Modeling Land Use/Cover Changes by the Combination of Markov Chain and Cellular Automata Markov (CA-Markov) Models

Nadoushan, Mozhgan Ahmadi; Soffianian, Alireza; Alebrahim, Alireza

doi:10.4103/2423-7752.159922

Cited by 25 publications

(19 citation statements)

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“…Whereas, for the second scenario the transition probability matrix has been calculated for the time periods of 2003-2008, 2008-2017 and 2003-2017. The transition probability matrices are derived from Markov chain analysis [42] and they are considered as is the key finder in the Markovian chain [23].…”

Section: The Markov Modelmentioning

confidence: 99%

“…In terms of the validation of CA-Markov prediction and in order to evaluate the performance of the induced model, the process of validation of the predicted map based on actual map is attained. In this study for validating the results, the land use condition of 2003 and 2017 was estimated and compared with actual land use maps in both scenarios [42,51]. Three indicators; Kappa for no ability (κ no) Kappa for location (κ location) and Kappa for quantity (κ quantity) were used for validating the CA-Markov model for prediction the LULC.…”

Section: The Ca-markov Chain Model (Ca-mcm)mentioning

confidence: 99%

“…Finally, the simulation is well-defined, when the value of indicators are equal to 1 and unsatisfactory when it is equal to 0 [4,42]. Eastman stated that 0.80 is acceptable accuracy rate to make plausible future predictions [55].…”

Section: The Ca-markov Chain Model (Ca-mcm)mentioning

confidence: 99%

“…During the second period (2008-2017), bare surface land has the highest amount of loss42.36 km 2 and gained 19.10 km 2 with net loss of 23.26 km 2 . Pasture land has been gained 43.64 km 2 and lost 16.70 km 2 with net gain of 26.94 km 2 .…”

mentioning

confidence: 99%

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Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

2018

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Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

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Section: The Markov Modelmentioning

confidence: 99%

Section: The Ca-markov Chain Model (Ca-mcm)mentioning

confidence: 99%

Section: The Ca-markov Chain Model (Ca-mcm)mentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

2018

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“…CA_MARKOV allocates land based on the suitability of the land for end covers along with a cellular automaton rule to promote spatial contiguity. It works well when historical land cover data is not available or is not a good predictor of future land cover [42,43]. Thus, we used CA_MARKOV to extrapolate the cropping patterns for the epochs of 2020, 2030, 2040, and 2050, and further provided some information that CA_MARKOV was well used in the satellite remote sensing analysis domain.…”

Section: Cropping Pattern Predictionmentioning

confidence: 99%

Time Tracking of Different Cropping Patterns Using Landsat Images under Different Agricultural Systems during 1990–2050 in Cold China

et al. 2018

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Rapid cropland reclamation is underway in Cold China in response to increases in food demand, while the lack analyses of time series cropping pattern mappings limits our understanding of the acute transformation process of cropland structure and associated environmental effects. The Cold China contains different agricultural systems (state and private farming), and such systems could lead to different cropping patterns. So far, such changes have not been revealed yet. Based on the Landsat images, this study tracked cropping information in five-year increments (1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015) and predicted future patterns for the period of 2020–2050 under different agricultural systems using developed method for determining cropland patterns. The following results were obtained: The available time series of Landsat images in Cold China met the requirements for long-term cropping pattern studies, and the developed method exhibited high accuracy (over 91%) and obtained precise spatial information. A new satellite evidence was observed that cropping patterns significantly differed between the two farm types, with paddy field in state farming expanding at a faster rate (from 2.66 to 68.56%) than those in private farming (from 10.12 to 34.98%). More than 70% of paddy expansion was attributed to the transformation of upland crop in each period at the pixel level, which led to a greater loss of upland crop in state farming than private farming (9505.66 km2 vs. 2840.29 km2) during 1990–2015. Rapid cropland reclamation is projected to stagnate in 2020, while paddy expansion will continue until 2040 primarily in private farming in Cold China. This study provides new evidence for different land use change pattern mechanisms between different agricultural systems, and the results have significant implications for understanding and guiding agricultural system development.

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Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh

Rahman

Tabassum²,

Rasheduzzaman

et al. 2017

Environ Monit Assess

162

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Change analysis of land use and land cover (LULC) is a technique to study the environmental degradation and to control the unplanned development. Analysis of the past changing trend of LULC along with modeling future LULC provides a combined opportunity to evaluate and guide the present and future land use policy. The southwest coastal region of Bangladesh, especially Assasuni Upazila of Satkhira District, is the most vulnerable to natural disasters and has faced notable changes in its LULC due to the combined effects of natural and anthropogenic causes. The objectives of this study are to illustrate the temporal dynamics of LULC change in Assasuni Upazila over the last 27 years (i.e., between 1989 and 2015) and also to predict future land use change using CA-ANN (cellular automata and artificial neural network) model for the year 2028. Temporal dynamics of LULC change was analyzed, employing supervised classification of multi-temporal Landsat images. Then, prediction of future LULC was carried out by CA-ANN model using MOLUSCE plugin of QGIS. The analysis of LULC change revealed that the LULC of Assasuni had changed notably during 1989 to 2015. "Bare lands" decreased by 21% being occupied by other land uses, especially by "shrimp farms." Shrimp farm area increased by 25.9% during this period, indicating a major occupational transformation from agriculture to shrimp aquaculture in the study area during the period under study. Reduction in "settlement" area revealed the trend of migration from the Upazila. The predicted LULC for the year 2028 showed that reduction in bare land area would continue and 1595.97 ha bare land would transform into shrimp farm during 2015 to 2028. Also, the impacts of the changing LULC on the livelihood of local people and migration status of the Upazila were analyzed from the data collected through focus group discussions and questionnaire surveys. The analysis revealed that the changing LULC and the occupational shift from paddy cultivation to shrimp farming were related to each other. Around 31.3% of the total respondents stated that at least one of their family members had migrated. Climate-driven southwestern coastal people usually migrate from the vulnerable rural areas towards the nearest relatively safe city due to adverse effects of natural disasters. To control the unplanned development and reduce the internal migration in Assasuni and other coastal areas, a comprehensive land use management plan was suggested that would accommodate the diversified uses of coastal lands and eventually lessen the threats to the life and livelihood of the local people.

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Modeling Land Use/Cover Changes by the Combination of Markov Chain and Cellular Automata Markov (CA-Markov) Models

Cited by 25 publications

References 0 publications

Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Time Tracking of Different Cropping Patterns Using Landsat Images under Different Agricultural Systems during 1990–2050 in Cold China

Temporal dynamics of land use/land cover change and its prediction using CA-ANN model for southwestern coastal Bangladesh

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