Chin J Plan Ecolo ›› 2012, Vol. 36 ›› Issue (10): 1095-1105.doi: 10.3724/SP.J.1258.2012.01095

• Research Articles • Previous Articles     Next Articles

Inversion of biomass components of the temperate forest using airborne Lidar technology in Xiaoxing’an Mountains, Northeastern of China

PANG Yong* and LI Zeng-Yuan   

  1. Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing 100091, China
  • Received:2012-02-13 Revised:2012-09-12 Online:2012-09-26 Published:2012-10-01
  • Contact: PANG Yong
  • Supported by:

    NSFC;State Forest Administration of China


Aims Our purpose was to demonstrate the potential of using airborne laser to estimate biomass components of temperate forest. The airborne Lidar data and field data of concomitant plots were used in a forest of the Northeastern China.
Methods A set of biomass components, i.e., leaf biomass, branch biomass, trunk biomass, aboveground biomass and belowground biomass, were calculated from field data using species-specific allometric equations. Canopy height indices and density indices were calculated from Lidar point cloud data. The height indices evaluated included maximum height of all points, mean height of all points, quadratic mean height (square root of the mean squared height of each Lidar point) as well as height percentiles. Canopy density indices were computed as the proportions of laser points above each percentile height to total number of points. Then statistical models between these biomass components from field data and Lidar indices were built. Stepwise regression was used for variable selection and the maximum coefficient of determination (R2) improvement variable selection techniques were applied to select the ALS-derived variables to be included in the models. The least squares method was used generally and repeated until all the independent variables of the regression equation were accord with the requirements of entering models.
Important findings There were good correlations between biomass components and Lidar indices. The R2 was >0.6 for all the biomass components when we put all three types of forest (i.e., needle-leaved, broad-leaved and mixed) together. Needle-leaved forest had best estimation followed by broad-leaved and mixed forests when we built separate models for the three types of forest. This estimation capability is better when the regression models are built for different forest types.

Axelsson P (2001). Ground estimation of laser data using adaptive TIN-models. Proceedings of OEEPE workshop on airborne laserscanning and interferometric SAR for detailed digital elevation models, pp 185-208
Gobakken T and N?sset E (2004). Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data. Scandinavian Journal of Forest Research, 19:529-542
Hall S A, Burke I C, Box D O, Kaufmann M R and Stoker J M (2005). Estimating stand structure using discrete-return lidar: an example from low density, fire prone ponderosa pine forests. Forest Ecology and Management, 208:189-209
Holmgren J and Persson ?. (2004a). Identifying species of individual trees using airborne laser scanner. Remote Sensing of Environment, 90(4):415-423
Holmgren J. (2004b). Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning. Scandinavian Journal of Forest Research, 19:543-553
Latifi, H., Nothdurft, A., Koch, B (2010). Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR–derived predictors. Forestry
Lim K, Treitz P, Baldwin K, Morrison I and Green J (2003). Lidar remote sensing of biophysical properties of tolerant northern hardwood forests. Canadian Journal of Remote Sensing, 29(5):658-678
Lim K S and Treitz P M (2004). Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Scandinavian Journal of Forest Research, 19:558-570
Maltamo M, Eerik?inen K, Pitk?nen J, Hyypp? J and Vehmas M (2004). Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sensing of Environment, 90(3):319-330
MacLean G A, Krabill W B (1986). Grossmerchantable timber volume estimation using an airborne LiDAR system[J].Canadian Journal of Remote Sensing,12:7~l8
N?sset E (1997). Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing, 52(2):49-56
N?sset E and Gobakken T (2008). Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sensing of Environment, 112(6):3079-3090
Nelson R, Krabill W and MacLean G (1984). Determining forest canopy characteristics using airborne laser data. Remote Sensing of Environment, 15(3):201-212
Nelson R, Krabill W and Tonelli J (1988). Estimating forest biomass and volume using airborne laser data. Remote Sensing of Environment, 24(2):247-267
Nelson R, Oderwald R and Gregoire T G (1997). Separating the ground and airborne laser sampling phases to estimate tropical forest basal area, volume, and biomass. Remote Sensing of Environment, 60(3):311-326
Nilsson M (1996). Estimation of tree heights and stand volume using an airborne lidar system. Remote sensing of Environment, 56: 1-7
Popescu S C. Estimating biomass of individual pine trees using airborne lidar. Biomass and Bioenergy, 2007, 31: 646-655
Popescu S C, Wynne R H and Nelson R F (2002). Estimating plot-level tree heights with lidar: local filtering with a canopy-height based variable window size. Computers and Electronics in Agriculture, 37(1-3):71-95
Wang, C., (2006). Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests. Forest Ecology and Management, 222(1-3), p.9-16.
Zhao,K.,Popescu,S.,Nelson,R (2009). Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers. Remote Sensing of Environment, 113:182-196
陈传国,朱俊凤 (1989).东北主要林木生物量手册.中国林业出版社.
冯宗炜,王效科,吴刚 (1999).中国森林生态系统的生产量和生产力.北京:科学出版社
付甜,庞勇,黄庆丰等 (2011). 亚热带森林参数的机载激光雷达估测,遥感学报,15(5):1092-1104
刘清旺,李增元,陈尔学等 (2010). 机载LIDAR点云数据估测单株木生物量,高技术通讯,7:765-770
庞勇,赵峰,李增元等 (2008). 机载激光雷达平均树高提取研究. 遥感学报,12(1):152-158
庞勇,李增元,陈尔学 (2005). 激光雷达技术及其在林业上的应用,林业科学,41(3):129~136
中国国家发展和改革委员会 (2007).中国应对气候变化国家方案,6月
No related articles found!
Full text



[1] Yan Xiao-hua Cai Zhu-ping. Effects of S-07, PP333 and Triadimefon on Peroxidaseisoentyme of Rice Seedling[J]. Chin Bull Bot, 1995, 12(专辑3): 109 -112 .
[2] . [J]. Chin Bull Bot, 1994, 11(专辑): 13 .
[3] Xiaomin Yu;Xingguo Lan;Yuhua Li. The Ub/26S Proteasome Pathway and Self-incompatible Responses in Flowering Plants[J]. Chin Bull Bot, 2006, 23(2): 197 -206 .
[4] WANG Ling-Li LIU Wen-Zhe. Contents of Camptothecin in Camptotheca acuminata from Different Provenances[J]. Chin Bull Bot, 2005, 22(05): 584 -589 .
[5] Dai Yun-ling and Xu Chun-hui. Advances in Research on Protein Components of Oxygen-evolving Complex[J]. Chin Bull Bot, 1992, 9(03): 1 -16 .
[6] . Advances in Research on Photosynthesis of Submerged Macrophytes[J]. Chin Bull Bot, 2005, 22(增刊): 128 -138 .
[7] Shaobin Zhang;Guoqin Liu. Research Advances in Plant Actin Isoforms[J]. Chin Bull Bot, 2006, 23(3): 242 -248 .
[9] MA Li-Hui, WU Pu-Te, and WANG You-Ke. Spatial pattern of root systems of dense jujube plantation with jujube age in the semiarid loess hilly region of China[J]. Chin J Plan Ecolo, 2012, 36(4): 292 -301 .
[10] PAN Yu-De, Melillo J. M., Kicklighter D. W., XIAO Xiang-Ming, McGuire A. D.. Modeling Structural and Functional Responses of Terrestria Ecosystems in China to Changes in Climate and Atmospheric CO2[J]. Chin J Plan Ecolo, 2001, 25(2): 175 -189 .