杉木人工林冠层高度无人机遥感估测
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摘要
冠层高度是森林资源调查的重要因子。传统的森林树高调查方法存在外业调查难度大,效率低等问题。无人机(UAV)的发展为快速估测森林树高提供了手段。以福建省闽清县的杉木Cunninghamia lanceolata人工林为研究对象,通过Eco Drone-UA无人机遥感系统获取研究区遥感影像,利用Pix4D Mapper软件对航拍多光谱影像进行预处理,构建数字表面模型(DSM),利用1∶10 000地形图生成数字高程模型(DEM);基于DSM和DEM叠加相减得到树冠高度模型(CHM),实现杉木树高的提取。结果表明:植被指数和多光谱波段结合随机森林算法能够有效识别真实树冠顶点;利用无人机遥感影像能够实现杉木树高估测,相对误差最小值为0.81%,最大值为23.48%,标准误差为1.48 m,估测精度为90.8%。高程变化对树高估测精度有影响,根据高程大小排序的3组样木实测树高与提取树高的决定系数(R2)分别是0.97, 0.84和0.78,标准误差分别是0.67, 1.17和1.99 m,在高程较高区域树高估测精度明显高于高程相对较低区域。
Tree height,an important parameter in a forest resource survey,has been problematic in traditional methods of a forest survey making it difficult and inefficient to conduct further investigations.To utilize the rapid development,in recent years,of unmanned aerial vehicle(UAV) technology as a means for quickly estimating the height of forest trees,tree height data were obtained from Cunninghamia lanceolata plantations in Minqing County,Fujian Province.Remote sensing imagery in the study area was obtained through the Eco Drone-UA drone remote sensing system,setting the flight altitude to 120 m and the flight belt overlap to 50%.Pix4D Mapper software was used to preprocess aerial multispectral images and build a DSM(Digital Surface Model) using kriging interpolation to obtain a DEM(Digital Elevation Model).Based on the estimated idea of the canopy height model(CHM) = digital surface model(DSM)-digital elevation model(DEM),the tree height of C.lanceolata was extracted.Results showed that combining the vegetation index,multispectral bands,and random forest algorithm were effective in identifying the true crown vertex,and it was feasible to use high resolution UAV imagery to extract tree height.The minimum relative error for tree height was 0.81%,the maximum was 23.48%,the estimation accuracy was 90.8%,and the standard error was 1.48 m.At the same time,the measurement of tree height was affected by the DEM with the R2 and root mean squared error(RMSE) value for the least DEM being R2= 0.781,RMSE= 1.99 m,for the next one was R2= 0.84,RMSE= 1.17 m,and for the largest it was R2= 0.966,RMSE= 0.67 m.The accuracy of the measured height of the larger DEM was higher than that of the smaller DEM.Therefore,This approach integrates UVA with random forest,which makes up of the shortcomings of each.In addition,the results also provide a reference guidelines for the tree height.
Tree height,an important parameter in a forest resource survey,has been problematic in traditional methods of a forest survey making it difficult and inefficient to conduct further investigations.To utilize the rapid development,in recent years,of unmanned aerial vehicle(UAV) technology as a means for quickly estimating the height of forest trees,tree height data were obtained from Cunninghamia lanceolata plantations in Minqing County,Fujian Province.Remote sensing imagery in the study area was obtained through the Eco Drone-UA drone remote sensing system,setting the flight altitude to 120 m and the flight belt overlap to 50%.Pix4D Mapper software was used to preprocess aerial multispectral images and build a DSM(Digital Surface Model) using kriging interpolation to obtain a DEM(Digital Elevation Model).Based on the estimated idea of the canopy height model(CHM) = digital surface model(DSM)-digital elevation model(DEM),the tree height of C.lanceolata was extracted.Results showed that combining the vegetation index,multispectral bands,and random forest algorithm were effective in identifying the true crown vertex,and it was feasible to use high resolution UAV imagery to extract tree height.The minimum relative error for tree height was 0.81%,the maximum was 23.48%,the estimation accuracy was 90.8%,and the standard error was 1.48 m.At the same time,the measurement of tree height was affected by the DEM with the R2 and root mean squared error(RMSE) value for the least DEM being R2= 0.781,RMSE= 1.99 m,for the next one was R2= 0.84,RMSE= 1.17 m,and for the largest it was R2= 0.966,RMSE= 0.67 m.The accuracy of the measured height of the larger DEM was higher than that of the smaller DEM.Therefore,This approach integrates UVA with random forest,which makes up of the shortcomings of each.In addition,the results also provide a reference guidelines for the tree height.
