Land cover and land use classification with TWOPAC: Towards automated processing for pixel- and object-based image classification

Remote Sensing

Volumn 4, Issue 9, 2012, Pages 2530-2553

  Huth, Juliane ^a   Kuenzer, Claudia ^a   Wehrmann, Thilo ^a   Gebhardt, Steffen ^b   Tuan, Vo Quoc ^a   Dech, Stefan ^a  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

^b UNIVERSITY OF WÜRZBURG   (Germany)

Author keywords

Automated processing; Classification; Decision tree; Land cover and land use; Supervised; TWOPAC; Validation; Web processing service

Indexed keywords

AUTOMATED PROCESSING; LAND COVER; SUPERVISED; TWOPAC; VALIDATION; WEB PROCESSING SERVICE;

AUTOMATION; DATA PROCESSING; DECISION TREES; LAND USE; LANDFORMS; OPEN SYSTEMS; PIXELS; REMOTE SENSING;

CLASSIFICATION (OF INFORMATION);

EID: 84868211646     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs4092530     Document Type: Article

References (76)

1. Brown, D.G.; Duh, J.-D. Spatial simulation for translating from land use to land cover. Int. J. Geogr. Inf. Sci. 2004, 18, 35-60.
2. Campbell, J.B. Introduction to Remote Sensing, 4th ed.; Taylor & Francis: London, UK, 2006; p. 626.
3. Cihlar, J.; Jansen, L.J.M. From land cover to land use: A methodology for efficient land use mapping over large areas. Prof. Geogr. 2001, 53, 275-289.
4. Herold, M. Assessment of the Status of the Development of the Standards for the Terrestrial Essential Climate Variables. Land. Land Cover; FAO: Rome, Italy, 2009.
5. Jensen, J.R. Remote Sensing of the Environment: An Earth Resource Perspective; Pearson Prentice Hall: Upper Saddle River, NJ, USA, 2007.
6. Cihlar, J. Land cover mapping of large areas from satellites: Status and research priorities. Int. J. Remote Sens. 2000, 21, 1093-1114.
7. Franklin, S.E.; Wulder, M.A. Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progr. Phys. Geogr. 2002, 26, 173-205.
8. Knorn, J.; Rabe, A.; Radeloff, V.C.; Kuemmerle, T.; Kozak, J.; Hostert, P. Land cover mapping of large areas using chain classification of neighboring Landsat satellite images. Remote Sens. Environ. 2009, 113, 957-964.
9. Rogan, J.; Franklin, J.; Stow, D.; Miller, J.; Woodcock, C.; Roberts, D. Mapping land-cover modifications over large areas: A comparison of machine learning algorithms. Remote Sens. Environ. 2008, 112, 2272-2283.
10. Schaaf, C.B.; Barry, R.G.; Brady, M.; Brown, J.; CEOS; Christiansen, H.H.; Cihlar, J.; Clow, G.; Csiszar, I.; Dolman, H.; et al. Terrestrial Essential Climate Variables: Climate Change Assessment, Mitigation and Adaptation; FAO: Rome, Italy, 2008.
11. Apel, H.; Thieken, A.H.; Merz, B.; Blöschl, G. Flood risk assessment and associated uncertainties. Natural Hazards Earth Syst. Sci. 2004, 4, 295-308.
12. Bates, P.D.; Horritt, M.S.; Smith, C.N.; Mason, D. Integrating remote sensing observations of flood hydrology and hydraulic modelling. Hydrol. Process. 1997, 11, 1777-1795.
13. Delgado, J.M.; Apel, H.; Merz, B. Flood trends and variability in the Mekong river. Hydrol. Earth Syst. Sci. 2010, 14, 407-418.
14. Matgen, P.; Schumann, G.; Henry, J.B.; Hoffmann, L.; Pfister, L. Integration of SAR-derived river inundation areas, high-precision topographic data and a river flow model toward near real-time flood management. Int. J. Appl. Earth Obs. Geoinf. 2007, 9, 247-263.
