Accuracy of LiDAR-based tree height estimation and crown recognition in a subtropical evergreen broad-leaved forest in Okinawa, Japan

Azita Ahmad Zawawi, Masami Shiba, Noor Janatun Naim Jemali


Aim of study: To present an approach for estimating tree heights, stand density and crown patches using LiDAR data in a subtropical broad-leaved forest.

Area of study: The study was conducted within the Yambaru subtropical evergreen broad-leaved forest, Okinawa main island, Japan.

Materials and methods: A digital canopy height model (CHM) was extracted from the LiDAR data for tree height estimation and a watershed segmentation method was applied for the individual crown delineation. Dominant tree canopy layers were estimated using multi-scale filtering and local maxima detection. The LiDAR estimation results were then compared to the ground inventory data and a high resolution orthophoto image for accuracy assessment.

Main results: A Wilcoxon matched pair test suggests that LiDAR data is highly capable of  estimating tree height in a subtropical forest (z = 4.0, p = 0.345), but has limitation to detect small understory trees and a single tree delineation. The results show that there is a statistically significant different type of crown detection from LiDAR data over forest inventory (z = 0, p = 0.043). We also found that LiDAR computation results underestimated the stand density and overestimated the crown size.

Research highlights: Most studies involving crown detection and tree height estimation have focused on the analysis of plantations, boreal forests and temperate forests, and less was conducted on tropical and/or subtropical forests. Our study tested the capability of LiDAR as an effective application for analyzing a highly dense forest.

Key words: Broad-leaved; inventory; LiDAR; subtropical; tree height.

Abbreviations: DBH: Diameter at Breast Height, CHM: Canopy Height Model, DEM: Digital Elevation Model, DSM: Digital Surface Model, LiDAR: Light Detection and Ranging, YFA: Yambaru Forest Area.

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Aldred AH, Bonner GM, 1985. Application of airborne laser to forest surveys. Petawawa National Forestry Institute, Canadian Forestry Service. Information Report PI-X-51. p. 62.

Asner GP, Palace M, Keller M, 2002. Estimating canopy structure in an Amazon forest from laser range finder and Ikonos satellite observations. Biotropica 344(4): 483-492.

Avery TE, Bukhart HE, 1994. Forest measurements, 4th ed. McGraw-Hill, Boston, USA. 480 pp.

Böhner J, McCloy KR, Strobl J [Eds.], 2006. SAGA-Analysis and modeling applications. Göttinger Geographische Abhandlungen Vol. 115. Göttingen, Germany.130 pp.

Brandtberg T, Warner TA, Landenberger RE, McGraw JW, 2002. Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America. Remote Sens Environ 85: 290-303.

Chen Q, Baldocchi DD, Gong P, Kelly M, 2006. Isolating individual trees in a savanna woodland using small footprint LiDAR data. Photogram Engineer Remote Sens 72: 923-932.

Elkie PC, Rempel RS, Carr AP, 1999. Patch analyst user`s manual. NWST Technical Manual TM-002, Northwest Science and Technology. 21 pp.

Erikson M, 2004. Segmentation and classification of individual tree crowns in high spatial resolution aerial images. Doctoral thesis. Swedish University of Agricultural Sciences. Sweden.

Falkowski MJ, Smith AMS, Gessler P, Hudak AT, Vierling LA, Evans JS, 2008. The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. Can J Remote Sens 34(2): 2-13.

Falkowski MJ, Smith AMS, Hudak AT, Gessler P, Vierling LA, Crookston NL, 2006. Automated estimation of individual conifer tree height and crown diameter via two- dimensional spatial wavelet analysis of Lidar data. Can J Remote Sens 32: 153-161.

González-Ferreiro E, Miranda D, Barreiro-Fernandez L, Bujan S, Garcia-Gutierrez J, Dieguez-Aranda U, 2013a. Modelling stand biomass fractions in Galician eucalyptus globulus plantations by use of different LiDAR pulse intensities. Forest Syst 22(3): 510-525.

González-Ferreiro E, Dieguez-Aranda U, Barreiro-Fernandez L, Bujan S, Barbosa M, Suárez JC, Bye U, Miranda D, 2013b. A mixed pixel and region-based approach using ALS data for individual tree crown delineation in Pinusradiate D. Don plantations. Int J Remote Sens 34: 7671-7690.

