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The Semi-Individual Tree Crown Approach

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Forestry Applications of Airborne Laser Scanning

Part of the book series: Managing Forest Ecosystems ((MAFE,volume 27))

Abstract

The individual tree crown (ITC) approach is a popular method for estimating forest parameters from airborne laser scanning data. One disadvantage of the approach is that errors in tree crown detection can result in estimates of forest parameters with considerable systematic errors. The semi-ITC approach is one method to reduce such systematic errors. In this chapter, we present different variations of the semi-ITC approach and review their application. Two variations of the semi-ITC approach are applied in a case study and compared with the ITC and the area-based approach. One of the semi-ITC approaches is based on the k nearest neighbors (kNN) method used to estimate forest parameters. In the case study, we analyze how different distance metrics and numbers of neighbors influence the accuracy and precision of forest parameter estimates at plot level and stand level.

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Notes

  1. 1.

    The feature space is spanned by the selected predictor variables. The distance is thus not geographic in nature but determined by the similarity of the predictor variables.

  2. 2.

    If we assume that the biomass obtained from field measurements is the true biomass or at least very close to it, we could use the term bias. However, biomass was not measured. Instead, biomass models were used to estimate the tree biomass from dbh and height measurements. Furthermore, height models were used to estimate the height of some trees. Without any assumptions, the term systematic error is technically more correct in our case.

  3. 3.

    Leave-one-plot-out cross validation was applied. Detailed results are not presented here.

  4. 4.

    Plots consisted of several sub plots such that their plot-level results were obtained in a similar way to the stand-level estimates in our study.

References

  • Borgefors G, Brandtberg T, Walter F (1999) Forest parameter extraction from airborne sensors. Int Arch Photogramm Remote Sens 32:151–158

    Google Scholar 

  • Bortolot ZJ (2006) Using tree clusters to derive forest properties from small footprint LiDAR data. Photogramm Eng Remote Sens 72:1389–1397

    Article  Google Scholar 

  • Breidenbach J, Næsset 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

    Article  Google Scholar 

  • Breidenbach J, Næsset E, Gobakken T (2012) Improving k-nearest neighbor predictions in forest inventories by combining high and low density airborne laser scanning data. Remote Sens Environ 117:358–365

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Crookston NL, Finley AO (2008) yaImpute: an R package for k-NN imputation. J Stat Softw 23:1–16

    Google Scholar 

  • Ene L, Næsset E, Gobakken T (2012) Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates. Int J Remote Sens 33:5171–5193

    Article  Google Scholar 

  • Eskelson BNI, Temesgen H, Lemay V, Barrett TM, Crookston NL, Hudak AT (2009) The roles of nearest neighbor methods in imputing missing data in forest inventory and monitoring databases. Scand J For Res 24:235–246

    Article  Google Scholar 

  • Flewelling JW (2008) Probability models for individually segmented tree crown images in a sampling context. In: Proceedings of the SilviLaser 2008 conference, Edinburgh, UK

    Google Scholar 

  • Flewelling JW (2009) Forest inventory predictions from individual tree crowns: regression modeling within a sample framework. In: McRoberts RE, Reams GA, Van Deusen PC, McWilliams WH (eds) Proceedings of the eighth annual forest inventory and analysis symposium; 2006 October 16--19; Monterey, CA. Gen. Tech. Report WO-79. Washington, DC: U.S. Department of Agriculture, Forest Service. 408 p

    Google Scholar 

  • Gjessing I, Werner M (2009) LIDAR rapport, Vestfold 2009. Blom ASA, Lardal

    Google Scholar 

  • 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 21:274–284

    Google Scholar 

  • Gougeon FA, Leckie DG (2003) Forest information extraction from high spatial resolution images using an individual tree crown approach. PFC information report, Victoria, BC, Canada

    Google Scholar 

  • Heinzel JN, Weinacker H, Koch B (2011) Prior-knowledge-based single-tree extraction. Int J Remote Sens 32:4999–5020

    Article  Google Scholar 

  • Holmgren J, Barth A, Larsson H, Olsson H (2012) Prediction of stem attributes by combining airborne laser scanning and measurements from harvesters. Silva Fenn 46:227–239

    Article  Google Scholar 

  • Hyyppä H, Hyyppä J (1999) Comparing the accuracy of laser scanner with other optical remote sensing data sources for stand attributes retrieval. Photogramm J Finl 16(2):5–15

    Google Scholar 

  • Hyyppä J, Inkinen M (1999) Detecting and estimating attributes for single trees using laser scanner. Photogramm J Finl 16:27–42

    Google Scholar 

  • Hyyppä J, Kelle O, Lehikoinen M, Inkinen M (2001a) 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

