پایش و مدل‌سازی تغییرات اراضی جنگلی در منطقه گرگان با استفاده از مدل Geomod
محورهای موضوعی :
سیستم اطلاعات جغرافیایی
سمیه گلدوی
1
(استادیار گروه علوم و مهندسی محیط زیست، مرکز آموزش عالی کاشمر. *(مسوول مکاتبات))
مرجان محمدزاده
2
(دانشیار گروه محیط زیست، دانشکده شیلات و محیط زیست، دانشگاه علوم کشاورزی و منابع طبیعی گرگان)
عبدالرسول سلمان ماهینی
3
(دانشیار گروه محیط زیست، دانشکده شیلات و محیط زیست، دانشگاه علوم کشاورزی و منابع طبیعی گرگان.)
علی نجفی نژاد
4
(دانشیار گروه آبخیزداری، دانشکده مرتع و آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی گرگان)
تاریخ دریافت : 1402/08/02
تاریخ پذیرش : 1402/09/02
تاریخ انتشار : 1402/10/01
کلید واژه:
اراضی جنگلی,
ایران,
مدل‌سازی تغییرات,
منطقه گرگان,
مدل Geomod,
چکیده مقاله :
زمینه و هدف: سالانه سطح وسیعی از جنگل ها به دلیل تبدیل به کاربری های دیگری مانند زمین های کشاورزی و مناطق مسکونی کاهش می‌یابد. اراضی جنگلی شمال ایران نیز از این قاعده مستثنی نیستند و این کاهش در اکثر مناطق دیده شده است. لذا، پژوهش حاضر با هدف مدل‌سازی تغییرات اراضی جنگلی در منطقه گرگان با استفاده از مدل Geomod انجام شده است.
روش بررسی: در مطالعه حاضر، نخست تغییرات اراضی جنگلی منطقه گرگان در بازه زمانی 20 ساله تعیین و سپس، مدل سازی این تغییرات با استفاده از مدل Geomod اجرا گردید. به این منظور، نقشه های کاربری زمین بازه های مطالعاتی با استفاده از تصاویر ماهواره ای تهیه و آشکارسازی تغییرات با روش مقایسه پس از طبقه‌بندی انجام شد. سپس، مدل Geomod برای مدل سازی تغییرات اراضی جنگلی اجرا گردید.
یافته ها: آشکارسازی تغییرات در دوره های زمانی مورد بررسی کاهش گستره اراضی جنگلی را نشان داد. همچنین، نتایج اجرای مدل با کسب کاپا بیش از 99/0 توانایی مدل را در مدل سازی تغییرات اراضی جنگلی این منطقه نشان داد. بنابراین، شرایط آینده اراضی جنگلی برای نیز با استفاده از مدل پیش بینی شد. نتایج حاصل نشان دهنده کاهش گستره اراضی جنگلی منطقه در دوره زمانی مورد مطالعه بود.
بحث و نتیجه گیری: نتایج کاهش گستره اراضی جنگلی منطقه در دوره زمانی مورد مطالعه را نشان داد که دلیل اصلی آن توسعه مناطق مسکونی و اراضی کشاورزی در منطقه بود. بنابراین، اقدامات مدیریتی و حفاظتی همچون تعیین محدوده اراضی کشاورزی و ممانعت از گسترش آن‌ها، جلوگیری از گسترش روستاها و نیز ممانعت از دسترسی بی‌رویه مردم به عرصه‌های جنگلی پیشنهاد می‌گردد.
چکیده انگلیسی:
Background and Objective: Each year, a wide range of forests change to the other uses such as agricultural and residential lands. Forests in northern Iran are no exception, and this decrease has been seen nearly everywhere. Therefore, the research was conducted with the aim of forest changes modeling in Gorgan area using Geomod model.
Material and Methodology: In the present study, forest changes occurred in Gorgan area was detected during 20 years. Then, forest change modeling was performed using Geomod. To do this, land use maps for the study time period were prepared using satellite imagery. Then, change detection process was performed by post-classification comparison technique. The Geomod was run to simulate forest changes in this area.
Findings: Forest change detection and its modelling showed the reduction of forest area in the region. Also, modeling results were validated using kappa indices which resulted in more than 0.99 and indicated model capability in the depicted forest changes in this area. Then, the future condition of forest areas were predicted using the model.
