Land cover harmonization using Latent Dirichlet Allocation

References

• Abercrombie, S.P. and Friedl, M.A., 2016. Improving the consistency of multitemporal land cover maps using a hidden Markov model. IEEE Transactions on Geoscience and Remote Sensing, 54, 703–713. doi:10.1109/TGRS.2015.2463689
   Web of Science ®Google Scholar
• Agriculture and Agri-Food Canada, 2018. Annual crop inventory [WWW document]. Available from: https://open.canada.ca/data/en/dataset/ba2645d5-4458-414d-b196-6303ac06c1c9 [Accessed 15 Nov 2018].
   Google Scholar
• Ahlqvist, O., 2005. Using uncertain conceptual spaces to translate between land cover categories. International Journal of Geographical Information Science, 19, 831–857. doi:10.1080/13658810500106729
   Web of Science ®Google Scholar
• Blanco, P.D., et al., 2013. A land cover map of Latin America and the Caribbean in the framework of the SERENA project. Remote Sensing of Environment, 132, 13–31. doi:10.1016/j.rse.2012.12.025
   Web of Science ®Google Scholar
• Blei, D.M., 2012. Probabilistic topic models. Communications of the ACM, 55, 77. doi:10.1145/2133806.2133826
   Web of Science ®Google Scholar
• Blei, D.M., 2019. Probabilistic topic models and user behavior [WWW document]. Available from: http://www.cs.columbia.edu/~blei/talks/Blei_User_Behavior.pdf [Accessed 13 Sept 2019].
   Google Scholar
• Blei, D.M., Ng, A.Y., and Jordan, M.I., 2003. Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
   Web of Science ®Google Scholar
• Buchhorn, M., et al., 2019. Copernicus global land service product user manual: moderate dynamic land cover 100m version 2. Available from: https://land.copernicus.eu/global/products/lc
   Google Scholar
• Cochran, W.G. 1977. Sampling techniques. 3rd ed. New York: John Wiley & Sons.
   Google Scholar
• Comber, A., Fisher, P., and Wadsworth, R., 2004a. Assessment of a semantic statistical approach to detecting land cover change using inconsistent data sets. Photogrammetric Engineering & Remote Sensing, 70, 931–938. doi:10.14358/PERS.70.8.931
   Web of Science ®Google Scholar
• Comber, A.J., Fisher, P., and Wadsworth, R., 2004b. Integrating land-cover data with different ontologies: identifying change from inconsistency. International Journal of Geographical Information Science, 18, 691–708. doi:10.1080/13658810410001705316
   Web of Science ®Google Scholar
• Comber, A.J., Fisher, P., and Wadsworth, R., 2018. Text mining analysis of land cover semantic overlap. In: O. Ahlqvist, D. Varanka, S. Fritz, K. Janowicz, ed. Land use and land cover semantics. Boca Raton: CRC Press, 208–227.
   Google Scholar
• Davidson, A.M., et al., 2017. Detailed crop mapping using remote sensing data (crop data layers). In: J. Delincé, ed. Handbook on remote sensing for agricultural statistics. Chapter 4. Rome: Handbook of the Global Strategy to improve Agricultural and Rural Statistics (GSARS), 91–130.
