds of rededge index is screened.Empirical model and machine learning inversion model are constructed respectively.Vegetation leaf area index inversion is completed by Sentinel-2image data and LAI-2000ground measured data synchronously.The results show that the vegetation index in the red-edge band is significantly correlated with the LAI sensitivity of vegetation,and the red-edge index is better in the inversion accuracy.Therefore,compared with the common vegetation index,the red edge index enhances its sensitivity to LAI and improves the accuracy of LAI estimation model. |
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| Keywords: | Sentinel-2 leaf area index inversion model red edge index |
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