{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T13:46:17Z","timestamp":1762004777306,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2016,3,22]],"date-time":"2016-03-22T00:00:00Z","timestamp":1458604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EU FP7-MC-IIF","award":["SIFCAS No. 627481"],"award-info":[{"award-number":["SIFCAS No. 627481"]}]},{"name":"EU FP7","award":["ERC grant Crowdland No. 617754"],"award-info":[{"award-number":["ERC grant Crowdland No. 617754"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Data fusion represents a powerful way of integrating individual sources of information to produce a better output than could be achieved by any of the individual sources on their own. This paper focuses on the data fusion of different land cover products derived from remote sensing. In the past, many different methods have been applied, without regard to their relative merit. In this study, we compared some of the most commonly-used methods to develop a hybrid forest cover map by combining available land cover\/forest products and crowdsourced data on forest cover obtained through the Geo-Wiki project. The methods include: nearest neighbour, naive Bayes, logistic regression and geographically-weighted logistic regression (GWR), as well as classification and regression trees (CART). We ran the comparison experiments using two data types: presence\/absence of forest in a grid cell; percentage of forest cover in a grid cell. In general, there was little difference between the methods. However, GWR was found to perform better than the other tested methods in areas with high disagreement between the inputs.<\/jats:p>","DOI":"10.3390\/rs8030261","type":"journal-article","created":{"date-parts":[[2016,3,22]],"date-time":"2016-03-22T11:50:37Z","timestamp":1458647437000},"page":"261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9846-3342","authenticated-orcid":false,"given":"Myroslava","family":"Lesiv","sequence":"first","affiliation":[{"name":"International Institute for Applied Systems Analysis, Schlossplatz 1, Laxenburg A-2361, Austria"}]},{"given":"Elena","family":"Moltchanova","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7814-4990","authenticated-orcid":false,"given":"Dmitry","family":"Schepaschenko","sequence":"additional","affiliation":[{"name":"International Institute for Applied Systems Analysis, Schlossplatz 1, Laxenburg A-2361, Austria"},{"name":"Moscow State Forest University, Institutskaya 1, Mytischi 141005, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2665-7065","authenticated-orcid":false,"given":"Linda","family":"See","sequence":"additional","affiliation":[{"name":"International Institute for Applied Systems Analysis, Schlossplatz 1, Laxenburg A-2361, Austria"}]},{"given":"Anatoly","family":"Shvidenko","sequence":"additional","affiliation":[{"name":"International Institute for Applied Systems Analysis, Schlossplatz 1, Laxenburg A-2361, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3652-7846","authenticated-orcid":false,"given":"Alexis","family":"Comber","sequence":"additional","affiliation":[{"name":"School of Geography, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK"}]},{"given":"Steffen","family":"Fritz","sequence":"additional","affiliation":[{"name":"International Institute for Applied Systems Analysis, Schlossplatz 1, Laxenburg A-2361, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2016,3,22]]},"reference":[{"key":"ref_1","unstructured":"Global Climate Observing System GCOS Essential Climate Variables. Available online: http:\/\/www.wmo.int\/pages\/prog\/gcos\/index.php?name=EssentialClimateVariables."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1038\/514434c","article-title":"China: Open access to Earth land-cover map","volume":"514","author":"Jun","year":"2014","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5851","DOI":"10.1080\/01431161.2013.798055","article-title":"Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: A segmentation-based approach","volume":"34","author":"Yu","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.rse.2012.01.010","article-title":"Opening the archive: How free data has enabled the science and monitoring promise of Landsat","volume":"122","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2014.06.016","article-title":"Building a hybrid land cover map with crowdsourcing and geographically weighted regression","volume":"103","author":"See","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.rse.2015.02.011","article-title":"Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics","volume":"162","author":"Schepaschenko","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_8","unstructured":"Fritz, S., Bartholom\u00e9, E., Belward, A., Hartley, A., Stibig, H.-J., Eva, H., and Mayaux, P. (2003). Harmonisation, Mosaicing and Production of the Global Land Cover 2000 Database (Beta Version), Office for Official Publications of the European Communities."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_10","unstructured":"Bicheron, P., Defourny, P., Brockmann, C., Schouten, L., Vancutsem, C., Huc, M., Bontemps, S., Leroy, M., Achard, F., and Herold, M. (2008). Globcover: Products Description and Validation Report, Medias France."