15. Schumann, G.; Di Baldassarre, G.; Bates, P.D. The utility of spaceborne radar to render flood inundation maps based on multialgorithm ensembles. IEEE Trans. Geosci. Remote Sens. 2009, 47, 2801-2807.
16. Eisfelder, C.; Kuenzer, C.; Dech, S. Derivation of biomass information for semi-arid areas using remote-sensing data. Int. J. Remote Sens. 2011, 33, 2937-2984.
17. Goetz, S.; Baccini, A.; Laporte, N.T.; Johns, T.; Walker, W.; Kellndorfer, J.; Houghton, R.; Sun, M. Mapping and monitoring carbon stocks with satellite observations: A comparison of methods. Carbon Balance Manag. 2009, 4, doi:10.1186/1750-0680-4-2.
18. GTOS. Assessment of the Status of the Development of the Standards for the Terrestrial Essential Climate Variables; GTOS 67; GTOS: Rome, Italy, 2009.
19. Guenther, K.P.; Borg, E.; Wisskirchen, K.; Schroedter-Homscheidt, M.; Fichtelmann, B. Remote Sensing and Modelling: A Tool to Provide the Spatial Information for Biomass Production Potential. In Proceedings of 2nd International Baltic Bioenergy Conference IBBC 2006, Stralsund, Germany, 2-4 November 2006; pp. 33-45.
20. Lu, D. The potential and challenge of remote sensing-based biomass estimation. Int. J. Remote Sens. 2006, 27, 1297-1328.
21. Dobson, J.E.; Bright, E.A.; Coleman, P.R.; Bhaduri, B.L. LandScan: A Global Population Database for Estimating Populations at Risk; Taylor & Francis: London, UK, 2003; pp. 267-281.
22. Sutton, P.; Roberts, D.; Elvidge, C.; Baugh, K. Census from Heaven: An estimate of the global human population using night-time satellite imagery. Int. J. Remote Sens. 2001, 22, 3061-3076.
23. Glade, T.; Birkmann, J. Assessment of different dimensions of vulnerability to natural hazards and climate change. Natural Hazards Earth Syst. Sci. 2009, 9, 315-1494.
24. Jacquin, A.; Misakova, L.; Gay, M. A hybrid object-based classification approach for mapping urban sprawl in periurban environment. Landsc. Urban Plan. 2008, 84, 152-165.
25. Wisner, B.; Blaikie, P.; Cannon, T.; Davies, I. At Risk; 2nd ed.; Routlegde: London, UK, 2004; p. 471.
26. Kuenzer, C.; Bluemel, A.; Gebhardt, S.; Vo Quoc, T.; Dech, S. Remote sensing of mangrove ecosystems: A review. Remote Sens. 2011, 3, 878-928.
27. Vo Quoc, T.; Kuenzer, C.; Vo Quang, M.; Oppelt, N. The concept of ecosystem services: A review. Ecol. Indic. 2012, 23, 431-446.
28. Defries, R.S.; Chan, J.C.-W. Multiple Criteria for Evaluating Machine Learning Algorithms for Land Cover Classification from Satellite Data. Remote Sens. Environ. 2000, 74, 503-515.
29. Coppin, P.; Jonckheere, I.; Nackaerts, K.; Muys, B.; Lambin, E. Review ArticleDigital change detection methods in ecosystem monitoring: a review. Int. J. Remote Sens. 2004, 25, 1565-1596.
30. Dekker, R.J.; Kuenzer, C.; Lacroix, V.; Lehner, M.; Niemeyer, I.; Nussbaum, S.; Schoepfer, E.; Sequeira, V.; Stringa, E. Change Detection Tools.n Remote Sensing from Space: Supporting International Peace and Security; Jasani, B., Pesaresi, M., Schneiderbauer, S., Zeug, G., Eds.; Springer: Dordrecht, The Netherlands, 2009; Volume VI, pp. 119-140.
31. Kuenzer, C.; Bartalis, Z.; Schmidt, M.; Wagner, W. Trend analyses of a 15 year global soil moisture time series derived from ERS-1/-2 and METOP scatterometer data-Floods, droughts and long term changes. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2008, 37, Part B7, 1363-1368.
