Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification

Remote Sensing of Environment

Volumn 114, Issue 6, 2010, Pages 1141-1154

  Ke, Yinghai ^a   Quackenbush, Lindi J ^a   Im, Jungho ^a  

^a STATE UNIVERSITY OF NEW YORK   (United States)

Author keywords

Decision tree; Forest classification; High spatial resolution multispectral imagery; LIDAR; Object based classification

Indexed keywords

FOREST CLASSIFICATION; HIGH SPATIAL RESOLUTION; MULTI-SPECTRAL IMAGERY; OBJECT BASED; OBJECT-BASED CLASSIFICATION;

DECISION TREES; DIGITAL IMAGE STORAGE; IMAGE RESOLUTION; IMAGE SEGMENTATION; IMAGE SENSORS; OPTIMIZATION; REMOTE SENSING;

OPTICAL RADAR;

ACCURACY ASSESSMENT; CLASSIFICATION; DATA SET; FOREST ECOSYSTEM; IMAGE CLASSIFICATION; LIDAR; MULTISPECTRAL IMAGE; QUICKBIRD; SATELLITE IMAGERY; SPATIAL RESOLUTION; STATISTICAL ANALYSIS;

EID: 77949657728     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rse.2010.01.002     Document Type: Article

References (54)

1. Baatz M., Benz U., Dehghani S., Heynen M., Höltje A., Hofmann P., et al. eCognition User Guide 4 (2004), Definiens Imaging, Germany, Munich, Germany 133-138
2. Baatz M., and Schape A. Multiresolution segmentation - an optimization approach for high quality multi-scale image segmentation. In: Strobl J., Baschke T., and Griesebner G. (Eds). Angewandte Geographische Informationsverarbeitung (2000), Wichmann-Verlag, Heidelberg 12-23
3. Baltsavias E.P. Airborne laser scanning: basic relations and formulas. ISPRS Journal of Photogrammetry & Remote Sensing 54 (1999) 199-214
4. Benz U.C., Hofmann P., Willhauck G., Lingenfelder I., and Heynen M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry & Remote Sensing 58 (2004) 239-258
5. Blaschke T. Object-based contextual image classification built on image segmentation. 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (2003) 113-119
6. Blaschke T. Towards a framework for change detection based on image objects. In: Erasmi S., Cyffka B., and Kappas M. (Eds). Göttinger Geographische Abhandlungen Vol. 113 (2005) 1-9. http://www.ggrs.uni-goettingen.de/ggrs2004/CD/Applications_in_Geography/ GGRS2004_Blaschke_G001.pdf Available online at: http://www.ggrs.uni-goettingen.de/ggrs2004/CD/Applications_in_Geography/ GGRS2004_Blaschke_G001.pdf (accessed 15 April 2009
7. Brandtberg T., Warner T., Landenberg R., and McGraw J. Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America. Remote Sensing of Environment 85 (2003) 290-303
8. Brown de Colstoun E.C., Story M.H., Thompson C., Commisso K., Smith T.G., and Irons J.R. National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier. Remote Sensing of Environment 85 (2003) 316-327
9. Chubey M.S., Franklin S.E., and Wulder M.A. Object-based analysis of IKONOS-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering and Remote Sensing 72 (2006) 383-394
10. Clark M.L., Roberts D.A., and Clark D.B. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment 96 (2005) 375-398
11. Congalton R.G., and Green K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd edition (2008), CRC Press, Boca Raton 105-110
12. Franklin S.E., Maudie A.J., and Lavigne M.B. Using spatial co-occurrence texture to increase forest structure and species classification accuracy. Photogrammetric Engineering and Remote Sensing 67 (2001) 849-855
13. Gitelson A.A., Kaufmann Y.J., and Merzlyak M.N. Use of Green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment 58 (1996) 289-298
14. Goodenough D.G., Chen H., Han D.T., and Li J.Y. Multisensor Data fusion for aboveground carbon estimation. Proceedings of XXVIIIth General Assembly of the International Union of Radio Science (URSI), October, 23-29, 2005, New Delhi, India (2005) 1-4
15. Goodenough D.G., Niemann D.A., Pearlman K.O., Han H.C., Murdoch T., and West M. Processing Hyperion and ALI for forest classification. IEEE Transactions on GeoScience and Remote Sensing 41 (2003) 1321-1331
