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Analysis of Sampling Size Effect of Aboveground Biomass Estimation of Rubber Forest Using Airborne LiDAR Data

  • Hongbin LUO ,
  • Qingtai SHU ,
  • Yong PANG ,
  • Qiang WANG ,
  • Dongling WANG
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  • 1. College of Forestry,Southwest Forestry University,Kunming,650224
    2. Resource Information Institute,Chinese Academy of Forestry,Beijing 100091

Received date: 2019-10-18

  Revised date: 2019-11-27

  Online published: 2020-05-18

Abstract

Remote sensing monitoring of forest biomass under climate change is a hot topic of current research.As an important remote sensing information source,airborne LiDAR's sampling size has a certain influence on the estimation accuracy of biomass.In this paper,the airborne LiDAR data is sampled in different sizes(21 sampling sizes,side length from 10m to 30m,interval is 1m),the LiDAR parameters of different sampling sizes are extracted,and the PLSR model is established with the biomass on the rubber forest.The effect of airborne LiDAR sampling size on the estimation accuracy of biomass on rubber forest was studied.The results show that the estimation accuracy of the aboveground biomass of rubber forest is affected by the sampling size of airborne LiDAR data.The results showed a certain regularity,but the difference was not significant.When the sampling size is less than 18m,the estimation accuracy increases with the increase of sampling size,while the sampling size is larger than 18m.The estimation accuracy decreases as the sampling size increases,and thus tends to be gentle.When the sampling size is 18m,the estimation result is the best.The model determination coefficient R 2 is 0.718,the root mean square error RMSE is 17.830 t/hm 2,and the cross-validation accuracy P and RMSEcv are 82.741% and 18.874t/hm 2.Compared to the estimated results at the actual sample size(30m), R 2 increased by 1.989% and RMSE decreased by 2.611% at the 18m sample size.Thus,the biomass estimation process for an actual case study and research of the sampling area size is selected,thereby increasing the biomass estimation accuracy.

Key words: sample; LiDAR; biomass; rubber forest

Cite this article

Hongbin LUO , Qingtai SHU , Yong PANG , Qiang WANG , Dongling WANG . Analysis of Sampling Size Effect of Aboveground Biomass Estimation of Rubber Forest Using Airborne LiDAR Data[J]. Forest and Grassland Resources Research, 2020 , 0(1) : 136 -142 . DOI: 10.13466/j.cnki.lyzygl.2020.01.017

References

[1] 李海奎, 雷渊才 . 中国森林植被生物量和碳储量评估[M]. 北京: 中国林业出版社, 2010,28.
[2] 陈迪 . 基于遥感技术的甘南州陆地生态系统NEP研究[D]. 甘肃:兰州大学, 2016.
[3] 张瑞英, 庞勇, 李增元 , 等. 结合机载LiDAR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.
[4] Gibbs H K, Brown S, Niles J O , et al. Monitoring and estimating tropical forest carbon stocks:making REDD a reality[J]. Environmental Research Letters, 2007,2(4):1-13.
[5] Duncanson L I, Niemann K O, Wulder M A . Integration of GLAS and Landsat TM data for aboveground biomass estimation[J]. Canadian Journal of Remote Sensing, 2010,36(2):129-141.
[6] Lefsky M A, Cohen W B, Parker G G , et al. Lidar Remote Sensing for Ecosystem Studies[J]. BioScience, 2002,52(1):19-30.
[7] Nelson R, Krabill W, Maclean G . Determining forest canopy characteristics using airborne laser data.[J]. Remote Sensing of Environment, 1984,15(3):201-212.
[8] 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.
[9] Popescu S C . Estimating biomass of individual pine trees using airborne LiDAR[J]. Biomass and Bioenergy, 2007,31(9):646-655.
[10] N?sset E, Gobakken T . Estimation of above and below-ground biomass across regions of the boreal forest zone using airborne laser[J]. Remote Sensing of Environment, 2008,112(6):3079-3090.
[11] 庞勇, 李增元 . 基于机载激光雷达的小兴安岭温带森林组分生物量反演[J]. 植物生态学报, 2012,36(10):1095-1105.
[12] Luo Shezhou, Chen Jing M, Wang Cheng , et al. Effects of LiDAR point density,sampling size and height threshold on estimation accuracy of crop biophysical parameters[J]. Optics Express, 2016,24(11):1578-1593.
[13] Streutker D R, Glenn N F . Li DAR measurement of sagebrush steppe vegetation heights[J]. Remote Sensing of Environment, 2006,102(1-2):135-145.
[14] Luo Shezhou, Wang Cheng, Xi Xiaohuan , et al. Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation[J]. Ecological Indicators, 2017,73:378-387.
[15] Estornell J, Ruiz L A, B. Velázquez-Martí, et al. Estimation of shrub biomass by airborne LiDAR data in small forest stands[J]. Forest Ecology and Management, 2011,262(9):1697-1703.
[16] 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.
[17] 罗洪斌, 舒清态, 王强 , 等. 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[J]. 东北林业大学学报, 2019(7):56-61.
[18] 赵晓庆, 杨贵军, 刘建刚 , 等. 基于无人机载高光谱空间尺度优化的大豆育种产量估算[J]. 农业工程学报, 2017(1):110-116.
[19] 舒清态, 唐守正 . 国际森林资源监测的现状与发展趋势[J]. 世界林业研究, 2005(3):33-37.
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