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Simulation and Prediction of Urban Land Use Change Considering Multiple Classes and Transitions by Means of Random Change Allocation Algorithms

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Autor(es):
Marques-Carvalho, Romulo ; de Almeida, Claudia Maria ; Escobar-Silva, Elton Vicente ; Alves, Rayanna Barroso de Oliveira ; Lacerda, Camila Souza dos Anjos
Número total de Autores: 5
Tipo de documento: Artigo Científico
Fonte: REMOTE SENSING; v. 15, n. 1, p. 29-pg., 2023-01-01.
Resumo

The great majority of the world population resides nowadays in urban areas. Understanding their physical and social structure, and especially their urban land use pattern dynamics throughout time, becomes crucial for successful, effective management of such areas. This study is committed to simulate and predict urban land use change in a pilot city belonging to the Sao Paulo Metropolitan Region, southeast of Brazil, by means of a cellular automata model associated with the Markov chain. This model is driven by data derived from orbital and airborne remotely sensed images and is parameterized by the Bayesian weights of evidence method. Several layers related to infrastructure and biophysical aspects of the pilot city, Sao Caetano do Sul, were used as evidence in the simulation process. Alternative non-stationary scenarios were generated for the short-run, and the results obtained from past simulations were statistically validated using a multiresolution "goodness-of-fit" metric relying on fuzzy logic. The best simulations reached fuzzy similarity indices around 0.25-0.58 for small neighborhood windows when an exponential decay approach was employed for the analysis, and approximately 0.65-0.95 when a constant decay and larger windows were considered. The adopted Bayesian inference method proved to be a good parameterization approach for simulating processes of urban land use change involving multiple classes and transitions. (AU)

Processo FAPESP: 20/09215-3 - Identificação de áreas de superfícies permeáveis e impermeáveis por meio de análise de imagens baseada em objetos geográficos (GEOBIA) e deep learning como entrada para um modelo de previsão de crescimento urbano
Beneficiário:Cláudia Maria de Almeida
Modalidade de apoio: Auxílio à Pesquisa - Regular
Processo FAPESP: 21/11435-4 - Projeções baseadas em cenário futuros de inundações urbanas por meio de um acoplamento fraco entre um modelo de mudança de uso do solo urbana baseado em CA e um modelo hidrodinâmico: um estudo de caso em São Caetano do Sul (SP)
Beneficiário:Elton Vicente Escobar Silva
Modalidade de apoio: Bolsas no Brasil - Doutorado