forest canopy gaps; ALS; raster-based approach; segmentation; random forest; geometric accuracy; stand types
Abstract :
[en] Canopy gaps are small-scale openings in forest canopies which offer suitable
micro-climatic conditions for tree regeneration. Field mapping of gaps is complex and
time-consuming. Several studies have used Canopy Height Models (CHM) derived from
airborne laser scanning (ALS) to delineate gaps but limited accuracy assessment has
been carried out, especially regarding the gap geometry. In this study, we investigate
three mapping methods based on raster layers produced from ALS leaf-off and leaf-on
datasets: thresholding, per-pixel and per-object supervised classifications with Random
Forest. In addition to the CHM, other metrics related to the canopy porosity are tested.
The gap detection is good, with a global accuracy up to 82% and consumer’s accuracy often
exceeding 90%. The Geometric Accuracy (GAc) was analyzed with the gap area, main
orientation, gap shape-complexity index and a quantitative assessment index of the matching
with reference gaps polygons. The GAc assessment shows difficulties in identifying a
method which properly delineates gaps. The performance of CHM-based thresholding was
exceeded by that of other methods, especially thresholding of canopy porosity rasters and the
per-pixel supervised classification. Beyond assessing the methods performance, we argue the
critical need for future ALS-based gap studies to consider the geometric accuracy of results.
Disciplines :
Agriculture & agronomy
Author, co-author :
Bonnet, Stéphanie ; Université de Liège > Ingénierie des biosystèmes (Biose) > Gestion des ressources forestières et des milieux naturels
Gaulton, Rachel
Lehaire, François
Lejeune, Philippe ; Université de Liège > Ingénierie des biosystèmes (Biose) > Gestion des ressources forestières et des milieux naturels
Language :
English
Title :
Canopy Gap Mapping from Airborne Laser Scanning: An Assessment of the Positional and Geometrical Accuracy
Alternative titles :
[fr] Cartographie de trouées forestières à partir de LiDAR aérien : Une évaluation de la précision de positionnement et géométrique
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Degen, T.; Devillez, F.; Jacquemart, A.L. Gaps promote plant diversity in beech forests (Luzulo-Fagetum), North Vosges, France. Ann. For. Sci. 2005, 62, 429-440.
Dobrowolska, D.; Veblen, T. Treefall-Gap structure and regeneration in mixed Abies alba stands in central Poland. For. Ecol. Manag. 2008, 255, 3469-3476.
Schliemann, S.; Bockheim, J. Methods for studying treefall gaps: A review. For. Ecol. Manag. 2011, 261, 1143-1151.
Blackburn, G.A.; Milton, E. Filling the gaps: Remote sensing meets woodland ecology. Glob. Ecol. Biogeogr. Lett. 1996, 5, 175-191.
Kane, V.R.; Gersonde, R.F.; Lutz, J.A.; McGaughey, R.J.; Bakker, J.D.; Franklin, J.F. Patch dynamics and the development of structural and spatial heterogeneity in pacific northwest forests. Can. J. For. Res. 2011, 41, 2276-2291.
Malcolm, D.; Mason, W.; Clarke, G. The transformation of conifer forests in Britain-regeneration, gap size and silvicultural systems. For. Ecol. Manag. 2001, 151, 7-23.
Runkle, J.R. Patterns of disturbance in some old-growth mesic forests of eastern North America. Ecology 1982, 63, 1533-1546.
Vehmas, M.; Packalen, P.; Maltamo, M.; Eerikainen, K. Using airborne laser scanning data for detecting canopy gaps and their understory type in mature boreal forest. Ann. For. Sci. 2011, 68, 825-835.
Watt, A.S. Pattern and process in the plant community. J. Ecol. 1947, 35, 1-22.
Koukoulas, S.; Blackburn, G. Spatial relationships between tree species and gap characteristics in broad-leaved deciduous woodland. J. Veg. Sci. 2005, 16, 587-596.
Lindenmayer, D.; Franklin, J.; Fischer, J. General management principles and a checklist of strategies to guide forest biodiversity conservation. Biol. Conserv. 2006, 131, 433-445.
Poulson, T.L.; Platt, W.J. Gap light regimes influence canopy tree diversity. Ecology 1989, 70, 553-555.
Getzin, S.; Wiegand, K.; Schöning, I. Assessing biodiversity in forests using very high-resolution images and unmanned aerial vehicles. Methods Ecol. Evol. 2012, 3, 397-404.
Betts, H.; Brown, L.; Stewart, G. Forest canopy gap detection and characterisation by the use of high-resolution digital eevation models. N. Zeal. J. Ecol. 2005, 29, 95-103.
Yamamoto, S.; Nishimura, N.; Torimaru, T.; Manabe, T.; Itaya, A.; Becek, K. A comparison of different survey methods for assessing gap parameters in old-growth forests. For. Ecol. Manag. 2011, 262, 886-893.
Vepakomma, U.; St-Onge, B.; Kneeshaw, D. Spatially explicit characterization of boreal forest gap dynamics using multi-temporal lidar data. Remote Sens. Environ. 2008, 112, 2326-2340.
Brokaw, N.V. The definition of treefall gap and its effect on measures of forest dynamics. Biotropica 1982, 14, 158-160.
Gaulton, R.; Malthus, T.J. LiDAR mapping of canopy gaps in continuous cover forests: A comparison of canopy height model and point cloud based techniques. Int. J. Remote Sens. 2010, 31, 1193-1211.
Asner, G.P.; Kellner, J.R.; Kennedy-Bowdoin, T.; Knapp, D.E.; Anderson, C.; Martin, R.E. Forest canopy gap distributions in the Southern Peruvian Amazon. PLoS ONE 2013, 8, e60875.
