Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia

References

• Altman, N. S. (1992). An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3), 175–185. doi: 10.1080/00031305.1992.10475879
   Web of Science ®Google Scholar
• Bagan, H., Kinoshita, T., & Yamagata, Y. (2012). Combination of AVNIR-2, PALSAR, and polarimetric parameters for land cover classification. IEEE Transactions on Geoscience and Remote Sensing, 50(4), 1318–1328. doi: 10.1109/TGRS.2011.2164806
   Web of Science ®Google Scholar
• Bannari, A., Morin, D., Huete, A. R., & Bonn, F. (1995). A review of vegetation indices. Remote Sensing Reviews, 13, 95–120. doi: 10.1080/02757259509532298
   Google Scholar
• Belward, A. S., & Skøien, J. O. (2015). Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 115–128. doi: 10.1016/j.isprsjprs.2014.03.009
   Web of Science ®Google Scholar
• Blaschke, T., Burnett, C., & Pekkarinen, A. (2005). Remote sensing image analysis: Including the spatial domain. Kluwer Academic Publishers, pp. 211–236. doi: 10.1007/978-1-4020-2560-0
   Google Scholar
• Breiman, R. E. (2001). Random forests. Machine Learning, 45, 5–32. doi: 10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167. doi: 10.1023/A:1009715923555
   Web of Science ®Google Scholar
• Chand, T. R. K., & Badarinath, K. V. S. (2007). Analysis of ENVISAT ASAR data for forest parameter retrieval and forest type classification – a case study over deciduous forests of central India. International Journal of Remote Sensing, 28(22), 4985–4999. doi: 10.1080/01431160701253295
   Web of Science ®Google Scholar
• Chilar, J. (2000). Land cover mapping of large areas from satellites: Status and research priorities. International Journal of Remote Sensing, 21(6), 1093–1114. doi: 10.1080/014311600210092
   Web of Science ®Google Scholar
• Cohen, W. B., & Goward, S. (2004). Landsat’s role in ecological applications of remote sensing. Bioscience, 54(2004), 535–545. doi: 10.1641/0006-3568(2004)054[0535:LRIEAO]2.0.CO;2
   Web of Science ®Google Scholar
• Congalton, R. G., & Green, K. (2009). Assessing the accuracy of remotely sensed data, principles and practices (2nd ed.). London: CRC Press, Taylor & Francis Group.
   Google Scholar
• Delegido, J., Verrelst, J., Alonso, L., & Moreno, J. (2011). Evaluation of Sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11, 7063–7081. doi: 10.3390/s110707063
   PubMed Web of Science ®Google Scholar
• Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., … Bargellini, P. (2012). Sentinel-2: ESA’s optical high-resolution mission for GMES operational services. Remote Sensing of Environment, 120, 25–36. doi:10.1080/014311600210092
   Web of Science ®Google Scholar
• eCognition. 2016. Retrieved from http://www.ecognition.com/
   Google Scholar
• Engdahl, M., & Hyyppa, J. (2003). Land-cover classification using multitemporal ERS-1/2 InSAR data. IEEE Transactions on Geoscience and Remote Sensing, 41, 1620–1628. doi: 10.1109/TGRS.2003.813271
   Web of Science ®Google Scholar
• Erasmi, S., & Twele, A. (2009). Regional land over mapping in the humid tropics using combined optical and SAR satellite data – a case study from Central Sulawesi, Indonesia. International Journal of Remote Sensing, 30 (10), 2465–2478.
   Google Scholar
• European Space Agency. (2012). Sentinel-2: ESA’s optical high resolution mission for GMES operational services (SP-1322/2). 70 pp.