    Article  Google Scholar 

  • Hyyppä J, Schardt M, Haggrén H, Koch B, Lohr U, Scherrer HU, Paananen R, Luukkonen H, Ziegler M, Hyyppä H, Pyysalo U, Friedländer H, Uuttera J, Wagner S, Inkinen M, Wimmer A, Kukko A, Ahokas A, Karjalainen M (2001b) HIGH-SCAN: the first European-wide attempt to derive single-tree information from laserscanner data. Photogramm J Finl 17:43–53

    Google Scholar 

  • Hyyppä J, Mielonen T, Hyyppä H, Maltamo M, Yu X, Honkavaara E, Kaartinen H (2005) Using individual tree crown approach for forest volume extraction with aerial images and laser point clouds. In: Vosselman G, Brenner C (eds) ISPRS workshop laser scanning 2005, Enschede, The Netherlands. Int Arch Photogramm Remote Sens Spat Inf Sci, pp 12–14

    Google Scholar 

  • Hyyppä J, Yu X, Hyyppä H, Maltamo M (2006) Methods of airborne laser scanning for forest information extraction. In: Koukal T, Schneider W (eds) Workshop on 3D remote sensing in forestry, Vienna, Austria

    Google Scholar 

  • Hyyppä J, Hyyppä H, Leckie D, Gougeon F, Yu X, Maltamo M (2008) Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. Int J Remote Sens 29:1339–1366

    Article  Google Scholar 

  • Kaartinen H, Hyyppä J, Yu X, Vastaranta M, Hyyppä H, Kukko A, Holopainen M, Heipke C, Hirschmugl M, Morsdorf F, Næsset E, Pitkänen J, Popescu S, Solberg S, Wolf B, Wu J-C (2012) An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sens 4:950–974

    Article  Google Scholar 

  • Koch B, Heyder U, Weinacker H (2006) Detection of individual tree crowns in airborne lidar data. Photogramm Eng Remote Sens 72:357–363

    Article  Google Scholar 

  • Lindberg E, Holmgren J, Olofsson K, Olsson H, Wallerman J (2010) Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods. Int J Remote Sens 31:1175–1192

    Article  Google Scholar 

  • Lindberg E, Holmgren J, Olofsson K, Wallerman J, Olsson H (2013) Estimation of tree lists from airborne laser scanning using tree model clustering and k-MSN imputation. Remote Sens 5:1932–1955

    Article  Google Scholar 

  • Maltamo M, Mustonen K, Hyyppä J, Pitkänen J, Yu X (2004) The accuracy of estimating individual tree variables with airborne laser scanning in a boreal nature reserve. Can J For Res 34:1791–1801

    Article  Google Scholar 

  • 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 Fenn 43:507–521

    Article  Google Scholar 

  • Marklund L (1988) Biomass functions for pine, spruce and birch in Sweden. Rapport-Sveriges Lantbruksuniversitet, Institutionen foer Skogstaxering, Sweden

    Google Scholar 

  • McGaughey R (2010) Fusion. Manual version 2.90. USDA, Pacific North-West Research Station, Seattle

    Google Scholar 

  • McRoberts RE (2009) Diagnostic tools for nearest neighbors techniques when used with satellite imagery. Remote Sens Environ 113:489–499

    Article  Google Scholar 

  • McRoberts RE (2012) Estimating forest attribute parameters for small areas using nearest neighbors techniques. For Ecol Manage 272:3–12

    Article  Google Scholar 

  • Means JE, Acker SA, Fitt BJ, Renslow M, Emerson L, Hendrix CJ (2000) Predicting forest stand characteristics with airborne scanning lidar. Photogramm Eng Remote Sens 66:1367–1371

    Google Scholar 

  • Mehtätalo L (2006) Eliminating the effect of overlapping crowns from aerial inventory estimates. Can J For Res 36:1649–1660

    Article  Google Scholar 

  • Moeur M, Stage AR (1995) Most similar neighbor: an improved sampling inference procedure for natural resource planning. For Sci 41:337–359

    Google Scholar 

  • Næsset E (1997) Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS J Photogramm Remote Sens 52:49–56

    Article  Google Scholar 

  • Næsset 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

    Article  Google Scholar 

  • Nilsson M (1996) Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens Environ 56:1–7

    Article  Google Scholar 

  • Ørka HO, Dalponte M, Gobakken T, Næsset E, Ene LT (2013) Characterizing forest species composition using multiple remote sensing data sources and inventory approaches. Scand J For Res 28:677–688

    Article  Google Scholar 

  • Packalén P, Vauhkonen J, Kallio E, Peuhkurinen J, Pitkänen J, Pippuri I, Maltamo M (2011) Comparison of the spatial pattern of trees obtained by ALS based forest inventory techniques. In: SilviLaser 2011, 11th international conference on LiDAR applications for assessing forest ecosystems, University of Tasmania, Hobart, Australia, 16–20 October 2011

