Rapid land cover map updates using change detection and robust random forest classifiers

dc.contributor.authorWessels, K.J. (Konrad)
dc.contributor.authorVan den Bergh, Frans
dc.contributor.authorRoy, David P.
dc.contributor.authorSalmon, Brian P.
dc.contributor.authorSteenkamp, Karen C.
dc.contributor.authorMacAlister, Bryan
dc.contributor.authorSwanepoel, Derick
dc.contributor.authorJewitt, Debbie
dc.date.accessioned2017-06-23T09:20:15Z
dc.date.available2017-06-23T09:20:15Z
dc.date.issued2016-10-28
dc.description.abstractThe paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM) system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) to identify areas of change and no change. The system then automatically generates large amounts of training samples (n > 1 million) in the no-change areas as input to an optimized Random Forest classifier. Experiments were conducted in the KwaZulu-Natal Province of South Africa using a reference land cover map from 2008, a change mask between 2008 and 2011 and Landsat ETM+ data for 2011. The entire system took 9.5 h to process. We expected that the use of the change mask would improve classification accuracy by reducing the number of mislabeled training data caused by land cover change between 2008 and 2011. However, this was not the case due to exceptional robustness of Random Forest classifier to mislabeled training samples. The system achieved an overall accuracy of 65%–67% using 22 detailed classes and 72%–74% using 12 aggregated national classes. “Water”, “Plantations”, “Plantations—clearfelled”, “Orchards—trees”, “Sugarcane”, “Built-up/dense settlement”, “Cultivation—Irrigated” and “Forest (indigenous)” had user’s accuracies above 70%. Other detailed classes (e.g., “Low density settlements”, “Mines and Quarries”, and “Cultivation, subsistence, drylands”)which are required for operational, provincial-scale land use planning and are usually mapped using manual image interpretation, could not be mapped using Landsat spectral data alone. However, the system was able to map the 12 national classes, at a sufficiently high level of accuracy for national scale land cover monitoring. This update approach and the highly automated, scalable LALCUM system can improve the efficiency and update rate of regional land cover mapping.en_ZA
dc.description.departmentGeography, Geoinformatics and Meteorologyen_ZA
dc.description.librarianam2017en_ZA
dc.description.sponsorshipThe CSIR and the Department Rural Development and Land Reform, National Geo-spatial Information (NGI).en_ZA
dc.description.urihttp://www.mdpi.com/journal/remotesensingen_ZA
dc.identifier.citationWessels, KJ, Van den Bergh, F, Roy, DP, Salmon, BP, Steenkamp, KC, MacAlister, B, Swanepoel, D & Jewitt, D 2016, 'Rapid land cover map updates using change detection and robust random forest classifiers', Remote Sensing, vol. 8, art. no. 888, pp. 1-24.en_ZA
dc.identifier.issn2072-4292
dc.identifier.other10.3390/rs8110888
dc.identifier.urihttp://hdl.handle.net/2263/61081
dc.language.isoenen_ZA
dc.publisherMDPI Publishingen_ZA
dc.rights© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license.en_ZA
dc.subjectLandsaten_ZA
dc.subjectLand coveren_ZA
dc.subjectChange detectionen_ZA
dc.subjectAutomated mappingen_ZA
dc.subjectRandom foresten_ZA
dc.subjectSouth Africa (SA)en_ZA
dc.titleRapid land cover map updates using change detection and robust random forest classifiersen_ZA
dc.typeArticleen_ZA

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