{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T16:36:09Z","timestamp":1772642169931,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003246","name":"Netherlands Organization for Scientific Research (NWO)","doi-asserted-by":"publisher","award":["15839"],"award-info":[{"award-number":["15839"]}],"id":[{"id":"10.13039\/501100003246","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003246","name":"Netherlands Organization for Scientific Research (NWO)","doi-asserted-by":"publisher","award":["101059548"],"award-info":[{"award-number":["101059548"]}],"id":[{"id":"10.13039\/501100003246","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open-Earth-Monitor Cyberinfratructure","award":["15839"],"award-info":[{"award-number":["15839"]}]},{"name":"Open-Earth-Monitor Cyberinfratructure","award":["101059548"],"award-info":[{"award-number":["101059548"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Handling multiple scales efficiently is one avenue for processing big remote sensing imagery data. Unfortunately, imagery is also affected by the infamous modifiable areal unit problem, which creates unpredictable errors at different scales. We developed a downsampling method that attempts to keep the data distribution in a downsampled image constant, reducing the modifiable areal unit problem. We tested our method against classic downsampling methods (mean, central pixel selection, random) under a range of typical remote sensing scenarios. Under our experimental conditions, our downsampling method consistently outperformed the classical downsampling methods within a 95% confidence level. The downsampling method can be used in most typical situations where downsampling is needed, but it is likely to shine when used as a pyramid building policy in geocomputing platforms, such as Google Earth Engine.<\/jats:p>","DOI":"10.3390\/rs14215538","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T03:53:07Z","timestamp":1667447587000},"page":"5538","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Downsampling Method Addressing the Modifiable Areal Unit Problem in Remote Sensing"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3654-2090","authenticated-orcid":false,"given":"Andrei","family":"M\u00eer\u021b","sequence":"first","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4327-4349","authenticated-orcid":false,"given":"Johannes","family":"Reiche","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7923-4309","authenticated-orcid":false,"given":"Jan","family":"Verbesselt","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0246-6886","authenticated-orcid":false,"given":"Martin","family":"Herold","sequence":"additional","affiliation":[{"name":"Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteg 3, 6708 PB Wageningen, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"von Mehren, M., Gieseke, F., Verbesselt, J., Rosca, S., Horion, S., and Zeileis, A. (2018, January 9\u201311). Massively-parallel break detection for satellite data. Proceedings of the 30th International Conference on Scientific and Statistical Database Management, Bozen-Bolzano, Italy.","DOI":"10.1145\/3221269.3223032"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gieseke, F., Rosca, S., Henriksen, T., Verbesselt, J., and Oancea, C.E. (2020, January 20\u201324). Massively-Parallel Change Detection for Satellite Time Series Data with Missing Values. Proceedings of the 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, USA.","DOI":"10.1109\/ICDE48307.2020.00040"},{"key":"ref_3","unstructured":"Pebesma, E., Wagner, W., Soille, P., Kadunc, M., Gorelick, N., Schramm, M., Verbesselt, J., Reiche, J., Appel, M., and Dries, J. (2018, January 8\u201313). OpenEO: An open API for cloud-based big Earth Observation processing platforms. Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Schramm, M., Pebesma, E., Milenkovi\u0107, M., Foresta, L., Dries, J., Jacob, A., Wagner, W., Mohr, M., Neteler, M., and Kadunc, M. (2021). The openEO API\u2013Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities. Remote Sens., 13.","DOI":"10.3390\/rs13061125"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hamunyela, E., Rosca, S., Mirt, A., Engle, E., Herold, M., Gieseke, F., and Verbesselt, J. (2020). Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data. Remote Sens., 12.","DOI":"10.3390\/rs12182953"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1007\/s11948-019-00171-7","article-title":"Big Data, Big Waste? A Reflection on the Environmental Sustainability of Big Data Initiatives","volume":"26","author":"Lucivero","year":"2020","journal-title":"Sci. Eng. Ethics"},{"key":"ref_8","unstructured":"Achard, F., and Hansen, M.C. (2012). Global Forest Monitoring from Earth Observation, Taylor & Francis."