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"044005","DOI":"10.1088\/1748-9326\/6\/4\/044005","article-title":"Highlighting continued uncertainty in global land cover maps for the user community","volume":"6","author":"Fritz","year":"2011","journal-title":"Environ. Res. Lett."},{"key":"ref_12","first-page":"25","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Sentin. Missions New Oppor. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"704504","DOI":"10.1155\/2013\/704504","article-title":"A review of data fusion techniques","volume":"2013","author":"Castanedo","year":"2013","journal-title":"Sci. World J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1080\/1747423X.2010.511681","article-title":"A new hybrid land cover dataset for Russia: A methodology for integrating statistics, remote sensing and in-situ information","volume":"6","author":"Schepaschenko","year":"2011","journal-title":"J. Land Use Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fritz, S., You, L., Bun, A., See, L., McCallum, I., Schill, C., Perger, C., Liu, J., Hansen, M., and Obersteiner, M. (2011). Cropland for sub-Saharan Africa: A synergistic approach using five land cover data sets. Geophys. Res. Lett., 38.","DOI":"10.1029\/2010GL046213"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/17538947.2013.856959","article-title":"Integrating global land cover products for improved forest cover characterization: An application in North America","volume":"7","author":"Song","year":"2014","journal-title":"Int. J. Digit. Earth"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s11004-014-9553-y","article-title":"Bayesian Markov Chain random field cosimulation for improving land cover classification accuracy","volume":"47","author":"Li","year":"2015","journal-title":"Math. Geosci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.rse.2003.10.002","article-title":"Delineation of forest\/nonforest land use classes using nearest neighbor methods","volume":"89","author":"Haapanen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"149","DOI":"10.2747\/1548-1603.44.2.149","article-title":"K Nearest neighbor method for forest inventory using remote sensing data","volume":"44","author":"Meng","year":"2007","journal-title":"GISci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3178","DOI":"10.1890\/0012-9658(2000)081[3178:CARTAP]2.0.CO;2","article-title":"Classification and regression trees: A powerful yet simple technique for ecological data analysis","volume":"81","author":"Fabricius","year":"2000","journal-title":"Ecology"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1890\/110236","article-title":"The current state of citizen science as a tool for ecological research and public engagement","volume":"10","author":"Dickinson","year":"2012","journal-title":"Front. Ecol. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1126\/science.333.6039.173","article-title":"Galaxy Zoo volunteers share pain and glory of research","volume":"333","author":"Clery","year":"2011","journal-title":"Science"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/13658816.2015.1018266","article-title":"Usability of VGI for validation of land cover maps","volume":"29","author":"Fonte","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Caruana, R., and Niculescu-Mizil, A. (2006, January 25\u201329). An empirical comparison of supervised learning algorithms. Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143865"},{"key":"ref_25","first-page":"57","article-title":"Geographic stacking: Decision fusion to increase global land cover map accuracy","volume":"103","author":"Clinton","year":"2015","journal-title":"Glob. Land Cover Mapp. Monit."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.envsoft.2011.11.015","article-title":"Geo-Wiki: An online platform for improving global land cover","volume":"31","author":"Fritz","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_27","unstructured":"Food and Agriculture Organisation of the United Nations (FAO) (2010). Global Forest Resources Assessment 2010, FAO. FAO Forestry Paper 163."},{"key":"ref_28","first-page":"1369","article-title":"A new global land cover map, GLCNMO","volume":"XXXVII","author":"Tateishi","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_29","unstructured":"Defourny, P., Vancustem, C., Bicheron, P., Brockmann, C., Nino, F., Schouten, L., and Leroy, M. (2006, January 8\u201311). GLOBCOVER: A 300m global land cover product for 2005 using ENVISAT MERIS time series. Proceedings of the ISPRS Commission VII Mid-Term Symposium: Remote Sensing: From Pixels to Processes, Enscede, The Netherlands."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1080\/17538947.2013.786146","article-title":"Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error","volume":"6","author":"Sexton","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_31","unstructured":"DiMiceli, C.M., Carroll, M.L., Sohlberg, R.A., Huang, C., Hansen, M.C., and Townshend, J.R.G. (2011). Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000\u20132010, Collection 5 Percent Tree Cover, University of Maryland."