摘要
【目的】探究基于卫星遥感数据的森林可燃物含水率反演,比较深度学习模型与传统机器学习模型的精度,并探索一种解决冠层遮挡问题的方案,为全国建立森林可燃物含水率数据库提供理论依据。【方法】以河北省张家口市崇礼区为研究区,基于实地测量数据,针对传统机器学习模型误差较大的问题,建立深度学习中的多层感知机(MLP)模型,研究光谱反射率与森林冠层植被和地表枯落物含水率之间的关系,并与传统机器学习中的支持向量回归(SVR)模型进行精度对比。选取与实地考察时间同季度的哨兵遥感数据,以光谱反射率、光谱水分指数等遥感估测法中常用变量作为反演森林冠层植被和地表枯落物含水率的影响因子,结合实地考察数据进行模型训练。针对以往采用遥感估测法反演地表枯落物含水率遇到的冠层遮挡问题,使用双向反射分布函数处理遥感数据获得不同观测角度的遥感数据,结合辐射传输模型,将冠层反射率映射到地表反射率后再训练模型。【结果】以红光、绿光、近红外和短波红外波段为输入变量的MLP模型在森林冠层植被含水率反演中的拟合度为0.843,优于SVR中最优模型的拟合度0.807,精度提高4.5%;MLP模型在地表枯落物含水率反演中拟合度为0.448,优于SVR中最优模型的拟合度0.408,精度提高9.8%。利用最优拟合模型反演崇礼区森林可燃物含水率灰度图和分布图,西部区域冠层植被含水率较高,东南地区地表枯落物含水率较高。【结论】本研究探索出一种解决光学遥感在冠层到地表间穿透性较差问题的优化方案,也为使用遥感估测法大尺度测定地区冠层植被以及地表枯落物含水率提供理论依据。
【Objective】This paper was carried out to study the water content measurement based on satellite remote sensing data and to compare the accuracy of deep learning model and traditional machine learning model in order to provide a theoretical basis for the establishment of forest fuel water content database in China.【Method】Taking Chongli District,Zhangjiakou City,Hebei Province as the research area,based on the field measurement data,aiming at the problem of a large error of traditional machine learning model,the mutilayer perceptron(MLP)model in deep learning was established.The relationships between remote sensing spectral reflectance and water content of forest canopy vegetation and litter were studied,and the accuracy was compared with the support vector regression(SVR)model in traditional machine learning.In the experiment,sentry remote sensing data of the same season as field investigation were selected,and then the model variables commonly used in remote sensing estimation method such as spectral reflectance and spectral moisture index were used as the influence factors of retrieving the moisture content of canopy vegetation and surface litter.Finally,the model training was carried out based on the data of field investigation.Inversion using the remote sensing estimation method for ever encountered in the process of surface litter moisture content of canopy cover problem,this study used in the processing of remote sensing data bidirectional reflectance distribution function to obtain samples of the remote sensing data of different observation angles,combined with the radiative transfer model,the canopy reflectance model mapped to the surface reflectance after training.【Result】In the experimental result,the fitting degree of MLP model with red light,green light,near-infrared and short-wave infrared bands as input variables in the inversion of canopy vegetation water content was 0.843,which was better than the fitting degree of the optimal model in SVR of 0.807,and the accuracy improved by 4.5%.The fitting degree of MLP model in the inversion of surface litter water content was 0.448,which was better than the optimal model in SVR of 0.408,and the accuracy improved by 9.8%.In this study,the optimal fitting model MLP was used to invert the distribution map of fuel water content,and it was concluded that the water content of canopy vegetation was higher in the western Chongli district,while the water content of surface litter was higher in the southeastern Chongli district.【Conclusion】The research result of this paper could demonstrate an optimization scheme to solve the problem of poor penetrability of optical remote sensing from canopy to surface,and could also provide a theoretical basis for using remote sensing estimation methods to measure moisture content of regional canopy vegetation and surface litter at a large scale.
作者
李贾
兰岚
张佐忠
袁文涛
高德民
宗树琴
业巧林
Li Jia;Lan Lan;Zhang Zuozhong;Yuan Wentao;Gao Demin;Zong Shuqin;Ye Qiaolin(College of Information Science and Technology,Nanjing Forestry UniversityNanjing 210037;Institute of Ecological Protection and Restoration,CAFBeijing 100091;Forestry and Grassland Bureau of Chongli District,Zhangjiakou City,Hebei ProvinceZhangjiakou 075000)
出处
《林业科学》
EI
CAS
CSCD
北大核心
2022年第10期47-58,共12页
Scientia Silvae Sinicae
基金
张家口市崇礼区森林防火综合体系建设无人机巡护监测系统(DA2020001)
国家自然科学基金面上项目(31971653)。
关键词
遥感数据
森林可燃物含水率
光谱水分指数
深度学习
remote sensing data
moisture content of forest combustibles
spectral moisture index
deep learning
