An object-based approach for the implementation of forest legislation in Greece using very high resolution satellite data

Lecture Notes in Geoinformation and Cartography

Volumn 0, Issue 9783540770572, 2008, Pages 309-325

  Mallinis, G ^a   Karamanolis, D ^a   Karteris, M ^a   Gitas, I ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

Author keywords

Environmental legislation; Forest cover; Image fusion; Logistic regression

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE ANALYSIS; IMAGE FUSION; LAWS AND LEGISLATION; LOGISTIC REGRESSION;

CLASSIFICATION PROCESS; CLASSIFIED OBJECTS; ENVIRONMENTAL LEGISLATIONS; FOREST COVER; MULTISPECTRAL IMAGES; OBJECT BASED IMAGE ANALYSIS; OBJECT-BASED CLASSIFICATIONS; VERY HIGH RESOLUTION;

FORESTRY;

EID: 84953366200     PISSN: 18632246     EISSN: 18632351     Source Type: Book Series    
DOI: 10.1007/978-3-540-77058-9_17     Document Type: Article

References (39)

1. Baatz M, Benz U, Dehghani S, Heynen M, Höltje A, Hofmann P, Lingenfelder I, Mimler M, Sohlbach M, Weber M, Willhauck G (2004) eCognition User Guide 4. Definiens Imaging, Munchen
2. Baatz M, Schäpe M (2000) Multiresolution Segmentation – an optimization approach for high quality multi-scale image segmentation. In: Strobl J, Blaschke T, Greisebener G (eds) Angewandte Geographische Informationsverarbeitung XII. Beiträge zum AGIT Symposium Salzburg, vol. 200. Herbert Wichmann Verlag, Karlsruhe, pp. 12–24
3. Bretschneider T, Kao O (2000) Image fusion in remote sensing. In: 1st Online Symposium of Electronic Engineers
4. Burnett C, Blaschke T (2003) A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecol. Model. 168:233-249
5. Chubey MS, Franklin SE, Wulder MA (2006) Object-based analysis of IKONOS- 2 imagery for extraction of forest inventory parameters. Photogram. Eng. Remote Sens. 72:383-394
6. Cohen J (1960) A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20: 37-40
7. Franklin SE, Wulder M (2003) Remote Sensing of Forest Environments, Challenges and Opportunities; In Wulder M, and Franklin SE (eds) Methods and Applications for Remote Sensing of Forests: Concepts and Case Studies. Kluwer Academic Publishing, Dordrecht, pp. 511-514
8. Gitas I, Mitri G, Ventura G (2004) Object-oriented image analysis for burned area mapping using NOAA AVHRR imagery in Creus Cape, Spain. Remote Sens. Environ. 92:409-413
9. Goetz SJ, Wright RK, Smith AJ, Zinecker E, Schaub E (2003) IKONOS imagery for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region. Remote Sens. Environ. 88: 195-208
10. Haapanen R, Ek AR, Bauer ME, Finley AO (2004) Delineation of forest/nonforest land use classes using nearest neighbor methods. Remote Sens. Environ. 89: 265-271
11. Hadjicostas P (2006) Maximizing proportions of correct classifications in binary logistic regression. J Appl. Stat. 33:629-640
12. Hair JF Jr, Anderson RE, Tatham RL, Black WC (1998) Multivariate Data Analysis (5th edn). Prentice-Hall, New Jersey
13. Hay GJ, Blaschke T, Marceau DJ, Bouchard A (2003) A comparison of three image- object methods for the multiscale analysis of landscape structure. ISPRS J Photogramm. 57:327–345
14. 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 Obs. Geoinform. 7:339-359
15. Hoffman RR, Markman AB, Carnahan WH (2001) Angles of Regard: Physiology meets Technology in the Perception and Interpretation of Nonliteral Imagery. In: Hoffman RR, Markman AB (Eds) Interpreting Remote Sensing Imagery. Human Factors, Lewis Publishers, Boca Raton FL, pp. 11-58
16. Hosmer DW, Lemeshow S (1989) Applied Logistic Regression. Wiley and Sons, New York
