Regional-Scale Assessment and Simulation of Land Salinization Using Cellular Automata-Markov Model

AbstractUnderstanding and modeling of land use change is of great significance to environmental protection and land use planning. The cellular automata-Markov chain (CA-Markov) model is a powerful tool to predict the change of land use, and the prediction accuracy is limited by many factors. To explore the impact of land use and socio-economic factors on the prediction of CA-Markov model on county scale, this paper uses the CA-Markov model to simulate the land use of Anren County in 2016, based on the land use of 1996 and 2006. Then, the correlation between the land use, socio-economic data and the prediction accuracy was analyzed. The results show that Shannon’s evenness index and population density having an important impact on the accuracy of model predictions, negatively correlate with kappa coefficient. The research not only provides a reference for correct use of the model but also helps us to understand the driving mechanism of landscape changes.

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Simulation of future forest and land use/cover changes (2019–2039) using the cellular automata-Markov model

Geocarto International ◽

10.1080/10106049.2020.1778102 ◽

2020 ◽

pp. 1-20

Author(s):

Hasan Aksoy ◽

Sinan Kaptan

Keyword(s):

Land Use ◽

Cellular Automata ◽

Markov Model

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GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

SSSA Special Publications - Applications of GIS to the Modeling of Non-Point Source Pollutants in the Vadose Zone ◽

10.2136/sssaspecpub48.c5 ◽

2015 ◽

pp. 69-100 ◽

Cited By ~ 4

Author(s):

Dennis L. Corwin

Keyword(s):

Point Source ◽

Solute Transport ◽

Vadose Zone ◽

Regional Scale ◽

Transport Models ◽

Gis Applications ◽

Scale Assessment

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Predicting Land use and land cover spatiotemporal changes utilizing CA-Markov model in Duhok district between 1999 and 2033

10.31222/osf.io/vngcp ◽

2020 ◽

Author(s):

shamal

Keyword(s):

Remote Sensing ◽

Land Use ◽

Cellular Automata ◽

Land Cover ◽

Markov Model ◽

Markov Chain Model ◽

United States Geological Survey ◽

Chain Model ◽

Lulc Changes ◽

Spatiotemporal Changes

AbstractTHE PROCESS OF SPATIOTEMPORAL CHANGES IN LAND USE LAND COVER (LULC) AND PREDICTING THEIR FUTURE CHANGES ARE HIGHLY IMPORTANT FOR LULC MANAGERS. ONE OF THE MOST IMPORTANT CHALLENGES IN LULC STUDIES IS CONSIDERED TO BE THE CREATION OF SIMULATION OF FUTURE CHANGE IN LULC THAT INVOLVE SPATIAL MODELING. THE PURPOSE OF THIS STUDY IS TO USE GIS AND REMOTE SENSING TO CLASSIFY LULC CLASSES IN DUHOK DISTRICT BETWEEN 1999 AND 2018, AND THEIR RESULTS CALCULATED USING AN INTEGRATED CELLULAR AUTOMATA AND CA-MARKOV CHAIN MODEL TO SIMULATE LULC CHANGES IN 2033. IN THIS STUDY, SATELLITE IMAGES FROM LANDSAT7 ETM AND LANDSAT8 OLI USED FOR DUHOK DISTRICT WHICH IS LOCATED IN THE NORTHERN PART OF IRAQ OBTAINED FROM UNITED STATES GEOLOGICAL SURVEY (USGS) FOR THE PERIODS (1999 AND 2018) ANALYZED USING REMOTE SENSING AND GIS TECHNIQUES IN ADDITION TO THE GROUND CONTROL POINTS, FOR EACH CLASS 60 GROUND POINTS HAVE TAKEN. TO SIMULATE FUTURE LULC CHANGES FOR 2033, INTEGRATED APPROACHES OF CELLULAR AUTOMATA AND CA-MARKOV MODELS UTILIZED IN IDRISI SELVA SOFTWARE. THE OUTCOMES DEMONSTRATE THAT DUHOK DISTRICT HAS EXPERIENCED A TOTAL OF 184.91KM CHANGES DURING THE PERIOD (TABLE 4). THE PREDICTION ALSO INDICATES THAT THE CHANGES WILL EQUAL TO 235.4 KM BY 2033 (TABLE 8). SOIL AND GRASS CONSTITUTES THE MAJORITY OF CHANGES AMONG LULC CLASSES AND ARE INCREASING CONTINUOUSLY. THE ACHIEVED KAPPA VALUES FOR THE MODEL ACCURACY ASSESSMENT HIGHER THAN 0.93 AND 0.85 FOR 1999 AND 2018 RESPECTIVELY SHOWED THE MODEL’S CAPABILITY TO FORECAST FUTURE LULC CHANGES IN DUHOK DISTRICT. THUS, ANALYZING TRENDS OF LULC CHANGES FROM PAST TO NOW AND PREDICT FUTURE APPLYING CA-MARKOV MODEL CAN PLAY AN IMPORTANT ROLE IN LAND USE PLANNING, POLICY MAKING, AND MANAGING RANDOMLY UTILIZED LULC CLASSES IN THE PROPOSED STUDY AREA