引文
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[32]王彬,孙虎,徐倩,等.基于无人3D摄影技术的雪松(Cedrus deodara)群落高度测定[J].生态学报,2018,38(10):1-9.WANG Bin,SUN Hu,XU Qian,et al.Height measurement of a Cedar(Cedrus deodara)community based on unmanned aerial vehicles(UAV)3D photogrammetry technology[J].Acta Ecol Sin,2018,38(10):1-9.
[2]唐守正,张会儒,胥辉.相容性生物量模型的建立及其估计方法研究[J].林业科学,2000,36(增刊1):19-27.TANG Shouzheng,ZHANG Huiru,XU Hui.Study on establish and estimate method of compatible biomass model[J].Sci Silv Sin,2000,36(suppl 1):19-27.
[3]隋宏大.树高测量综合技术比较研究[D].北京:北京林业大学,2009.SUI Hongda.Comparative Study on Comprehensive Technologies of Tree Height Measurement[D].Bei jing:Beijing Forestry University,2009.
[4]周志强,岳彩荣,徐天蜀,等.森林高度遥感估测研究综述[J].现代农业科技,2012(2):198-199,203.ZHOU Zhiqiang,YUE Cairong,XU Tianshu,et al.Review on forest height estimation using remote sensing[J].Modern Agric Sci Technol,2012(2):198-199,203.
[5]庞勇,李增元,谭炳香,等.点云密度对机载激光雷达林分高度反演的影响[J].林业科学研究,2008,21(增刊):14-19.PANG Yong,LI Zengyuan,TAN Bingxiang,et al.The effects of airborne LiDAR point density on forest height estimation[J].For Res,2008,21(suppl):14-19.
[6]于大洋,董贵威,杨健,等.基于干涉极化SAR数据的森林树高反演[J].清华大学学报(自然科学版),2005,45(3):334-336.YU Dayang,DONG Guiwei,YANG Jian,et al.Forest tree height estimates based on polarimetric SAR interferometry[J].J Tsinghua Univ Sci Technol,2005,45(3):334-336.
[7]王涛,龚建华,张利辉,等.基于机载激光雷达点云数据提取林木参数方法研究[J].测绘科学,2010,35(6):47-49.WANG Tao,GONG Jianhua,ZHANG Lihui,et al.Estimating parameters of trees in forest using point cloud data of airborne LIDAR[J].Sci Surv Map,2010,35(6):47-49.
[8]ZIMBLE D A,EVANS D L,CARLSON G C,et al.Characterizing vertical forest structure using small-footprint airborne LiDAR[J].Remote Sens Environ,2003,87(2/3):171-182.
[9]CLARK M L,CLARK D B,ROBERTS D A.Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape[J].Remote Sens Environ,2004,91(1):68-89.
[10]SAMANTA S,PAL D K,LOHAR D,et al.Interpolation of climate variables and temperature modeling[J].Ther Appl Climatol,2012,107:35-45.
[11]OPPELT N,MAUSER W.Airborne visible/infrared imaging spectrometer AVIS:design,characterization and calibration[J].Sensors,2007,7(9):1934-1953.
[12]高珍,邓甲昊,孙骥,等.微型无人机图像无线传输系统分类方案与关键技术[J].北京理工大学学报,2008,28(12):1078-1082.GAO Zhen,DENG Jiahao,SUN Ji,et al.Scheme and key technologies of wireless image transmission system for micro unmanned air vehicles[J].Trans Beijing Inst Technol,2008,28(12):1078-1082.
[13]RANGO A,LALIBERTE A S,HERRICK J E,et al.Unmanned aerial vehicle-based remote sensing for rangeland assessment,monitoring,and management[J].J Appl Remote Sens,2009,3(1):33542.
[14]孙中宇,陈燕乔,杨龙,等.轻小型无人机低空遥感及其在生态学中的应用进展[J].应用生态学报,2017,28(2):528-536.SUN Zhongyu,CHEN Yanqiao,YANG Long,et al.Small unmanned aerial vehicles for low-altitude remote sensing and its application progress in ecology[J].Chin J Appl Ecol,2017,28(2):528-536.
[15]庞勇,赵峰,李增元,等.机载激光雷达平均树高提取研究[J].遥感学报,2008,12(1):152-158.PANG Yong,ZHAO Feng,LI Zengyuan,et al.Forest height inversion using airborne lidar technology[J].J Remote Sens,2008,12(1):152-158.