Discussion and Conclusions: Results showed that forest areas have been decreased in this time period that development of residential areas and agricultural lands are the main reason for this. So, managerial and protectoral programs such as determining agricultural lands' boundaries, preventing their expansion, preventing rural expansion, and restricting accessible to forest areas were suggested.
منابع و مأخذ:
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Abuelaish, B. & Camacho Olmedo, M. T. 2016. Scenario of land use and land cover change in the Gaza Strip using remote sensing and GIS models. Arab J Geosci. Vol 9: 1-14.
Eman A. A. & & Bharti, W. G. 2022. Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE. Journal of Sensors. Vol 2022: 1-15.
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Naseri Rad, S. Naghavi, H., Soosani, J., Nouredini, A. R. & Vafaei, S. 2021. Investigating the transmission potential of land use and land cover using Similarity Weighted Instance based Learning, Logistic regression and Geomod methods (Case study: Bastam basin, Selseleh city). Journal Environmental Sciences and Technology. Vol 22. No.11.
Nozari, F. Salehi, A. Armin, M. & Farzin, M. 2020. Prediction of forest cover changes for Boyer-Ahmad region using Geomod model. Journal of Forest Research and Development. Vol 6. No 3: 463-476.
Irmadi, N., Turmudi, R. W., Jaka S., Ratna, S. D. & Sri, L. 2018. Comparing of Land Change Modeler and Geomod Modeling for the Assessment of Deforestation (Case Study: Forest Area at Poso Regency, Central Sulawesi Province). International Journal of Advanced Engineering, Management and Science (IJAEMS). Vol. 4. No 8: 597-607.
Aksoy, H. & Kaptan, S. 2022. Simulation of Future Forest and Land Use/Cover Changes (2019-2039) Using the Cellular Automata Markov Model. Geocarto International. Vol 37. No 4: 1183-1202.
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Rahimi, J. & Almodaresi, S. A. 2020. Investigation and Prediction of Land Use Change in Shahrekord City Using Land Change Model and GIS. Journal of Radar and Optical Remote Sensing. Vol 4: 72–86.
Serra, P., Pons, X. & Sauri, D. 2008. Land-cover and land-use change in a Mediterranean landscape: A spatial analysis of driving forces integrating biophysical and human factors. Applied Geography. Vol. 28: 189-209.
Pontius Jr, R. G. & Malanson, J. 2005. Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science.Vol. 19. No. 2: 243-265.
Cabral, P. & Zamyatin, A. 2006. Three land change models for urban dynamics analysis in Sintra-Cascais area. 1st Earsel workshop of the sig urban Remote Sensing Humboldt-universitst Zu Berlin, 2-3 March.
Pontius Jr, R. G. Cornell, J. D. & Hall, C. A. S. 2001. Modeling the spatial pattern of land- use change with GEOMOD2: Application and validation for CostaRica.Agriculture Ecosystems & Environment. Vol. 1775: 1-13.
Pickard, B., Gray, J. & Meentemeyer, R. 2017. Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models. Land. Vol 6. No 52: 1-21.
Azizi, P., Soltani, A., Bagheri, F., Sharifi, Sh. & Mikaeili, M. 2022. An Integrated Modelling Approach to Urban Growth and Land Use/Cover Change. Land. Vol 11: 1-26.
Brown, S., Hall, M., Andrasko, K., Ruiz, F., Marzoli, W., Guerrero, G., Masera, O., Dushku, A. & DeJong, B. 2007. Baselines for land-use change in the tropics: application to avoided deforestation projects. Mitig Adapt Start Glob Chenge. Vol. 12: 1001-1026.
Pontius Jr, R. G. 2002. Statistical Methods to Partition Effects of Quantity and Location During Comparison of Categorical Maps at Multiple Resolutions. Photogrammetric Engineering & Remote Sensing. Vol. 68. No. 10: 1041-1049.
Soffianian, A. & Ahmadi Nadoushan, M. 2010. Modelling urban changes using Geomod Model in Arak, Iran.3rd International Conference on Cartography and GIS.15-20 June. Nessebar, Bulgaria.
