Publicação
Mixed-effects modeling for analyzing land use change in the Brazilian Pantanal subregion of Cáceres
| Resumo: | In this paper, we discuss the use of mixed-effects modeling for analyzing land use change in the Brazilian Pantanal subregion of Cáceres, Mato Grosso State, Brazil. The proposed method, easily extendable to similar case studies, consisted of two steps. First, spatio-temporal data, consisting of Landsat images of the study area from 1993, 1999, 2004, 2009, and 2015, were obtained. The data are polygons with numerical data (year and area) and categorical data (land use and soil type). Second, we analyzed the data using four linear mixed models able to incorporate both the fixed and the random effects underlying the clustered data. The proposed models allowed analyzed complex data structures, such as multilevel data, taking into account particularities of each land use type as a function of the year. The models were fitted to identify land use changes over time. In particular, the point estimate of the random slope in the case of the Pasture class is 0.34, which indicates an increase of about 40% in hectare and the point estimate for the Forest is −0.32, which indicates a decrease of about 27% in hectare in next 5 years. |
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| Autores principais: | Galvanin, Edinéia A. S. |
| Outros Autores: | Menezes, Raquel; Pereira, Murilo H. X.; Neves, Sandra M. A. S. |
| Assunto: | Anthropogenic processes Remote sensing Statistical models Vegetation cover Wetland |
| Ano: | 2019 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | In this paper, we discuss the use of mixed-effects modeling for analyzing land use change in the Brazilian Pantanal subregion of Cáceres, Mato Grosso State, Brazil. The proposed method, easily extendable to similar case studies, consisted of two steps. First, spatio-temporal data, consisting of Landsat images of the study area from 1993, 1999, 2004, 2009, and 2015, were obtained. The data are polygons with numerical data (year and area) and categorical data (land use and soil type). Second, we analyzed the data using four linear mixed models able to incorporate both the fixed and the random effects underlying the clustered data. The proposed models allowed analyzed complex data structures, such as multilevel data, taking into account particularities of each land use type as a function of the year. The models were fitted to identify land use changes over time. In particular, the point estimate of the random slope in the case of the Pasture class is 0.34, which indicates an increase of about 40% in hectare and the point estimate for the Forest is −0.32, which indicates a decrease of about 27% in hectare in next 5 years. |
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