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

2019 ◽  
Vol 8 (10) ◽  
pp. 454 ◽  
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.

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

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

scholarly journals Study on Coastline Changes of Xiamen City Based on Remote Sensing Images

2019 ◽  
Vol 136 ◽  
pp. 05003
Author(s):  
Yanfang Qin ◽  
Lin Ye ◽  
Siming Chen
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Changes ◽  
Remote Sensing Data ◽  
Length Change ◽  
Vector Data ◽  
Remote Sensing Images ◽  
Change Rate ◽  
Xiamen City ◽  
Spatio Temporal

Based on the Landsat remote sensing data, this paper had monitored the coastline changes of Xiamen city in recent 20 years. By extracting the coastline vector data of 1999, 2005, 2011 and 2017 respectively, the spatio-temporal characteristics of coastline changes on coastline length, change rate and land change area were analyzed, and the main driving factors were analyzed combined with the land use changes in the coastal swing area. The results show that: the total length of Xiamen's coastline increased from 235.16 km to 264.98 km during 1999-2017, and the land area increased from 1558.84 km2 to 1594.29 km2. The most significant changes occurred in Xiang'an district and Huli district with the coastline length increased by 16.38% during 2011-2017 and 22.14% during 1999-2005 respectively, while the changes were not very conspicuous in other areas. According to the land use changes in the coastal areas, the coastline changes in Xiamen City were mainly related to the expansion of construction land and port constructions in Haicang district, Xiang'an district and Huli district, as well as the expansion of aquaculture in the Xiang'an district.

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

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.

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

2020 ◽  
Author(s):  
Ismael Abdulrahman Ismael Abdulrahman Abdulrahman ◽  
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.

scholarly journals Research and Application of Remote Sensing and GIS Technologies in Determining and Forecasting Land Use Changes by Markov Chain in Y Yen District - Nam Dinh Province

2016 ◽  
Vol 24 (3) ◽  
pp. 27-39 ◽  
Author(s):  
Giang Thi Le ◽  
Thuan Duc Nguyen ◽  
Vinh Quoc Tran
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Markov Chain ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Remote Sensing And Gis ◽  
Strong Impact ◽  
Gis Technology ◽  
New Directions ◽  
Residential Living

Abstract The land's natural resources are invaluable and a requisite for the existence and development of humans and other organisms on Earth. In recent years, under the strong impact of new directions in economic and social development, the demand for land has been increasing. The percentage of land used for residential living, transportation, irrigation and infrastructure tends to increase, while the share of agricultural land is continuously decreasing. Consequently, the allocation and efficient use of land is one of the most important concerns in order to enable sustainable development, environmental protection and ecology. Therefore, research to determine the volatility and changing trends in land use is necessary. This study uses remote sensing and GIS technology, combined with the Markov Chain to determine variation and forecast the changes in land use in the Y Yen district of the Nam Dinh province of Vietnam. This will create a basis for helping land managers grasp the situation in local land use management.

scholarly journals The Evaluation of Regional Spatial Plan for 2011-2031 Based on Land Use Changes Prediction Using Cellular Automata-Markov Model in Sleman

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Linggar Esty Hardini ◽  
Ana Noveria
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Markov Model ◽  
Dynamic Properties ◽  
Land Use Changes ◽  
Mathematical Concept ◽  
Kappa Coefficient ◽  
Markov Modeling ◽  
Von Neumann ◽  
Building Area

In the past years, the development of Sleman Regency has been considered rapid as evidenced by the emergence of built up areas including expansion of the university areas, shopping malls, and housing. Along with the increase in the total population, university students and workers from other regions coming to this regency, the land use in Sleman Regency has started to shift. Land use changes need to be controlled by predicting land use using the CA-Markov model. CA-Markov modeling has dynamic properties that integrate the dimensions of space and time, where the occurrence of events is determined by events that directly precede them and can be used to predict the next event. The accuracy of the CA-Markov concept can be determined by validation and expressed in the Kappa coefficient value (≥ 0.70). This CA-Markov concept has been developed since the 1940s in the field of computers by Von Neumann and Ulam. In this concept it is assumed that pixels are the beginning of the mathematical concept. When a pixel changes, its new status is only affected by its old status and the neighbor status.  This research was conducted to predict the land use in 2031 using the Cellular Automata-Makov model, evaluate the use of land in 2031 in relation to RTRW or city plan, and create a scenario of the direction for land use control in 2031 for disaster-prone areas. Based on the prediction of land use in Sleman Regency in 2031, Kappa coefficient was obtained at 0.7399, implying that the suitability of spatial area and distribution reached 73.99% which is considered good. The results of the prediction also showed that in 2031, the land use would be dominated by building area which was predicted to reach 43.53% out of the total area. The evaluation of land use prediction in 2031 based on RTRW method showed that as large as 40.137,39 ha land would be used according to the RTRW, while 17.411,00 ha would not be used accordingly. The improper use of land might be due to the shift in the use of 4.659,18 ha of rice fields into buildings.

scholarly journals Evaluating The Relationship Between Spatial Temporal Land Use Changes And Climate Pattern Of Nilgiris District

2021 ◽  
Vol 23 (09) ◽  
pp. 381-390
Author(s):  
M.Suguna Devakumari ◽  
◽  
S. Carolin Jeeva ◽  
R.Susan Poonguzhali ◽  
◽  
...  
Keyword(s):  
Land Use ◽  
Tamil Nadu ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Current Trend ◽  
Remote Sensing Images ◽  
Landsat 7 ◽  
The Nilgiris ◽  
The Impact ◽  
The Relationship

Due to deforestation, urbanization and land use changes, the Nilgiris district of Tamil Nadu has been impacted by climate variation. This study was carried out to explore the impact of the land use changes in Nilgiris District from 2011 to 2017using remote sensing images acquired from Landsat 7. Temperature and rainfall data of the study area were also obtained from IMD Pune. The results show that the current trend of decreasing forest area and increasing agricultural land, and the area also experienced a temperature increase of 0.4 ◦C between 2011 and 2017. This study is crucial for land planners and environmentalists to understand the impacts of land use change on the climate in Nilgiris District.

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