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 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 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.

scholarly journals Analyzing and Predicting Land Use and Land Cover Changes in New Jersey Using Multi-Layer Perceptron–Markov Chain Model

Earth ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 845-870
Author(s):  
Kikombo Ilunga Ngoy ◽  
Feng Qi ◽  
Daniela J. Shebitz
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
New Jersey ◽  
Land Cover ◽  
Markov Chain Model ◽  
Mixed Forest ◽  
The United States ◽  
Chain Model ◽  
Multi Layer Perceptron ◽  
Lulc Changes

This study analyzed the changes of land use and land cover (LULC) in New Jersey in the United States from 2007 to 2012. The goal was to identify the driving factors of these changes and to project the five-year trend to 2100. LULC data was obtained from the New Jersey Department of Environmental Protection. The original 86 classes were reclassified to 11 classes. Data analysis and projection were performed using TerrSet 2020. Results from 2007 to 2012 showed that the rate of LULC changes was relatively small. Most changes happened to brush/grasslands, mixed forest lands, farmlands and urban/developed lands. Urban/developed lands and the mixed-forest cover gained while farmlands lost. Using a multi-layer perceptron–Markov chain (MLP–MC) model, we projected the 2015 LULC and validated by actual data to produce a 2100 LULC. Changes from 2012 to 2100 showed that urban/developed lands, as well as brush/grasslands, would continue to gain, while farmlands would lose, although the projected landscape texture would likely be identical to the 2012 landscape. Human and natural factors were discussed. It was concluded that the MLP–MC model could be a useful model to predict short-term LULC change. Unexpected factors are likely to interfere in a long-term projection.

scholarly journals Landscape pattern and economic factors’ effect on prediction accuracy of cellular automata-Markov chain model on county scale

2020 ◽  
Vol 12 (1) ◽  
pp. 626-636
Author(s):  
Wang Song ◽  
Zhao Yunlin ◽  
Xu Zhenggang ◽  
Yang Guiyan ◽  
Huang Tian ◽  
...  
Keyword(s):  
Land Use ◽  
Markov Chain ◽  
Cellular Automata ◽  
Markov Model ◽  
Prediction Accuracy ◽  
Driving Mechanism ◽  
Economic Factors ◽  
Chain Model ◽  
The Impact ◽  
County Scale

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.

Remote sensing and GIS forest landscape assessment for sustainable forest management

2021 ◽  
Vol 17 (1) ◽  
pp. 12-26
Author(s):  
A.F. Chukwuka ◽  
A. Alo ◽  
O.J. Aigbokhan
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Forest Cover ◽  
Sustainable Forest Management ◽  
Markov Chain Model ◽  
Remote Sensing Data ◽  
Chain Model ◽  
Spatial Metrics ◽  
Forest Reserve ◽  
Land Cover Types

This study set out to assess the dynamic characteristics of the Ikere forest reserve landscape between 1985 and 2017 using remote sensing data and spatial metrics. Landscape of the study area maintained complex patterns of spatial heterogeneity over the years. Forest cover loss to other land cover types results in new large non-forest area at increasing rate. As at the year 2017, the changes in land cover types were not yet at equilibrium, thus the need to determine the future forest cover extent using a three-way markov Chain model. The decrease in number of patches of forest land (NumP) with increase in its mean patch size (MPS) shows that the forest is becoming a single unit probably due to clearing of existing patches of forest trees. The decrease in class diversity and evenness (SDI and SEI) of the general landscape over the years strengthens this assertion. The findings of this study would be very helpful to government and other stakeholders responsible for ensuring sustainable forest and general environment. Keyword: Landscape, Spatial metrics, sustainable forest and Environment

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 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.

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