Dynamic simulation of land use change based on logistic-CA-Markov and WLC-CA-Markov models: a case study in three gorges reservoir area of Chongqing, China

Guan, Dongjie; Zhao, Zulun; Tan, Jing

doi:10.1007/s11356-019-05127-9

Cited by 79 publications

(47 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research results of domestic and foreign scholars on scenario simulation models of LULC showed that single model cannot satisfy both quantitative simulation and spatial pattern analysis simultaneously. Therefore, scenario simulation is gradually changing from using single model to multiple integrated models 14 – 26 . Previous studies have suggested that logistic regression model can better reveal the main driving forces and interaction mechanisms of LULC change 11 , 26 – 29 .…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, scenario simulation is gradually changing from using single model to multiple integrated models 14 – 26 . Previous studies have suggested that logistic regression model can better reveal the main driving forces and interaction mechanisms of LULC change 11 , 26 – 29 . Binary logistic regression model (BLRM) is good for binary dependent variables, while multinomial logistic regression model is more suitable for multivariate dependent variables.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Scenario simulation of land use and land cover change in mining area

Xiao-yan

Zhang

Cong

et al. 2021

Sci Rep

View full text Add to dashboard Cite

In this study, we selected 11 townships with severe ground subsidence located in Weishan County as the study area. Based on the interpretation data of Landsat images, the Binary logistic regression model was used to explore the relationship between land use and land cover (LULC) change and the related 7 driving factors at a resolution of 60 m. Using the CLUE-S model, combined with Markov model, the simulation of LULC under three scenarios—namely, natural development scenario, ecological protection scenario and farmland protection scenario—were explored. Firstly, using LULC map in 2005 as input data, we predicted the land use spatial distribution pattern in 2016. By comparing the actual LULC map in 2016 with the simulated map in 2016, the prediction accuracy was evaluated based on the Kappa index. Then, after validation, the spatial distribution pattern of LULC in 2025 under the three scenarios was simulated. The results showed the following: (1) The driving factors had satisfactory explanatory power for LULC changes. The Kappa index was 0.82, which indicated good simulation accuracy of the CLUE-S model. (2) Under the three scenarios, the area of other agricultural land and water body showed an increasing trend; while the area of farmland, urban and rural construction land, subsided land with water accumulation, and tidal wetland showed a decreasing trend, and the area of urban and rural construction land and tidal wetland decreased the fastest. (3) Under the ecological protection scenario, the farmland decreased faster than the other two scenarios, and most of the farmland was converted to ecological land such as garden land and water body. Under the farmland protection scenario, the area of tidal wetland decreased the fastest, followed by urban and rural construction land. We anticipate that our study results will provide useful information for decision-makers and planners to take appropriate land management measures in the mining area.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Scenario simulation of land use and land cover change in mining area

Xiao-yan

Zhang

Cong

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The research results of domestic and foreign scholars on scenario simulation models of land use showed that single model cannot satisfy both quantitative simulation and spatial pattern analysis simultaneously. Therefore, scenario simulation is gradually changing from using single model to multiple integrated models [14][15][16][17][18][19][20][21][22][23][24][25][26] . Previous studies have suggested that logistic regression model can better reveal the main driving forces and interaction mechanisms of landscape pattern evolution 11,[26][27][28][29] .…”

Section: Introductionmentioning

confidence: 99%

“…Binary logistic regression model (BLRM) is good for binary dependent variables, while multinomial logistic regression model is more suitable for multivariate dependent variables. Logistic regression model is often used for driving factors analysis of land use change in ecologically fragile areas such as reservoir area 11,26 , mountainous area 29 , etc. And it is also mainly used for driving factors analysis of urban landscape pattern evolution 27,28 .…”

Section: Introductionmentioning

confidence: 99%

Scenario Simulation and Landscape Pattern Dynamic Changes of Land Use in Mining Area

