Light detection and ranging (lidar) data is increasingly being used for ecosystem monitoring across geographic scales. This work concentrates on delineating individual trees in topographically-complex, mixed conifer forest across the California’s Sierra Nevada. We delineated individual trees using vector data and a 3D lidar point cloud segmentation algorithm, and using raster data with an object-based image analysis (OBIA) of a canopy height model (CHM). The two approaches are compared to each other and to ground reference data. We used high density (9 pulses/m 2), discreet lidar data and WorldView-2 imagery to delineate individual trees, and to classify them by species or species types. We also identified a new method to correct artifacts in a high-resolution CHM. Our main focus was to determine the difference between the two types of approaches and to identify the one that produces more realistic results. We compared the delineations via tree detection, tree heights, and the shape of the generated polygons. The tree height agreement was high between the two approaches and the ground data (r 2: 0.93–0.96). Tree detection rates increased for more dominant trees (8–100 percent). The two approaches delineated tree boundaries that differed in shape: the lidar-approach produced fewer, more complex, and larger polygons that more closely resembled real forest？structure.
References
[1]
Hyypp？, J.; Hyypp？, H.; Yu, X.; Kaartinen, H.; Kukko, A.; Holopainen, M. Forest Inventory Using Small-Footprint Airborne Lidar. In Topographic Laser Ranging and Scanning: Principles and Processing; Shan, J., Toth, C.K., Eds.; Taylor & Francis Group: New York, NY, USA, 2009; pp. 335–370.
[2]
Jensen, J.R. Remote Sensing of the Environment: An Earth Resource Perspective, 2nd ed. ed.; Prentice Hall: Upper Saddle River, NJ, USA, 2007; pp. 1–591.
[3]
Lefsky, M.A.; Cohen, W.B.; Harding, D.J.; Parker, G.G.; Acker, S.A.; Gower, S.T. Lidar remote sensing of above-ground biomass in three biomes. Glob. Ecol. Biogeogr 2002, 11, 393–399.
[4]
Clark, M.L.; Clark, D.B.; Roberts, D.A. Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape. Remote Sens. Environ 2004, 91, 68–89.
[5]
Falkowski, M.J.; Smith, A.M.S.; Gessler, P.E.; Hudak, A.T.; Vierling, L.A.; Evans, J.S. The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. Can. J. Remote Sens 2008, 34, 338–350.
[6]
Vepakomma, U.; St-Onge, B.; Kneeshaw, D. Response of a boreal forest to canopy opening: assessing vertical and lateral tree growth with multi-temporal lidar data. Ecol. Appl 2011, 21, 99–121.
[7]
Garcia-Feced, C.; Temple, D.; Kelly, M. Characterizing California Spotted Owl nest sites and their associated forest stands using Lidar data. J. For 2011, 108, 436–443.
[8]
Zhao, F.; Sweitzer, R.A.; Guo, Q.; Kelly, M. Characterizing habitats associated with fisher den structures in the Southern Sierra Nevada, California using discrete return lidar. Forest Ecol. Manag 2012, 280, 112–119.
[9]
Popescu, S.C.; Wynne, R.H.; Nelson, R.F. Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Can. J. Remote Sens 2003, 29, 564–577.
[10]
Zhao, F.; Guo, Q.; Kelly, M. Allometric equation choice impacts lidar-based forest biomass estimates: A case study from the Sierra National Forest, CA. Agric. For. Meteorol 2012, 165, 64–72.
Maltamo, M.; Mustonen, K.; Hyypp？, J.; Pitk？nen, J.; Yu, X. The accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve. Can. J. Forest Res 2004, 34, 1791–1801.
[13]
Pitk？nen, J. Individual tree detection in digital aerial images by combining locally adaptive binarization and local maxima methods. Can. J. Forest Res 2001, 31, 832–844.
Brandtberg, T.; Walter, F. Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis. Mach. Vis. Appl 1998, 11, 64–73.
[16]
Gougeon, F.A.; Leckie, D.G. The individual tree crown approach applied to Ikonos images of a coniferous plantation area. Photogramm. Eng. Remote Sensing 2006, 72, 1287–1297.
[17]
Kaartinen, H.; Hyypp？, J.; Yu, X.; Vastaranta, M.; Hyypp？, H.; Kukko, A.; Holopainen, M.; Heipke, C.; Hirschmugl, M.; Morsdorf, F.; N？sset, E.; Pitk？nen, J.; Popescu, S.; Solberg, S.; Wolf, B.M.; Wu, J.-C. An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning. Remote Sens 2012, 4, 950–974.
[18]
Hyypp？, J.; Inkinen, M. Detecting and estimating attributes for single trees using laser scanner. Photogramm. J. Finl 1999, 16, 27–42.
