地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (2): 308-315.doi: 10.12082/dqxxkx.2020.190254

• 遥感科学与应用技术 • 上一篇    下一篇

路域植被等效水厚度估算模型研究

郭云开1,2, 张晓炯1,2,*(), 许敏1,2, 刘雨玲1, 钱佳1, 章琼1   

  1. 1. 长沙理工大学交通运输工程学院,长沙 410014
    2. 长沙理工大学测绘遥感应用技术研究所,长沙 410076
  • 收稿日期:2019-05-22 修回日期:2019-09-20 出版日期:2020-02-25 发布日期:2020-04-13
  • 通讯作者: 张晓炯 E-mail:1183582609@qq.com
  • 作者简介:郭云开(1958— ),男,湖南常德人,教授,主要从事高等级路域环境遥感研究。E-mail: guoyunkai226@163.com
  • 基金资助:
    国家自然科学基金面上项目(41671489)

Estimation Model of Equivalent Water Thickness in the Road Area

GUO Yunkai1,2, ZHANG Xiaojiong1,2,*(), XU Min1,2, LIU Yuling1, QIAN Jia1, ZHANG Qiong1   

  1. 1. School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410014, China
    2. Institute of Surveying and Mapping Remote Sensing Application Technology, Changsha University of Science & Technology, Changsha 410076, China
  • Received:2019-05-22 Revised:2019-09-20 Online:2020-02-25 Published:2020-04-13
  • Contact: ZHANG Xiaojiong E-mail:1183582609@qq.com
  • Supported by:
    National Natural Science Foundation of China(41671489)

摘要:

植被等效水厚度对路域生态环境的监测评估具有重要意义。本研究以湖南醴潭高速一段为研究对象,以地面实测光谱和等效水厚度以及PRO4SAIL模拟光谱和模拟等效水厚度为数据源,利用PRO4SAIL冠层模型模拟光谱与地面实测光谱建立12种常用水分指数,引入随机森林算法对水分指数与等效水厚度进行重要性分析,得到12种水分指数的重要性排序;利用调整R 2确定建立等效水厚度估算模型中输入水分指数的最佳个数;在优选水分指数基础上,以PRO4SAIL模拟光谱计算得到水分指数和等效水厚度为训练集,分别构建随机森林耦合偏最小二乘(RF-PLS)、随机森林耦合支持向量机(RF-SVM)和随机森林耦合遗传算法优化支持向量机(RF-GA-SVM)等效水估算模型,并用地面实测等效水厚度对估算模型进行精度验证与分析。结果表明:RF-SVM估算模型中输入重要性前9的水分指数(NDWI、NMDI、SRWI、SR、NDII、WI、DWI、MSI、SAVI)时,调整R 2最高,验证集决定系数为0.8877;RF-PLS和RF-GA-SVM估算模型中输入重要性前4的水分指数(NDWI、NMDI、SRWI、SR)时,调整R 2最高,验证集决定系数分别为0.8053、0.8952,其中RF-GA-SVM模型估算等效水厚度效果最佳,其精度满足路域植被等效水厚度监测要求。本文研究成果为等效水厚度估算提供一种有效且精确的方法,同时为发展基于高光谱遥感的路域环境监测提供重要支撑。

关键词: 等效水厚度, 随机森林, PRO4SAIL模型, 水分指数, 机器学习, 路域植被, 湖南醴潭高速

Abstract:

Vegetation water content is an important evaluation index for vegetation health monitoring. The Equivalent Water Thickness (EWT) of vegetation is of great significance for the monitoring and evaluation of the ecological conditions in the road area, it could provide a guideline in road area environment management. Taking the Litan highway in Hunan Provinces as an example, this research used field data of canopy reflectance and equivalent water thickness of vegetation on the ground, and simulated reflectance and simulated equivalent water thickness established by PRO4SAIL. In total, 12 kinds of water indices were established by using the simulated reflectance of the PRO4SAIL canopy model and the ground measured reflectance. The random forest algorithm (RF) was introduced to analyze the importance of the 12 water indices and equivalent water thickness. We determined the ordination between water indices and equivalent water thickness as well as the optimal number of input water index in the equivalent water thickness estimation model by using the adjusted coefficient of determination. Based on the selected water index, the water index and equivalent water thickness were calculated by the PRO4SAIL simulation reflectance as the training set. Three equivalent water thickness estimation models were constructed: Random Forest Coupled Partial Least Squares (RF-PLS), Random Forest Coupled Support Vector Machine (RF-SVM) model, and Random Forest coupled Genetic Algorithm to optimize the Support Vector Machine (RF-GA-SVM) model. The applicability of 12 water indices in the estmation of equivalent water thickness in road-area of vegetation was also analyzed. The accuracy of the model was validated by measured equivalent water thickness on the ground. The experimental results show: (1) The adjusted determination coefficient of RF-SVM model was the highest, established by Normalized Difference Water Index (NDWI), Normalized Multi-band Drought Index (NMDI), Simple Ratio Water Index (SRWI), Simple Ratio (SR), Normalized Difference Infrared Index (NDII), Water Index (WI), Dattwater Index (DWI), Moisture Stress Index (MSI) and Soil Adjusted Vegetation Index (SAVI), with the determination coefficient of verification set reaching 0.8877. (2) The RF-PLS and RF-GA-SVM models with the four water indices of NDWI, NMDI, SRWI, and SR had the highest adjusted determination coefficient, with the validation set's determination coefficients reaching 0.8053 and 0.8952, respectively. (3) Among them, the RF-GA-SVM model was the best for estimating equivalent water thickness, which met the requirements of vegetation equivalent water thickness monitoring in road area. Our findings provide an effective and accurate method for the estimation of equivalent water thickness, and provide support for road area environment monitoring based on hyper-spectral remote sensing.

Key words: Equivalent water thickness, random forest, PRO4SAILmodel, water index, machine learning, vegetation of road area, Litan highway in Hunan Province