16. Haralick R.M. Statistical image texture analysis. In: Young T.Y., and Fu K.S. (Eds). Handbook of Pattern Recognition and Image Processing (1986), New York, New York 247-279
17. Heller R.C., Doverspike G.E., and Aldrich R.C. Identification of tree species on large scale panchromatic and color aerial photographs. USDA Handbook No. 261 (1964) 1-17
18. Hill R.A., and Thomson A.G. Mapping woodland species composition and structure using airborne spectral and LiDAR data. International Journal of Remote Sensing 26 (2005) 3763-3779
19. Hodgson M.E., and Bresnahan P. Accuracy of airborne Lidar-derived elevation: empirical assessment and error budget. Photogrammetric Engineering & Remote Sensing 70 (2004) 331-339
20. Hodgson M.E., Jensen J.R., Tullis J.A., Riordan K., and Archer R. Synergistic use of LIDAR and color aerial photography for mapping urban parcel imperviousness. Photogrammetric Engineering & Remote Sensing 69 (2003) 973-980
21. Holmgren J., and Persson A. Identifying species of individual trees using airborne laser scanner. Remote Sensing of Environment 90 (2004) 415-423
22. Holmgren J., Persson A., and Soderman U. Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images. International Journal of Remote Sensing 29 (2008) 1537-1552
23. Im J., Jensen J.R., and Hodgson M.E. Object-based land cover classification using high-posting-density LIDAR data. GIScience & Remote Sensing 45 (2008) 209-228
24. Im J., Jensen J.R., and Tullis J.A. Object-based change detection using correlation image analysis and image segmentation. International Journal of Remote Sensing 29 (2007) 399-423
25. Jensen J.R., Im J., Hardin P., and Jensen R.R. Image classification. In: Warner T.A., Nellis M.D., and Foody G.M. (Eds). The SAGE Handbook of Remote Sensing (2009), London, United Kingdom 269-281
26. Kim M., Madden M., and Warner T. Object-based forest stand mapping using multispectral IKONOS Imagery: estimation of optimal image object size. In: Blacshke T., Lang S., and Hay G.J. (Eds). Object-Based Image Analysis (2008), Springer-Verglag, New York 291-307
27. Lawrence R.L., Wood S.D., and Sheley R.L. Mapping invasive plants using hyperspectral imagery and Breiman Culter classifications (randomForest). Remote Sensing of Environment 100 (2006) 356-362
28. Leckie D., Gougeon F., Hill D., Quinn R., Armstrong L., and Shreenan R. Combined high density LiDAR and multispectral imagery for individual tree crown analysis. Canadian Journal of Remote Sensing 29 (2003) 633-649
29. Leonard J. Technical Approach for LIDAR Acquisition and Processing (2005), EarthData Inc, Frederick, MD 1-20
30. Liang X., Hyyppa J., and Matikainen L. First-last pulse signatures of airborne laser scanning for tree species classification, Deciduous-coniferous tree classification using difference between first and last pulse laser signatures. Proceedings of the ISPRS workshop, Laser Scanning 2007 and Silvilaser 2007 (2007) 253-257
31. Martin M.E., Newman S.D., Aber J.D., and Congalton R.G. Determining forest species composition using high spectral resolution remote sensing data. Remote Sensing of Environment 65 (1998) 249-254
32. Maune D.F., Kopp S.M., Crawford C.A., and Zervas C.E. Introduction. In: Maune D.F. (Ed). Digital Elevation Model Technologies and Applications: The DEM Users Manual (2001), American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland 537 pp
33. Meinel G., and Neubert M. A comparison of segmentation programs for high resolution remote sensing data. International Archives of Photogrammetry and Remote Sensing 35 (2004) 1097-1105
34. Möller M., Lymburner L., and Volk M. The comparison index: a tool for assessing the accuracy of image segmentation. International Journal of Applied Earth Observation and Geoinformation 9 (2007) 311-321
