Monitoring and prediction spatiotemporal vegetation changes using NDVI index and CA-Markov model (case study: Kermanshah city)
Introduction: Monitoring and evaluation of land surface condition is one of the basic needs in investigating the changes occurring at different levels, including global, regional and local, which include environmental changes. Today, the rapid growth of remote sensing technology, GIS and computer sc...
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Shahid Beheshti University
2020-12-01
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| author | Shadman Darvishi Karim Solaimani |
| author_facet | Shadman Darvishi Karim Solaimani |
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| description | Introduction: Monitoring and evaluation of land surface condition is one of the basic needs in investigating the changes occurring at different levels, including global, regional and local, which include environmental changes. Today, the rapid growth of remote sensing technology, GIS and computer science has led to the emergence of many models to present current and future patterns of land use change. In order to high population growth in large cities and the population's need for land resources, this provides for the destruction of land use, especially vegetation. Kermanshah city as one of the growing areas in recent years has experienced a large population growth and due to the role of population in land use changes and vegetation cover, this issue requires awareness of the vegetation status of this area for proper management of natural resources. The purpose of this study is to monitor and predict vegetation changes in Kermanshah city using NDVI index and CA-Markov model. Material and methods: In this study, vegetation density of Kermanshah city using NDVI index in four classes of low, medium, dense and highest dense vegetation was extracted from Landsat images in 1987, 2002 and 2017 and then the results were validated using ground control points. Also, in order to predict vegetation density for 2032, vegetation map of 2017 was first simulated by applying CA-Markov model and then results were validated using actual vegetation map of the same year using validate module in IDRISI Terrset software followed by validation results and by applying the mentioned model, vegetation density map was predicted in 2032. Results and discussion: The results of vegetation maps with over 85% accuracy show that the area with low, medium and highest dense vegetation classes had a decreased and dense vegetation class had an increased trend during the period of 1987 to 2017. Changes in vegetation classes in elevation classes over the 30 year period show low vegetation in classes of 1042 to 1587 and 2133 to 2678 meters, medium vegetation in classes of 1042 to 1587, 1587 to 2133 and 2678 to 3224 meters, dense vegetation in classes of 2133 to 2678 meters and highest dense vegetation in classes of 1042 to 1587 and 1587 to 2133 meter had a decreased trend. Also, vegetation density in slope classes showed that slope of 0-25% had the highest and slopes of 50-75% and more than 75% had the lowest vegetation density. Also, CA-Markov model results with more than 80% accuracy show that vegetation density in 2032 will be similar to previous periods and medium vegetation cover will have the highest vegetation area in Kermanshah city. The increasing and decreasing trend of vegetation classes indicates that the medium vegetation class will decrease compared to 2017 and the classes with low, dense and high dense vegetation will increase and assessment of vegetation classes in elevation and slope classes shows that at altitudes of 1042 to 1587, 1587 to 2133 and 2133 to 2678 meters and slopes of 0 to 25 percent, the highest vegetation density was related to medium and dense vegetation classes but at altitudes of 2678 to 3224 meters and the slope of 50 to 75 and more than 75 percent the highest vegetation density will be the low vegetation class. Conclusion: In general, the results of this study showed that using NDVI and CA-Markov models with respect to the validation results of these methods can provide acceptable results from the vegetation status of an area. |
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| spelling | doaj-art-09416b2ef17f40bba044ed668cc2892c2025-08-20T02:30:46ZfasShahid Beheshti Universityعلوم محیطی1735-13242588-61772020-12-0118416118210.52547/envs.18.4.16198164Monitoring and prediction spatiotemporal vegetation changes using NDVI index and CA-Markov model (case study: Kermanshah city)Shadman Darvishi0Karim Solaimani1Department of Remote Sensing and Geographic Information Systems, Faculty of Environmental Sciences, Aban Haraz Institute of Higher Education, Amol, Mazandaran, IranDepartment of Watershed Management, Faculty of Natural Resources, Sari Agricultural Sciences and Natural Resources University, Mazandaran, IranIntroduction: Monitoring and evaluation of land surface condition is one of the basic needs in investigating the changes occurring at different