A hybrid analytical-heuristic method for calibrating land-use change models

Soares‐Filho, Britaldo; Rodrigues, Henrique; Follador, Marco

doi:10.1016/j.envsoft.2013.01.010

Cited by 122 publications

(83 citation statements)

References 52 publications

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“…O índice de similaridade fuzzy adaptado não opera sobre o mapa final real e o mapa final simulado, como o originalmente criado por Hagen (2002), mas sim entre os mapas-diferença resultantes da subtração desses dois mapas do mapa inicial real. Na prática, índices de similaridade fuzzy adaptados com valores oscilando entre 0.45 e 0.50 para janelas de amostragem com tamanhos de 3x3 a 5x5 têm denotado concordâncias aceitáveis entre o mapa simulado e o mapa real, considerando-se que esse índice desconsidera as áreas de não-mudança, as quais são responsáveis por sobrestimar índices de ajuste em geral, incluindo o índice de similaridade fuzzy propriamente dito (ALMEIDA et al, 2008;SOARES-FILHO et al, 2013). …”

Section: Parametrização E Validação Do Modelounclassified

Modelagem dinâmica espacial das alterações de cobertura e uso da terra relacionadas à expansão canavieira

et al. 2013

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RESUMOA avaliação das mudanças na paisagem é fundamental para a eficiência na gestão territorial. O objetivo deste trabalho é parametrizar e calibrar um modelo de mudança de cobertura e uso, além de validar as simulações associadas à expansão canavieira em Arealva-SP, no período compreendido entre 2005 e 2010. Os mapas inicial e final foram corregistrados e, após a rasterização, foi realizada uma tabulação cruzada, gerando-se um mapa de mudanças e a respectiva matriz de transição. O modelo adotado foi o Dinamica EGO, e seu desempenho foi avaliado por meio de um método baseado no conceito de incerteza de localização (fuzziness of location), no qual a representação de uma célula é influenciada por ela mesma, e, em menor magnitude, pela sua vizinhança. Há predominância de pastagens e baixo índice de área de vegetação nativa. As mudanças mais relevantes estão relacionadas à expansão canavieira e à retração de pastagens. O valor da similaridade fuzzy entre o mapa simulado e o mapa-referência, para a janela de tamanho 11x11 e função de decaimento constante, foi de 0.52. Foi possível aprimorar o conhecimento dos fatores direcionadores das mudanças de cobertura e uso, propiciando a revelação das forçantes dessas mudanças. Palavras-chave: Detecção de Mudanças; Modelagem Dinâmica Espacial; Cana-deAçúcar; Dinamica EGO.

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Section: Parametrização E Validação Do Modelounclassified

Modelagem dinâmica espacial das alterações de cobertura e uso da terra relacionadas à expansão canavieira

et al. 2013

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“…Furthermore, a statistical analysis allows for relating factors of change (such as slope or accessibility) to the observed changes. That relation is established by different methods, such as regression models, weights of evidence [49] and artificial intelligence [50], among others. The product is a map containing values indicating the propensity for change.…”

Section: Training or Calibration Of The Modelmentioning

confidence: 99%

Modeling Historical Land Cover and Land Use: A Review fromContemporary Modeling

Chang-Martínez

Mas

Valle

et al. 2015

IJGI

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Spatially-explicit land cover land use change (LCLUC) models are becoming increasingly useful tools for historians and archaeologists. Such kinds of models have been developed and used by geographers, ecologists and land managers over the last few decades to carry out prospective scenarios. In this paper, we review historical models to compare them with prospective models, with the assumption that the ample experience gained in the development of models of prospective simulation can benefit the development of models having as their objective the simulation of changes that happened in the past. The review is divided into three sections: in the first section, we explain the functioning of contemporary LCLUC models; in the second section, we analyze historical LCLUC models; in the third section, we compare the former two types of models, and finally, we discuss the contributions to historical LCLUC models of contemporary LCLUC models.

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“…Published analyses of forest loss have employed a range of statistical techniques including logistic regression (Chowdhury 2006;Echeverria et al 2008), genetic algorithms (Venema et al 2005;Soares-Filho et al 2013), weights of evidence (Soares-Filho et al 2010;Maeda et al 2011) and cellular automata (Thapa et al 2013). These models are based on a number of environmental and anthropogenic landscape features that are thought to drive deforestation rates.…”

Section: Introductionmentioning

confidence: 99%

Multiple-scale prediction of forest loss risk across Borneo

et al. 2017

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Context The forests of Borneo have among the highest biodiversity and also the highest forest loss rates on the planet. Objectives Our objectives were to: (1) compare multiple modelling approaches, (2) evaluate the utility of landscape composition and configuration as predictors, (3) assess the influence of the ratio of forest loss and persistence points in the training sample, (4) identify the multiple-scale drivers of recent forest loss and (5) predict future forest loss risk across Borneo. Methods We compared random forest machine learning and logistic regression in a multi-scale approach to model forest loss risk between 2000 and 2010 as a function of topographical variables and landscape structure, and applied the highest performing model to predict the spatial pattern of forest loss risk between 2010 and 2020. We utilized a naïve model as a null comparison and used the total operating characteristic AUC to assess model performance. Results Our analysis produced five main results. We found that: (1) random forest consistently outperformed logistic regression and the naïve model; (2) including landscape structure variables substantially improved predictions; (3) a ratio of occurrence to nonoccurrence points in the training dataset that does not match the actual ratio in the landscape biases the predictions of both random forest and logistic regression; (4) forest loss risk differed between the three nations that comprise Borneo, with patterns in Kalimantan highly related to distance from the edge of the previous frontier of forest loss, while Malaysian Borneo showed a more diffuse pattern related to the structure of the landscape; (5) we predicted continuing 123Landscape Ecol (2017) 32:1581-1598 DOI 10.1007 very high rates of forest loss in the 2010-2020 period, and produced maps of the expected risk of forest loss across the full extent of Borneo. Conclusions These results confirm that multiplescale modelling using landscape metrics as predictors in a random forest modelling framework is a powerful approach to landscape change modelling. There is immense immanent risk to Borneo's forests, with clear spatial patterns of risk related to topography and landscape structure that differ between the three nations that comprise Borneo.

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A hybrid analytical-heuristic method for calibrating land-use change models

Cited by 122 publications

References 52 publications

Modelagem dinâmica espacial das alterações de cobertura e uso da terra relacionadas à expansão canavieira

Modelagem dinâmica espacial das alterações de cobertura e uso da terra relacionadas à expansão canavieira

Modeling Historical Land Cover and Land Use: A Review fromContemporary Modeling

Multiple-scale prediction of forest loss risk across Borneo

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