Prospective scenario of land use and land cover change at Rio Capivara watershed, municipality of Botucatu, São Paulo State, through dynamic spatial modeling

• Autor(a) principal: Pisani, Rodrigo José
• Data de Publicação: 2016
• Outros Autores: Demarchi , Julio Cesar, Riedel, Paulina Setti
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Revista Cerrados (Montes Claros. Online)
• Texto Completo: https://www.periodicos.unimontes.br/index.php/cerrados/article/view/1369
• topic: Pesos de Evidência; autômatos celulares; similaridade fuzzy.; Weights of Evidence; cellular automata; fuzzy similarity.; Pesos de Evidencia; autómatas celulares; similitud fuzzy.

There are several modeling approaches to model land use and land cover changes of a given region, which can, for example, analyze and evaluate several landscape variables based on integrated approaches (i.e. lithology, soil, relief and drainage). The aim of this research was to test the applicability of the Dinamica EGO model for simulating and evaluating scenarios of land use and land cover changes in Capivara river watershed, municipality of Botucatu, São Paulo State, Brazil. Using satellite images (Landsat 5 TM), topographic maps and the softwares ENVI 4.7, ArcGIS 10, Spring 5.1 and Dinamica EGO platform. Firstly, land use and land cover maps for the years 1988, 1997 and 2007 were produced. Secondly, the Dinamica EGO model was calibrated by the analysis and comparing the real and simulated maps, so it was validated by the use of exponential decay method by means of fuzzy similarity index. Finally, using the validated model, a scenario for this watershed for the year 2017 was built. Results showed, from used method, for example, a decrease in grasslands areas and its substitution by reforestation until 2007, that tends to overtake native forest class in the simulated map of 2017. As a conclusion, the Dinamica EGO model can strongly aid in the monitoring of land use and land cover change in the context of watershed. In addition, the proposed methodology can be used to build probabilistic scenarios and, consequently, predict diagnoses and strategies for land use planning.