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

See, L. ORCID: https://orcid.org/0000-0002-2665-7065, Laso Bayas, J.C. ORCID: https://orcid.org/0000-0003-2844-3842, Schepaschenko, D. ORCID: https://orcid.org/0000-0002-7814-4990, Perger, C., Dresel, C., Maus, V. ORCID: https://orcid.org/0000-0002-7385-4723, Salk, C., Weichselgartner, J., Lesiv, M. ORCID: https://orcid.org/0000-0001-9846-3342, McCallum, I. ORCID: https://orcid.org/0000-0002-5812-9988, Moorthy, I., & Fritz, S. ORCID: https://orcid.org/0000-0003-0420-8549 (2017). LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya. Remote Sensing 9 (7) e754. 10.3390/rs9070754.

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Abstract

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.

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