Estimation of diameter and height of individual trees for Pinus sylvestris L. based on the individualising of crowns using airborne LiDAR and the National Forestry Inventory data.

  1. Valbuena-Rabadán, Manuel-Ángel 1
  2. Santamaría-Peña, Jacinto 2
  3. Sanz-Adán, Félix 2
  1. 1 Departamento de Educación del Gobierno Vasco, IES Murgia BHI, Spain
  2. 2 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revista:
Forest systems

ISSN: 2171-5068

Año de publicación: 2016

Volumen: 25

Número: 1

Tipo: Artículo

DOI: 10.5424/FS/2016251-05790 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Forest systems

Repositorio institucional: lock_openAcceso abierto Editor

Resumen

The possibility of generalising the use of airborne LIDAR data not only gives rise to cost reductions in acquisition and universal distribution, following their inclusion in the National Aerial Orthophotography Plan, but also enables automatic methods to be drawn up for inventorying and assessing forestry resources cheaply and very accurately.This paper describes a procedure developed for estimating the diameter at breast height (DBH) and total height of individual trees that can be applied to stands of Pinus sylvestris L. in Álava, based on data from the National Forestry Inventory [Inventario Forestal Nacional] (IFN4) and a crown individualisation algorithm that uses data from airborne LIDAR point clouds obtained in the 2008 overflight. The procedure was drawn up using free-license software at all stages. It represents an innovation on conventional methods which use digital models of crowns or height percentiles from point clouds. The DBH and total height data for trees are obtained from the plots in IFN4: these data are used to adjust the models for DBH and total height of trees located by the delineation of their crowns from the LIDAR point clouds corresponding to the plots in question. Obtaining data on each individual tree enables stand variable values to be calculated for both complete hillsides and for their silvicultural and management zoning divisions. 