Shayesteh, K. Abedian, S. & Galdavi, S. 2018. Urban Expansion Modeling Using Logistic Regression Method based on Geomod Model Case study: Kordkuy city. Vol 16. No 51: 44-64.
Poelmans, L. & Romoaey, A. V. 2009. Detection and modeling spatial patterns of urban sprawl in highly fragmented areas: A case study in the Flunders region. Landscape and Urban Planning. (93): 10-19.
Pontius Jr, R. G & Pacheco, P. 2004. Calibration and validation of model of forest disturbance in the western ghats, India 1920- 1990. Geo Journal. Vol. 61: 325-334.
Galdavi, S., Mohammadzadeh, M., Salman Mahini, A. & Najafi Nejad, A. 2013. Urban Change Detection Using Multi-Temporal Remotely Sensed imagery (Case study: Gorgan Area, Northern Iran). Environment & Urbanization ASIA. Vol 4: 339-348.
Andaryani, S., Sloan, S., Nourani, V. & Keshtkar, H. 2021. The utility of a hybrid GEOMOD-Markov Chain model of land-use change in the context of highly water-demanding agriculture in a semi-arid region. Ecological Informatics. Vol 64: 1-12.
Abdollahi, S. & Nasiri, V. 2021. Forest change Detecting and predicting in Gilan province using satellite images and geomed. Environmental research and technology. Vol 7. No 5: 141-151. (In Persian)
Nasiri, V. Darvishsefat, A. A. Shirvani, A. & Avatefi Hemmat. 2019. Forest change detecting and modeling in Arsbaran using regression Logistic, Markov chain and geomed model. Vol 19. No 65: 171-189.
Landis, J. & Koch, G. 1977. The measurement of observer agreement for categorical data. Biometrics. Vol 33: 159–174.
Heidarizadi, Z. & Mohammadian Behbahani, A. 2019. Performance comparison of Geomod and LCM models to predict land use changes (case study: Abughovair plain, Ilam province). Iranian Journal of Range and Desert Research. Vol 26. No 3: 660-674. (In Persian)
Yang, Ch., Wu, G., Chend, J., Lia, Q., Dinge, K., Wangf, G & Zhanga, Ch. 2019. Simulating and forecasting spatio-temporal characteristic of land-use/cover change with numerical model and remote sensing: a case study in Fuxian Lake Basin, China. European journal of remote sensing. VOl 52. NO 1: 374–384.
Thiam, S., Ariel, A., Salas, Houngue, N. R., Santos Almoradie, A. D., Verleysdonk, S., Adounkpe, J. & Komi, K. 2022. Modelling Land Use and Land Cover in the Transboundary Mono River Catchment of Togo and Benin Using Markov Chain and Stakeholder’s Perspectives. Sustainability. Vol 14: 1-22.
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Hua, A. K. 2017. Application of ca-markov model and land use/land cover changes in Malacca river watershed, Malaysia. Applied ecology and environmental research. Vol 15. No 4:605-622.
Galdavi, S.; Mohammadzadeh, M., salman Mahiny, A. R. & Najafi Nejad, A. 2014. Forest change modeling using logistic regression in the period 1988-2007 and prediction of future condition in gorgan area. Geographic Space, 14(46), 51-70. (In Persian)
Abuelaish, B. & Camacho Olmedo, M. T. 2016. Scenario of land use and land cover change in the Gaza Strip using remote sensing and GIS models. Arab J Geosci. Vol 9: 1-14.
Eman A. A. & & Bharti, W. G. 2022. Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE. Journal of Sensors. Vol 2022: 1-15.
Smaeily, A. & Ashjaei. 2019. Modeling land use change through Markoff and GIS (Case study: Qom Province). Geography and territorial Spatial Arrangement. Vol 9. No 31: 153-172. (In Persian)
Echeverria, C., Coomes, D. A., Hall, M. & Newton, A. C. 2008. Spatially explicit models to analyze forest loss and fragmentation between 1976 and 2020 in southern chile. Ecological Modeling. Vol. 212: 439-449.
Echeverria, C., Coomes, D., Salas, J., rey-Benayas, J. M., Lara, A. & Newton, A. 2006. Rapid deforestation and fragmentation of Chilean Temperate Forests.Biological Conservation. Xxx-xxx.
