Examinando por Autor "Camacho Olmedo, MT"

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    Modeling Land-Use Scenarios in Protected Areas of an Urban Region in Spain
    (Springer, 2018) Gallardo Beltrán, Marta; Martínez Vega, Javier; Camacho Olmedo, MT; Paegelow, M; Mas, JF; Escobar, F; https://orcid.org/0000-0002-8519-120X
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    Pontius Jr. Methods Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps
    (Springer, 2022) Paegelow, Martin; Mas, Jean François; Gallardo Beltrán, Marta; Camacho Olmedo, María Teresa; García Álvarez, David; García Álvarez, D.; Camacho Olmedo, MT; Paegelow, M; Mas , JF; http://orcid.org/0000-0001-9254-5312; http://orcid.org/0000-0002-6138-9879; https://orcid.org/0000-0003-3178-1543; https://orcid.org/0000-0003-3611-8003
    Several validation techniques based on the cross-tabulation matrix can be applied to validate Land Use Cover (LUC) maps. The exercises in this chapter focus, in particular, on the cross-tabulation techniques proposed by Robert Gilmore Pontius Jr., who has developed many indices and techniques in this field. Given his major contribution to this family of validation techniques, we have associated his name here with cross-tabulation techniques without this in any way implying that his scientific activity is limited to this field. The null model (Sect. 1) is especially useful for validating simulations, comparing the modelled map to a reference map with full persistence. LUCC budget (Sect. 2) only focusses on changes, which it splits into different components. This method can be used to compare the changes we want to validate with a reference set of changes, so providing interesting information as to how well our maps capture the dynamics of the landscape. Quantity and allocation disagreement (Sect. 3) 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. 4) technique is used to validate a set of LUC changes by comparing them with a reference, distinguishing between different components of agreement: correctly simulated change, wrongly simulated or missing change. Incidents and States (Sect. 5) allows us to identify illogical transitions in a time series of maps by providing the number of states and transitions that a cell undergoes over the course of the series. Intensity analysis (Sect. 6) and Flow matrix (Sect. 7) also enable us to validate the logic of LUC changes in a time series of maps. Intensity analysis provides information on the speed of changes, identifying those transitions or changes that do not follow a logical trend, while the flow matrix enables us to spot unstable changes in a series of maps. In this chapter, we present examples of how these techniques can be used in different cases: to validate single LUC maps, to validate a series of maps with two or more time points, to validate simulated changes against a reference map of changes and to validate changes simulated by various models. All these techniques are illustrated by exercises using datasets from the Asturias Central Area and the Ariège Valley.
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    Validation of Land Use Cover Maps: A Guideline
    (Springer, 2022) Camacho Olmedo, María Teresa; García Álvarez, David; Gallardo Beltrán, Marta; Mas, Jean Francois; Paegelow, Martin; Castillo Santiago, Miguel Ángel; Molinero Parejo, Ramón; García Álvarez, D.; Camacho Olmedo, MT; Paegelow, M; Mas, JF; https://orcid.org/0000-0003-3178-1543; https://orcid.org/0000-0003-3611-8003; https://orcid.org/0000-0002-6138-9879; http://orcid.org/0000-0001-9254-5312; https://orcid.org/0000-0002-3024-5514; http://orcid.org/0000-0001-7406-8604
    This chapter offers a general overview of the available tools and strategies for validating Land Use Cover (LUC) data—specifically LUC maps—and Land Use Cover Change Modelling (LUCCM) exercises. We give readers some guidelines according to the type of maps they want to validate: single LUC maps (Sect. 3), time series of LUC maps (Sect. 4) or the results of LUCCM exercises (Sect. 5). Despite the fact that some of the available methods are applicable to all these maps, each type of validation exercise has its own particularities which must be taken into account. Each section of this chapter starts with a brief introduction about the specific type of maps (single, time series or modelling exercises) and the reference data needed to validate them. We also present the validation methods/functions and the corresponding exercises developed in Part III of this book. To this end, we address, in this order, the tools for validating Land Use Cover data based on basic and Multiple-Resolution Cross-Tabulation (see chapter “Basic and Multiple-Resolution Cross Tabulation to Validate Land Use Cover Maps”), metrics based on the Cross-Tabulation matrix (see chapter “Metrics Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps”), Pontius Jr. methods based on the Cross-Tabulation matrix (see chapter “Pontius Jr. Methods Based on a Cross-Tabulation Matrix to Validate Land Use Cover Maps”), validation practices with soft maps produced by Land Use Cover models (see chapter “Validation of Soft Maps Produced by a Land Use Cover Change Model”), spatial metrics (see chapter “Spatial Metrics to Validate Land Use Cover Maps”), advanced pattern analysis (see chapter “Advanced Pattern Analysis to Validate Land Use Cover Maps”) and geographically weighted methods (see chapter “Geographically Weighted Methods to Validate Land Use Cover Maps”).