Gougeon FA, 1995. A crown following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Can J Remote Sens 21(3): 274-284.

Gougeon FA, Leckie DG, 2006. The individual tree crown approach applied to IKONOS images of a coniferous plantation area. Photogram Engineer Remote Sens 72: 1287-1297.

Gougeon FA, Leckie DG, 1999. Forest regeneration: Individual tree crown detection techniques for density and stocking assessment. Proc Sym Int Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, Victoria, February 10-12.pp. 169-178.

Heurich M, Weinacker H, 2004. Automated tree detection and measurement in temperate forests of central Europe using laser scanning data. Int Arch Photogramm Rem Sens Spatial Inform Sci, Vol. XXXVI-8/W2. pp. 198-203.

Holmgren J, Nilsson M, Olsson H, 2003. Estimation of tree height and stem volume on plots using airborne laser scanning. Forest Sci 49: 419-428.

KeY, Quackenbush LJ, 2011. A review method for automatic individual tree-crown detection and delineation from passive remote sensing. Int J Remote Sense 32 (17): 4725-4747.

Koch B, Heyder U, Weinacker H, 2006. Detection of individual tree crowns in airborne lidar data. Photogramm Eng Rem S 72(4): 357-363.

Korpela I, 2004. Individual tree measurements by means of digital aerial photogramettry. Silva Monographs 3: 1-93.

Kwak DA, Lee WK, Lee JK, Biging GS, Gong P, 2007. Detection of individual trees and estimation of tree height using LiDAR data. J For Res 12: 425-434.

Lamar WR, McGraw JB, Warner TA, 2005. In Ke Y, Quackenbush LJ, 2011. A review method for automatic individual tree-crown detection and delineation from passive remote sensing. Int J Remote Sens 32 (17): 4725-4747.

Larsen M, Rudemo M, 1998. Optimizing templates for finding trees in aerial photographs, Pattern Recogn Lett 19 (12):1153-1162.

Liu X, Zhang Z, Peterson J, Chandra S, 2007. The effect of LiDAR data density on DEM accuracy. MODSIM07: Int Con on Modelling and Simulation: Land, Water and Environmental Management: Integrated Systems for Sustainability, Christchurch (New Zealand), December 10-13. pp. 1363-1369.

Maltamo M, Mustonen K, Hyyppa J, Pitkanen J, Yu X, 2004. The accuracy of estimating individual tree variables with airborne laser scanning in boreal nature reserve. Canadian J For Res 28: 1016-1031.

Mandelbrot BB, 1982. The fractal geometry of nature. W.H, Freeman, New York.460pp.

Matsue K, Itoh T, Naito K, 2006. Estimating forest resources using airborne LiDAR- estimating stand parameters of Sugi (Cryptomeria japonica D. Don) and Hinoki (Chamaecyparis obtuse Endl.) stands with differing densities. Journal of Japan Soc. Photogramm Remote Sense 45(1): 4-13.

Meng X, Currit N, Zhao K, 2010. Ground filtering algoritms for airborne LiDAR data: A review of critical issues. Remote Sens 2: 833-860.

Oono K, Numata Y, Hirano A, 2008. An improved method of individual tree detection using airborne LiDAR. SilviLaser, Edinburgh (UK). September 17-19. pp. 508-516.

Palace M, Keller M, Asner GP, Hagen S, Braswell B, 2007. Amazon forest structure from IKONOS satellite data and automated characterization of forest canopy properties. Biotropica 40(2): 141-150.

Persson A, Holmgren J, Soderman U, 2002. Detecting and measuring individual trees using airborne laser scanning. Photogramm Eng Rem S 68 (9): 925-932.

Popescu SC, Wynne RH and Nelson RF, 2003. Measuring individual tree diameter with lidar and assessing its influence on estimating forest volume and biomass. Can J Remote Sens 29:564-577.

Popescu SC, Wynne RH and Nelson RF, 2002. Estimating plot-level tree heights with lidar: local filtering with a canopy height based variable window size. Computers and Electronics in Agric 37: 71-95.