[19]
Popescu, S.C.; Wynne, R.H. Seeing the trees in the forest: using lidar and multispectral data fusion with local filtering and variable window size for estimating tree height. Photogramm. Eng. Remote Sensing 2004, 70, 589–604.
[20]
Chen, Q.; Baldocchi, D.; Gong, P.; Kelly, M. Isolating individual trees in a savanna woodland using small footprint LIDAR data. Photogramm. Eng. Remote Sensing 2006, 72, 923–932.
[21]
Morsdorf, F.; Meier, E.; K？tz, B.; Itten, K.I.; Dobbertin, M.; Allg？wer, B. LIDAR-based geometric reconstruction of boreal type forest stands at single tree level for forest and wildland fire management. Remote Sens. Environ 2004, 92, 353–362.
[22]
Kwak, D.A.; Lee, W.K.; Lee, J.H.; Biging, G.S.; Gong, P. Detection of individual trees and estimation of tree height using LiDAR data. J. Forest Res 2007, 12, 425–434.
[23]
Brandtberg, T.; Warner, T.A.; Landenberger, R.E.; McGraw, J.B. 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 2003, 85, 290–303.
[24]
Gleason, C.J.; Im, J. A fusion approach for tree crown delineation from LiDAR data. Photogramm. Eng. Remote Sensing 2012, 78, 679–692.
[25]
Koch, B.; Heyder, U.; Weinacker, H. Detection of individual tree crowns in airborne lidar data. Photogramm. Eng. Remote Sensing 2006, 72, 357–363.
[26]
Yu, X.; Hyypp？, J.; Vastaranta, M.; Holopainen, M.; Viitala, R. Predicting individual tree attributes from airborne laser point clouds based on the random forests technique. ISPRS J. Photogramm 2011, 66, 28–37.
[27]
Hyypp？, J.; Kelle, O.; Lehikoinen, M.; Inkinen, M. A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners. ISPRS J. Photogramm 2001, 39, 969–975.
[28]
Solberg, S.; Naesset, E.; Hanssen, K.H.; Christiansen, E. Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning. Remote Sens. Environ 2006, 102, 364–376.
[29]
Breidenbach, J.; N？sset, E.; Lien, V.; Gobakken, T.; Solberg, S. 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 2010, 114, 911–924.
[30]
Edson, C.; Wing, M.G. Airborne light Detection and Ranging (LiDAR) for individual tree stem location, height, and biomass measurements. Remote Sens 2011, 3, 2494–2528.
[31]
Féret, J.-B.; Asner, G.P. Semi-supervised methods to identify individual crowns of lowland tropical canopy species using imaging spectroscopy and LiDAR. Remote Sens 2012, 4, 2457–2476.
[32]
Lee, H.; Slatton, K.C.; Roth, B.E.; Cropper, W.P. Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests. Int. J. Remote Sens 2010, 31, 117–139.
[33]
Li, W.; Guo, Q.; Jakubowski, M.; Kelly, M. A new method for segmenting individual trees from the lidar point cloud. Photogramm. Eng. Remote Sensing 2012, 78, 75–84.
[34]
Cleve, C.; Kelly, M.; Kearns, F.R.; Moritz, M. Classification of the wildland–urban interface: A comparison of pixel-and object-based classifications using high-resolution aerial photography. Compu. Environ. Urban Syst 2008, 32, 317–326.
[35]
Guo, Q.C.; Kelly, M.; Gong, P.; Liu, D. An object-based classification approach in mapping tree mortality using high spatial resolution imagery. GIScience Remote Sens 2007, 44, 24–47.
[36]
Kelly, M.; Blanchard, S.; Kersten, E.; Koy, K. Object-based analysis of imagery in support of public health: new avenues of research. Remote Sens 2011, 3, 2321–2345.
[37]
Blaschke, T. Object-based image analysis for remote sensing. ISPRS J. Photogramm 2010, 65, 2–16.
[38]
Chubey, M.S.; Franklin, S.E.; Wulder, M.A. Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters. Photogramm. Eng. Remote Sensing 2006, 72, 383–394.
[39]
Brennan, R.; Webster, T. Object-oriented land cover classification of lidar-derived surfaces. Can. J. Remote Sens 2006, 32, 162–172.
[40]
Antonarakis, A.S.; Richards, K.S.; Brasington, J. Object-based land cover classification using airborne LiDAR. Remote Sens. Environ 2008, 112, 2988–2998.
[41]
Ke, Y.; Quackenbush, L.J.; Im, J. Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification. Remote Sens. Environ 2010, 114, 1141–1154.
Trimble. eCognition Developer. Version 8.8;; Trimble: Munich, Germany, 2012.
