Process-based approach to automated classification of forest structures using medium format digital aerial photos and ancillary GIS information

European Journal of Forest Research

Volumn 127, Issue 2, 2008, Pages 115-124

  Hájek, Filip ^a,b  

^a Ministerstvo zemedelstvi   (Czech Republic)

^b CZECH UNIVERSITY OF LIFE SCIENCES PRAGUE   (Czech Republic)

Author keywords

GLCM texture; Medium format digital images; Object based image analysis; Tree species

Indexed keywords

AERIAL PHOTOGRAPH; DATA ACQUISITION; DATA INTERPRETATION; DATA PROCESSING; DATABASE; DIGITAL IMAGE; FOREST INVENTORY; FOREST MANAGEMENT; GIS; IMAGE CLASSIFICATION; NDVI; PATTERN RECOGNITION; PIXEL; REMOTE SENSING; SATELLITE DATA; SATELLITE IMAGERY; SPATIAL DISTRIBUTION;

EID: 39749095146     PISSN: 16124669     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10342-007-0188-0     Document Type: Article

References (33)

1. Baatz M, Schǎpe A (1999) Object-oriented and multi-scale image analysis in semantic network. In: Proceedings of the 2nd international symposium on operationalization of remote sensing, Enschede ITC, 16-20 August 1999
2. Baatz M, Schǎpe A (2000) Multiresolution segmentation - an optimization approach for high quality multiscale image segmentation. In: Strobl J et al (Hrsg.): Angewandte Geographische Informationsverarbeitung XII. Beiträge zum AGIT-Symposium Salzburg, September 2000, Karlsruhe, Herbert Wichmann Verlag, pp 12-23
3. Brandtberg T (1999) Automatic individual tree-based analysis of high spatial resolution aerial images on naturally regenerated boreal forests. Can J Forest 29:1464-1478
4. Benz UC, Hofmann P, Willhanck G Lingenfelder I, Heynen M (2004) Multiresolution, object oriented fuzzy analysis of remote sensing data for GIS ready information. ISPRS J Photogramm Remote Sens 58(3-4):239-258
5. Bucha T (1998) Posúdenie možností satelitného mapovania drevinového zloženia lesov. [Evaluation of possibilities of mapping tree species composition by using satellite images]. International conference "Lesy a lesnícky výskum pre tretie tisí cročie", LVÚ Zvolen (in Slovak)
6. Burnett C, Blaschke T (2003) A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecol Model 168(3): 33-249
7. Burnett C, Heurich M, Tiede D (2003) Exploring segmentation-based mapping of Tree Crowns: Experiences with the Bavarian Forest NP Lidar/ Digital Image Dataset. Poster presented at the ScandLaser 2003 international conference and workshop, Umeå, 2-4 September 2003
8. Definiens Imaging (2006a) Definiens Professional 5 User Guide. Definiens Imaging, Munchen
9. Definiens Imaging (2006b) Definiens professional 5 reference book. Definiens Imaging, Munchen
10. De Kok R (2006) Sequential processing of image objects and its consequences for automatic analysis. In: Lang S, Blaschke T, Schöpfer E (eds) Proceedings of the 1st international conference on object-based image analysis, 4-5 July 2006, Salzburg
11. Dralle K, Rudemo M (1997) Automatic estimation of individual tree positions from aerial photos. Can J For Res 27:1728-1736
12. Förster M, Kleinschmit B (2006) Integration of ancillary information into object-based classification for detection of forest structures and habitats. In: Lang S, Blaschke T, Schöpfer E (eds) First international conference on object-based image analysis. ISPRS, vol XXXVI-4/C42. Salzburg, pp 1-6
13. 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(3):274-284
14. Hájek F (2006) Object analysis of Ikonos XS and pan-sharpened imagery in comparison for purpose of tree species estimation. In: Lang S, Blaschke T, Schöpfer E (eds) Proceedings of the 1st international conference on object-based image analysis, 4-5 July 2006