Referencias bibliográficas

  • References
  • Andersen HE, Breidenbach J, 2007. Statistical properties of mean stand biomass estimators in a LiDAR-bases double sampling forest survey design. In: Proceedings of the ISPRS workshop laser scanning 2007 and SilviLaser 2007, Espoo, Finland, 12–14 Sept 2007, IAPRS, vol XXXVI, Part 3/W52, 2007, pp. 8–13.
  • Bravo F, Del Río M, Pando V, San Martín R, Montero G, Ordoñez C, Cañellas I, 2002. El diseño de las parcelas del Inventario Forestal Nacional y la estimación de variables dasométricas. In: Bravo F, Del Río M, Del Peso C, (eds): El Inventario Forestal Nacional Elemento clave para la Gestión Forestal Sostenible. Fundación General de la Universidad de Valladolid, Spain. 19-35.
  • Breidenbach J, Kublin E, Mcgaughey R, Andersen HE, Reutebuch S, 2008. Mixed-effects models for estimating stand volume by means of small footprint airborne laser scanner data. Photogramm J Finland 21: 4–15.
  • Breidenbach J, Naesset E, Lien V, Gobakken T, Solberg S, 2010. Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data. Remote Sens Environ 114: 911–924. http://dx.doi.org/10.1016/j.rse.2009.12.004
  • Breusch TS, Pagan AR, 1979. A Simple Test for Heteroscedaticity and Random Coefficient Variation. Econometrica, 47: 1287-1294. http://dx.doi.org/10.2307/1911963
  • Cuasante D, García C, 2009. Estimación de recursos forestales con tecnología LiDAR aerotransportada. Aplicación práctica en varios montes de la Provincia de Burgos. V Congreso Forestal Español.
  • Condes S, Riaño D, 2005. El uso del escáner laser aerotransportado para la estimación de la biomasa foliar del Pinus sylvestris L. en Canencia (Madrid). Cuad Soc Esp Cienc For 19: 63-70.
  • Duan N, 1983. Smearing estimate: a nonparametric retransformation method. J Am Stat Assoc 78: 605-10. http://dx.doi.org/10.1080/01621459.1983.10478017
  • Durbin J, Watson GS, 1971. Testing for serial correlation in least squares regression. III. Biometrika, 58: 1–19. http://dx.doi.org/10.2307/2334313
  • Gobakken T, Naesset E, 2008. Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data. Can J Forest Res 38:1095–1109. http://dx.doi.org/10.1139/X07-219
  • Heurich M, Persson A, Holmgren J, Kennel E, 2004. Detection and measuring individual trees with laser scanning in mixed mountain forest of central Europe using an algorithm developed for Swedish boreal forest conditions. In: Proceedings of the international Conference. Laser-Scanners for Forest and Landscape Assessment - Instruments, Processing Methods and Applications. Freiburg im Breisgau, Germany.
  • Holmgren J, 2004. Prediction of tree height, basal area and stem volume using airborne laser scanning. Scand J For Res 19:543–553. http://dx.doi.org/10.1080/02827580410019472
  • Holmgren J, Barth A, Larsson H, Olsson H, 2012. Prediction of stem attributes by combining airborne laser scanning and measurements from harvesters. Silva Fennica 46(2): 227–239. http://dx.doi.org/10.14214/sf.56
  • Holmgren J, Lindberg E, 2013. Tree crown segmentation based on a geometric tree crown model for prediction of forest variables. Can J Remote Sens 2013, 39: 86-98. http://dx.doi.org/10.5589/m13-025
  • Hyyppä J, Kelle O, Lehikoinen M, Inkinen M, 2001. A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. IEEE Trans Geosci Remote Sens 39: 969–975. http://dx.doi.org/10.1109/36.921414
  • Kini AU, Popescu SC, 2004. Treevaw: a versatile tool for analysing forest canopy LiDAR data: A preview with an eye towards future. Remote Sensing Foundation for GIS Applications September 12-16.
  • Koch B, Heyder U, Weinacker H, 2006. Detection of Individual Tree Crowns in Airborne LiDAR Data. Photogramm Eng Rem Sen, April 2006 :357-363.
  • Kopela I, 2007. Incorporation of allometry in single-tree remote sensing with LiDAR and multiple images. In: Heipke C, Jacobsen K Gerke M, (eds.). Proceedings of ISPRS Hannover workshop 2007 on High Resolution Earth Imaging for Geospatial Information. Hannover, Germany, May 29–June 1, 2007. IAPRS XXXVI Pt I/W51, 6 pp.
  • Kopela I, Dahlin B, Schäfer H, Bruun E, Haapaniemi F, Honkasalo J, Ilvesniemi S, Kuutti V, Linkosalmi M, Mustonen J, Salo M, Suomi O, Virtanen H, 2007. Single-tree forest inventory using lidar and aerial images for 3D treetop positioning, species recognition, height and crown width estimation. In: Rönnholm P, Hyyppä H, Hyyppä, J, (eds.). Proceedings of ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007, September 12–14, 2007, Espoo, Finland. IAPRS Vol. XXXVI, Part 3 / W52, pp. 227–233.
  • Maltamo M, Peuhkurinen J, Malinen J, Vauhkonen J, Packalén P, Tokola T, 2009. Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data. Silva Fennica 43(3): 507–521. http://dx.doi.org/10.14214/sf.203
  • Naesset E, 1997. Estimating timber volume of forest stands using airborne laser scanner data. Remote Sens Environ 51: 246–253. http://dx.doi.org/10.1016/S0034-4257(97)00041-2
  • Naesset E, 2002. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens Environ 80: 88–99. http://dx.doi.org/10.1016/S0034-4257(01)00290-5
  • Palomino MP, 2009. Algoritmo para la localización y estimación de masa forestal a partir de imágenes LiDAR. Proyecto fin de master en Sistemas Inteligentes. Facultad de Informática. Universidad Complutense de Madrid, Spain.
  • Prodan M, 1968. Forest bimetrics. Pergamon Press. 447. Oxford, UK.
  • Rahman MZ, Gorte BG, 2009. Tree crown delineation from high resolution airborne LiDAR based on densities of high points. ISPRS workshop Laserscanning 2009. Paris, France.
  • Ramsey JB, 1969. Tests for Specification Errors in Classical Linear Least Squares Regression Analysis. J Royal Stat Soc B, 31(2): 350–371.
  • Suárez J, Di Lucca M, Goudie J, Polsson K, Xenadis G, Gardiner B, Perks M, 2009. An individual canopy delineation algorithm based on object- oriented segmentation and classification. Silvilaser 2009 October 14-16 2009 – College Station Texas, USA.
  • Vauhkonen J, 2010. Estimating single-tree attributes by airborne laser scanning: methods based on computational geometry of the 3-D point data. Dissertationes Forestales 104. 44 pp. http://www.metla.fi/dissertationes/df104.htm.
  • Vauhkonen J, Korpela I, Maltamo M, Tokola T, 2010. Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics. Remote Sens Environ 114:1263–1276. http://dx.doi.org/10.1016/j.rse.2010.01.016
  • Wang Y, Weinacker H, Koch B, 2008. A LiDAR point cloud based procedure for vertical canopy structure analysis and 3D single tree modelling in forest. Sensors, 8: 3938-3951. http://dx.doi.org/10.3390/s8063938
  • Yao W, Krzystek P, Heurich M, 2012. Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data. Remote Sens Environ 123:368–380. http://dx.doi.org/10.1016/j.rse.2012.03.027
  • Zhao K, Popescu SC, 2007. Hierarchical watershed segmentation of canopy height model for multi-escale forest inventory. ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007 Espoo September 12-14 2007, Finland.