Object-based class modelling for multi-scale riparian forest habitat mapping

International Journal of Applied Earth Observation and Geoinformation

Volumn 37, Issue , 2015, Pages 29-37

  Strasser, Thomas ^a   Lang, Stefan ^a  

^a UNIVERSITY OF SALZBURG   (Austria)

Author keywords

Multilevel class modelling; Natura 2000; Obia; Object hierarchy; Semi automated habitat mapping; Worldview 2

Indexed keywords

HABITAT STRUCTURE; IMAGE ANALYSIS; MODELING; RIPARIAN FOREST; RIPARIAN ZONE; SATELLITE IMAGERY; SEGMENTATION; VEGETATION MAPPING; WORLDVIEW;

EID: 84945299335     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2014.10.002     Document Type: Article

References (47)

1. Allen, T.F.H., Starr, T.B., 1982. Hierarchy. University of Chicago Press, Chicago.
2. Baatz, M., Schäpe, A., 2000. Multiresolution segmentation - an optimization approach for high quality multi-scale image segmentation. In: Strobl, J.,. Blaschke, T., Griesebner, G. (Eds.), Angewandte Geographische Informationsverarbeitung. Wichmann-Verlag, Heidelberg, pp. 12-23.
3. Bunting, P., Lucas, R.M., Jones, K., Bean, A.R., 2010. Characterisation and mapping of forest communities by clustering individual tree crowns. Remote Sens. Environ. 114, 2536-2547.
4. Burnett, C, Blaschke, T., 2003. A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecol. Model. 168, 233-249.
5. Carleer, A.P., Wolff, E., 2004. Exploitation of very high resolution satellite data for tree species identification. Photogramm. Eng. Remote Sens. 70,135-140.
6. Dalponte, M., Bruzzone, L, Gianelle, D., 2012. Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sens. Environ. 123, 258-270.
7. Davies, C.E., Moss, D., Hill, M.O., 2004. EUNIS Habitat Classification Revised 2004.
8. Draguţ, L, Tiede, D., Levick, S.R., 2010. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inform. Sci. 24, 859-871.
9. Eisfelder, C, Kraus, T., Bock, M., Werner, M., Buchroithner, M.F., Strunz, G., 2009. Towards automated forest-type mapping - a service within GSE Forest Monitoring based on SPOT-5 and IKONOS data. Int. J. Remote Sens. 30, 5015-5038.
10. Ellmauer, T., 2005. Entwicklung von Kriterien, Indikatoren und Schwellenwerten zur Beurteilung des Erhaltungszustandes der Natura 2000-Schutzgüter. Band 3: Lebensraumtypen des Anhangs I der Fauna-Flora-Habitat-Richtlinie. Im Auftrag der neun österreichischen Bundesländer, des Bundesministerium für Landund Forstwirtschaft, Umwelt und Wasserwirtschaft und der Umweltbundesamt GmbH.
11. Förster, M., Kleinschmit, B., 2008. Object-based classification of QuickBird data using ancillary information for the detection of forest types and NATURA 2000 habitats. In: Blaschke, T., Lang, S., Hay, G.J. (Eds.), Object-Based Image Analysis -Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Springer, Berlin, pp. 275-290.
12. Fuchs, M., Bushart, M., Krisai, R., Nowotny, G., 1999. Die Vegetation der Salzachauen im Bereich der Bundesländer Bayern, Oberösterreich und Salzburg. Ad-hoc-Arbeitsgruppe der Ständigen Gewässerkommission nach dem Regensburger Vertrag, München.
13. Gergel, S., Stange, Y., Coops, N., Johansen, K., Kirby, K., 2007. What is the value of a Good Map? An example using high spatial resolution imagery to aid riparian restoration. Ecosystems 10,688-702.
14. Ghosh, A., Joshi, P.K., 2014. A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery. Int. J. Appl. Earth Observ. Geoinform. 26, 298-311.
15. Gougeon, F.A., Leckie, D.G., 2006. The individual tree crown approach applied to IKONOS images of a coniferous plantation area. Photogramm. Eng. Remote Sens. 72,1287-1297.
16. Hay, G.J., Castilla, G., Wulder, M.A., Ruiz, J.R., 2005. An automated object-based approach for the multiscale image segmentation of forest scenes. Int. J. Appl. Earth Observ. Geoinform. 7,339-359.
17. Hay, G.J., Marceau, D.J., Dubé, P., Bouchard, A., 2001. A multiscale framework for landscape analysis - object-specific analysis and upscaling. Landsc. Ecol. 16, 471-490.
18. Hearn, S.M., Healey, J.R., McDonald, M.A., Turner, A.J., Wong, J.L.G., Stewart, G.B., 2011. The repeatability of vegetation classification and mapping. J. Environ. Manag. 92, 1174-1184.
19. Hernando, A., Arroyo, LA, Velázquez, J., Tejera, R., 2012. Objects-based image analysis for mapping Natura 2000 habitats to improve forest management. Photogramm. Eng. Remote Sens. 78, 991-999.
20. Jakubowski, M.K., Guo, Q., Kelly, M., 2013. Tradeoffs between lidar pulse density and forest measurement accuracy. Remote Sens. Environ. 130, 245-253.
21. Johansen, K., Coops, N.C., Gergel, S.E., Stange, Y., 2007. Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification. Remote Sens. Environ. 110, 29-44.
