Mapping CORINE land cover from Sentinel-1A SAR and SRTM digital elevation model data using random forests

Remote Sensing

Volumn 7, Issue 11, 2015, Pages 14876-14898

  Balzter, Heiko ^a   Cole, Beth ^a   Thiel, Christian ^b   Schmullius, Christiane ^b  

^a UNIVERSITY OF LEICESTER   (United Kingdom)

^b FRIEDRICH SCHILLER UNIVERSITY JENA   (Germany)

Author keywords

Copernicus; CORINE; Geomorphometry; Habitat mapping; Land cover; Land use; SAR; Shuttle Radar Topography Mission (SRTM)

Indexed keywords

DECISION TREES; FORESTRY; IMAGE ACQUISITION; IMAGE TEXTURE; INTERFEROMETRY; LAND USE; MAPPING; POLARIZATION; RADAR; RADAR IMAGING; SATELLITE IMAGERY; SYNTHETIC APERTURE RADAR; TRACKING RADAR;

COPERNICUS; CORINE; GEOMORPHOMETRY; HABITAT MAPPING; LAND COVER; SAR; SHUTTLE RADAR TOPOGRAPHY MISSION;

SPACE-BASED RADAR;

EID: 84950132986     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs71114876     Document Type: Article

References (42)

1. Feranec, J.; Jaffrain, G.; Soukup, T.; Hazeu, G. Determining changes and flows in European landscapes 1990-2000 using CORINE land cover data. Appl. Geogr. 2010, 30, 19-35.
2. Kallimanis, A.S.; Koutsias, N. Geographical patterns of Corine land cover diversity across Europe: The effect of grain size and thematic resolution. Prog. Phys. Geogr. 2013, 37, 161-177.
3. Van der Sande, C.; De Jong, S.; De Roo, A. A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. Int. J. Appl. Earth Obs. Geoinf. 2003, 4, 217-229.
4. Koellner, T.; Scholz, R.W. Assessment of land use impacts on the natural environment. Int. J. Life Cycle Assessment 2008, 13, 32-48.
5. Koellner, T.; de Baan, L.; Beck, T.; Brandão, M.; Civit, B.; Goedkoop, M.; Margni, M.; i Canals, L.M.; Müller-Wenk, R.; Weidema, B.; et al. Principles for life cycle inventories of land use on a global scale. Int. J. Life Cycle Assessment 2013, 18, 1203-1215.
6. Falcucci, A.; Maiorano, L.; Boitani, L. Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation. Landsc. Ecol. 2007, 22, 617-631.
7. Gallego, F.J.; Batista, F.; Rocha, C.; Mubareka, S. Disaggregating population density of the European Union with CORINE land cover. Int. J. Geogr. Inf. Sci. 2011, 25, 2051-2069.
8. Genovese, G.; Vignolles, C.; Nègre, T.; Passera, G. A methodology for a combined use of normalised difference vegetation index and CORINE land cover data for crop yield monitoring and forecasting. A case study on Spain. Agronomie 2001, 21, 91-111.
9. Stathopoulou, M.; Cartalis, C. Daytime urban heat islands from Landsat ETM+ and CORINE land cover data: An application to major cities in Greece. Sol. Energy 2007, 81, 358-368.
10. Büttner, G.; Kosztra, B. CLC2006 Technical Guidelines; European Environment Agency: Copenhagen, Denmark, 2007.
11. Torres, R.; Snoeij, P.; Geudtner, D.; Bibby, D.; Davidson, M.; Attema, E.; Potin, P.; Rommen, B.; Floury, N.; Brown, M.; et al. GMES Sentinel-1 mission. Remote Sens. Environ. 2012, 120, 9-24.
12. Schmullius, C.; Thiel, C.; Pathe, C.; Santoro, M. Radar time series for land cover and forest mapping. In Remote Sensing Time Series; Kuenzer, C., Dech, S., Wagner, W., Eds.; Remote Sensing and Digital Image Processing; Springer International Publishing: Basel, Switzerland, 2015; pp. 323-356.
13. Kwarteng, A.Y.; Dobson, M.C.; Kellndorfer, J.; Williams, R. SAR-based land cover classification of Kuwait. Int. J. Remote Sens. 2008, 29, 6739-6778.
14. Mishra, P.; Singh, D. A statistical-measure-based adaptive land cover classification algorithm by efficient utilization of polarimetric SAR observables. IEEE Trans. Geosci. Remote Sens. 2014, 52, 2889-2900.
15. Wagner, W.; Sabel, D.; Doubkova, M.; Hornacek, M.; Schlaffer, S.; Bartsch, A. Prospects of Sentinel-1 for land applications. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22-27 July 2012; pp. 1741-1744.
16. Thiel, C.; Cartus, O.; Eckardt, R.; Richter, N.; Thiel, C.; Schmullius, C. Analysis of multi-temporal land observation at C-band. In Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12-17 July 2009; doi:10.1109/IGARSS.2009.5417764.
17. Millin-Chalabi, G.; McMorrow, J.; Agnew, C. Detecting a moorland wildfire scar in the Peak District, UK, using synthetic aperture radar from ERS-2 and Envisat ASAR. Int. J. Remote Sens. 2014, 35, 54-69.
18. Park, N.-W.; Chi, K.-H. Integration of multitemporal/polarization C-band SAR data sets for land-cover classification. Int. J. Remote Sens. 2008, 29, 4667-4688.
