运用机载激光雷达和陆地卫星数据对橡胶林地上生物量的估测
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摘要
以景洪市机载激光雷达飞行条带内橡胶林地上生物量为研究对象,以机载激光雷达(LiDAR)和陆地卫星(Landsat8/OLI)影像数据为主要信息源,结合地面44块实测样地数据,采用偏最小二乘回归模型(PLSR)分别依据单一信息源、多源信息联合对橡胶林地上生物量进行遥感估测。结果表明:在单一信息源生物量遥感估测建模中,Landsat8/OLI光学影像数据的模型决定系数比激光雷达(LiDAR)数据低,模型决定系数(R~2)分别为0.56和0.68;联合LiDAR和Landsat8/OLI数据对橡胶林地上生物量的估测效果比单一信息源精度高,模型决定系数(R~2)为0.75,均方根误差为16.50 t/hm~2,验证精度为81%;飞行区域橡胶林地上生物平均值为98.86 t/hm~2,总生物量为556 074 t。
With the above ground biomass of rubber forest in the flight strip of airborne LiDAR in Jinghong City as the research object,the airborne LiD AR and Landsat8/OLI Landsat image data are the main information sources,combined with 44 pieces of ground sample data of 30 m×30 m in size. Using partial least squares regression model( PLSR),a single information source and multi-source information was used to estimate above ground biomass of rubber forest. In the single information source biomass remote sensing estimation modeling,the model determination coefficient based on Landsat8/OLI optical image data was lower than that of LiD AR data,with R~2 of 0.56 and 0.68,respectively; Combining LiDAR and Landsat8/OLI data,the estimation accuracy of above ground biomass of rubber forest was better than that of single information source,with model determination coefficient( R~2) of 0.75,the root mean square error( RMSE) of 16.50 t/hm~2,and the verification accuracy( Pcv) of 81%. The average biomass of the rubber forest in the flight area was 98.86 t/hm~2,and the total biomass was 556074 t.
With the above ground biomass of rubber forest in the flight strip of airborne LiDAR in Jinghong City as the research object,the airborne LiD AR and Landsat8/OLI Landsat image data are the main information sources,combined with 44 pieces of ground sample data of 30 m×30 m in size. Using partial least squares regression model( PLSR),a single information source and multi-source information was used to estimate above ground biomass of rubber forest. In the single information source biomass remote sensing estimation modeling,the model determination coefficient based on Landsat8/OLI optical image data was lower than that of LiD AR data,with R~2 of 0.56 and 0.68,respectively; Combining LiDAR and Landsat8/OLI data,the estimation accuracy of above ground biomass of rubber forest was better than that of single information source,with model determination coefficient( R~2) of 0.75,the root mean square error( RMSE) of 16.50 t/hm~2,and the verification accuracy( Pcv) of 81%. The average biomass of the rubber forest in the flight area was 98.86 t/hm~2,and the total biomass was 556074 t.
引文
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[18]罗批,郭继昌,李锵,等.基于偏最小二乘回归建模的探讨[J].天津大学学报(自然科学与工程技术版),2002,35(6):783-786.
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[20]曲苑婷,汪垚,刘观潮,等.基于GLAS激光雷达反演森林生物量[J].测绘通报,2014(11):73-77.
[21]于颖,范文义,李明泽,等.利用大光斑激光雷达数据估测树高和生物量[J].林业科学,2010,46(9):84-87.
[22]王云飞,庞勇,舒清态.基于随机森林算法的橡胶林地上生物量遥感反演研究:以景洪市为例[J].西南林业大学学报,2013,33(6):38-45.
[23]张瑞英,庞勇,李增元,等.结合机载Li DAR和LANDSAT ETM+数据的温带森林郁闭度估测[J].植物生态学报,2016,40(2):102-115.
[2]WARING R H,SCHLESINGER W H.Forest ecosystems.concepts and management[M].Florida:Academic Press,1985.
[3]罗云建,张小全,王效科,等.森林生物量的估算方法及其研究进展[J].林业科学,2009,45(8):129-134.
[4]庞勇.星载干涉雷达和激光雷达数据森林参数反演[D].北京:中国科学院遥感应用研究所,2005.
[5]冯仲科,罗旭,石丽萍.森林生物量研究的若干问题及完善途径[J].世界林业研究,2005,18(3):25-28.
[6]闾妍宇,李超,欧光龙,等.基于地理加权回归模型的思茅松生物量遥感估测[J].林业资源管理,2017(1):82-90.
[7]李丹丹.北京市森林生物量遥感反演模型研究[D].北京:北京林业大学,2013.
[8]曹林,徐婷,申鑫,等.集成Landsat OLI和机载Li DAR条带数据的亚热带森林生物量制图[J].遥感学报,2016,20(4):665-678.
[9]NELSON R,KRABILL W,MACLEAN G.Determining forest canopy characteristics using airborne laser data[J].Remote Sensing of Environment,1984,15(3):201-212.
[10]NELSON R,KRABILL W,TONELLI J.Estimating forest biomass and volume using airborne laser data[J].Remote Sensing of Environment,1988,24(2):247-267.
[11]POPESCU S C,WYNNE R H,SCRIVANI J A.Fusion of smallfootprint lidar and multispectral data to estimate plot-level volume and biomass in deciduous and pine forests in virginia,USA[J].Forest Science,2004,50(4):551-565.
[12]徐婷.基于Li DAR和OLI多光谱数据的森林生物量估算方法及应用[D].南京:南京林业大学,2015.
[13]LUO S Z,WANG C,XI X H,et al.Fusion of airborne Li DARdata and hyperspectral imagery for aboveground and belowground forest biomass estimation[J].Ecological Indicators,2017,73:378-387.
[14]LAURIN G V,CHEN Q,LINDSELL J A,et al.Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,89:49-58.
[15]唐建维,庞家平,陈明,等.西双版纳橡胶林的生物量及其模型[J].生态学杂志,2009,28(10):1942-1948.
[16]NILSSON M.Estimation of tree heights and stand volume using an airborne lidar system[J].Remote Sensing of Environment,1996,56(1):1-7.
[17]陈楚,关泽群,张鹏林,等.利用RS和GIS的森林蓄积量偏最小二乘估测研究[J].湖北林业科技,2004,33(4):25-28.
[18]罗批,郭继昌,李锵,等.基于偏最小二乘回归建模的探讨[J].天津大学学报(自然科学与工程技术版),2002,35(6):783-786.
[19]付甜.基于机载激光雷达的亚热带森林参数估测[D].合肥:安徽农业大学,2010.
[20]曲苑婷,汪垚,刘观潮,等.基于GLAS激光雷达反演森林生物量[J].测绘通报,2014(11):73-77.
[21]于颖,范文义,李明泽,等.利用大光斑激光雷达数据估测树高和生物量[J].林业科学,2010,46(9):84-87.
[22]王云飞,庞勇,舒清态.基于随机森林算法的橡胶林地上生物量遥感反演研究:以景洪市为例[J].西南林业大学学报,2013,33(6):38-45.
[23]张瑞英,庞勇,李增元,等.结合机载Li DAR和LANDSAT ETM+数据的温带森林郁闭度估测[J].植物生态学报,2016,40(2):102-115.