32. Lu, D.; Mausel, P.; Brondazio, E.; Moran, E. Change detection techniques. Int. J. Remote Sens. 2004, 25, 2365-2401.
33. Rogan, J.; Chen, D. Remote sensing technology for mapping and monitoring land-cover and land-use change. Progr. Plan. 2004, 61, 301-325.
34. Lillesand, T.M.; Kiefer, R.W.; Chipman, J.W. Remote Sensing and Image Interpretation, 6th ed.; John Wiley & Sons, Inc.: Chichester, UK, 2008; p. 756.
35. Richards, J.A.; Jia, X. Remote Sensing Digital Image Analysis. An Introduction, 3rd ed.; Springer: Berlin, Germany, 1999; p. 363.
36. Quinlan, J.R. C4.5: Programs for Machine Learning; Morgan Kaufmann: San Mateo, CA, USA, 1993.
37. Roth, A.; Huber, M.; Kosmann, D. Geocoding of TerraSAR-X Data. In Proceedings of 20th Congress of ISPRS, Istanbul, Turkey, 12-23 July 2004.
38. Richter, R. A spatially adaptive fast atmospheric correction algorithm. Int. J. Remote Sens. 1996, 17, 1201-1214.
39. Blaschke, T. A Framework for Change Detection Based on Image Objects. In Remote Sensing & GIS for Environmental Studies: Applications in Geography; Erasmi, S., Cyffka, B., Kappas, M., Eds.; Göttinger Geographische Abhandlungen: Göttingen, Germany, 2005; Volume 113, pp. 1-9.
40. Möller, M.; Lymburner, L.; Volk, M. The comparison index: A tool for assessing the accuracy of image segmentation. Int. J. Appl. Earth Obs. Geoinf. 2007, 9, 311-321.
41. Neubert, M.; Herold, H. Assessment of Remote Sensing Image Segmentation Quality. In Proceedings of GEOBIA 2008, Pixels, Objects, Intelligence-GEOgraphic Object Based Image Analysis for the 21st Century, Calgary, AB, Canada, 5-8 August 2008.
42. Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. 2010, 65, 2-16.
43. Haralick, R.M.; Shapiro, L.G. Image segmentation techniques. Comput. Vis. Graph. Image Process. 1985, 29, 100-132.
44. Bannari, A.; Morin, D.; Bonn, F.; Huete, A.R. A review of vegetation indices. Remote Sens. Rev. 1995, 13, 95-120.
45. Crist, E.P.; Cicone, R.C. A physically-based transformation of thematic mapper data-The TM Tasseled Cap. IEEE Trans. Geosci. Remote Sens. 1984, 22, 256-263
46. Kauth, R.J.; Thomas, G.S. The tasseled Cap-A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by Landsat. In Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, Lafayette, IN, USA, 29 June-1 July 1976; pp. 4B-41-44B-51.
47. Schowengerdt, R.A. Remote Sensing. Models and Methods for Image Processing, 3 ed.; Elsevier Academic Press: Amsterdam, The Netherlands, 2007; p. 515.
48. Singh, A.; Harrison, A. Standardized principal components. Int. J. Remote Sens. 1985, 6, 883-896.
49. Tso, B.; Mather, P.M. Classification Methods for Remotely Sensed Data, 2nd ed.; Taylor & Francis Ltd: Abingdon, UK, 2001; p. 356.
50. Quantum-GIS. Available online: http://qgis.osgeo.org (accessed on 15 March 2012).
51. Friedl, M.A.; Sulla-Menashe, D.; Tan, B.; Schneider, A.; Ramankutty, N.; Sibley, A.; Huang, X. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 2010, 114, 168-182.
52. Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data. Principles and Practices, 2 ed.; CRC Press: Boca Raton, FL, USA, 2007; p. 183.
53. Quinlan, J.R. Introduction of decision trees. Machine Learn. 1986, 1, 81-106.
54. Quinlan, J.R. Improved use of continuous attributes in C4.5. J. Artif. Intell. Res. 1996, 4, 77-90.