   Google Scholar
• Feng, C.C. and Flewelling, D.M., 2004. Assessment of semantic similarity between land use/land cover classification systems. Computers, Environment and Urban Systems, 28, 229–246. doi:10.1016/S0198-9715(03)00020-6
   Google Scholar
• Franklin, S.E. and Wulder, M.A., 2002. Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography: Earth and Environment, 26, 173–205. doi:10.1191/0309133302pp332ra
   Google Scholar
• Fritz, S., et al., 2011. Highlighting continued uncertainty in global land cover maps for the user community. Environmental Research Letters, 6, 044005. doi:10.1088/1748-9326/6/4/044005
   Web of Science ®Google Scholar
• Fritz, S. and See, L., 2008. Identifying and quantifying uncertainty and spatial disagreement in the comparison of Global Land Cover for different applications. Global Change Biology, 14, 1057–1075. doi:10.1111/j.1365-2486.2007.01519.x
   Web of Science ®Google Scholar
• Giri, C., Zhu, Z., and Reed, B., 2005. A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sensing of Environment, 94, 123–132. doi:10.1016/j.rse.2004.09.005
   Web of Science ®Google Scholar
• Griffiths, T.L. and Steyvers, M., 2004. Finding scientific topics. Proceedings of the National Academy of Sciences, 101, 5228–5235. doi:10.1073/pnas.0307752101
   PubMed Web of Science ®Google Scholar
• Hermosilla, T., et al., 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment, 158, 220–234. doi:10.1016/j.rse.2014.11.005
   Web of Science ®Google Scholar
• Hermosilla, T., et al., 2018. Disturbance-informed annual land cover classification maps of Canada’s forested ecosystems for a 29-year landsat time series. Canadian Journal of Remote Sensing, 44, 1–21. doi:10.1080/07038992.2018.1437719
   Web of Science ®Google Scholar
• Herold, M., et al., 2006. A joint initiative for harmonization and validation of land cover datasets. IEEE Transactions on Geoscience and Remote Sensing, 44, 1719–1727. doi:10.1109/TGRS.2006.871219
   Web of Science ®Google Scholar
• Herold, M., et al., 2008. Some challenges in global land cover mapping: an assessment of agreement and accuracy in existing 1 km datasets. Remote Sensing of Environment, 112, 2538–2556. doi:10.1016/j.rse.2007.11.013
   Web of Science ®Google Scholar
• Iwao, K., et al., 2011. Creation of new global land cover map with map integration. Journal Geographical Information Systems, 03, 160–165. doi:10.4236/jgis.2011.32013
   Google Scholar
• Jepsen, M.R. and Levin, G., 2013. Semantically based reclassification of Danish land-use and land-cover information. International Journal of Geographical Information Science, 27, 2375–2390. doi:10.1080/13658816.2013.803555
   Web of Science ®Google Scholar
• Jung, M., et al., 2006. Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment, 101, 534–553. doi:10.1016/j.rse.2006.01.020
   Web of Science ®Google Scholar
• Kaptué Tchuenté, A.T., Roujean, J.-L., and De Jong, S.M., 2011. Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale. International Journal of Applied Earth Observation and Geoinformation, 13, 207–219. doi:10.1016/j.jag.2010.11.005
   Web of Science ®Google Scholar
• Kavouras, M. and Kokla, M., 2002. A method for the formalization and integration of geographical categorizations. International Journal of Geographical Information Science, 16, 439–453. doi:10.1080/13658810210129120
   Web of Science ®Google Scholar
• Kinoshita, T., Iwao, K., and Yamagata, Y., 2014. Creation of a global land cover and a probability map through a new map integration method. International Journal of Applied Earth Observation and Geoinformation, 28, 70–77. doi:10.1016/j.jag.2013.10.006
   Web of Science ®Google Scholar
• Latifovic, R., Pouliot, D., and Olthof, I., 2017. Circa 2010 land cover of Canada: local optimization methodology and product development. Remote Sensing, 9, 1098. doi:10.3390/rs9111098
   Web of Science ®Google Scholar
• Lesiv, M., et al., 2016. Comparison of data fusion methods using crowdsourced data in creating a hybrid forest cover map. Remote Sensing, 8 (3), 261. doi:10.3390/rs8030261
   Web of Science ®Google Scholar
• Matthews, E., 1983. Global vegetation and land use: new high-resolution data bases for climate studies. Journal of Climate and Applied Meteorology, 22, 474–487. doi:10.1175/1520-0450(1983)022<0474:GVALUN>2.0.CO;2
   Google Scholar
• McCallum, I., et al., 2006. A spatial comparison of four satellite derived 1 km global land cover datasets. International Journal of Applied Earth Observation and Geoinformation, 8, 246–255. doi:10.1016/j.jag.2005.12.002
   Web of Science ®Google Scholar