Xiao-yan¹,

Zhang²,

Cong³

et al. 2021

Preprint

View full text Add to dashboard Cite

In this study, we selected 11 townships with severe ground subsidence located in Weishan County as the study area. Based on the interpretation data of Landsat images, the Binary logistic regression model was used to explore the relationship between land use change and the related 7 driving factors at a scale of 60m * 60m. Using the CLUE-S model, combined with Markov model, the simulation of land use under three scenarios–namely, natural development scenario, ecological protection scenario and farmland protection scenario–were explored. Firstly, using land use map in 2005 as input data, we predicted the land use spatial distribution pattern in 2016. By comparing the actual land use map in 2016 with the simulated map of land use pattern in 2016, the prediction accuracy was evaluated based on the Kappa index. Then, after validation, the distribution of land use pattern in 2025 under the three scenarios was simulated. The results showed the following: (1) The driving factors had satisfactory explanatory power for land use changes. The Kappa index was 0.82, which indicated good simulation accuracy of the CLUE-S model. (2) Under the three scenarios, the area of other agricultural land and water body showed an increasing trend; while the area of farmland, urban and rural construction land, subsided land with water accumulation, and tidal wetland showed a decreasing trend, and the area of urban and rural construction land and tidal wetland decreased the fastest. (3) Under the ecological protection scenario, the farmland decreased faster than the other two scenarios, and most of the farmland was converted to ecological land such as garden land and water body. Under the farmland protection scenario, the area of tidal wetland decreased the fastest, followed by urban and rural construction land. We anticipate that our study results will provide useful information for decision-makers and planners to take appropriate land management measures in the mining area.

show abstract

“…The analytical equation-based models (Shamsi 2010) are often employed for estimating LULC changes. There are also the statistical models (Aitkenhead and Aalders 2009;Hyandye 2015), Markov models (Guan et al 2019), multi-agent models (Ralha et al 2013), expert system models (Stefanov et al 2001), cellular models (Singh et al 2015) and hybrid models (Subedi et al 2013). Currently, the most extensively used models in LULC change monitoring and prediction are the cellular and agent-based models or the mixed model based on these two types of models (Sohl and Claggett 2013;Zhao and Peng 2012;Stevens and Dragićević 2007).…”

Section: Introductionmentioning

confidence: 99%

Geospatial assessment of land use and land cover dynamics in the mid-zone of Ghana

Koranteng

Adu-Poku

Donkor

et al. 2020

Folia Forestalia Polonica

View full text Add to dashboard Cite

Land use and land cover (LULC) terrain in Ghana has undergone profound changes over the past years emanating mainly from anthropogenic activities, which have impacted countrywide and sub-regional environment. This study is a comprehensive analysis via integrated approach of geospatial procedures such as Remote Sensing (RS) and Geographic Information System (GIS) of past, present and future LULC from satellite imagery covering Ghana’s Ashanti regional capital (Kumasi) and surrounding districts. Multi-temporal satellite imagery data sets of four different years, 1990 (Landsat TM), 2000 (Landsat ETM+), 2010 (Alos and Disaster Monitoring Constellation-DMC) and 2020 (SENTINEL), spanning over a 30-year period were mapped. Five major LULC categories – Closed Forest, Open Forest, Agriculture, Built-up and Water – were delineated premised on the prevailing geographical settings, field study and remote sensing data. Markov Cellular Automata modelling was applied to predict the probable LULC change consequence for the next 20 years (2040). The study revealed that both Open Forest and Agriculture class categories decreased 51.98 to 38.82 and 27.48 to 20.11, respectively. Meanwhile, Built-up class increased from 4.8% to 24.8% (over 500% increment from 1990 to 2020). Rapid urbanization caused the depletion of forest cover and conversion of farmlands into human settlements. The 2040 forecast map showed an upward increment in the Built-up area up to 35.2% at the expense of other LULC class categories. This trend from the past to the forecasted future would demand that judicious LULC resolutions have to be made to keep Ghana’s forest cover, provide arable land for farming activities and alleviate the effects of climate change.

show abstract

Dynamic simulation of land use change based on logistic-CA-Markov and WLC-CA-Markov models: a case study in three gorges reservoir area of Chongqing, China

Cited by 79 publications

References 44 publications

Scenario simulation of land use and land cover change in mining area

Scenario simulation of land use and land cover change in mining area

Scenario Simulation and Landscape Pattern Dynamic Changes of Land Use in Mining Area

Geospatial assessment of land use and land cover dynamics in the mid-zone of Ghana

Contact Info

Product

Resources

About