35. Moore D., Grayson R.B., and Ladson A.R. Digital terrain modeling - a review of hydrological, geomorphological and biological applications. Hydrological Processes 5 (1991) 3-30
36. Pal M., and Mather P.M. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment 86 (2003) 554-565
37. Pugh, M.L. (2005). Forest Terrain Feature Characterization using multi-sensor neural image fusion and feature extraction methods. Ph.D. Dissertation, State University of New York College of Environmental Science and Forestry. 56-85.
38. Quackenbush L.J., and Ke Y. Investigating new advances in forest species classification. Proceedings of 2007 ASPRS Annual Conference (American Society of Photogrammetry and Remote Sensing, Bethesda, Maryland), May 7-11, 2007, Tampa, Florida (2007) Unpaginated CD-ROM
39. Quinlan, J.R. (2003). Data Mining Tools See5 and C5.0.St. Ives NSW, Australia: RuleQuest Research. http://www.rulequest.com/see5-info.html (accessed on 14 April 2009).
40. Radoux J., and Defourny P. Quality assessment of segmentation results devoted to object-based classification. In: Blaschke T., Lang S., and Hay G.J. (Eds). Object-Based Image Analysis-Spatial Concepts for Knowledge-Driven Remote Sensing Applications (2008), Springer-Verlag, Berlin 818 pp
41. Radoux J., Defourny P., and Bogaert P. Comparison of pixel- and object-based sampling strategies for the thematic accuracy assessment. Proceedings of International conference GEOBIA, 2008 - Pixels, Objects, Intelligence: Geographic-Object Based Image Analysis for the 21st Century, August 5-8, 2008, Calgary, Alberta, Canada (2008)
42. Reitberger J., Krzystek P., and Stilla U. Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. International Journal of Remote Sensing 29 (2008) 1407-1431
43. Stehman S.V., and Czaplewski R.L. Design and analysis for thematic map accuracy assessment: fundamental principles. Remote Sensing of Environment 64 (1998) 331-344
44. Thomas N., Hendrix C., and Congalton R.G. A comparison of urban mapping methods using high-resolution digital imagery. Photogrammetric Engineering and Remote Sensing 69 (2003) 963-972
45. Treitz P.M., and Howarth P.J. Integrating spectral, spatial, and terrain variables for forest ecosystem classification. Photogrammetric Engineering and Remote Sensing 66 (2000) 305-317
46. Vieira I.G., Silva de Almeida A., Davidson E.A., Stone T.A., Reis de Carvalho C.J., and Guerrero J.B. Classifying successional forests using Landsat spectral properties and ecological characteristics in eastern Amazonia. Remote Sensing of Environment 87 (2003) 470-481
47. Walsh S.J. Coniferous tree species mapping using Landsat data. Remote Sensing of Environment 9 (1980) 11-26
48. Wang L., Sousa W.P., and Gong P. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. International Journal of Remote Sensing 25 (2004) 5655-5668
49. Wolter P.T., Host G.E., Mladenoff D.J., and Crow T.R. Improved forest classification in the northern Lake States using multi-temporal Landsat imagery. Photogrammetric Engineering & Remote Sensing 61 (1995) 1129-1143
50. Wulder M.A., Hall R.J., Coops N.C., and Franklin S.E. High spatial resolution remotely sensed data for ecosystem characterization. BioScience 54 (2004) 511-521
51. Wulder M.A., and Seemann D. Forest inventory height update through the integration of lidar data with segmented Landsat imagery. Canadian Journal of Remote Sensing 29 (2003) 536-543
52. Yu Q., Gong P., Clinton N., Biging G., Kelly M., and Schirokauer D. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing 72 (2006) 799-811
53. Zhang Y.J. A survey on evaluation methods for image segmentation. Pattern Recognition 29 (1996) 1335-1346
54. Zhang C., Franklin S.E., and Wulder M.A. Geostatistical and texture analysis of airborne-acquired images used in forest classification. International Journal of Remote Sensing 25 (2004) 859-865