scholarly journals An Analysis of Land-Use and Land-Cover Change in the Zhujiang–Xijiang Economic Belt, China, from 1990 to 2017

2018 ◽  
Vol 8 (9) ◽  
pp. 1524 ◽  
Author(s):  
Yunfeng Hu ◽  
Batunacun
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Autocorrelation ◽  
Land Cover Change ◽  
Markov Chain Model ◽  
Chain Model ◽  
Environment Change ◽  
Long Time ◽  
Global And Local ◽  
Year 2000

Land-use and land-cover change (LUCC) are currently contested topics in the research of global environment change and sustainable change. Identifying the historic land-use change process is important for the new economic development belt (the Zhujiang–Xijiang Economic Belt, ZXEB). During this research, based on long-time-series land-use and land-cover data, while using a combination of a transition matrix method and Markov chain model, the authors derive the patterns, processes, and spatial autocorrelations of land-use and land-cover changes in the ZXEB for the periods 1990–2000 and 2000–2017. Additionally, the authors discuss the spatial autocorrelation of land-use in the ZXEB and the major drivers of urbanization. The results indicate the following: (1) The area of cropland reduces during the two periods, and woodland decreases after the year 2000. The woodland is the most stable land-use type in both periods. (2) Built-up land expansion is the most important land-use conversion process; the major drivers of built-up land expansion are policy intervention, GDP (gross domestic product), population growth, and rural population migration. (3) Transition possibilities indicate that after 2000, most land-use activities become stronger, the global and local Moran’s I of all land-use types show that the spatial autocorrelations have become more closely related, and the spatial autocorrelation of built-up land has become stronger. Policies focus on migration from rural to urban, and peri-urban development is crucial for future sustainable urbanization.

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

Modelling Land-Use and Land-Cover Changes

2021 ◽  
pp. 57-102
Author(s):  
Raquel Faria de Deus ◽  
José António Tenedório ◽  
Jorge Rocha
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Land Cover ◽  
Land Use Planning ◽  
Markov Chain Model ◽  
Hybrid Approach ◽  
Chain Model ◽  
Lulc Change ◽  
Massive Growth ◽  
Urban Settlements

In this chapter, a hybrid approach integrating cellular automata (CA), fuzzy logic, logistic regression, and Markov chains for modelling and prediction of land-use and land-cover (LULC) change at the local scale, using geographic information with fine spatial resolution is presented. A spatial logistic regression model was applied to determine the transition rules that were used by a conventional CA model. The overall dimension of LULC change was estimated using a Markov chain model. The proposed CA-based model (termed CAMLucc) in combination with physical variables and land-use planning data was applied to simulate LULC change in Portimão, Portugal between 1947 and 2010 and to predict its future spatial patterns for 2020 and 2025. The main results of this research show that Portimão has been facing massive growth in artificial surfaces, particularly near the main urban settlements and along the coastal area, and reveal an early and intensive urban sprawl over time.

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

2020 ◽  
Vol 9 (4) ◽  
pp. 71
Author(s):  
Ashti I. Abdulrahman ◽  
Shamal A. Ameen
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Markov Model ◽  
Land Use Planning ◽  
Markov Chain Model ◽  
United States Geological Survey ◽  
Chain Model ◽  
Lulc Changes ◽  
Spatiotemporal Changes

The 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 automaton 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 constitute 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 Land Use/ Land Cover Dynamics Study and Prediction In Jaipur City us­ing CA Markov Model Integrated with Road Network

2021 ◽  
Author(s):  
Nitesh Kumar Mourya ◽  
Sana Rafi ◽  
Saima Shamoo
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Road Network ◽  
Markov Chain Model ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Visual Interpretation ◽  
Lulc Change ◽  
Barren Land ◽  
Jaipur City