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/17538947.2012.713190","article-title":"Global characterization and monitoring of forest cover using Landsat data: Opportunities and challenges","volume":"5","author":"Townshend","year":"2012","journal-title":"Int. J. Digit. Earth"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1080\/07038992.2014.987376","article-title":"Forest Monitoring Using Landsat Time Series Data: A Review","volume":"40","author":"Banskota","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1080\/17538947.2016.1187673","article-title":"Mass data processing of time series Landsat imagery: Pixels to data products for forest monitoring","volume":"9","author":"Hermosilla","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.foreco.2015.06.014","article-title":"Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015","volume":"352","author":"Keenan","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2513","DOI":"10.1109\/TIP.2006.877415","article-title":"Adaptive downsampling to improve image compression at low bit rates","volume":"15","author":"Lin","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3291","DOI":"10.1109\/TIP.2011.2158226","article-title":"Interpolation-Dependent Image Downsampling","volume":"20","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","unstructured":"Youssef, A. (April, January 30). Analysis and comparison of various image downsampling and upsampling methods. Proceedings of the DCC \u201998 Data Compression Conference (cat. No.98TB100225), Snowbird, UT, USA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dumitrescu, D., and Boiangiu, C.A. (2019). A Study of Image Upsampling and Downsampling Filters. Computers, 8.","DOI":"10.3390\/computers8020030"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1109\/76.585926","article-title":"Fast algorithms for DCT-domain image downsampling and for inverse motion compensation","volume":"7","author":"Merhav","year":"1997","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, Y., and Jin, Z. (2008, January 18\u201320). Down-Sampling Face Images and Low-Resolution Face Recognition. Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control, Dalian, China.","DOI":"10.1109\/ICICIC.2008.234"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/IGARSS.2002.1025749","article-title":"Aliasing effects mitigation by optimised sampling grids and impact on image acquisition chains","volume":"Volume 2","author":"Vitulli","year":"2002","journal-title":"Proceedings of the IEEE International Geoscience and Remote Sensing Symposium"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1117\/12.477569","article-title":"Aliasing in remote sensing imagery","volume":"Volume 4736","author":"Rahman","year":"2002","journal-title":"Proceedings of the Visual Information Processing XI"},{"key":"ref_21","unstructured":"Weigel, S. (1996). Scale, Resolution and Resampling: Representation and Analysis of Remotely Sensed Landscapes Across Scale in Geographic Information Systems. [Ph.D. Thesis, Louisiana State University and Agricultural & Mechanical College]."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4900","DOI":"10.1080\/01431161.2013.781289","article-title":"Analysing the effect of different aggregation approaches on remotely sensed data","volume":"34","author":"Raj","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1533656","article-title":"How up-scaling of remote-sensing images affects land-cover classification by comparison with multiscale satellite images","volume":"40","author":"Xu","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"49","DOI":"10.4081\/gh.2006.280","article-title":"Upscale or downscale: Applications of fine scale remotely sensed data to Chagas disease in Argentina and schistosomiasis in Kenya","volume":"1","author":"Kitron","year":"2006","journal-title":"Geospat. Health"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.3390\/s90301768","article-title":"Scale Issues in Remote Sensing: A Review on Analysis, Processing and Modeling","volume":"9","author":"Wu","year":"2009","journal-title":"Sensors"},{"key":"ref_26","unstructured":"Openshaw, S. (1983). The Modifiable Areal Unit Problem, Geo Books."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Buchhorn, M., Lesiv, M., Tsendbazar, N.E., Herold, M., Bertels, L., and Smets, B. (2020). Copernicus Global Land Cover Layers\u2014Collection 2. Remote Sens., 12.","DOI":"10.3390\/rs12061044"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2012.02.022","article-title":"Near real-time disturbance detection using satellite image time series","volume":"123","author":"Verbesselt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_29","unstructured":"Ho, T.K. (1995, January 14\u201316). Random decision forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_30","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","unstructured":"R Core Team (2022). R: A Language and Environment for Statistical Computing. Manual, R Foundation for Statistical Computing."},{"key":"ref_32","first-page":"503","article-title":"The arrangement of field experiments","volume":"33","author":"Fisher","year":"1926","journal-title":"J. Minist. Agric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5538\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:09:42Z","timestamp":1760144982000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5538"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,3]]},"references-count":32,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215538"],"URL":"https:\/\/doi.org\/10.3390\/rs14215538","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,3]]}}}