Koukoulas, S.; Blackburn, G. Quantifying the spatial properties of forest canopy gaps using LiDAR imagery and GIS. Int. J. Remote Sens. 2004, 25, 3049-3072.
Zhang, K. Identification of gaps in mangrove forests with airborne LiDAR. Remote Sens. Environ. 2008, 112, 2309-2325.
Korhonen, L.; Korhonen, K.T.; Rautiainen, M.; Stenberg, P. Estimation of forest canopy cover: A comparison of field measurement techniques. Silva Fenn. 2006, 40, 577-588.
Korhonen, L.; Korpela, I.; Heiskanen, J.; Maltamo, M. Airborne discrete-return LiDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index. Remote Sens. Environ. 2011, 115, 1065-1080.
Korhonen, L.; Morsdorf, F. Estimation of canopy cover, gap fraction and leaf area index with airborne laser scanning. In Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies; Maltamo, M., Nässet, E., Vauhkonen, J., Eds.; Springer Science & Business Media: Dordrecht, The Netherlands, 2014; Volume 27, pp. 397-417.
Jennings, S.; Brown, N.; Sheil, D. Assessing forest canopies and understorey illumination: Canopy closure, canopy cover and other measures. Forestry 1999, 72, 59-74.
Saint-Onge, B.; Vepakomma, U.; Sénécal, J.F.; Kneeshaw, D.; Doyon, F. Canopy gap dectection and analysis with airborne laser scanning. In Forestry Applications of Airborne Laser Scanning: Concepts and Case Studies; Maltamo, M., Nässet, E., Vauhkonen, J., Eds.; Springer Science & Business Media: Dordrecht, The Netherlands, 2014; Volume 27, pp. 419-438.
Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.
Ligot, G.; Balandier, P.; Fayolle, A.; Lejeune, P.; Claessens, H. Height competition between Quercus Petraea and Fagus Sylvatica natural regeneration in mixed and uneven-aged stands. For. Ecol. Manag. 2013, 304, 391-398.
Alderweireld, M.; Ligot, G.; Latte, N.; Claessens, H. Le chêne en forêt ardennaise, un atout à préserver. Forêt Wallonne 2010, 109, 10-24.
Bruciamacchie, M.; Grandjean, G.; Jacobée, F. Installation de régénérations feuillues dans de petites trouées en peuplements irréguliers. Revue For. Fr. 1994, 6, 639-652.
Nagel, T.; Svoboda, M.; Rugani, T.; Diaci, J. Gap regeneration and replacement patterns in an old-growth Fagus-Abies forest of Bosnia-Herzegovina. Plant Ecol. 2010, 208, 307-318.
Isenburg, M. LAStools-Efficient tools for LiDAR processing (Version 150406, Academic), 2015. Available online: http://rapidlasso.com/LAStools (accessed on 28 August 2015).
Lee, A.; Lucas, R. A LiDAR-derived canopy density model for tree stem and crown mapping in Australian forests. Remote Sens. Environ. 2007, 111, 493-518.
Liaw, A.; Wiener, M. Classification and regression by randomForest. R News 2002, 2, 18-22.
R Development Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2015.
Genuer, R.; Poggi, J.M.; Tuleau-Malot, C. Variable selection using Random Forests. Pattern Recognit. Lett. 2010, 31, 2225-2236.
Baatz, M.; Hoffmann, C.; Willhauck, G. Progressing from object-based to object-oriented image analysis. In Object-Based Image Analysis; Springer: Berlin, Germany, 2008; pp. 29-42.
Benz, U.C.; Hofmann, P.; Willhauck, G.; Lingenfelder, I.; Heynen, M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 2004, 58, 239-258.
Baatz, M.; Schäpe, A. Multiresolution segmentation-An optimization approach for high quality multi-scale image segmentation. In Angewandte Geographische Informationsverarbeitung XII; Strobl, J., Blaschke, T., Griesebner, G., Eds.; Wichmann: Heidelberg, Germany, 2000; pp. 12-23.
Sing, T.; Sander, O.; Beerenwinkel, N.; Lengauer, T. ROCR: Visualizing classifier performance in R. Bioinformatics 2005, 21, 3940-3941.
Lillesand, T.; Kiefer, R.W.; Chipman, J. Remote Sensing and Image Interpretation, 6th ed.; John Wiley & Sons: Hoboken, NJ, USA, 2008.
Möller, M.; Lymburner, L.; Volk, M. The comparison index: A tool for assessing the accuracy of image segmentation. Int. J. Appl. Earth Observ. Geoinf. 2007, 9, 311-321.
Getzin, S.; Nuske, R.S.; Wiegand, K. Using Unmanned Aerial Vehicles (UAV) to quantify spatial gap patterns in forests. Remote Sens. 2014, 6, 6988-7004.
Lisein, J.; Pierrot-Deseilligny, M.; Bonnet, S.; Lejeune, P. A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests 2013, 4, 922-944.
Hytteborn, H.; Verwijst, T. Small-scale disturbance and stand structure dynamics in an old-growth Picea abies forest over 54 year in central Sweden. J. Veg. Sci. 2014, 25, 100-112.
Blackburn, G.A.; Abd Latif, Z.; Boyd, D.S. Forest disturbance and regeneration: A mosaic of discrete gap dynamics and open matrix regimes? J. Veg. Sci. 2014, 25, 1341-1354.
Vepakomma, U.; Kneeshaw, D.; St-Onge, B. Interactions of multiple disturbances in shaping boreal forest dynamics: A spatially explicit analysis using multi-temporal lidar data and high-resolution imagery. J. Ecol. 2010, 98, 526-539.
Similar publications
Sorry the service is unavailable at the moment. Please try again later.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.