Automated detection of forest gaps in spruce dominated stands using canopy height models derived from stereo aerial imagery

Remote Sensing

Volumn 8, Issue 3, 2016, Pages

  Zielewska Büttner, Katarzyna ^a   Adler, Petra ^b   Ehmann, Michaela ^a   Braunisch, Veronika ^a,c  

^a Baden Württemberg Forest Research Institute   (Germany)

^b UNIVERSITY OF FREIBURG   (Germany)

^c UNIVERSITY OF BERN   (Switzerland)

Author keywords

Aerial imagery; Canopy cover; Canopy opening; CHM; DSM; Forest gap; LiDAR DEM; NDSM; Photogrammetry; RGBI

Indexed keywords

AERIAL PHOTOGRAPHY; ALGORITHMS; CONSERVATION; ECOLOGY; FORESTRY; IMAGE MATCHING; OPTICAL RADAR; PHOTOGRAMMETRY; SURVEYING;

AERIAL IMAGERY; CANOPY COVER; CANOPY OPENING; FOREST GAP; NDSM; RGBI;

STEREO IMAGE PROCESSING;

EID: 84962561634     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8030175     Document Type: Article

References (76)

1. Lindenmayer, D.B.; Margules, C.R.; Botkin, D.B. Indicators of Biodiversity for Ecologically Sustainable Forest Management. Conserv. Biol. 2000, 14, 941-950.
2. Lindenmayer, D.B.; Franklin, J.F.; Fischer, J. General management principles and a checklist of strategies to guide forest biodiversity conservation. Conserv. Biol. 2006, 131, 433-445.
3. Noss, R.F. Indicators for Monitoring of Biodiversity. A Hierarchical Approach. Conserv. Biol. 1990, 4, 355-364.
4. Smith, G.F.; Gittings, T.;Wilson, M.; French, L.; Oxbrough, A.; O'Donoghue, S.; O'Halloran, J.; Kelly, D.L.; Mitchell, F.J.G.; Kelly, T.; et al. Identifying practical indicators of biodiversity for stand-level management of plantation forests. Biodivers. Conserv. 2008, 17, 991-1015.
5. Müller, J.; Brandl, R. Assessing biodiversity by remote sensing in mountainous terrain: The potential of LiDAR to predict forest beetle assemblages. J. Appl. Ecol. 2009, 46, 897-905.
6. Zellweger, F.; Braunisch, V.; Baltensweiler, A.; Bollmann, K. Remotely sensed forest structural complexity predicts multi species occurrence at the landscape scale. For. Ecol. Manag. 2013, 307, 303-312.
7. Koukoulas, S.; Blackburn, G.A. Quantifying the spatial properties of forest canopy gaps using LiDAR imagery and GIS. Int. J. Remote Sens. 2004, 25, 3049-3071.
8. Sierro, A.; Arlettaz, R.; Naef-Daenzer, B.; Strebel, S.; Zbinden, N. Habitat use and foraging ecology of the nightjar (Caprimulgus europaeus) in the Swiss Alps: Towards a conservation scheme. Conserv. Biol. 2001, 98, 325-331.
9. Braunisch, V. Spacially Explicit Species-Habitat Models for Large-Scale Conservation Planning. Modelling Habitat Potential and Habitat Connectivity for Capercaillie (Tetrao urogallus). Ph. D. Thesis, Albert-Ludwigs-Universität Freiburg im Breisgau, Freiburg im Breisgau, Germany, 2008.
10. Hölzinger, J.; Mahler, U. Die Vögel Baden-Württembergs. Band 2.3: Nicht-Singvögel 3; Verlag Eugen Ulmer: Stuttgart, Germany, 2001.
11. Aschoff, T.; Spiecker, H.; Holdenried, M.W. Terrestrische Laserscanner und akustische Ortungssysteme: Jagdlebensräumen von Fledermäusen. AFZ-DerWald 2006, 4, 172-175.