19. Balzter, H. Forest mapping and monitoring with interferometric synthetic aperture radar (InSAR). Prog. Phys. Geogr. 2001, 25, 159-177.
20. Gaveau, D.L.A.; Balzter, H.; Plummer, S. Forest woody biomass classification with satellite-based radar coherence over 900 000 km2 in Central Siberia. For. Ecol. Manag. 2003, 174, 65-75.
21. Li, G.; Lu, D.; Moran, E.; Dutra, L.; Batistella, M. A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. ISPRS J. Photogramm. Remote Sens. 2012, 70, 26-38.
22. Longepe, N.; Rakwatin, P.; Isoguchi, O.; Shimada, M.; Uryu, Y.; Yulianto, K. Assessment of ALOS PALSAR 50 m Orthorectified FBD data for regional land cover classification by Support Vector Machines. IEEE Trans. Geosci. Remote Sens. 2011, 49, 2135-2150.
23. Esch, T.; Schenk, A.; Ullmann, T.; Thiel, M.; Roth, A.; Dech, S. Characterization of land cover types in TerraSAR-X images by combined analysis of speckle statistics and intensity information. IEEE Trans. Geosci. Remote Sens. 2011, 49, 1911-1925.
24. Riedel, T.; Thiel, C.; Schmullius, C. Fusion of multispectral optical and SAR images towards operational land cover mapping in Central Europe. In Object-Based Image Analysis; Blaschke, T., Lang, S., Hay, G.J., Eds.; Lecture Notes in Geoinformation and Cartography; Springer International Publishing: Berlin, Germany, 2008; pp. 493-512.
25. Satalino, G.; Panciera, R.; Balenzano, A.; Mattia, F.; Walker, J. COSMO-SkyMed multi-temporal data for landcover classification and soil moisture retrieval over an agricultural site in Southern Australia. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22-27 July 2012; pp. 5701-5704.
26. Cable, J.W.; Kovacs, J.M.; Shang, J.; Jiao, X. Multi-temporal polarimetric RADARSAT-2 for land cover monitoring in northeastern Ontario, Canada. Remote Sens. 2014, 6, 2372-2392.
27. Niu, X.; Ban, Y. Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. Int. J. Remote Sens. 2013, 34, 1-26.
28. Evans, T.L.; Costa, M.; Telmer, K.; Silva, T.S.F. Using ALOS/PALSAR and RADARSAT-2 to map land cover and seasonal inundation in the Brazilian Pantanal. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2010, 3, 560-575.
29. Dobson, M.C.; Pierce, L.E.; Ulaby, F.T. Knowledge-based land-cover classification using ERS-1/JERS-1 SAR composites. IEEE Trans. Geosci. Remote Sens. 1996, 34, 83-99.
30. Niu, X.; Ban, Y. Multitemporal polarimetric RADARSAT-2 SAR data for urban land cover mapping through a dictionary-based and a rule-based model selection in a contextual SEM algorithm. Can. J. Remote Sens. 2013, 39, 138-151.
31. Ullmann, T.; Schmitt, A.; Roth, A.; Duffe, J.; Dech, S.; Hubberten, H.-W.; Baumhauer, R. Land cover characterization and classification of arctic tundra environments by means of polarized synthetic aperture X-and C-Band Radar (PolSAR) and Landsat 8 multispectral imagery-Richards Island, Canada. Remote Sens. 2014, 6, 8565-8593.
32. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32.
33. Waske, B.; Braun, M. Classifier ensembles for land cover mapping using multitemporal SAR imagery. ISPRS J. Photogramm. Remote Sens. 2009, 64, 450-457.
34. Van Beijma, S.; Comber, A.; Lamb, A. Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data. Remote Sens. Environ. 2014, 149, 118-129.
35. Farr, T.G.; Kobrick, M. Shuttle radar topography mission produces a wealth of data. Eos Trans. Am. Geophys. Union 2000, 81, 583-585.
36. Farr, T.G.; Rosen, P.A.; Caro, E.; Crippen, R.; Duren, R.; Hensley, S.; Kobrick, M.; Paller, M.; Rodriguez, E.; Roth, L.; et al. The shuttle radar topography mission. Rev. Geophys. 2007, doi:10.1029/2005RG000183.
37. Kobrick, M. On the toes of giants-How SRTM was born. Photogramm. Eng. Remote Sens. 2006, 72, 206-210.
38. Hensley, S.; Rosen, P.; Gurrola, E. Topographic map generation from the shuttle radar topography mission C-band SCANSAR interferometry. Proc. SPIE 2000, 4152, 179-189.
39. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2014.
40. Liaw, A.; Wiener, M. Classification and Regression by randomForest. R News 2002, 2, 18-22.
41. Pebesma, E.J.; Bivand, R.S. Classes and methods for spatial data in R. R News 2005, 5, 9-13.
42. Bivand, R.S.; Pebesma, E.; Gomez-Rubio, V. Applied Spatial Data Analysis with R, 2nd ed.; Springer: New York, NY, USA, 2013.