   Google Scholar
• European Space Agency. (2015). Sentinel-1 userbook. European Space Agency. Retrieved May 2016, from https://sentinels.copernicus.eu/web/sentinel/user-guides/document-library/-/asset_publisher/xlslt4309D5h/content/sentinel-2-user-handbook
   Google Scholar
• European Space Agency. (2016a). Sentinel scientific hub. Retrieved May 2016, from https://scihub.copernicus.eu/dhus/#/home
   Google Scholar
• European Space Agency. (2016b). Sentinel application platform. Retrieved July 2016, from http://step.esa.int/main/toolboxes/snap
   Google Scholar
• European Space Agency. (2016c). Sen2Cor. Retrieved June 2016, from http://step.esa.int/main/third-party-plugins-2/sen2cor
   Google Scholar
• Etter, A., McAlpine, C., & Possingham, H. (2008). Historical patterns and drivers of landscape change in Colombia since 1500: A regionalized spatial approach. Annals of the Association of American Geographers, 98(1), 2–23. doi: 10.1080/00045600701733911
   Web of Science ®Google Scholar
• Feddema, J. J., Oleson, K. W., Bonan, G. B., Mearns, L. O., Buja, L. E., Meehl, G. A., & Washington, W. M. (2005). The importance of land-cover change in simulating future climates. Science, 310, 1674–1678. doi: 10.1126/science.1118160
   PubMed Web of Science ®Google Scholar
• Frampton, W. J., Dash, J., Watmough, G., & Milton, E. J. (2013). Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 82, 83–92. doi: 10.1016/j.isprsjprs.2013.04.007
   Web of Science ®Google Scholar
• Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A. M., Ramakutty, N., Sibley, A., & Huang, X. (2010). MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114, 168–182. doi: 10.1016/j.rse.2009.08.016
   Web of Science ®Google Scholar
• Fritz, S., See, L., McCallum, I., You, L., Bun, A., Moltchanova, E., … Obersteiner, M. (2015). Mapping global cropland and field size. Global Change Biology, 21, 1980–1992. doi: 10.1111/gcb.12838
   PubMed Web of Science ®Google Scholar
• Gessner, U., Machwitz, M., Esch, T., Tillack, A., Naeimi, V., Kuenzer, C., & Dech, S. (2015). Multi-sensor mapping of West African land cover using MODIS, ASAR and TanDEM-X/TerraSAR-X data. Remote Sensing of Environment, 164, 282–297. doi: 10.1016/j.rse.2015.03.029
   Web of Science ®Google Scholar
• Gitelson, A., & Merzlyak, M. (1998). Remote sensing of chlorophyll concentration in higher plant leaves. Advances in Space Research, 22, 689–692. doi: 10.1016/S0273-1177(97)01133-2
   Web of Science ®Google Scholar
• Gómez, C., White, J. C., & Wulder, M. A. (2016). Optical remotely sensed time series data for land cover classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 55–72. doi: 10.1016/j.isprsjprs.2016.03.008
   Web of Science ®Google Scholar
• Grimm, N. B., Faeth, S. H., Golubiewski, N. E., Redman, C. L., Wu, J., Bai, X., & Briggs, J. M. (2008). Global change and the ecology of cities. Science, 319, 756–760. doi: 10.1126/science.1150195
   PubMed Web of Science ®Google Scholar
• Guyot, G., & Baret, F. (1988, January 18–22). Utilisation de la haute resolution spectrale pour suivre I’etat des couverts vegetaux. Paper presented at the proceedings, 4th international colloquium “Spectral Signatures of Objects in Remote Sensing”, Aussois, Paris, ESA, ESA, Publication SP-287, pp. 279–286.
   Google Scholar
• Haack, B., & Bechdol, M. (2000). Integrating multisensor data and radar texture measures for land cover mapping. Computers & Geosciences, 26, 411–421. doi: 10.1016/S0098-3004(99)00121-1
   Web of Science ®Google Scholar
• Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., … ChiniL, J. R. G. (2013). Townshend. High-resolution global maps of 21st century forest cover change. Science, 342(6160), 850–853. doi: 10.1126/science.1244693
   PubMed Web of Science ®Google Scholar
• Haralick, R. M., Shanmugat, M. K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610–621. doi: 10.1109/TSMC.1973.4309314
   Web of Science ®Google Scholar
• Herold, N. D., & Haack, B. N. (2002). Fusion of radar and optical data for land cover mapping. Geocarto International, 17(2), 21–30. doi: 10.1080/10106040208542232
   Google Scholar
• Herold, N. D., Haack, B. N., & Solomon, E. (2004). An evaluation of radar texture for land use/cover extraction in varied landscapes. International Journal of Applied Earth Observation and Geoinformation, 5, 113–128. doi: 10.1016/j.jag.2004.01.005
   Google Scholar
• Herold, M., Woodcock, C., Di Gregorio, A., Mayaux, P., Belward A., Latham, J., & Schmullius, C. C. (2006). A joint initiative for harmonization and validation of land cover datasets. IEEE Transactions on Geoscience and Remote Sensing, 44 (7), 1719–1727.
