Li, Z, Comber, A, White, JC et al. (4 more authors) (2021) Land cover harmonization using Latent Dirichlet Allocation. International Journal of Geographical Information Science, 35 (2). pp. 348-374. ISSN 1365-8816
Abstract
Large-area land cover maps are produced to satisfy different information needs. Land cover maps having partial or complete spatial and/or temporal overlap, different legends, and varying accuracies for similar classes, are increasingly common. To address these concerns and combine two 30-m resolution land cover products, we implemented a harmonization procedure using a Latent Dirichlet Allocation (LDA) model. The LDA model used regionalized class co-occurrences from multiple maps to generate a harmonized class label for each pixel by statistically characterizing land attributes from the class co-occurrences. We evaluated multiple harmonization approaches: using the LDA model alone and in combination with more commonly used information sources for harmonization (i.e. error matrices and semantic affinity scores). The results were compared with the benchmark maps generated using simple legend crosswalks and showed that using LDA outputs with error matrices performed better and increased harmonized map overall accuracy by 6–19% for areas of disagreement between the source maps. Our results revealed the importance of error matrices to harmonization, since excluding error matrices reduced overall accuracy by 4–20%. The LDA-based harmonization approach demonstrated in this paper is quantitative, transparent, portable, and efficient at leveraging the strengths of multiple land cover maps over large areas.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Copyright of the Crown in Canada. Natural Resources Canada. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | agriculture; bayesian; Forest; land cover; land use; landsat; LDA |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Funding Information: | Funder Grant number NERC (Natural Environment Research Council) NE/S009124/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Jul 2020 11:39 |
Last Modified: | 25 Jun 2023 22:21 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/13658816.2020.1796131 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163476 |
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