15. Halounová L (2003) Textural classification of B&W serial photos for the forest classification. In: Proceedings of 23rd symposium of European association of remote sensing laboratories, 2-5 June 2003, Gent, pp 173-179
16. Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3, pp 610-621
17. Haralick R, Shapiro L (1985) Survey-image segmentation techniques. Comput Vis Graph Image Process 29:100-132
18. Hauta-Kasari M, Parkkinen J, Jaaskelainen T, Lenz R (1999) Multi-spectral texture segmentation based on the spectral co-occurrence matrix. Pattern Anal Appl 2:275-284
19. Hay GJ, Castilla G, Wulder MA, Ruiz JR (2005) An automated object-based approach for the multiscale image segmentation of forest scenes. Int J Appl Earth Observ Geoinf 7:339-359
20. Larsen M, Rudemo M (1998) Optimizing templates for finding trees in aerial photographs. Pattern Recognit Lett 19:1153-1162
21. Le'Vesque J, King DJ (2003) Spatial analysis of radiometric fractions from high-resolution multi-spectral imagery for modelling forest structure and health. Remote Sens Environ 84:589-602
22. Malenovský Z (2001) Possibilities of using satellite data for mapping the vegetation formation types in the forested area of Mediterranean region. J Forest Sci 47(3):114-123
23. Radoux J, Defourny P (2006) Influence of image segmentation parameters on positional and spectral quality of derived objects. In: Lang S, Blaschke T, Schöpfer E (eds) Proceedings of the 1st international conference on object-based image analysis, 4-5 July 2006 Salzburg, Austria (pages pending)
24. Ryherd S, Woodcock CE (1996) Combining spectral and texture data in the segmentation of remotely sensed images. Photogramm Eng Remote Sens 62(2):181-194
25. Schiewe J (2002) Segmentation of high-resolution remotely sensed data-concepts, applications and problems. Symposium on geospatial theory, processing and applications, Ottawa
26. Schöpfer E, Lang S (2006) Object fate analysis - a virtual overlay method for the categorisation of object transition and object-based accuracy assessment. In: Lang S, Blaschke T, Schöpfer E (eds) Proceedings of the 1st international conference on object-based image analysis, 4-5 July 2006 Salzburg
27. Stone Ch, Coops N (2004) Assessment and monitoring of damage from insects in Australian eucalypt forests and commercial plantations. Aus J Entomol 43(3):283-292
28. Šumbera S. Žídek V. (2003) Digital classification of tree species and spatial structure of forest stands from a remote sensing source. In: Proceedings of the 22nd EARSeL symposium geoinformation for European-wide integration, 4-6 June 2002, Prague, pp 439-446
29. Tiede D, Hoffmann Ch (2006) Process oriented object-based algorithms for single tree detection using laser scanning data. In: EARSeL-proceedings of the workshop on 3D remote sensing in forestry, 14-15 February 2006, Vienna, pp 151-156
30. Tuominen S, Pekkarinen A (2004) Local radiometric correction of digital aerial photographs for multi source forest inventory. Remote Sens Environ 89:72-82
31. Wack R, Stelzl H (2005) Assessment of forest stand parameters form Laser-scanner data in mixed forests. In: Proceedings of conference ForestSat 2005, Boras, 31 May-3 June 2005, pp 56-60
32. Willhauck G, Schneider T, De Kok R, Ammer U (2000) Comparison of object-oriented classification techniques and standard image analysis for the use of change detection betweeen SPOT multispectral satellite images and aerial photos. In: Proceedings of XIX ISPRS congress, Amsterdam, 16-22 July
33. Zhang Y (2001) A spectral and spatial information integrated approach for tree extraction from high-resolution digital imagery. Beyond information infrastructure, In: Proceedings of the digital earth 2001 conference, Fredericton, 24-28 June, Paper Number de-a-081