{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T15:53:43Z","timestamp":1782402823422,"version":"3.54.5"},"reference-count":49,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,7,31]],"date-time":"2019-07-31T00:00:00Z","timestamp":1564531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sustainable Development Goals (SDGs) are a set of priorities the United Nations and World Bank have set for countries to reach in order to improve quality of life and environment globally by 2030. Free satellite images have been identified as a key resource that can be used to produce official statistics and analysis to measure progress towards SDGs, especially those that are concerned with the physical environment, such as forest, water, and crops. Satellite images can often be unusable due to missing data from cloud cover, particularly in tropical areas where the deforestation rates are high. There are existing methods for filling in image gaps; however, these are often computationally expensive in image classification or not effective at pixel scale. To address this, we use two machine learning methods\u2014gradient boosted machine and random forest algorithms\u2014to classify the observed and simulated \u2018missing\u2019 pixels in satellite images as either grassland or woodland. We also predict a continuous biophysical variable, Foliage Projective Cover (FPC), which was derived from satellite images, and perform accurate binary classification and prediction using only the latitude and longitude of the pixels. We compare the performance of these methods against each other and inverse distance weighted interpolation, which is a well-established spatial interpolation method. We find both of the machine learning methods, particularly random forest, perform fast and accurate classifications of both observed and missing pixels, with up to 0.90 accuracy for the binary classification of pixels as grassland or woodland. The results show that the random forest method is more accurate than inverse distance weighted interpolation and gradient boosted machine for prediction of FPC for observed and missing data. Based on the case study results from a sub-tropical site in Australia, we show that our approach provides an efficient alternative for interpolating images and performing land cover classifications.<\/jats:p>","DOI":"10.3390\/rs11151796","type":"journal-article","created":{"date-parts":[[2019,7,31]],"date-time":"2019-07-31T11:37:07Z","timestamp":1564573027000},"page":"1796","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["A Decision Tree Approach for Spatially Interpolating Missing Land Cover Data and Classifying Satellite Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4608-5313","authenticated-orcid":false,"given":"Jacinta","family":"Holloway","sequence":"first","affiliation":[{"name":"ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Queensland University of Technology, Brisbane 4001, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0201-5348","authenticated-orcid":false,"given":"Kate J.","family":"Helmstedt","sequence":"additional","affiliation":[{"name":"ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Queensland University of Technology, Brisbane 4001, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kerrie","family":"Mengersen","sequence":"additional","affiliation":[{"name":"ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Queensland University of Technology, Brisbane 4001, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Schmidt","sequence":"additional","affiliation":[{"name":"German Aerospace Centre (DLR), 51147 Cologne, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,31]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2018, June 29). Sustainable Development Goals: Sustainable Development Knowledge Platform. Available online: https:\/\/sustainabledevelopment.un.org\/?menu=1300."},{"key":"ref_2","unstructured":"(2018, July 07). GEO Earth Observations and Geospatial Information: Supporting Official Statistics in Monitoring the SDGs. Available online: http:\/\/www.un.org\/ga\/search\/view_doc.asp?symbol=A\/RES\/70\/1&Lang=E."},{"key":"ref_3","unstructured":"(2018, April 10). United Nations United Nations Global Working Group on Big Data for Official Statistics. Available online: https:\/\/unstats.un.org\/bigdata\/."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1080\/10095020.2017.1333230","article-title":"Earth observation in service of the 2030 Agenda for Sustainable Development","volume":"20","author":"Anderson","year":"2017","journal-title":"Geo Spat. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Holloway, J., and Mengersen, K. (2018). Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10091365"},{"key":"ref_6","unstructured":"(2018, April 10). Committee on Earth Observation Satellites CEOS EO HANDBOOK Special 2018 Edition. Available online: http:\/\/eohandbook.com\/sdg\/index.html."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.compenvurbsys.2017.05.003","article-title":"A review of the emergent ecosystem of collaborative geospatial tools for addressing environmental challenges","volume":"65","author":"Palomino","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_8","unstructured":"Killough, B. (2018, June 29). Open Data Cube Background and Vision 2017. Available online: https:\/\/docs.wixstatic.com\/ugd\/f9d4ea_805b7a80f1b74184ad411d3a85f0953f.pdf."},{"key":"ref_9","unstructured":"(2018, July 25). USGS GloVis\u2014The USGS Global Visualization Viewer, Available online: https:\/\/glovis.usgs.gov."},{"key":"ref_10","unstructured":"(2018, April 10). United Nations Earth Observations for Official Statistics: Satellite Imagery and Geospatial Data Task Team Report. Available online: https:\/\/unstats.un.org\/bigdata\/taskteams\/satellite\/UNGWG_Satellite_Task_Team_Report_WhiteCover.pdf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3855","DOI":"10.1080\/01431160010006926","article-title":"Cloud cover in Landsat observations of the Brazilian Amazon","volume":"22","author":"Asner","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1109\/JSTARS.2012.2228167","article-title":"A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping","volume":"6","author":"Griffiths","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/MGRS.2015.2441912","article-title":"Missing Information Reconstruction of Remote Sensing Data: A Technical Review","volume":"3","author":"Shen","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1080\/00031305.1989.10475658","article-title":"Geostatistics","volume":"43","author":"Cressie","year":"1989","journal-title":"Am. Stat."