12. Runkel, V. Mikrohabitatnutzung syntoper Waldfledermäuse. Ein Vergleich der genutzten Strukturen in anthropogen geformten Waldbiotopen Mitteleuropas. Ph. D. Thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen-Nürnberg, Germany, 2008.
13. Patriquin, K.J.; Barclay, R.M.R. Foraging by bats in cleared, thinned and unharvested boreal forest. J. Appl. Ecol. 2003, 40, 646-657.
14. Seibold, S.; Brandl, R.; Buse, J.; Hothorn, T.; Schmidl, J.; Thorn, S.; Müller, J. Association of extinction risk of saproxylic beetles with ecological degradation of forests in Europe. Conserv. Biol. 2014, 29, 382-390.
15. Braunisch, V.; Coppes, J.; Arlettaz, R.; Suchant, R.; Zellweger, F.; Bollmann, K. Temperate mountain forest biodiversity under climate change: Compensating negative effects by increasing structural complexity. PLoS ONE 2014, 9, e97718.
16. 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.
17. Boncina, A. Conceptual approaches to integrate nature conservation into forest management: A central European perspective. Int. For. Rev. 2011, 13, 13-22.
18. Noss, R.F. Assessing and monitoring forest biodiversity: A suggested framework and indicators. For. Ecol. Manag. 1999, 115, 135-146.
19. Scherzinger, W. Naturschutz im Wald: Qualitätsziele einer Dynamischen Waldentwicklung; Ulmer: Stuttgart, Germany, 1996; p. 447.
20. Johann, E. Historical development of nature-based forestry in Central Europe. In Nature-Based Forestry in Central Europe. Alternatives to Industrial Forestry and Strict Preservation. Studia Forestalia Slovenica Nr. 126; Diaci, J., Ed.; Department of Forestry and Renewable Forest Resources-Biotechnical Faculty, University of Ljubljana: Ljubljana, Slovenia, 2006; p. 167.
21. Bässler, C.; Stadler, J.; Müller, J.; Förster, B.; Göttlein, A.; Brandl, R. LiDAR as a rapid tool to predict forest habitat types in Natura 2000 networks. Biodivers. Conserv. 2010, 20, 456-481.
22. Kathke, S.; Bruelheide, H. Gap dynamics in a near-natural spruce forest at Mt. Brocken, Germany. For. Ecol. Manag. 2010, 259, 624-632.
23. Garbarino, M.; Borgogno Mondino, E.; Lingua, E.; Nagel, T.A.; Dukić, V.; Govedar, Z.; Motta, R. Gap disturbances and regeneration patterns in a Bosnian old-growth forest: A multispectral remote sensing and ground-based approach. Ann. For. Sci. 2012, 69, 617-625.
24. Hobi, M.L.; Commarmot, B.; Ginzler, C.; Bugmann, H. Gap pattern of the largest primeval beech forest of Europe revealed by remote sensing. Ecosphere 2015, 6. art76.
25. Vepakomma, U. Spatial contiguity and continuity of canopy gaps in mixed wood boreal forests: Persistence, expansion, shrinkage and displacement. J. Ecol. 2012, 100, 1257-1268.
26. Vepakomma, U.; Kneeshaw, D.; Fortin, M.-J. 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.
27. Rugani, T.; Diaci, J.; Hladnik, D. Gap dynamics and structure of two old-growth beech forest remnants in Slovenia. PLoS ONE 2013, 8, e52641.
28. 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.
29. Zellweger, F.; Morsdorf, F.; Purves, R.S.; Braunisch, V.; Bollmann, K. Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment. Biodivers. Conserv. 2013, 23, 289-307.
30. Ginzler, C.; Hobi, M.L. Countrywide stereo-image matching for updating digital surface models in the framework of the swiss national forest inventory. Remote Sens. 2015, 7, 4343-4370.