    Google Scholar 

  • Packalén P, Vauhkonen J, Kallio E, Peuhkurinen J, Pitkänen J, Pippuri I, Strunk J, Maltamo M (2013) Predicting the spatial pattern of trees by airborne laser scanning. Int J Remote Sens 34:5154–5165

    Article  Google Scholar 

  • Persson A, Holmgren J, Söderman U (2002) Detecting and measuring individual trees using an airborne laser scanner. Photogramm Eng Remote Sens 68:925–932

    Google Scholar 

  • Peuhkurinen J, Mehtätalo L, Maltamo M (2011) Comparing individual tree detection and the area-based statistical approach for the retrieval of forest stand characteristics using airborne laser scanning in Scots pine stands. Can J For Res 41:583–598

    Article  Google Scholar 

  • Suvanto A, Maltamo M (2010) Using mixed estimation for combining airborne laser scanning data in two different forest areas. Silva Fenn 44:91–107

    Article  Google Scholar 

  • Vastaranta M, Holopainen M, Yu X, Haapanen R, Melkas T, Hyyppä J, Hyyppä H (2011) Individual tree detection and area-based approach in retrieval of forest inventory characteristics from low-pulse airborne laser scanning data. Photogramm J Finl 22:1–13

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Vauhkonen J, Packalén P, Pitkänen J (2011) Airborne laser scanning-based stem volume imputation in a managed, boreal forest area: a comparison of estimation units. In: Proceedings of SilviLaser 2011, 11th international conference on LiDAR applications for assessing forest ecosystems, University of Tasmania, Hobart, Australia, 16–20 October 2011

    Google Scholar 

  • Vauhkonen J, Ene L, Gupta S, Heinzel J, Holmgren J, Pitkänen J, Solberg S, Wang Y, Weinacker H, Hauglin KM (2012) Comparative testing of single-tree detection algorithms under different types of forest. Forestry 85:27–40

    Article  Google Scholar 

  • Vauhkonen J, Packalén P, Malinen J, Pitkänen J, Maltamo M (2013) Airborne laser scanning based decision support for wood procurement planning. Scand J For Res. doi:10.1080/02827581.2013.813063

    Google Scholar 

  • Wallerman J, Bohlin J, Fransson JES (2012) Forest height estimation using semi-individual tree detection in multi-spectral 3D aerial DMC data. In: Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22–27 July 2012. IEEE international, New York, USA, pp 6372–6375

    Google Scholar 

  • Yu X, Hyyppä J, Holopainen M, Vastaranta M (2010) Comparison of area-based and individual tree-based methods for predicting plot-level forest attributes. Remote Sens 2:1481–1495

    Article  Google Scholar 

  • Yu X, Hyyppä J, Vastaranta M, Holopainen M, Viitala R (2011) Predicting individual tree attributes from airborne laser point clouds based on the random forests technique. ISPRS J Photogramm Remote Sens 66:28–37

    Article  Google Scholar 

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Acknowledgements

We thank Dr. Jim Flewelling, Seattle Biometrics, USA, for improving the description of the parametric semi-ITC approach and many other useful comments that considerably improved this chapter. Dr. Ronald E. McRoberts, Northern Research Station, St. Paul, USA, Dr. Christoph Straub, Bavarian State Institute of Forestry, Freising, Germany, and Mr. Johannes Rahlf, Norwegian Forest and Landscape Institute, Ås, Norway, are thanked for their valuable comments on an early version of the manuscript. In addition we thank Dr. Jari Vauhkonen and Dr. Barbara Koch for their review statements. We acknowledge the help of Mr. Wiley Bogren, Norwegian Forest and Landscape Institute, Ås, Norway, who assisted in improving the language of this chapter.

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Authors and Affiliations

  1. National Forest Inventory, Norwegian Forest and Landscape Institute, Ås, Norway

    Johannes Breidenbach & Rasmus Astrup

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  1. Johannes Breidenbach
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  2. Rasmus Astrup
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Correspondence to Johannes Breidenbach .

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Editors and Affiliations

  1. School of Forest Sciences, University of Eastern Finland, Joensuu, Finland

    Matti Maltamo

  2. Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway

    Erik Næsset

  3. Department of Forest Sciences, University of Helsinki, Helsinki, Finland

    Jari Vauhkonen

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Breidenbach, J., Astrup, R. (2014). The Semi-Individual Tree Crown Approach. In: Maltamo, M., Næsset, E., Vauhkonen, J. (eds) Forestry Applications of Airborne Laser Scanning. Managing Forest Ecosystems, vol 27. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8663-8_6

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