   Google Scholar
• Jarvis, A., Reuter, H. I., Nelson, A., & Guevara, E. (2008). Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90 m Database. Retrieved from http://srtm.csi.cgiar.org
   Google Scholar
• Jin, H., Mountrakis, G., & Stehman, S. V. (2014). Assessing integration of intensity, polarimetric scattering, interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 98, 70–84. doi: 10.1016/j.isprsjprs.2014.09.017
   Web of Science ®Google Scholar
• Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., … Waske, B. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8, 70. doi: 10.3390/rs8010070
   Web of Science ®Google Scholar
• Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., … Kessler, M. (2016). Climatologies at high resolution for the Earth land surface areas. arXiv:1607.00217 [physics].
   Google Scholar
• Karlson, M., Ostwald, M., Reese, H., Roméo Bazié, H., & Tankoano, B. (2016). Assessing the potential of multi-seasonal WorldView-2 imagery for mapping West African agroforestry tree species. International Journal of Applied Earth Observation and Geoinformation, 50, 80–88. doi: 10.1016/j.jag.2016.03.004
   Web of Science ®Google Scholar
• Kuemmerle, T., Erb, K., Meyfroidt, P., Müller, D., Verburg, P. H., Estel, S., … Reenberg, A. (2013). Challenges and opportunities in mapping land use intensity globally. Current Opinion in Environmental Sustainability, 5(5), 484–493. doi: 10.1016/j.cosust.2013.06.002
   PubMed Web of Science ®Google Scholar
• Malenovský, H. R., Cihlar, J., Schaepman, M. E., García-Santos, G., Fernandes, R., & Berger, M. (2012). Sentinels for science: Potential of sentinel-1, -2, and -3 missions for scientific observations of ocean, cryosphere, and land. Remote Sensing of Environment, 120, 91–101. doi: 10.1016/j.rse.2011.09.026
   Web of Science ®Google Scholar
• Maxwell, S. K., Meliker, J. R., & Goovaerts, P. (2009). Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research. Journal of Exposure Science and Environmental Epidemiology, 20(2010), 176–185. doi: 10.1038/jes.2009.7
   PubMed Web of Science ®Google Scholar
• Miettinen, J., & Liew, S. C. (2011). Separability of insular southeast Asian woody plantation species in the 50 m resolution ALOS PALSAR mosaic product. Remote Sensing Letters, 2, 299–307. doi: 10.1080/01431161.2010.520345
   Web of Science ®Google Scholar
• Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., … Kassem, K. R. (2001). Terrestrial ecoregions of the world: A new map of life on Earth: A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience, 51(11), 933–938. e Change 3, 385–390. doi: 10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2
   Web of Science ®Google Scholar
• Pesaresi, M., Corbane, C., Julea, A., Florczyk, A. J., Syrris, V., & Soille, P. (2016). Assessment of the added-value of Sentinel-2 for detecting built-up areas. Remote Sensing, 8(4), 299. doi: 10.3390/rs8040299
   Web of Science ®Google Scholar
• Qi, J., Chehbouni, A., Huete, A. R., & Kerr, Y. H. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48, 119–126. doi: 10.1016/0034-4257(94)90134-1
   Web of Science ®Google Scholar
• Qin, Y., Xiao, X., Dong, J., Zhang, G., Roy, P. S., Joshi, P. K., … Moore III, B. (2016). Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010. Scientific Reports, 6, 570. doi: 10.1038/srep20880
   Google Scholar
• Recio, J. A., Helmholz, P., & Muller, S. (2011). Potential evaluation of different types of images and their combination for the classification of GIS objects cropland and grassland. The Internat archives of the Photogramm, Remote Sens and Spatial Info Sc, 28-4/W19, 6pp. doi: 10.5194/isprsarchives-XXXVIII-4-W19-251-2011
   Google Scholar
• Rodrigues, A., Marcal, A. R. S., Furlan, D., Ballester, M. V., & Cunha, M. (2013). Land cover map production for Brazilian Amazon using NDVI SPOT VEGETATION time series. Canadian Journal of Remote Sensing, 39(4), 277–289. doi: 10.5589/m13-037
   Web of Science ®Google Scholar
• Rouse, J. W., Haas, R. W., Schell, J. A., Deering, D. W., & Harlan, J. C. (1974). Monitoring the vernal advancement and retrogradation (Greenwave effect) of natural vegetation. NASA/GSFCT Type III Final Report, Greenbelt, MD, USA.
