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.

Manuel-Ángel Valbuena-Rabadán, Jacinto Santamaría-Peña, Félix Sanz-Adán

Abstract


Aim of study: The objective of this study is to test the validity of the DBH and total height allometric models fitted to the crown polygon data obtained by the application of a crown delineation and individualisation algorithm which uses the geometrical relationships between the points in the original LiDAR point clouds in the Pinus sylvestris L. stands.

Area of study: The study area is located in the province of Álava in the Autonomous Community of the Basque Country.

Material and Methods: The crowns are delineated using data from airborne LiDAR point clouds obtained in the 2008 overflight of the Basque Autonomous Community. The DBH and total height data for field trees are obtained from the plots in the 4th National forest inventory.

Main Results: For the adjusted total height and DBH models coefficients of determination of 0.87 and 0.74 respectively were obtained. The root mean squared errors were 10.67% and 18.97% respectively. The distributions of obtained DBH and total height fitted values and the distributions of the DBH and total height of the field trees are very similar except for the DBH below 15 cm.

Research highlights: For stands of Pinus sylvestris L. in Álava, the geometrical relationships between the points that correspond to laser signal echoes obtained with airborne LiDAR sensors can be used directly to delineate approximations of the horizontal projections of the crowns of the trees. Although the procedure set out here was developed for stands of P. sylvestris L. in Álava, it can be applied to other conifers in regular stands by adjusting the working parameters of the function which delineates the crowns on the basis of the point cloud.

Abbreviations used: IFN4: 4th National Forest Inventory; Ht: Field Tree Height; Hl: LiDAR Tree Height; DCL: LiDAR Crown Diameter.


Keywords


LiDAR Forest Inventory; LiDAR points cloud; Segmentation method; Tree Crown Individualisation

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References


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.




DOI: 10.5424/fs/2016251-05790

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