Spatial pattern development of selective logging over several years

Anwar, Salma; Stein, Alfred

doi:10.1016/j.spasta.2015.03.001

Cited by 4 publications

(5 citation statements)

References 27 publications

(34 reference statements)

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“…Different spatial patterns can be observed depending on human activities contributing to deforestation as cattle ranching, shifting cultivation, commercial agriculture or logging (Anwar and Stein, 2015;Lorena and Lambin, 2007). This approach can be used to calibrate LUCC models and other types of models aiming at simulating landscape patterns.…”

Section: Resultsmentioning

confidence: 99%

Calibrating Cellular Automata of Land Use/Cover Change Models Using a Genetic Algorithm

Mas

Soares‐Filho

Rodrigues

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Spatially explicit land use / land cover (LUCC) models aim at simulating the patterns of change on the landscape. In order to simulate landscape structure, the simulation procedures of most computational LUCC models use a cellular automata to replicate the land use / cover patches. Generally, model evaluation is based on assessing the location of the simulated changes in comparison to the true locations but landscapes metrics can also be used to assess landscape structure. As model complexity increases, the need to improve calibration and assessment techniques also increases. In this study, we applied a genetic algorithm tool to optimize cellular automata's parameters to simulate deforestation in a region of the Brazilian Amazon. We found that the genetic algorithm was able to calibrate the model to simulate more realistic landscape in term of connectivity. Results show also that more realistic simulated landscapes are often obtained at the expense of the location coincidence. However, when considering processes such as the fragmentation impacts on biodiversity, the simulation of more realistic landscape structure should be preferred to spatial coincidence performance.

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Section: Resultsmentioning

confidence: 99%

Calibrating Cellular Automata of Land Use/Cover Change Models Using a Genetic Algorithm

Mas

Soares‐Filho

Rodrigues

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…For the choice of irregular parameters, because the likelihood is not differentiable with respect to them, we used a maximum profile likelihood approach based on the logistic likelihood estimation procedure and AIC values for model selection. Introduced for the pseudo-likelihood estimates in [1] and applied to the logistic likelihood approach by us using the results in [3], this method consists in fixing irregular parameters and maximizing the composite likelihood with respect to the regular ones. This technique is a computationally-intensive method.…”

Section: Discussionmentioning

confidence: 99%

“…Generalizing the Strauss process, the Geyer saturation process [26] intends to model both inhibition and clustering. It is able to take into account the clustering nature of a pattern due to interactions between points in absence of covariate information [1]. [7] defined a new class of multi-scale Gibbs point processes, so-called hybrid models.…”

Section: Introductionmentioning

confidence: 99%

A spatio-temporal multi-scale model for Geyer saturation point process: Application to forest fire occurrences

2021

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“…A spatial point pattern analysis, then, may serve as an appropriate tool for analysing the process that determines the LLS distribution. On the other hand, as logging operations also vary in time (Matricardi et al, 2005); a spatial-temporal statistical analysis of the spatial distribution of selective logging is necessary to reveal its important temporal characteristics (Anwar and Stein, 2014a) .…”

Section: Introductionmentioning

confidence: 99%

“…The objective is to discover important spatial characteristics of selective logging in the south-western part of the Brazilian Amazonia using J inhom and then modelling a LLS pattern using a model suitable for inhomogeneous processes. While the purely spatial aspects are dealt with in this paper, the spatial-temporal aspects are treated in (Anwar and Stein, 2014a).…”

Section: Introductionmentioning

confidence: 99%

Spatial point process modelling of land use and land cover (LULC) change

Anwar¹

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I feel greatly indebted to my friend and an excellent person Clarisse Kagoyire for always saying yes whenever I needed her for software related issues. I am thankful to Bas Retsios for helping me in programming. I feel highly obliged to Prof. Adrian Baddeley and Rolf Turner for their very kind response and always helping me whenever I wrote to them about sofware related problems.My heartiest and sincere gratitude are paid to Theresa van der Boogaard, Marie Chantal, Bettine Geerdik and Marion Pierik for their professional as well as moral support. I am very thankful to Ginie Gerrits for always promptly responding to my travel related requests and making arrangements in almost no time. I am thankful to Helpdesk people, particularly Mr. Aiko Mulder, for their technical support. And thanks to Roeloff schoppers for his cheerful greetings every day. I am thankful to my office mates and friends Xudan Xu, Biao, Meisam and Hajer for their invaluable company, and to Sumbal, Coco, Liang and Rahul for their cheering talks. My special thanks are to my lovely friend Alka, her husband Amit and her daughter Bhuvan for having been like a blessing to me. I am also very thankful to Ms. Loes Minkman of NUFFIC for her appreciations and encouraging words which meant a lot to me.

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Spatial pattern development of selective logging over several years

Cited by 4 publications

References 27 publications

Calibrating Cellular Automata of Land Use/Cover Change Models Using a Genetic Algorithm

Calibrating Cellular Automata of Land Use/Cover Change Models Using a Genetic Algorithm

A spatio-temporal multi-scale model for Geyer saturation point process: Application to forest fire occurrences

Spatial point process modelling of land use and land cover (LULC) change

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