55. Friedl, M.A.; Brodley, C.E. Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 1997, 61, 399-409.
56. Hansen, M.; Dubayah, R.; Defries, R. Classification trees: An alternative to traditional land cover classifiers. Int. J. Remote Sens. 1996, 17, 1075-1081.
57. Quinlan, J.R., Generating Production Rules from Decision Trees. In Proceedings of the 10th International Joint Conference on Artificial Intelligence; Morgan Kaufmann Publishers Inc.: Milan, Italy, 1987; Volume 1, pp 304-307.
58. Wu, X.D.; Kumar, V.; Quinlan, J.R.; Ghosh, J.; Yang, Q.; Motoda, H.; McLachlan, G.J.; Ng, A.; Liu, B.; Yu, P.S.; et al. Top 10 algorithms in data mining. Knowl. Inf. Syst. 2008, 14, 1-37.
59. Friedl, M.A.; Brodley, C.E.; Strahler, A.H. Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Trans. Geosci. Remote Sens. 1999, 37, 969-977.
60. Rokach, L.; Maimon, O.Z. Data Mining with Decision Trees: Theroy and Applications; World Scientific Pub. Co. Inc.: Hackensack, NJ, USA, 2008; p. 243.
61. Kamber, M.; Winstone, L.; Gong, W.; Cheng, S.; Han, J.W. Generalization and Decision Tree Induction: Efficient Classification in Data Mining. In Proceedings of Seventh International Workshop on Research Issues in Data Engineering, Birmingham, UK, 7-8 April 1997; pp. 111-120.
62. Pal, M.; Mather, P.M. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens. Environ. 2003, 86, 554-565.
63. Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991, 37, 35-46.
64. Cohen, J. A coefficient of agreement for nominal scales. Educ. Psych. Meas. 1960, 20, 37-46.
65. Foody, G.M. Status of land cover classification accuracy assessment. Remote Sens. Environ. 2002, 80, 185-201.
66. Schut, P. OpenGIS Standards-Web Processing Service (WPS). Available online: http://www.opengeospatial.org/standards/wps (accessed on 15 March 2012).
67. Bielski, C.; Gentilini, S.; Pappalardo, M. Post-disaster image processing for damage analysis using GENESI-DR, WPS and Grid computing. Remote Sens. 2011, 3, 1234-1250.
68. Cepicky, J.; Becchi geospatial processing via internet on remote servers-PyWPS. OSGeo J. 2007, 1, 39-42.
69. Rouse, J.W.; Haas, R.H.; Schell, J.A.; and, D.W.D.; Harlan, J.C. Monitoring the Vernal Advancements and Retrogradation of Natural Vegetation; NASA/GSFC: Greenbelt, MD, USA, 1974; p. 371.
70. Zarco-Tejada, P.J.; Berjón, A.; López-Lozano, R.; Miller, J.R.; Martín, P.; Cachorro, V.; González, M.R.; de Frutos, A. Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ. 2005, 99, 271-287.
71. Haboudane, D.; Miller, J.R.; Pattey, E.; Zarco-Tejada, P.J.; Strachan, I.B. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens. Environ. 2004, 90, 337-352.
72. Klein, I.; Gessner, U.; Kuenzer, C. Regional land cover mapping in Central Asia using MODIS time-series. Appl. Geogr. 2012, doi: 10.1016/j.apgeog.2012.06016.
73. Duro, D.C.; Franklin, S.E.; Dubé, M.G. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ. 2012, 118, 259-272.
74. Reinartz, P. The CATENA Processing Chain-Multi-Sensor Pre-processing: Orthorectification, Atmospheric Correction, Future Aspects. In Proceedings of Geoland Forum_6, Toulouse, France, 24-25 March 2010.
75. Breiman, L. Random Forests. Machine Learn. 2001, 45, 5-32.
76. Vapnik, V.N.; Chervonenkis, A.Y. Teoriya Raspoznavaniya Obrazov: Statisticheskie Problemy Obucheniya; Izdat. Nauka, FizMatLit.: Moscow, Russia, 1974; p. 416.