scholarly journals Predicting Land Use Changes within the Tazekka Park and Its Borders via a Cellular Automata-Markov Modeling of Satellite Images

2021 ◽  
Vol 56 (2) ◽  
pp. 534-541
Author(s):  
Mohammed Zouiten ◽  
Jamal Chaaouan ◽  
Ibtissam Naoui
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Changes ◽  
Markov Modeling ◽  
New Approach ◽  
Environmental Statistics ◽  
Markov Transition Matrix ◽  
Markov Transition ◽  
Predictive Map ◽  
Derived Data

This article describes a new approach of land cover study to predicting and combatting deforestation based on satellite imagery as environmental statistics. Specifically, a stochastic mathematical cellular automata-Markov model was used to predict land-use changes in the Tazekka Park and its borders in TAZA province in Morocco. The model was used mainly to create thematic forecast maps. Through the proposed approach, we derived data and statistics covering the period 2000 to 2020 and then constructed a predictive map for the year 2040 using ArcGIS 10.4. The evaluation of our model’s effectiveness was confirmed by calculating the Markov transition matrix in the derivation of the final map. These results can improve the management of forest areas and serve as a reference in addressing the direct effects of forests on the environment.

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 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 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 Modeling Major Rural Land-Use Changes Using the GIS-Based Cellular Automata Metronamica Model: The Case of Andalusia (Southern Spain)

2020 ◽  
Vol 9 (7) ◽  
pp. 458 ◽  
Author(s):  
Rafael M. Navarro Cerrillo ◽  
Guillermo Palacios Rodríguez ◽  
Inmaculada Clavero Rumbao ◽  
Miguel Ángel Lara ◽  
Francisco Javier Bonet ◽  
...  
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Cover ◽  
Land Use Changes ◽  
Growth Patterns ◽  
Kappa Index ◽  
Rural Land ◽  
Dry Land ◽  
Rural Land Use ◽  
Clustered Network

The effective and efficient planning of rural land-use changes and their impact on the environment is critical for land-use managers. Many land-use growth models have been proposed for forecasting growth patterns in the last few years. In this work; a cellular automata (CA)-based land-use model (Metronamica) was tested to simulate (1999–2007) and predict (2007–2035) land-use dynamics and land-use changes in Andalucía (Spain). The model was calibrated using temporal changes in land-use covers and was evaluated by the Kappa index. GIS-based maps were generated to study major rural land-use changes (agriculture and forests). The change matrix for 1999–2007 showed an overall area change of 674971 ha. The dominant land uses in 2007 were shrubs (30.7%), woody crops on dry land (17.3%), and herbaceous crops on dry land (12.7%). The comparison between the reference and the simulated land-use maps of 2007 showed a Kappa index of 0.91. The land-cover map for the projected PRELUDE scenarios provided the land-cover characteristics of 2035 in Andalusia; developed within the Metronamica model scenarios (Great Escape; Evolved Society; Clustered Network; Lettuce Surprise U; and Big Crisis). The greatest differences were found between Great Escape and Clustered Network and Lettuce Surprise U. The observed trend (1999–2007–2035) showed the greatest similarity with the Big Crisis scenario. Land-use projections facilitate the understanding of the future dynamics of land-use change in rural areas; and hence the development of more appropriate plans and policies

scholarly journals CELLULAR AUTOMATA MODEL – LANDSCAPE DYNAMICS SIMULATION TOOL IN THE PROCESS OF CHANGE IN LAND USE AND COVER IN THE CITY OF GAÚCHA DO NORTE – MT

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 447-451
Author(s):  
E. A. L. Pinheiro ◽  
N. A. Camini ◽  
M. R. S. Soares ◽  
S. S. Sumida
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Change ◽  
Land Use Changes ◽  
Environmental Modeling ◽  
Dynamics Simulation ◽  
Cellular Automata Model ◽  
The City ◽  
Land Use And Cover ◽  
Main Factor

Abstract. The factors that contribute to land use change in the municipality of Gaúcha do Norte - MT, are entirely linked to the economic process and agricultural production. This process has left Brazil in a state of alert due to the process of deforestation and loss of tropical forests. From 2000 to 2010, the forest areas converted into agriculture accounted for 13.3%, the main factor that directly potentiated with deforestation was the cultivation of soybeans, which in turn was occupying places previously occupied by livestock and pushing the livestock forest inside. The phenomena of land use change and land cover start from multidimensional issues in the environmental and economic context. The use of environmental modeling through cellular automata to analyze land use change phenomena and reproduce the trajectory through future land use simulations and evolution establishes an integration associated by mathematical models and flow integration systems. That predict the trajectory of land use change, thus generating a dynamic model capable of predicting future land use changes by replicating possible patterns of landscape evolution and enabling assessments of future ecological implications for the environment.

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