17. Kennedy P, Bertolo F (2002) Mapping sub-pixel forest cover in Europe using AVHRR data and national and regional statistics. Can J Remote Sens 28: 302-321
18. Key T, Warner TA, McGraw JB, Fajvan MA (2001) A comparison of multispectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest. Remote Sens. Environ. 75: 100-112
19. King RL, Wang, J (2001) A wavelet based algorithm for pan sharpening Landsat 7 imagery. Proc. IGARSS 2001, pp 849–851.
20. Kosaka N, Akiyama T, Tsai T, Kojima T (2005) Forest Type Classification Using Data Fusion of Multispectral and Panchromatic High-Resolution Satellite Imageries. Proc. IGARSS 2005, pp 2980-2983
21. Koutsias N, Karteris M (1998) Logistic regression modelling of multitemporal Thematic Mapper data for burned area mapping. Int. J. Remote Sens. 19:3499-3514
22. Koutsias N, Karteris M, Chuvieco E (2000) The Use of Intensity-Hue-Saturation Transformation of Landsat-5Thematic Mapper Data for Burned Land Mapping. Photogram. Eng. Remote Sens. 66:829-839
23. Laben CA, Brower BV (2000) Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. U.S. Patent 6 011 875
24. López-Caloca, AA, Mora F, Escalante-Ramírez B (2004) Mapping and characterization of urban forest in Mexico City. Proc. SPIE 5239:522-531
25. Lu D, Weng Q (2005) Urban classification using full spectral information of Landsat ETM+ imagery in Marion County, Indiana. Photogram. Eng. Remote Sens. 71:1275-1284
26. Lund HG (2002) When is a forest not a forest?. J. Forest. 100:21–28
27. Mallinis G (2006) A remote sensing approach for the generation of a National Forest Geodatabase. Phd thesis Aristotle University of Thessaloniki, Thessaloniki, Greece.
28. McRoberts RE (2006) A model-based approach to estimating forest area. Remote Sens. Environ. 103: 56-66
29. Phol C, van Genderen JL (1998) Multisensor Image Fusion in Remote Sensing: Concepts, Methods and Applications. Int. J. Remote Sens. 19:823-854
30. Ranchin T, Wald L (2000) Fusion of high spatial and spectral resolution images: The ARSIS concept and its implementation. Photogram. Eng. Remote Sens. 66:49-61
31. Tanaka S, Sugimura T (2001) Cover: A new frontier of remote sensing from IKONOS images. Int. J. Remote Sens. 22:1-5
32. Thiel C, Drezet P, Weise C, Quegan S, Schmullius C (2006) Radar remote sensing for the delineation of forest cover maps and the detection of deforestation Forestry 79: 589-597
33. Trias-Sanz R., Boldo D (2005) A high-reliability, high-resolution method for land cover classification into forest and non-forest. Lecture Notes in Computer Science 3540: 831-840
34. Woodcock CE, Harward VJ (1992) Nested-Hierarchical Scene Models and Image Segmentation. Int. J. Remote Sens. 13:3167-3187
35. Woodcock CE, Strahler A (1987) The factor of scale in remote sensing. Remote Sens. Environ. 21:311-322
36. Wulder M (1998) Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Prog. Phys. Geog. 22:449-476
37. Wulder MA, White JC, Hay GJ, Castilla G (2007) Pixels to objects to information: Spatial context to aid in forest characterization with remote sensing. In: Blaschke T, Lang S, Hay G (eds.) Object-Based Image Analysis Spatial concepts for knowledge-driven remote sensing applications, Springer-Verlag (under publication)
38. Wald L, Ranchin T, Mangolini M (1997) Fusion of Satellite Images of Different Spatial Resolutions: Assessing the Quality of Resulting Images. Photogram. Eng. Remote Sens. 63: 691-699
39. Zhou J, Civco DL, Silander JA (1998) A wavelet transform method to merge Landsat TM and SPOT panchromatic data. Int. J. Remote Sens. 19: 743-757