31. Waser, L.T.; Ginzler, C.; Kuechler, M.; Baltsavias, E.; Hurni, L. Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data. Remote Sens. Environ. 2011, 115, 76-85.
32. Straub, C.; Stepper, C.; Seitz, R.; Waser, L.T. Potential of UltraCamX stereo images for estimating timber volume and basal area at the plot level in mixed European forests. Can. J. For. Res. 2013, 43, 731-741.
33. Wang, Z.; Waser, L.T.; Ginzler, C. A novel method to assess short-term forest cover changes based on digital surface models from image-based point clouds. Forestry 2015, 88, 1-12.
34. Kotremba, C. Hochauflösende fernerkundliche Erfassung von Waldstrukturen im GIS am Beispiel der Kernzone "Quallgebiet derWieslauter" im Pfälzerwald. AFZ-DerWald 2014, 9, 12-15.
35. Betts, H.D.; Brown, L.J.; Stewart, G.H. Forest canopy gap detection and characterisation by use of high-resolution Digital Elevation Models. N. Z. J. Ecol. 2005, 29, 95-103.
36. Braunisch, V.; Suchant, R. Using ecological forest site mapping for long-term habitat suitability assessments in wildlife conservation-Demonstrated for capercaillie (Tetrao urogallus). For. Ecol. Manag. 2008, 256, 1209-1221.
37. Zellweger, F.; Braunisch, V.; Morsdorf, F.; Baltensweiler, A.; Abegg, M.; Roth, T.; Bugmann, H.; Bollmann, K. Disentangling the effects of climate, topography, soil and vegetation on stand-scale species richness in temperate forests. For. Ecol. Manag. 2015, 349, 36-44.
38. AG Boden. Bodenkundliche Kartieranleitung, Ad-hoc-Arbeitsgruppe Boden der Geologischen Landesämter und der Bundesanstalt für Geowissenschaften und Rohstoffe der Bundesrepublik Deutschland; 4. Auflage, Nachdruck; Finnern, H., Grottenthaler, W., Kühn, D., Pälchen, W., Schraps, W.-G., Sponagel, H., Eds.; Bundesanstalt für Geowissenschaften und Rohstoffe und Geologische Landesämter: Hannover, Germany, 1996; p. 392.
39. Landesamt für Geoinformation und Landentwicklung Baden-Württemberg, LGL, Geobasisdaten. © www.lgl-bw.deAz.:2851.9-1/19. 2015.
40. Mantel, K. Wald und Forst in der Geschichte: Ein Lehr-und Handbuch. Mit e. Vorw. von Helmut Brandl; Schaper: Alfeld, Hannover, Germany, 1990; p. 512.
41. Moosmayer, H.U. Ausscheidung von Waldschutzgebieten: Hier: Schonwald "Kleemiss" im Forstbezirk Klosterreichenbach; V 794.2/1-4; Ministerium für Ernährung, Landwirtschaft, Weinbau und Forsten Baden-Württemberg: Stuttgart, Germany, 1972; p. 2.
42. Landesamt für Geoinformation und Landentwicklung Baden-Württemberg, LGL. Digitale Geländemodelle (DGM). Available online: https://www.lgl-bw.de/lgl-internet/opencms/de/05_Geoinformation/Geotopographie/Digitale_Gelaendemodelle/(accessed on 30 October 2015).
43. Landesamt für Geoinformation und Landentwicklung Baden-Württemberg, LGL. ATKIS®-Amtliches Topographisch-Kartographisches Informationssystem. Available online: http://www.lgl-bw.de/lgl-internet/opencms/de/05_Geoinformation/AAA/ATKIS/(accessed on 30 October 2015).
44. Mathow, T.; ForstBW; Regierungspräsidium Freiburg, Forstdirektion. Referat 84 Forsteinrichtung und Forstliche Geoinformation, Freiburg, Germany. Personal communication, 3 November 2015.