levels, including global, regional and local, which include environmental changes. Today, the rapid growth of remote sensing technology, GIS and computer science has led to the emergence of many models to present current and future patterns of land use change. In order to high population growth in large cities and the population's need for land resources, this provides for the destruction of land use, especially vegetation. Kermanshah city as one of the growing areas in recent years has experienced a large population growth and due to the role of population in land use changes and vegetation cover, this issue requires awareness of the vegetation status of this area for proper management of natural resources. The purpose of this study is to monitor and predict vegetation changes in Kermanshah city using NDVI index and CA-Markov model. Material and methods: In this study, vegetation density of Kermanshah city using NDVI index in four classes of low, medium, dense and highest dense vegetation was extracted from Landsat images in 1987, 2002 and 2017 and then the results were validated using ground control points. Also, in order to predict vegetation density for 2032, vegetation map of 2017 was first simulated by applying CA-Markov model and then results were validated using actual vegetation map of the same year using validate module in IDRISI Terrset software followed by validation results and by applying the mentioned model, vegetation density map was predicted in 2032. Results and discussion: The results of vegetation maps with over 85% accuracy show that the area with low, medium and highest dense vegetation classes had a decreased and dense vegetation class had an increased trend during the period of 1987 to 2017. Changes in vegetation classes in elevation classes over the 30 year period show low vegetation in classes of 1042 to 1587 and 2133 to 2678 meters, medium vegetation in classes of 1042 to 1587, 1587 to 2133 and 2678 to 3224 meters, dense vegetation in classes of 2133 to 2678 meters and highest dense vegetation in classes of 1042 to 1587 and 1587 to 2133 meter had a decreased trend. Also, vegetation density in slope classes showed that slope of 0-25% had the highest and slopes of 50-75% and more than 75% had the lowest vegetation density. Also, CA-Markov model results with more than 80% accuracy show that vegetation density in 2032 will be similar to previous periods and medium vegetation cover will have the highest vegetation area in Kermanshah city. The increasing and decreasing trend of vegetation classes indicates that the medium vegetation class will decrease compared to 2017 and the classes with low, dense and high dense vegetation will increase and assessment of vegetation classes in elevation and slope classes shows that at altitudes of 1042 to 1587, 1587 to 2133 and 2133 to 2678 meters and slopes of 0 to 25 percent, the highest vegetation density was related to medium and dense vegetation classes but at altitudes of 2678 to 3224 meters and the slope of 50 to 75 and more than 75 percent the highest vegetation density will be the low vegetation class. Conclusion: In general, the results of this study showed that using NDVI and CA-Markov models with respect to the validation results of these methods can provide acceptable results from the vegetation status of an area.https://envs.sbu.ac.ir/article_98164_03e77990dcd935497cde01a670925eba.pdfvegetation densityslopecellular automataelevation classeskermanshah |
| spellingShingle | Shadman Darvishi Karim Solaimani Monitoring and prediction spatiotemporal vegetation changes using NDVI index and CA-Markov model (case study: Kermanshah city) علوم محیطی vegetation density slope cellular automata elevation classes kermanshah |
| title | Monitoring and prediction spatiotemporal vegetation changes using NDVI index and CA-Markov model (case study: Kermanshah city) |
| title_full | Monitoring and prediction spatiotemporal vegetation changes using NDVI index and CA-Markov model (case study: Kermanshah city) |
| title_fullStr | Monitoring and prediction spatiotemporal vegetation changes using NDVI index and CA-Markov model (case study: Kermanshah city) |
| title_full_unstemmed | Monitoring and prediction spatiotemporal vegetation changes using NDVI index and CA-Markov model (case study: Kermanshah city) |
| title_short | Monitoring and prediction spatiotemporal vegetation changes using NDVI index and CA-Markov model (case study: Kermanshah city) |
| title_sort | monitoring and prediction spatiotemporal vegetation changes using ndvi index and ca markov model case study kermanshah city |
| topic | vegetation density slope cellular automata elevation classes kermanshah |
| url | https://envs.sbu.ac.ir/article_98164_03e77990dcd935497cde01a670925eba.pdf |
| work_keys_str_mv | AT shadmandarvishi monitoringandpredictionspatiotemporalvegetationchangesusingndviindexandcamarkovmodelcasestudykermanshahcity AT karimsolaimani monitoringandpredictionspatiotemporalvegetationchangesusingndviindexandcamarkovmodelcasestudykermanshahcity |