Popescu SC, Wynne RH, 2004. Seeing the trees in the forest: Using LiDAR and musltispectral data fusion with local filtering and variable window size for estimating tree height. Photogramm Eng Rem S 70(5): 589-604.

Read JM, Clark DB, Venticinque EM, Moreira MP, 2003. Application of merged 1-m and 4-m resolution satellite data to research and management in tropical forests. J Appli Ecol 40: 592-600.

Scarth P, Phinn S, 2000. Determining forest structural attributes using an inverted geometric- optical model in mixed eucalypt forests, Southeast Queensland, Australia. Remote Sens Env 71: 141-157.

Schaaf CB, Li X, Strahler AH, 1994. Topographic effects on bidirectional and hemispherical reflectances calculated with a geometric optical canopy model. IEEE Trans Geosci Remote Sens 32(6): 1186-1193.

Shataee SJ, 2013. Forest attributes estimation using aerial laser scanner and TM data. Forest Syst 22: 484-496.

Shinohara T, Florence R, Asato I, 1996. Some observations on forests and forestry on the Ryukyuan Islands. Sci Bull Coll Agric Univ of the Ryukyus 43: 31-41.

Shivers BD, Borders BE, 1996. Sampling techniques for forest inventory.Wiley. New York, USA. 368 pp.

Stereńczak K, 2013. Factors influencing individual tree crowns detection based on airborne laser scanner data. For Res Papers 74(4): 323-333.

Stereńczak K, Będkowski K, Weinacker H, 2008. Accuracy of crown segmentation and estimation of selected trees and forest stand parameters in order to resolution of used DSM and nDSM models generated from dense small footprint LiDAR data. ISPRS Congress, Beijing, China, Commission VI, WG VI/5. pp. 27-32.

Stereńczak K, Zasada M, 2011. Accuracy of tree height estimation based on LIDAR data analysis. Folia For Pol: A, 53(2): 123-129.

Tiede D, Burnett C, Heurich M, 2004. Objekt-basierteanalyse von laserscanner und multispectraldaten zur einzelbaumdelinierung im Nationalpark Bayerischer Wald. In: Strobl J, Blaschke T, Griesebner G (eds.): Angewandte Geoinformatik 2004, Wichmann Verlag, Heidelberg: 690-695. (English abstract).

Tiede D, Hochleitner G, Blaschke T, 2005. A full GIS-based workflow for tree identification and tree crown delineation using laser scanning. In: Stilla U, Rottensteiner F, Hins S. (Eds) CMRT05. IAPRS.Vol XXXVI, Part 3/W24. Vienna (Austria), August 29- 30. pp 9-14.

Toutin T, 2002. Impact of terrain slope and aspect on radargrametric DEM accuracy, ISPRS Photogramm Rem S 57(3): 154-170.

Vincent G, Sabatier D, Blanc L, Chave J, Weissenbacher E, Pelissier R, Fonty E, Molino JF, Couteron P, 2012. Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure. Remote Sense Environ 125: 23-33.

Wang C, Menenti M, Stoll MP, Alessandra F, Enrica B, Marco M, 2009. Separation of round and low vegetation signatures in LiDAR measurements of Salt Marsh Environments. IEEE Trans Geosci Remote Sens 47: 2014-2023.

Wang L, Gong P, Biging GS, 2004. Individual tree-crown delineation and tree top detection in high spatial resolution aerial imagery. Photogramme Eng Rem S 70: 351-357.

Wulder M, Niemann KO, Goodenough D, 2000. Local maximum filtering for the extraction of tree locations and basal area from high spatial resolution imagery. Rem Sens Environ. 73: 103-114.

Xu XN, Wang Q, Shibata H, 2008. Forest structure, productivity and soil properties in a subtropical evergreen broad-leaved forest in Okinawa, Japan. J Forest Res 19 (4): 271-276.

Yamamori N, 1979. Studies on the characteristics of water and silvilcultural techniques for avoiding drought damages of Pinus luchuensis stands. Sci Bull Coll Agric Univ of the Ryukyus 26: 573-716.

Zhao K, Popescu S, 2007. Hierarchical watershed segmentation of canopy height model for multi-scale forest inventory (2007). ISPRS Workshop on Laser Scanning and SilviLaser, Espoo. pp 436-441.

DOI: 10.5424/fs/2015241-05476