• Olofsson, P., et al., 2014. Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42–57. doi:10.1016/j.rse.2014.02.015
   Web of Science ®Google Scholar
• Pedregosa, F., et al., 2011. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
   Web of Science ®Google Scholar
• Pérez-Hoyos, A., et al., 2017. Comparison of global land cover datasets for cropland monitoring. Remote Sensing, 9, 1118. doi:10.3390/rs9111118
   Web of Science ®Google Scholar
• Pérez-Hoyos, A., García-Haro, F.J., and San-Miguel-Ayanz, J., 2012a. Conventional and fuzzy comparisons of large scale land cover products: application to CORINE, GLC2000, MODIS and GlobCover in Europe. ISPRS Journal of Photogrammetry and Remote Sensing, 74, 185–201. doi:10.1016/j.isprsjprs.2012.09.006
   Web of Science ®Google Scholar
• Pérez-Hoyos, A., García-Haro, F.J., and San-Miguel-Ayanz, J., 2012b. A methodology to generate a synergetic land-cover map by fusion of different land-cover products. . International Journal of Applied Earth Observation and Geoinformation, 19, 72–87. doi:10.1016/j.jag.2012.04.011
   Web of Science ®Google Scholar
• Pflugmacher, D., et al., 2011. Comparison and assessment of coarse resolution land cover maps for Northern Eurasia. Remote Sensing of Environment, 115, 3539–3553. doi:10.1016/j.rse.2011.08.016
   Web of Science ®Google Scholar
• Running, S.W., 2008. CLIMATE CHANGE: ecosystem disturbance, carbon, and climate. Science, 80, 321, 652–653. doi:10.1126/science.1159607
   Google Scholar
• Schepaschenko, D., et al., 2015. Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics. Remote Sensing of Environment, 162, 208–220. doi:10.1016/j.rse.2015.02.011
   Web of Science ®Google Scholar
• See, L., et al., 2015. Building a hybrid land cover map with crowdsourcing and geographically weighted regression. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 48–56. doi:10.1016/j.isprsjprs.2014.06.016
   Web of Science ®Google Scholar
• Sellers, P.J., et al., 1997. Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science, 80 (275), 502–509. doi:10.1126/science.275.5299.502
   Google Scholar
• Stehman, S.V. and Czaplewski, R.L., 2003. Introduction to special issue on map accuracy. Environmental and Ecological Statistics, 10, 301–308. doi:10.1023/A:1025138423071
   Web of Science ®Google Scholar
• Sulla-Menashe, D., et al., 2019. Hierarchical mapping of annual global land cover 2001 to present: the MODIS Collection 6 Land Cover product. Remote Sensing of Environment, 222, 183–194. doi:10.1016/j.rse.2018.12.013
   Web of Science ®Google Scholar
• Townshend, J.R.G., 1992. Land cover. International Journal of Remote Sensing, 13, 1319–1328. doi:10.1080/01431169208904193
   Web of Science ®Google Scholar
• Tsendbazar, N.-E., et al., 2015. Spatial accuracy assessment and integration of global land cover datasets. Remote Sensing, 7, 15804–15821. doi:10.3390/rs71215804
   Web of Science ®Google Scholar
• Tsendbazar, N.-E., de Bruin, S., and Herold, M., 2017. Integrating global land cover datasets for deriving user-specific maps. International Journal of Digital Earth, 10, 219–237. doi:10.1080/17538947.2016.1217942
   Web of Science ®Google Scholar
• Tuanmu, M.N. and Jetz, W., 2014. A global 1-km consensus land-cover product for biodiversity and ecosystem modelling. Global Ecology and Biogeography, 23, 1031–1045. doi:10.1111/geb.12182
   Web of Science ®Google Scholar
• Vancutsem, C., et al., 2013. Harmonizing and combining existing land cover/land use datasets for cropland area monitoring at the African continental scale. Remote Sensing, 5, 19–41. doi:10.3390/rs5010019
   Web of Science ®Google Scholar
• Wadsworth, R.A., Comber, A.J., and Fisher, P.F., 2006. Expert knowledge and embedded knowledge: or why long rambling class descriptions are useful, in: Progress in Spatial Data Handling - 12th International Symposium on Spatial Data Handling, 12–14 July 2006. Vienna, Austria: SDH. doi: 10.1007/3-540-35589-8_13
   Google Scholar
• White, J.C., et al., 2014. Pixel-based image compositing for large-area dense time series applications and science. Canadian Journal of Remote Sensing, 40, 192–212. doi:10.1080/07038992.2014.945827
   Web of Science ®Google Scholar
• Wulder, M.A., et al., 2008. Monitoring Canada’s forests. Part 1: completion of the EOSD land cover project. Can. Journal of Remote Sensing, 34, 549–562. doi:10.5589/m08-066
   Google Scholar
• Wulder, M.A., et al., 2018. Land cover 2.0. Int. International Journal of Remote Sensing, 39, 4254–4284. doi:10.1080/01431161.2018.1452075
   Web of Science ®Google Scholar