45. Schliemann, S.A.; Bockheim, J.G. Methods for studying treefall gaps: A review. For. Ecol. Manag. 2011, 261, 1143-1151.
46. Brokaw, N.V.L. The definition of treefall gap and its effect on measures of forest dynamics. Biotropica 1982, 14, 158-160.
47. Runkle, J.R. Gap regeneration in some old-growth forests of the eastern United States. Ecology 1981, 62, 1041-1051.
48. Hytteborn, H.; Verwijst, T. Small-scale disturbance and stand structure dynamics in an old-growth Picea abies forest over 54 yr in central Sweden. J. Veg. Sci. 2013, 25, 100-112.
49. Qinghong, L.; Hytteborn, H. Gap structure, disturbance and regeneration in a primeval Picea abies forest. J. Veg. Sci. 1991, 2, 391-402.
50. Ahrens, W.; Brockamp, U.; Pisoke, T. Zur Erfassung vonWaldstrukturen im Luftbild. Arbeitsanleitung für Waldschutzgebiete Baden-Württemberg. Waldschutzgebiete Baden-Württemberg 2004, 5, 54.
51. Müller, K.H.; Wagner, S. Störungslücken in Fichtenbeständen des Erzgebirges: Initiale eines Waldumbaues. Forst Holz, 58. Jahrgang 2003, 13/14, 407-411.
52. ERDAS,. © Intergraph Corporation. LPS eATE. From Image to Point Clouds: Model Your World Like never before, Product Sheet. 2012; Vol. 5/12 GEO-US-0033A-ENG. Available online: https://www.intergraphgovsolutions.com/assets/product-sheet/LPS%20eATE%20Product%20Sheet.pdf (accessed on 2 November 2015).
53. Hexagon Geospatial,. © Intergraph®Corporation. ERDAS IMAGINE Help. XPro Semi-Global Matching. Available online: https://hexagongeospatial.fluidtopics.net/en/book#!book;uri=f2790e0ca81311531f1a57c6b7bc80b2;breadcrumb=23b983d2543add5a03fc94e16648eae7-b70c75349e3a9ef9f26c972ab994b6b6-572fc16c2299fb0cf9dea36da3f6df73-77e5f6815e5da847bab34f6d88a2efec-895bc642e4bd922f332936d584d19af7 (accessed on 2 November 2015).
54. Adler, P.; Naake, T.; Peters, S.; Ginzler, C.; Bauerhansl, C.; Stepper, C. Reliability of forest canopy height extraction from digital aerial images. In Proceedings of the ForestSAT Conference, Riva del Garda (TN), Italy, 4-7 November 2014.
55. Rapidlasso GmbH. LAStools-Fast Tools To Catch Reality,. © 2007-2014, rapidlasso GmbH, Germany. Available online: http://rapidlasso.com/LAStools (accessed on 3 December 2014).
56. Environmental Systems Resource Institute (ESRI). ArcMap 10.3; ESRI: Redlands, CA, USA, 2014.
57. GEOSYSTEMS GmbH,. © Intergraph® Corporation. Stereoauswertung mit Erdas extensions für ArcGIS®; Product sheet; Intergraph: Germering, Germany, 2014; Available online http://www.geosystems.de/infomaterial/2_Produktinfos/ArcGISExtensions/SAfAG_Flyer_dt_09-2015.pdf (accessed on 2 November 2015).
58. AFL, Arbeitsgruppe Forstlicher Luftbildinterpreten. Luftbildinterpretationsschlüssel-Bestimmungsschlüssel für die Beschreibung von strukturreichen Waldbeständen im Color-Infrarot-Luftbild; Troyke, A., Habermann, R., Wolff, B., Gärtner, M., Engels, F., Brockamp, U., Hoffmann, K., Scherrer, H.-U., Kenneweg, H., Kleinschmit, B., et al, Eds.; Landesforstpräsidium (LFP) Freistaat Sachsen: Pirna, Germany, 2003.
59. Story, M.; Congalton, R.G. Accuracy assessment: A user's perspective. Photogram. Eng. Remote Sens. 1986, 52, 397-399.