   Google Scholar
• Saadat, H., Adamowski, J., Bonnell, R., Sharifi, F., Namdar, M., & Ale-Ebrahim, S. (2011). Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 66(5), 608–619. doi: 10.1016/j.isprsjprs.2011.04.001
   Web of Science ®Google Scholar
• Schmidt, M., Pringle, M., Devadas, R., Denham, R., & Tindall, D. A. (2016). A framework for large-area mapping of past and present cropping activity using seasonal Landsat images and time series metrics. Remote Sensing, 8, 312. doi: 10.3390/rs8040312
   Web of Science ®Google Scholar
• Schuster, C., Förster, M., & Kleinschmit, B. (2012). Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data. International Journal of Remote Sensing, 33, 5583–5599. doi: 10.1080/01431161.2012.666812
   Web of Science ®Google Scholar
• Spoto, F., Sy, O., Laberinti, P., Martiort, P., Fernandez, V., Colin, O., … Meygret, A. (2012). Overview of Sentinel-2. IEEE International Geoscience and Remote Sensing Symposium, Munich, 2012, 1707–1710. doi: 10.1109/IGARSS.2012.6351195
   Google Scholar
• Sterling, S. M., Ducharne, A., & Polcher, J. (2013). The impact of global land-cover change on the terrestrial water cycle. Nature Climate Change, 3, 385–390. doi: 10.1038/nclimate1690
   Web of Science ®Google Scholar
• Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., … Rostan, F. (2012). GMES Sentinel-1 mission. Remote Sensing of Environment, 120, 9–24. doi: 10.1016/j.rse.2011.05.028
   Web of Science ®Google Scholar
• Turner, B. L., II, Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104(52), 20666–20671. doi:10.1073/pnas.0800052105
   PubMed Web of Science ®Google Scholar
• Usman, M., Liedl, R., Shahid, M. A., & Abbas, A. (2015). Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data. Journal of Geographical Sciences, 25, 1479. doi: 10.1007/s11442-015-1247-y
   Web of Science ®Google Scholar
• Vaglio Laurin, G., Puletti, N., Hawthorne, W., Liesenberg, V., Corona, P., Papale, D., … Valentini, R. (2016). Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data. Remote Sensing of Environment, 176, 163–176. doi: 10.1016/j.rse.2016.01.017
   Web of Science ®Google Scholar
• Walker, W. S., Stickler, C. M., Kellndorfer, J. M., Kirsch, K. M., Nepstad, D. C (2010). Large-area classification and mapping of forest and land cover in the Brazilian Amazon: A comparative analysis of ALOS/PALSAR and Landsat data sources. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, 3(4), 594–604.
   Google Scholar
• Wagner, W., Sabel, D., Doubkova, M., Hornáček, M., Schlaffer, S., & Bartsch, A. (2012). Prospects of Sentinel-1 for land applications. IEEE International Geoscience and Remote Sensing Symposium, Munich, 1741–1744. doi: 10.1109/IGARSS.2012.6351184
   Google Scholar
• Wang, X., Ge, X., & Li, X. (2012). Evaluation of filters for Envisat ASAR speckle suppression in pasture area. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-7, 2012 XXII ISPRS Congress, 25 August–01 September 2012, Melbourne, Australia. doi: 10.5194/isprsannals-I-7-341-2012
   Google Scholar