[16]李廷伟,梁甸农,朱炬波.极化干涉SAR森林高度反演综述[J].遥感信息,2009(3):85-91.LI Tingwei,LIANG Diannong,ZHU Jubo.A review of inversion of the forest height by polarimetric interferometric SAR[J].Remote Sens Inf,2009(3):85-91.
[17]刘晓双,黄建文,鞠洪波.高空间分辨率遥感的单木树冠自动提取方法与应用[J].浙江林学院学报,2010,27(1):126-133.LIU Xiaoshuang,HUANG Jianwen,JU Hongbo.Research progress in the methods and applications of individual tree crown’s automatic extraction by high spatial resolution remote sensing[J].J Zhejiang For Coll,2010,27(1):126-133.
[18]汪沛,罗锡文,周志艳,等.基于微小型无人机的遥感信息获取关键技术综述[J].农业工程学报,2014,30(18):1-12.WANG Pei,LUO Xiwen,ZHOU Zhiyan,et al.Key technology for remote sensing information acquisition based on micro UAV[J].Trans Chin Soc Agric Eng,2014,30(18):1-12.
[19]张雄清,张建国,段爱国.基于贝叶斯法估计杉木人工林树高生长模型[J].林业科学,2014,50(3):69-75.ZHANG Xiongqing,ZHANG Jianguo,DUAN Aiguo.Tree-height growth model for Chinese fir plantation based on Bayesian method[J].Sci Silv Sin,2014,50(3):69-75.
[20]李燕,张建国,段爱国,等.杉木人工林生物量估算模型的选择[J].应用生态学报,2010,21(12):3036-3046.LI Yan,ZHANG Jianguo,DUAN Aiguo,et al.Selection of biomass estimation models for Chinese fir plantation[J]Chin J Appl Ecol,2010,21(12):3036-3046.
[21]王玉宏,陈永立.低空遥感影像匹配与拼接技术研究[J].测绘信息与工程,2009,34(2):47-49.WANG Yuhong,CHEN Yongli.Automatic matching and panorama generation of low altitude aerial images[J].J Geom,2009,34(2):47-49.
[22]陈信华.SIFT特征匹配在无人机低空遥感影像处理中的应用[J].地矿测绘,2008,24(2):10-12.CHEN Xinhua.Application of SIFT feature matching in the process of unmanned air vehicle remotely-sensed imagery on the low altitude[J].Surv Map Geol Miner Res,2008,24(2):10-12.
[23]王佳,杨慧乔,冯仲科,等.利用轻小型飞机遥感数据建立人工林特征参数模型[J].农业工程学报,2013,29(8):164-170.WANG Jia,YANG Huiqiao,FENG Zhongke,et al.Model of characteristic parameter for forest plantation with data obtained by light small aerial remote sensing system[J].Trans Chin Soc Agric Eng,2013,29(8):164-170.
[24]KINKAID T J.Study of Mirco-sized Technology,Mirco Air Vehicles,and Design of A Payload Carrying Flapping Wing Micro Air Vehicle[D].Monterey:Naval Postgraduate School,2006.
[25]刘国峰,孟小红,岳延波,等.克里金法在GPS数据内插中的应用[J].物探与化探,2006,30(2):175-178.LIU Guofeng,MENG Xiaohong,YUE Yangbo,et al.The application of Kriging to GPS data interpolation[J].Geophys Geochem Explor,2006,30(2):175-178.
[26]王艳梅,李楠,魏林,等.基于无人机航测的DEM数据生产及编辑[J].物探装备,2014,24(5):330-336.WANG Yanmei,LI Nan,WEI Lin,et al.Processing and editing of DEM data from the UVA aerial[J].Equip Geophys Prospect,2014,24(5):330-336.
[27]OLIVER M A,WEBSTER R.Kriging:a method of interpolation for geographical information systems[J].Int J Geogr Inf Syst,1990,4(3):313-332.
[28]PARIS C,BRUZZONE L.A three-dimensional model-based approach to the estimation of the tree top height by fusing low-density lidar data and very high resolution optical images[J].Trans Geosc Remote Sens,2015,53(1):467-480.
[29]吴迪,范文义.激光雷达协同多角度光学遥感数据反演树高[J].北京林业大学学报,2014,36(4):8-15.WU Di,FAN Wenyi.Forest canopy height estimation using LiDAR and optical multi-angler data[J].J Beijing For Univ,2014,36(4):8-15.
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