60. Rossiter, D.G. Technical Note: Statistical Methods for Accuracy Assesment of Classified Thematic Maps; International Institute for Geo-information Science & Earth Observation (ITC): Enschede, The Netherlands, 2014.
61. Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed Data. Remote Sens. Environ. 1991, 37, 35-46.
62. Stehman, S.V.; Czaplewski, R.L. Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sens. Environ. 1998, 64, 331-344.
63. Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Measur. 1960, 20, 37-46.
64. Kuhn, M.; Wing, J.; Weston, S.; Williams, A.; Keefer, C.; Engelhardt, A.; Cooper, T.; Mayer, Z.; Kenkel, B.; the R Core Team; et al. Package "Caret", Classification and Regression Training, 6.0-52. Reference Manual. Available online: https://cran.r-project.org/web/packages/caret/caret.pdf (accessed on 3 August 2015).
65. Wrbka, T., Szerencisits, E., Peterseil, J., Thurner, B., Eds.; SINUS Kulturlandschaft-Methoden. Kulturlandschaftskartierung. Abteilung für Vegetationsökologie und Naturschutzforschung. Institut für ökologie und Naturschutz, UniversitätWien: Wien, österreich. Available online: http://131.130.59.133/projekte/sinus/klkart10/methoden/kartierung.htm (accessed on 20 February 2015).
66. Hothorn, T.; Hornik, K.; Zeileis, A. Unbiased recursive partitioning: A conditional inference framework. J. Comput. Graph. Stat. 2006, 15, 651-674.
67. Hothorn, T.; Zeileis, A. Partykit: A modular toolkit for recursive partytioning in R. Reference Manual. Available online: https://cran.r-project.org/web/packages/partykit/partykit.pdf (accessed on 3 August 2015).
68. Hobi, M.L.; Ginzler, C. Accuracy assessment of digital surface models based onWorldView-2 and ADS80 stereo remote sensing data. Sensors 2012, 12, 6347-6368.
69. McElhinny, Ch.; Gibbons, P.; Brack, C.; Bauhus, J. Forest and woodland stand structural complexity: Its definition and measurement. For. Ecol. Manag. 2005, 218, 1-24.
70. Gaulton, R.; Malthus, T.J. LiDAR Mapping of Canopy Gaps in Continuous Cover Forests; A Comparison of a Canopy Height Model and Point Cloud Based Techniques; SilviLaser: Edinburgh, UK, 2008; pp. 407-416.
71. Braunisch, V.; Suchant, R. Aktionsplan Auerhuhn Tetrao urogallus im Schwarzwald: Ein integratives Konzept zum Erhalt einer überlebensfähigen Population. Vogelwelt 2013, 134, 29-41.
72. Graf, R.F.; Mathys, L.; Bollmann, K. Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps. For. Ecol. Manag. 2009, 257, 160-167.
73. Bollmann, K.; Mollet, P.; Ehrbar, R. Das Auerhuhn Tetrao urogallus im Alpinen Lebensraum: Verbreitung, Bestand, Lebensraumansprüche und Förderung. Vogelwelt 2013, 134, 19-28.
74. Russo, D.; Cistronec, L.; Jones, G. Emergence time in forest bats: The influence of canopy closure. Acta Oecol. 2007, 31, 119-126.
75. Napal, M.; Garin, I.; Goiti, U.; Salsamendi, E.; Aihartza, J. Habitat selection by Myotis bechsteinii in the southwestern Iberian Peninsula. Ann. Zool. Fennici 2010, 47, 239-250.
76. Ewald, M.; Dupke, C.; Heurich, M.; Müller, J.; Reineking, B. LiDAR remote sensing of forest structure and GPS telemetry data provide insights on winter habitat selection of European roe deer. Forests 2014, 5, 1374-1390.