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.isprsjprs.2009.06.001","article-title":"Geostatistical interpolation of SLC-off Landsat ETM+ images","volume":"64","author":"Pringle","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2173","DOI":"10.1080\/01431160802549294","article-title":"Restoration of clouded pixels in multispectral remotely sensed imagery with cokriging","volume":"30","author":"Zhang","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1002\/env.699","article-title":"Complementary co-kriging: Spatial prediction using data combined from several environmental monitoring networks","volume":"16","author":"Zimmerman","year":"2005","journal-title":"Environmetrics"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1111\/j.1467-9868.2007.00633.x","article-title":"Fixed rank kriging for very large spatial data sets","volume":"70","author":"Cressie","year":"2008","journal-title":"J. R. Stat. Soc. Ser. B (Statistical Methodol.)"},{"key":"ref_20","unstructured":"Longley, P.A., Goodchild, M.F., Maguire, D.J., and Rhind, D.W. (2005). Spatial Statistics in Geographical Information Systems, University of Edinburgh. [2nd ed.]."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.isprsjprs.2014.10.001","article-title":"An effective approach for gap-filling continental scale remotely sensed time-series","volume":"98","author":"Weiss","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A working guide to boosted regression trees","volume":"77","author":"Elith","year":"2008","journal-title":"J. Anim. Ecol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1080\/01431160701250416","article-title":"Gaps-fill of SLC-off Landsat ETM+ satellite image using a geostatistical approach","volume":"28","author":"Zhang","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","first-page":"159","article-title":"Spatial Interpolation with Applications","volume":"103","year":"2016","journal-title":"Course Topol. Comb."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1559\/152304083783914958","article-title":"Spatial Interpolation Methods: A Review","volume":"10","author":"Lam","year":"1983","journal-title":"Am. Cartogr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1111\/tgis.12458","article-title":"Real-time inverse distance weighting interpolation for streaming sensor data","volume":"22","author":"Liang","year":"2018","journal-title":"Trans. GIS"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2014.11.015","article-title":"Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia","volume":"158","author":"Schmidt","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"033540","DOI":"10.1117\/1.3216031","article-title":"Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery","volume":"3","author":"Armston","year":"2009","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","unstructured":"United Nations (2018, August 30). Available online: https:\/\/unstats.un.org\/sdgs\/indicators\/Global Indicator Framework after refinement_Eng.pdf."},{"key":"ref_30","unstructured":"Hijmans, R.J. (2018, July 25). Package \u2019raster\u2019: Geographic Data Analysis and Modeling 2017. Available online: https:\/\/cran.r-project.org\/web\/packages\/raster\/raster.pdf."},{"key":"ref_31","unstructured":"Queensland Department of Science (2019, May 24). Land Cover Change in Queensland 2010\u201311: A Statewide Landcover and Trees Study (SLATS) Report, Available online: https:\/\/publications.qld.gov.au\/dataset\/84d44f82-17c2-4633-b245-8bdc08e8552e\/resource\/e9891989-e028-49f5-bd43-c308babfc579\/download\/slats-report-2010-11.pdf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1111\/1365-2664.13108","article-title":"Valuable habitat and low deforestation can reduce biodiversity gains from development rights markets","volume":"55","author":"Helmstedt","year":"2018","journal-title":"J. Appl. Ecol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1214\/ss\/1009213726","article-title":"Statistical Modeling: The Two Cultures","volume":"16","author":"Breiman","year":"2001","journal-title":"Stat. Sci."},{"key":"ref_34","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2008). The Elements of Statistical Learning, Springer. [2nd ed.]."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1016\/j.procs.2015.05.157","article-title":"A Fuzzy Decision Tree for Processing Satellite Images and Landsat Data","volume":"52","author":"Belacel","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1007\/s12040-013-0339-2","article-title":"Decision tree approach for classification of remotely sensed satellite data using open source support","volume":"122","author":"Sharma","year":"2013","journal-title":"J. Earth Syst. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203450","article-title":"Greedy Function Approximation: A Gradient Boosting Machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Source Ann. Stat."},{"key":"ref_39","unstructured":"Ridgeway, G. (2018, July 12). Generalized Boosted Models: A guide to the gbm package. Available online: http:\/\/www.saedsayad.com\/docs\/gbm2.pdf."},{"key":"ref_40","unstructured":"Greenwell, B., Boehmke, B., and Cunningham, J. (2018, March 22). gbm: Generalized Boosted 2.1.4., Regression Models. Available online: https:\/\/cran.r-project.org\/package=gbm."},{"key":"ref_41","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_42","unstructured":"Pebesma, E., and Graeler, B. (2019, May 16). Package \u201cgstat\u201d 2019. Available online: https:\/\/cran.r-project.org\/web\/packages\/gstat\/gstat.pdf."},{"key":"ref_43","unstructured":"Food and Agriculture Organization of the United Nations (2016). Map Accuracy Assessment and Area Estimation: A Practical Guide, Food and Agriculture Organization of the United Nations."},{"key":"ref_44","unstructured":"Bruce, P.C. (2015). Introductory Statistics and Analytics: A Resampling Perspective, Wiley."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sammut, C., and Webb, G.I. (2011). Encyclopedia of Machine Learning, Springer.","DOI":"10.1007\/978-0-387-30164-8"},{"key":"ref_46","unstructured":"Kuhn, M. (2019, January 14). Package \u201cCaret\u201d. Available online: https:\/\/cran.r-project.org\/web\/packages\/caret\/caret.pdf."},{"key":"ref_47","unstructured":"Illowsky, B., and Dean, S.L. (2017). Introductory Statistics, Rice University."},{"key":"ref_48","unstructured":"Google (2018, November 07). Google Earth Engine. Available online: https:\/\/earthengine.google.com\/."},{"key":"ref_49","unstructured":"R Core Team (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1796\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:11:40Z","timestamp":1760188300000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1796"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,31]]},"references-count":49,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11151796"],"URL":"https:\/\/doi.org\/10.3390\/rs11151796","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,31]]}}}