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5061","DOI":"10.5194\/bg-9-5061-2012","article-title":"Mapping Congo Basin vegetation types from 300 m and 1 km multi-sensor time series for carbon stocks and forest areas estimation","volume":"9","author":"Verhegghen","year":"2012","journal-title":"Biogeosciences"},{"key":"ref_33","first-page":"390004","article-title":"Pan-European forest maps derived from optical satellite imagery","volume":"5","author":"Kempeneers","year":"2012","journal-title":"Earthzine"},{"key":"ref_34","unstructured":"Australian Bureau of Agricultural and Resource Economics and Science (2011). Guidelines for Land Use Mapping in Australia: Principles, Procedures and Definitions."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.rse.2010.10.001","article-title":"Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia","volume":"115","author":"Potapov","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3274","DOI":"10.1073\/pnas.1222465110","article-title":"Climate change effects on agriculture: Economic responses to biophysical shocks","volume":"111","author":"Nelson","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_37","first-page":"37","article-title":"Using control data to determine the reliability of volunteered geographic information about land cover","volume":"23","author":"Comber","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.rse.2012.09.005","article-title":"Spatial analysis of remote sensing image classification accuracy","volume":"127","author":"Comber","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"345","DOI":"10.3390\/rs1030345","article-title":"Geo-Wiki.Org: The use of crowdsourcing to improve global land cover","volume":"1","author":"Fritz","year":"2009","journal-title":"Remote Sens."},{"key":"ref_40","unstructured":"See, L., Fritz, S., Thornton, P., You, L., Becker-Reshef, I., Justice, C.O., Leo, O., and Herrero, M. Building a Consolidated Community Global Cropland Map. Earthzine, Available online: http:\/\/www.earthzine.org\/2012\/01\/24\/building-a-consolidated-community-global-cropland-map\/."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Efron, B., and Tibshirani, R.J. (1994). An Introduction to the Bootstrap, CRC Press.","DOI":"10.1201\/9780429246593"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_43","unstructured":"Russell, S.J., and Norvig, P. (1995). Artificial Intelligence: A Modern Approach, Prentice Hall."},{"key":"ref_44","unstructured":"Fotheringham, A.S., Brunsdon, C., and Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, John Wiley & Sons."},{"key":"ref_45","first-page":"431","article-title":"Geographically weighted regression-modelling spatial non-stationarity","volume":"47","author":"Brunsdon","year":"1998","journal-title":"J. R. Stat. Soc. Ser. Stat."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Waller, L.A., and Gotway, C.A. (2004). Applied Spatial Statistics for Public Health Data, John Wiley & Sons, Inc.","DOI":"10.1002\/0471662682"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1111\/j.1466-822X.2005.00153.x","article-title":"Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems","volume":"14","author":"Wang","year":"2005","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10021-005-0054-1","article-title":"Newer classification and regression tree techniques: bagging and random forests for ecological prediction","volume":"9","author":"Prasad","year":"2006","journal-title":"Ecosystems"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1001\/jama.293.5.572","article-title":"Risk stratification for in-hospital mortality in acutely decompensated heart failure: Classification and regression tree analysis","volume":"293","author":"Fonarow","year":"2005","journal-title":"J. Am. Med. Assoc."},{"key":"ref_50","unstructured":"Martin, K.J., and Hirschberg, D.S. (1996). Small Sample Statistics for Classification Error Rates II: Confidence Intervals and Significance Tests, University of California."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Pampel, F.C. (2000). Logistic Regression: A Primer, SAGE.","DOI":"10.4135\/9781412984805"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy","volume":"70","author":"Foody","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_53","unstructured":"Rish, I. (2015). An Empirical Study of the Naive Bayes Classifier, IBM Research Division, Thomas J. Watson Research Center. Computer Science."},{"key":"ref_54","unstructured":"Zhang, H. The Optimality of Naive Bayes. Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference;."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Schneider, K.-M. (2005, January 13\u201319). Techniques for improving the performance of naive bayes for text classification. Proceedings of the 6th International Conference on Computational Linguistics and Intelligent Text Processing, Mexico City, Mexico.","DOI":"10.1007\/978-3-540-30586-6_76"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s10109-005-0155-6","article-title":"Multicollinearity and correlation among local regression coefficients in geographically weighted regression","volume":"7","author":"Wheeler","year":"2005","journal-title":"J. Geogr. Syst."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/3\/261\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:21:05Z","timestamp":1760210465000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/3\/261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3,22]]},"references-count":56,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2016,3]]}},"alternative-id":["rs8030261"],"URL":"https:\/\/doi.org\/10.3390\/rs8030261","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2016,3,22]]}}}