LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/78602 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/78602 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/78602 archive 353 disk0/00/01/47/49 2017-07-24 06:37:40 2025-03-12 05:00:24 2017-07-24 06:37:40 article show See L. 8571 0000-0002-2665-7065 Laso Bayas J.C. 2106 0000-0003-2844-3842 Schepaschenko D. 7435 0000-0002-7814-4990 Perger C. 1899 Dresel C. 2105 Maus V. 8780 0000-0002-7385-4723 Salk C. 8556 Weichselgartner J. Lesiv M. 8477 0000-0001-9846-3342 McCallum I. 1731 0000-0002-5812-9988 Moorthy I. 2089 0000-0002-3664-3855 Fritz S. 8130 0000-0003-0420-8549 LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya pub prog_esm land cover; accuracy assessment; validation; statistical inference; GlobeLand30; sampling design; Kenya; Geo-Wiki Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set of four simple steps including uploading a land cover map, creating a sample from the map, interpreting the sample with very high resolution satellite imagery and generating a report with accuracy measures. The aim of this paper is to present the main features of this new tool followed by an example of how it can be used for accuracy assessment of a land cover map. For the purpose of illustration, we have chosen GlobeLand30 for Kenya. Two different samples were interpreted by three individuals: one sample was provided by the GlobeLand30 team as part of their international efforts in validating GlobeLand30 with GEO (Group on Earth Observation) member states while a second sample was generated using LACO-Wiki. Using satellite imagery from Google Maps, Bing and Google Earth, the results show overall accuracies between 53% to 61%, which is lower than the global accuracy assessment of GlobeLand30 but may be reasonable given the complex landscapes found in Kenya. Statistical models were then fit to the data to determine what factors affect the agreement between the three interpreters such as the land cover class, the presence of very high resolution satellite imagery and the age of the image in relation to the baseline year for GlobeLand30 (2010). The results showed that all factors had a significant effect on the agreement. 2017-07-22 published Molecular Diversity Preservation International (MDPI) 10.3390/rs9070754 276 172 279 228 69 3138 262 181 202 211 98 public Remote Sensing 9 7 e754 TRUE 2072-4292 Harnessing the power of crowdsourcing to improve land cover and land-use information (CROWDLAND, FP7 617754) A Citizen Observatory and Innovation Marketplace for Land Use and Land Cover Monitoring (LANDSENSE, H2020 689812) Geo-Wiki FALSE yes info:eu-repo/grantAgreement/EC/FP7/617754/EU//CROWDLAND; info:eu-repo/grantAgreement/EC/H2020/689812/EU//LANDSENSE info:eu-repo/semantics/article info:eu-repo/semantics/openAccess