22. Leckie, D.G., Gougeon, F.A., Tinis, S., Nelson, T., Burnett, C.N., Paradine, D., 2005. Automated tree recognition in old growth conifer stands with high resolution digital imagery. Remote Sens. Environ. 94,311-326.
23. Lang, S., Schöpfer, E., Langanke, T., 2009. Combined object-based classification and manual interpretation-Synergies for a quantitative assessment of parcels and biotopes. Geocarto Int. 24,99-114.
24. Lang, S., 2008. Object-based image analysis for remote sensing applications - modelling reality- dealing with complexity. In: Blaschke, T., Lang, S., Hay, G.J. (Eds.), Object-Based Image Analysis - Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Springer, Berlin, pp. 3-27.
25. Lang, S., Langanke, T., 2006. Object-based mapping and object-relationship modelling for land-use classes and habitats. Photogramm. Fernerkund. Geoinform. 1/2006, 5-18.
26. Lang, S., Burnett, C, Blaschke, T., 2004. Multi-scale object-based image analysis - a key to the hierarchical organisation of landscapes. Ekol. Suppl. 23,1-9.
27. Lucas, R., Medcalf, K., Brown, A., Bunting, P., Breyer, J., Clewley, D., Keyworth, S., Blackmore, P., 2011. Updating the Phase 1 habitat map of Wales, UK, using satellite sensor data. ISPRS J. Photogram. Rem. Sens. 66, 81-102.
28. Maier, B., Tiede, D., Dorren, L., 2008. Characterising mountain forest structure using landscape metrics on LiDAR-based canopy surface models. In: Blaschke, T., Lang, S., Hay, G. (Eds.), Object-Based Image Analysis. Springer, Berlin, Heidelberg, pp. 625-643.
29. Miller, J.G., 1975. The nature of living systems. Behav. Sci. 20,343-365.
30. Naiman, R.J., Decamps, H., 1997.The ecology of interfaces: riparian zones. Annu. Rev. Ecol. Syst. 28, 621-658.
31. Nagendra, H., Lucas, R., Honrado, J.P., Jongman, R.H.G., Tarantino, C, Adamo, M., Mairota, P., 2013. Remote sensing for conservation monitoring: assessing protected areas, habitat extent, habitat condition, species diversity, and threats. Ecol. Ind. 33, 45-59.
32. Nagendra, H., 2001. Using remote sensing to assess biodiversity. Int. J. Remote Sens. 22, 2377-2400.
33. O'Neill, R.V., Deangelis, D.L., Waide, J.B., Allen, G.E., 1986. A Hierarchical Concept of Ecosystems. Princeton University Press, Princeton.
34. Palenichka, R., Doyon, F., Lakhssassi, A., Zaremba, M., 2012. Hierarchical multi-scale segmentation of LiDAR images in forest areas. In: 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 5462-5465.
35. Puissant, A., Rougier, S., Stumpf, A., 2014. Object-oriented mapping of urban trees using Random Forest classifiers. Int. J. Appl. Earth Observ. Geoinform. 26, 235-245.
36. Simon, H.A., 1973. The organization of complex systems. In: Pattee, H.H. (Ed.), Hierarchy Theory- The Challenge of Complex Systems. George Braziller, New York, pp. 1-27.
37. Strasser, T., Stefan, L., Thomas, B., 2014a. Class modelling of complex riparian forest habitats. South-East. Eur. J. Earth Observ. Geomat. 3s, 531-534.
38. Strasser, T., Lang, S., Riedler, B., Pernkopf, L., Paccagnel, K., 2014b. Multiscale object feature library for habitat quality monitoring in riparian forests. IEEE Geosci. Remote Sens. Lett. 11, 559-563.
39. Suchenwirth, L, Förster, M., Cierjacks, A., Lang, F., Kleinschmit, B., 2012. Knowledgebased classification of remote sensing data for the estimation of below- and above-ground organic carbon stocks in riparian forests. Wetl. Ecol. Manag. 20, 151-163.
40. Tiede, D., Lang, S., Albrecht, F., Hoelbling, D., 2010. Object-based class modelling for cadastre-constrained delineation ofgeo-objects. Photogramm. Eng. Remote Sens. 76, 193-202.
41. Tockner, K., Stanford, J.A., 2002. Riverine flood plains: present state and future trends. Environ. Conserv. 29,308-330.
42. Turner, M.G., Gardner, R.H., O'Neill, R.V., 2001. Landscape Ecology in Theory And Practice: Pattern and Process. Springer, New York.
43. Vanden Borre, J., Paelinckx, D., Mücher, C.A., Kooistra, L., Haest, B., De Blust, G., Schmidt, A.M., 2011. Integrating remote sensing in Natura 2000 habitat monitoring: prospects on the way forward. J. Nat. Conserv. 19, 116-125.
44. Wantzen, K.M., Junk, W.J., 2008. Riparian wetlands. In: Sven Erik, J., Brian, F. (Eds.), Encyclopedia of Ecology. Academic Press, Oxford, pp. 3035-3044.
45. Ward, J.V., Tockner, K., Arscott, D.B., Claret, C, 2002. Riverine landscape diversity. Freshw. Biol. 47, 517-539.
46. Wu, J., 1999. Hierarchy and scaling: extrapolating information along a scaling ladder. Can. J. Remote Sens. 25,367-380.
47. Wulder, M.A., Hall, R.J., Coops, N.C., Franklin, S.E., 2004. High spatial resolution remotely sensed data for ecosystem characterization. Bioscience 54, 511-521.