Special Column:Celebration of the 70th Anniversary of IGSNRR, CAS

Reconstruction of Sparse Forest Canopy Height Using Small Footprint LiDAR Data

  • 1. State Key Laboratory of Remote Sensing Science,Institute of Remote Sensing Applications,CAS,Beijing 100101,China;
    2. Institute of Applied Ecology,CAS,Shenyang 110016,China;
    3. Graduate University of Chinese Academy of Sciences,Beijing 100049,China

Received date: 2006-10-31

  Revised date: 2007-10-31

  Online published: 2008-05-28


This study estimates canopy height using multi-returns data acquired by the small footprint lidar of sparse forest and explores the precision of different methods such as IDW(Inverse distance weight),Spline method,OK(Ordinary Kriging) to reconstruct the canopy elevation and the ground elevation.It is found out that the performance of the different methods is different between the condition of the forest canopy and the ground.Thereinto the Spline method has the best precision on the reconstruction of forest canopy,the mean of absolute value error is 0.95m,and the variance is 3.42.But in the case of ground,the OK method's mean of absolute value error is the lowest,being 0.35m,but the variance of Spline method is the lowest,being 0.48.But integrating the precision of ground elevation with canopy elevation,and choosing the canopy elevation reconstructed by Spline and the ground elevation reconstructed by OK,it is possible to achieve the abstraction of the canopy height of the study area.

Cite this article

QIN Yu-chu, WU Yun-chao, NIU Zheng, ZHAN Yu-lin, XIONG Zai-ping . Reconstruction of Sparse Forest Canopy Height Using Small Footprint LiDAR Data[J]. JOURNAL OF NATURAL RESOURCES, 2008 , 23(3) : 507 -513 . DOI: 10.11849/zrzyxb.2008.03.018


[1] 李小文,王锦地. 植被光学遥感模型与植被结构参数化[M]. 北京:科学出版社, 1995. [L I Xiao-wen, WANG J ing-di. Vegetation Op tical Remote SensingModel and Vegetation Structure Paramterization. Beijing: Science Press, 1995.] [2] 赵宪文,李崇贵,斯林,等. 基于信息技术的森林资源调查新体系[J]. 北京林业大学学报, 2002, 24 (5~6): 147~155. [ZHAO Xian-wen, L I Chong-gui, SI Lin, et al. Building a new system of forest resources inventory by information technology. Journal of B eijing Forestry University, 2002, 24 (5-6): 147-155.] [3] Dubayah Jason Drake. Lidar remote sensing for forestry[J]. Journal of Forestry, 2000, 98: 44-46. [4] Kevin Lima, Paul Treitza, MichaelWulderb. Lidar remote sensing of forest structure[J]. Progress in Physical Geography, 2003, (27): 88-106. [5] Nelson R F, KrabillW B, et al. Determining forest canopy characteristics using airborne laser data[J]. Rem ote Sensing of Environm ent, 1984, 15: 201-212. [6] Matthew L Clark,David Clark, Dar Roberts. Small2footp rint lidar estimation of sub2canopy elevation and tree height in a trop ical rain forest landscape[J]. Rem ote Sensing of Environm ent, 2004, 91: 68-89. [7] K Kraus, N Pfeifer. Determination of terrain models in wooded areaswith airborne laser scanner data[J]. ISPRS Journal of Photogramm etry and Rem ote Sensing, 1998, 53: 193-203. [8] M Roggero. Airborne laser scanning: Clustering in raw data[J]. InternationalA rchives of Photogramm etry, 2001, 34: 227-232. [9] P Axelsson. DEM generation from laser scanner data using adap tive tin models[J]. International A rchives of Photogramm etry, 2000, 33: 85-92. [10] Streutker D, Glenn N. Lidar measurement of sagebrush steppe vegetation heights[J]. Rem ote Sensing of Environm ent, 2006, 102: 135-145. [11] Hasenauer H, Merganicova K, Petritsch R, et a1. Validation daily climate interpolations over comp lex terrain in Austria [J]. Agricultural and ForestM eteorology, 2003, 119 (1-2): 87-107. [12] Latypov D, Zosse E. Lidar data quality control and system calibration using overlapp ing flight lines in commercial environment[A]. In: Proceedings of the American Society of Photogrammetry and Remote Sensing Annual Conference [C]. Washington D C, 2002.