Abstract Land Use Land Cover (LULC) dynamics analysis is critical and should be done regularly. It draws attention to LULC developments that can be addressed before they become unmanageable disasters or circumstances. For the years 2000, 2010, and 2020, LULC change analysis was carried out in Jaipur City, Rajasthan, India. The LULC maps were created using Landsat data through a visual interpretation technique at a scale of 1:50,000. These maps were classified into vegetation, agriculture, built-up areas, barren land, and water bodies. LULC was predicted by extrapolating the current LULC change pattern. Using a Cellular Automata-Markov Chain Model (CA Markov) integrated with road network, the current LULC change trend was extrapolated and utilized to estimate the LULC map for the years 2020, 2030, 2040, and 2050. The strategy was validated by estimating LULC change for 2020 and comparing it to the actual LULC map for that year. The urban area contributed to 4. 75% in 2000 of the total area in Jaipur city. The percentage of area under urban class has increased to 9.68% in 2010 and 12.96% in 2020. The prediction based on 2000-2010 and 2010-2020 has shown an unprecedented decadal growth in the built-up area till 2050. The prediction based on the 2000-2010 period has shown a rise of 92.04 % during 2020-2030, 77.13 % during 2030-2040 and, 64.34 % during 2040-2050. The prediction based on the 2010-2020 period has shown a rise of 102.42% during 2020-2030, 73.56% during 2030-2040 and, 54.47 % during 2040-2050. This study is, therefore, calls for policy interventions to manage population and urban growth.

scholarly journals Monitoring and Predicting Spatio-Temporal Land Use/Land Cover Changes in Zaria City, Nigeria, through an Integrated Cellular Automata and Markov Chain Model (CA-Markov)

2020 ◽  
Vol 12 (24) ◽  
pp. 10452
Author(s):  
Auwalu Faisal Koko ◽  
Wu Yue ◽  
Ghali Abdullahi Abubakar ◽  
Roknisadeh Hamed ◽  
Akram Ahmed Noman Alabsi
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Land Cover ◽  
Markov Chain Model ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Lulc Change ◽  
Barren Land ◽  
Spatio Temporal

Monitoring land use/land cover (LULC) change dynamics plays a crucial role in formulating strategies and policies for the effective planning and sustainable development of rapidly growing cities. Therefore, this study sought to integrate the cellular automata and Markov chain model using remotely sensed data and geographical information system (GIS) techniques to monitor, map, and detect the spatio-temporal LULC change in Zaria city, Nigeria. Multi-temporal satellite images of 1990, 2005, and 2020 were pre-processed, geo-referenced, and mapped using the supervised maximum likelihood classification to examine the city’s historical land cover (1990–2020). Subsequently, an integrated cellular automata (CA)–Markov model was utilized to model, validate, and simulate the future LULC scenario using the land change modeler (LCM) of IDRISI-TerrSet software. The change detection results revealed an expansion in built-up areas and vegetation of 65.88% and 28.95%, respectively, resulting in barren land losing 63.06% over the last three decades. The predicted LULC maps of 2035 and 2050 indicate that these patterns of barren land changing into built-up areas and vegetation will continue over the next 30 years due to urban growth, reforestation, and development of agricultural activities. These results establish past and future LULC trends and provide crucial data useful for planning and sustainable land use management.

scholarly journals IMPACT OF LAND USE AND LAND COVER CHANGES ON RUNOFF PREDICTION IN OGBESE RIVER WATERSHED

2021 ◽  
Vol 15 (2) ◽  
pp. 297-308
Author(s):  
Obinna Obiora-Okeke
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Land Cover ◽  
Markov Chain Model ◽  
Hydrologic Model ◽  
Landsat 8 ◽  
Chain Model ◽  
Runoff Simulation ◽  
Flow Rates ◽  
Lulc Changes

Land use and land cover (LULC) changes in Ogbese watershed due to urbanization implies increased areas of low infiltration. This results to higher flow rates downstream the watershed. This study estimates the changes in peak flow rates at the watershed’s outlet for present and future LULC. Rainfall-runoff simulation was achieved with Hydrologic Engineering Centre-Hydrologic Modeling System (HEC-HMS) version 4.2 while future LULC was projected with Markov Chain model. Rainfall inputs to the hydrologic model were obtained from intensity-duration-frequency curves for Ondo state. Landsat 7, Enhanced Thematic mapper plus (ETM+) image and Landsat 8 operational land imager (OLI) with path 190 and row 2 were used to generate LULC images for the years 2002, 2015 and 2019. Six LULC classes were extracted as follows: built up area, bare surface, vegetation, wetland, rock outcrop and waterbody.  Future LULC in year 2025 and 2029 were projected with Markov Chain model. The model prediction was verified with Nash Sutcliffe Efficiency index (NSE). NSE value of 0.79 was calculated indicating LULC changes in the watershed was Markovian. Results show that built up area cover in 2019 is projected to increase by 26.1% in 2024 and 39.9% in 2029 and wetland is projected to decreased by 1.2% in 2024 and 2.3% by 2029. Runoff peaks for these LULC projections indicate increase by 0.24% in 2024 and 1.19% in 2029 at the watershed’s outlets for 100-year return period rainfall.

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