[44]
Baatz, M.; Hoffmann, C.; Willhauck, G. Progressing from Object-Based to Object-Oriented Image Analysis. In Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications; Blaschke, T., Lang, S., Hay, G.J., Eds.; Springer: Berlin/Heidelberg, Germany, 2008; pp. 29–42.
[45]
Stephens, S.L.; Collins, B.M. Fire regimes of mixed conifer forests in the north-central Sierra Nevada at multiple spatial scales. Northwest Sci 2004, 78, 12.
[46]
Jakubowski, M.K.; Guo, Q.; Kelly, M. Tradeoffs between lidar pulse density and forest measurement accuracy. Remote Sens. Environ 2013, 130, 245–253.
[47]
Exelis. ENVI Software; Exelis: McLean, VA, USA, 2012.
[48]
Laben, C.A.; Brower, B.V. Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-SharpeningUS Patent No. 6,011,875. 4, January, 2000.
[49]
Chang, Y.; Habib, A.; Lee, D.; Yom, J. Automatic classification of lidar data into ground and non-ground points. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci 2008, 37, 463–468.
[50]
Leckie, D.; Gougeon, F.; Hill, D.; Quinn, R.; Armstrong, L.; Shreenan, R. Combined high-density lidar and multispectral imagery for individual tree crown analysis. Can. J. Remote Sens 2003, 29, 633–649.
[51]
Lee, J.-S. Digital Image Enhancement and Noise Filtering by Use of Local Statistics. IEEE Trans. Pattern Anal. Mach. Intell. 1980, PAMI–2, 165–168.
[52]
Lopes, A.; Touzi, R.; Nezry, E. Adaptive speckle filters and scene heterogeneity. IEEE Trans. Geosci. Remote Sens 1990, 28, 992–1000.
Dra？gu？, L.; Tiede, D.; Levick, S.R. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inf. Sci 2010, 24, 859–871.
[55]
Leonard, J. Technical Approach for LIDAR Acquisition and Processing; EarthData Inc.: Frederick, MD, USA, 2005; pp. 1–20.
[56]
Im, J.; Jensen, J.R.; Hodgson, M.E. Object-based land cover classification using high-posting-density LiDAR data. GIScience Remote Sens 2008, 45, 209–228.
[57]
Baltsavias, E.P. Airborne laser scanning: basic relations and formulas. ISPRS J. Photogram 1999, 54, 199–214.
[58]
Frank, E.; Hall, M.; Holmes, G.; Kirkby, R.; Pfahringer, B.; Witten, I.H.; Trigg, L. Weka—A Machine Learning Workbench for Data Mining. In Data Mining and Knowledge Discovery Handbook; Maimon, O., Rokach, L., Eds.; Springer: New York, NY, USA, 2010; pp. 1269–1277.
[59]
Jakubowski, M.K.; Guo, Q.; Collins, B.; Stephens, S.; Kelly, M. Predicting surface fuel models and fuel metrics using lidar and imagery in dense, mountainous forest. Photogramm. Eng. Remote Sensing 2013, 79, 37–50.
[60]
Fan, R.E.; Chang, K.W.; Hsieh, C.J.; Wang, X.R.; Lin, C.J. LIBLINEAR: A library for large linear classification. J. Mach. Learn. Res 2008, 9, 1871–1874.
[61]
Keerthi, S.S.; Shevade, S.K.; Bhattacharyya, C.; Murthy, K.R.K. Improvements to Platt's SMO algorithm for SVM classifier design. Neural Comput 2001, 13, 637–649.
[62]
Buhmann, M.D. Radial basis functions. Acta Numer 2000, 9, 1–38.
[63]
Forman, R.T.T.; Godron, M. Landscape Ecology; JohnWiley and Sons: New York, NY, USA, 1986; pp. 1–619.
[64]
McGarigal, K.; Cushman, S.A.; Neel, M.C.; Ene, E. FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. Version 4;; University of Massachusetts Amherst: MA, USA, 2002.
[65]
Jiao, L.; Liu, Y. Analyzing the shape characteristics of land use classes in remote sensing imagery. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2012, I-7, 135–140.
[66]
Trani, M.K.; Giles, R.H., Jr. An analysis of deforestation: Metrics used to describe pattern change. Forest Ecol. Manag 1999, 114, 459–470.
[67]
Brokaw, N.; Thompson, J. The H for DBH. Forest Ecol. Manag 2000, 129, 89–91.
[68]
Suarez, J.C.; Ontiveros, C.; Smith, S.; Snape, S. Use of airborne LiDAR and aerial photography in the estimation of individual tree heights in forestry. Comput. Geosci 2005, 31, 253–262.
