Markov chain models for vegetation dynamics

Balzter, Heiko

doi:10.1016/s0304-3800(00)00262-3

Cited by 199 publications

(126 citation statements)

References 28 publications

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“…A Markov chain is a stochastic process (based on probabilities) with discrete state space and discrete or continuous parameter space [18]. In this random process, the state of a system s at time (t+1) depends only on the state of the system at time t, not on the previous states.…”

Section: Stochastic Markov Modelmentioning

confidence: 99%

Modeling Urban Land Cover Growth Dynamics Using Multi‑Temporal Satellite Images: A Case Study of Dhaka, Bangladesh

Ahmed¹,

Ahmed

2012

IJGI

216

130

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Abstract:The primary objective of this research is to predict and analyze the future urban growth of Dhaka City using the Landsat satellite images of 1989, 1999 and its surrounding impact areas have been selected as the study area. At the beginning, a fisher supervised classification method has been applied to prepare the base maps with five land cover classes. In the next stage, three different models have been implemented to simulate the land cover map of Dhaka city of 2009. These have been named as "Stochastic Markov (St_Markov)" Model, "Cellular Automata Markov (CA_Markov)" Model and "Multi Layer Perceptron Markov (MLP_Markov)" Model. Then the best-fitted model has been selected by implementing a method to compare land cover categories in three maps: a reference map of time 1, a reference map of time 2 and a simulation map of time 2. This is how the "Multi Layer Perceptron Markov (MLP_Markov)" Model has been qualified as the most appropriate model for this research. Later, using the MLP_Markov model, the land cover map of 2019 has been predicted. The MLP_Markov model extrapolates that built-up area increases from 46% to 58% of the total study area during 2009-2019.

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Section: Stochastic Markov Modelmentioning

confidence: 99%

Modeling Urban Land Cover Growth Dynamics Using Multi‑Temporal Satellite Images: A Case Study of Dhaka, Bangladesh

Ahmed¹,

Ahmed

2012

IJGI

216

130

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“…Recent studies have demonstrated plant spatialpattern formation as a result of different process using different kind of models, ranging from analytical models (HilleRisLambers et al, 2001), Markov chains (Balzter, 2000;Logofet and Lesnaya, 2000), cellular automata (Bak et al, 1988;Solé and Manrubia, 1995). All these models are limited by the fact that they can not include all the variability of the system into the model (Whilhelm and Brüggemann, 2000).…”

Section: Fractal Dimension Of Plant Spatial Patternsmentioning

confidence: 99%

Change in plant spatial patterns and diversity along the successional gradient of Mediterranean grazing ecosystems

Alados¹,

El-Aich

Papanastasis³

et al. 2004

Ecological Modelling

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In this study, we analyze the complexity of plant spatial patterns and diversity along a successional gradient resulting from grazing disturbance in four characteristic ecosystems of the Mediterranean region. Grazing disturbance include not only defoliation by animals, but also associated disturbances as animal trampling, soil compaction, and mineralization by deposition of urine and feces. The results show that woodland and dense matorral are more resistant to species loss than middle dense and scattered matorral, or grassland. Information fractal dimension declined as we moved from a dense to a discontinuous matorral, increasing as we moved to a more scattered matorral and a grassland. In all studied cases, the characteristic species of the natural vegetation declined in frequency and organization with grazing disturbance. Heliophyllous species and others with postrate or rosette twigs increased with grazing pressure, particularly in dense matorral. In the more degraded ecosystem, only species with well-adapted traits, e.g., buried buds or unpalatable qualities showed a clear increase with grazing. Indeed, the homogeneity of species distribution within the plant community declined monotonically with grazing impact. Conversely, the spatial organization of the characteristic plants of each community increased in the better-preserved areas, being also related to the sensitivity of the species to grazing impact. The degree of autocorrelation of plant spatial distribution at the species level and the information fractal dimension at the community level allow us to quantify the degree of degradation of natural communities and to determine the sensitivity of key species to disturbance.

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“…Alternatively, the limitations of individual model are also discussed in many studies (Araya & Cabral, 2010;Balzter, 2000;Triantakonstantis & Mountrakis, 2012). Therefore, the integrated modeling approaches are widely used for LULC change simulation and projection to overcome the limitations of individual models (Al-Sharif & Pradhan, 2015;Basse, Omrani, Charif, Gerber, & Bódis, 2014;Guan et al, 2011;Mishra, Rai, & Mohan, 2014).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

Wang

Maduako

2018

European Journal of Remote Sensing

106

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An accurate information on the amount and location of Land use and land cover (LULC) changes is necessary to develop and implement a sustainable-urban planning.This research investigates the potential of an integrated Multi-Layer Perceptron and Markov Chain Analysis (MLP-MCA) to map and accurately predict the future LULC change scenarios in Lagos Metropolitan Region of Nigeria. Multi-temporal LULC datasets derived from remotely sensed Landsat images from 1984, 2000 and 2015 were used for modeling, validation and prediction. Predicted LULC changes for 2030 and 2050 were performed based on the LULC map of 2015 using MLP-MCA method. The result reveals a significant expansion of built-up areas during the whole study period. Analysis of LULC distribution in Lagos metropolitan region shows that about 50% of urban land expansion happened beyond the administrative boundary of Lagos State during the period of 2000-2015. It is predicted that more than 75% of future urban growth will occur across the border of Lagos State, in the neighbouring Ogun State by 2050. These results imply that a strong and consistent collaboration between different states is crucial to establish an effective regional planning framework and ensure a proper planned growth of the metropolitan region.ARTICLE HISTORY

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Markov chain models for vegetation dynamics

Cited by 199 publications

References 28 publications

Modeling Urban Land Cover Growth Dynamics Using Multi‑Temporal Satellite Images: A Case Study of Dhaka, Bangladesh

Modeling Urban Land Cover Growth Dynamics Using Multi‑Temporal Satellite Images: A Case Study of Dhaka, Bangladesh

Change in plant spatial patterns and diversity along the successional gradient of Mediterranean grazing ecosystems

Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

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