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Dynamics of landscape patterns in an inland river delta of Central Asia based on a cellular automata-Markov model

Regional Environmental Change ◽

10.1007/s10113-014-0638-4 ◽

2014 ◽

Vol 15 (2) ◽

pp. 277-289 ◽

Cited By ~ 15

Author(s):

Geping Luo ◽

Tureniguli Amuti ◽

Lei Zhu ◽

Bulkajyr T. Mambetov ◽

Bagila Maisupova ◽

...

Keyword(s):

Cellular Automata ◽

Markov Model ◽

Central Asia ◽

River Delta ◽

Landscape Patterns ◽

Inland River

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Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8100454 ◽

2019 ◽

Vol 8 (10) ◽

pp. 454 ◽

Cited By ~ 3

Author(s):

Junfeng Kang ◽

Lei Fang ◽

Shuang Li ◽

Xiangrong Wang

Keyword(s):

Remote Sensing ◽

Land Use ◽

Cellular Automata ◽

Markov Model ◽

Agricultural Land ◽

Land Use Changes ◽

Remote Sensing Images ◽

Mapreduce Framework ◽

Construction Land ◽

Model Based

The Cellular Automata Markov model combines the cellular automata (CA) model’s ability to simulate the spatial variation of complex systems and the long-term prediction of the Markov model. In this research, we designed a parallel CA-Markov model based on the MapReduce framework. The model was divided into two main parts: A parallel Markov model based on MapReduce (Cloud-Markov), and comprehensive evaluation method of land-use changes based on cellular automata and MapReduce (Cloud-CELUC). Choosing Hangzhou as the study area and using Landsat remote-sensing images from 2006 and 2013 as the experiment data, we conducted three experiments to evaluate the parallel CA-Markov model on the Hadoop environment. Efficiency evaluations were conducted to compare Cloud-Markov and Cloud-CELUC with different numbers of data. The results showed that the accelerated ratios of Cloud-Markov and Cloud-CELUC were 3.43 and 1.86, respectively, compared with their serial algorithms. The validity test of the prediction algorithm was performed using the parallel CA-Markov model to simulate land-use changes in Hangzhou in 2013 and to analyze the relationship between the simulation results and the interpretation results of the remote-sensing images. The Kappa coefficients of construction land, natural-reserve land, and agricultural land were 0.86, 0.68, and 0.66, respectively, which demonstrates the validity of the parallel model. Hangzhou land-use changes in 2020 were predicted and analyzed. The results show that the central area of construction land is rapidly increasing due to a developed transportation system and is mainly transferred from agricultural land.

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Integration of Boosted Regression Trees and Cellular Automata—Markov Model to Predict the Land Use Spatial Pattern in Hotan Oasis

Sustainability ◽

10.3390/su12041396 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1396

Author(s):

Shufang Wang ◽

Xiyun Jiao ◽

Liping Wang ◽

Aimin Gong ◽

Honghui Sang ◽

...

Keyword(s):

Land Use ◽

Cellular Automata ◽

Markov Model ◽

Land Use Changes ◽

Regression Trees ◽

Boosted Regression Trees ◽

Utilization Rate ◽

Protection Measures ◽

Unused Land ◽

The Government

The simulation and prediction of the land use changes is generally carried out by cellular automata—Markov (CA-Markov) model, and the generation of suitable maps collection is subjective in the simulation process. In this study, the CA-Markov model was improved by the Boosted Regression Trees (BRT) to simulate land use to make the model objectively. The weight of ten driving factors of the land use changes was analyzed in BRT, in order to produce the suitable maps collection. The accuracy of the model was verified. The outcomes represent a match of over 84% between simulated and actual land use in 2015, and the Kappa coefficient was 0.89, which was satisfactory to approve the calibration process. The land use of Hotan Oasis in 2025 and 2035 were predicted by means of this hybrid model. The area of farmland, built-up land and water body in Hotan Oasis showed an increasing trend, while the area of forestland, grassland and unused land continued to show a decreasing trend in 2025 and 2035. The government needs to formulate measures to improve the utilization rate of water resources to meet the growth of farmland, and need to increase ecological environment protection measures to curb the reduction of grass land and forest land for the ecological health.

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