Titlebook: Land Use Cover Datasets and Validation Tools; Validation Practices David García-Álvarez,María Teresa Camacho Olmedo,J Book‘‘‘‘‘‘‘‘ 2022 The

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David García-Álvarez,Javier Lara Hinojosa,Jaime Quintero Villarasot 10 years. Only for dominant ataxias, more than 30 diseases have now been unravelled. The number of ataxic disorders will increase with aging, the cerebellum being the structure of the brain with the most impo978-94-007-1333-8

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Basic and Multiple-Resolution Cross-Tabulation to Validate Land Use Cover Mapspoints could also be used as reference data for these analyses. Examples of Cross-Tabulation analyses at one and multiple resolutions are presented for four different cases: to validate single LUC maps, to validate the soft maps produced by a model, to validate a simulation exercise and to validate

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Metrics Based on a Cross-Tabulation Matrix to Validate Land Use Cover Mapsd LUCC map with a reference map of changes. Section . introduces the Kappa indices, which allow us to assess the agreement between two datasets, given the agreement expected by random coincidence. We used the indices to compare observed or simulated maps with a reference map. In Sect. . we evaluate

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Pontius Jr. Methods Based on a Cross-Tabulation Matrix to Validate Land Use Cover Mapsion as to how well our maps capture the dynamics of the landscape. Quantity and allocation disagreement (Sect. .) analyse the differences between the reference map and the map being validated using two indices: disagreement in quantity and disagreement in allocation. The Figure of Merit (FoM) (Sect.

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Validation of Soft Maps Produced by a Land Use Cover Change Modeld and the values estimated by the soft map. One would expect real change areas to coincide with high change potential values (near 1) and real no-change areas with low change potential values (near 0). This comparison can be made using various statistical approaches including Correlation Coefficient