Projection of Future Changes in Landuse/Landcover Using Cellular Automata/Markov Model over Akure City, Nigeria

Balogun, Ifeoluwa Adebowale; Ishola, Kazeem A.

doi:10.18005/jrst0501003

Cited by 41 publications

(13 citation statements)

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“…To simulate the FCC of the study area, the Markov-chain model was used to determine the forest cover pattern for the year 2030. The Cellular automata were used to stimulate the time-space and underlie the dynamics of changes in the study area (Balogun and Ishola, 2017). However, the Markov chain and cellular automata were further supported with dependent and independent variables in the iDrisi selva environment.…”

Section: Simulation Pattern Analysismentioning

confidence: 99%

“…To simulate the FCC of the study area, the Markov-chain model was used to determine the forest cover pattern for the year 2030. The Cellular automata were used to stimulate the time-space and underlie the dynamics of changes in the study area (Balogun and Ishola, 2017). However, the 2Here, S represents the set of states of the finite cells; t and t+1 are the early years and the later year; N is the neighborhood of cells, and f is the conversion rule of local space.…”

Section: Simulation Pattern Analysismentioning

confidence: 99%

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Simulation Pattern of Forest Cover Change in Onigambari Reserve, Ibadan, Oyo State, Nigeria using spectral vegetation index and Markov chain Techniques

Tobore

Alabi

Oyerinde

et al. 2021

Preprint

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Forest cover change (FCC) varies from region to region and is thus considered as one of the drivers of climate change. This study identified the pattern of the FCC for the years 2010 and 2020 using spectral vegetation index and Markov chain Techniques. The Markov chain (MC) and Cellular automata (CA) methods were utilized to simulate the forest cover map for the year 2030. The spectral vegetation index of Landsat 7 Enhanced thematic mapper plus (ETM+) and Landsat 8 Operational land images (OLI) were used to assess the forest cover loss for the year 2010 and 2020 focusing on the Normalized difference vegetation index, (NDVI), Green normalized difference vegetation index (GNDVI) and Difference vegetation index (DVI). Based on the validation result, the accuracy of the forest cover simulation model is more than 75 percent (%). The simulation result shows that if the current deforestation and encroachment continues, the forest cover will continue to be endangered and thus leading to a decrease in dense forest, plantation, and sparse vegetation by 20.9%, 16.1%, and 20% respectively. This study will be helpful for planners and decision-makers in ensuring sustainable forest management.

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Section: Simulation Pattern Analysismentioning

confidence: 99%

“…To simulate the FCC of the study area, the Markov-chain model was used to determine the forest cover pattern for the year 2030. The Cellular automata were used to stimulate the time-space and underlie the dynamics of changes in the study area (Balogun and Ishola, 2017). However, the 2Here, S represents the set of states of the finite cells; t and t+1 are the early years and the later year; N is the neighborhood of cells, and f is the conversion rule of local space.…”

Section: Simulation Pattern Analysismentioning

confidence: 99%

Simulation Pattern of Forest Cover Change in Onigambari Reserve, Ibadan, Oyo State, Nigeria using spectral vegetation index and Markov chain Techniques

Tobore

Alabi

Oyerinde

et al. 2021

Preprint

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“…The identified changes were transformations within the nine LULC categories. This allowed us quantifying the modification that occurs from time 1 to time 2, and it also helps us understand the increase and decrease between land use covers, the conversion in the area as well as contribution between LULC classes (Eastman, 2016;Balogun, Ishola, 2017). The format of these data should be similar and simultaneously.…”

Section: Detection Of Land Use/land Cover Changesmentioning

confidence: 99%

Land Use/Land Cover Changes Using Multi-Temporal Satellite

Nguyen

Chau

Pham

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Producing the map of land use land cover change (LULCC) at the local extent is fundamental for a variety of applications such as vegetation, forest covers, soil degradation, and global warming. Understanding the directions and spread trend of LULCC plays significant role in obtaining useful data for the local authorities in making land-use policies under the context of climate change. Dak Nong is located in the Central Highlands of Vietnam having the largest tropical forest. Over the past decades, the natural forest in the region has significantly declined due to the pressure of population growth and social-economic development. The current study analyzed the LULCC in the province over the four periods: 2005–2018, 2005–2010, 2010–2015, and 2015–2018. Information from Landsat satellite imagery captured in 2005, 2010, 2015, and 2018 was utilized to create the LULC maps and detect the land-use changes. The Random Forest (RF) was employed to categorize the images into nine different LULC classes. The study showed that classification accuracy was achieved from 72.49% to 84.55% with a kappa coefficient of 0.69 to 0.81. The findings revealed a significant decrease in the natural forest over time from 53.1% to 42.7%, 36.8%, and 34.6% in 2005, 2010, 2015, and 2018, respectively. Meanwhile, the other types of LULC showed an increase in the area over the periods, especially croplands. It was noticeable that the continuous decrease in the forest area over the years has put pressure on the natural environmental resources and generated the risk of climate change.

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“…Numerous methods and algorithms, i.e., Markov Chain (MC) [ 1 , 15 , 31 , 32 ]. Cellular Automata (CA) [ 33 , 34 , 35 ], Logistic Regression (LR) [ 36 ], and Artificial Neural Network (ANN) [ 14 , 37 , 38 ] were used to predict the LULC and LST changes in several studies. Every method consists of its own strength and limitations.…”

Section: Introductionmentioning

confidence: 99%

“…Every method consists of its own strength and limitations. If variations in land cover are known, but there is no geographical dependence and distribution, the MC is preferred for the prediction [ 31 , 35 ]. Depending on the prior position of cells in a region, the CA model specifies the position of cells in an array according to a set of transition laws [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh

Kafy

Faisal

Rakib

et al. 2021

Heliyon

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Projection of Future Changes in Landuse/Landcover Using Cellular Automata/Markov Model over Akure City, Nigeria

Cited by 41 publications

References 0 publications

Simulation Pattern of Forest Cover Change in Onigambari Reserve, Ibadan, Oyo State, Nigeria using spectral vegetation index and Markov chain Techniques

Simulation Pattern of Forest Cover Change in Onigambari Reserve, Ibadan, Oyo State, Nigeria using spectral vegetation index and Markov chain Techniques

Land Use/Land Cover Changes Using Multi-Temporal Satellite

Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh

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