Naseri Rad, S. Naghavi, H., Soosani, J., Nouredini, A. R. & Vafaei, S. 2021. Investigating the transmission potential of land use and land cover using Similarity Weighted Instance based Learning, Logistic regression and Geomod methods (Case study: Bastam basin, Selseleh city). Journal Environmental Sciences and Technology. Vol 22. No.11.
Nozari, F. Salehi, A. Armin, M. & Farzin, M. 2020. Prediction of forest cover changes for Boyer-Ahmad region using Geomod model. Journal of Forest Research and Development. Vol 6. No 3: 463-476.
Irmadi, N., Turmudi, R. W., Jaka S., Ratna, S. D. & Sri, L. 2018. Comparing of Land Change Modeler and Geomod Modeling for the Assessment of Deforestation (Case Study: Forest Area at Poso Regency, Central Sulawesi Province). International Journal of Advanced Engineering, Management and Science (IJAEMS). Vol. 4. No 8: 597-607.
Aksoy, H. & Kaptan, S. 2022. Simulation of Future Forest and Land Use/Cover Changes (2019-2039) Using the Cellular Automata Markov Model. Geocarto International. Vol 37. No 4: 1183-1202.
Schneider, L. C. & Pontius Jr, R. G. 2001. Modeling land-use change in the Ipswich watershed, Massachusetts, USA. Agriculture Ecosystems & Environment. Vol. 85: 83-94.
Rahimi, J. & Almodaresi, S. A. 2020. Investigation and Prediction of Land Use Change in Shahrekord City Using Land Change Model and GIS. Journal of Radar and Optical Remote Sensing. Vol 4: 72–86.
Serra, P., Pons, X. & Sauri, D. 2008. Land-cover and land-use change in a Mediterranean landscape: A spatial analysis of driving forces integrating biophysical and human factors. Applied Geography. Vol. 28: 189-209.
Pontius Jr, R. G. & Malanson, J. 2005. Comparison of the structure and accuracy of two land change models. International Journal of Geographical Information Science.Vol. 19. No. 2: 243-265.
Cabral, P. & Zamyatin, A. 2006. Three land change models for urban dynamics analysis in Sintra-Cascais area. 1st Earsel workshop of the sig urban Remote Sensing Humboldt-universitst Zu Berlin, 2-3 March.
Pontius Jr, R. G. Cornell, J. D. & Hall, C. A. S. 2001. Modeling the spatial pattern of land- use change with GEOMOD2: Application and validation for CostaRica.Agriculture Ecosystems & Environment. Vol. 1775: 1-13.
Pickard, B., Gray, J. & Meentemeyer, R. 2017. Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models. Land. Vol 6. No 52: 1-21.
Azizi, P., Soltani, A., Bagheri, F., Sharifi, Sh. & Mikaeili, M. 2022. An Integrated Modelling Approach to Urban Growth and Land Use/Cover Change. Land. Vol 11: 1-26.
Brown, S., Hall, M., Andrasko, K., Ruiz, F., Marzoli, W., Guerrero, G., Masera, O., Dushku, A. & DeJong, B. 2007. Baselines for land-use change in the tropics: application to avoided deforestation projects. Mitig Adapt Start Glob Chenge. Vol. 12: 1001-1026.
Pontius Jr, R. G. 2002. Statistical Methods to Partition Effects of Quantity and Location During Comparison of Categorical Maps at Multiple Resolutions. Photogrammetric Engineering & Remote Sensing. Vol. 68. No. 10: 1041-1049.
Soffianian, A. & Ahmadi Nadoushan, M. 2010. Modelling urban changes using Geomod Model in Arak, Iran.3rd International Conference on Cartography and GIS.15-20 June. Nessebar, Bulgaria.
Shayesteh, K. Abedian, S. & Galdavi, S. 2018. Urban Expansion Modeling Using Logistic Regression Method based on Geomod Model Case study: Kordkuy city. Vol 16. No 51: 44-64.
Poelmans, L. & Romoaey, A. V. 2009. Detection and modeling spatial patterns of urban sprawl in highly fragmented areas: A case study in the Flunders region. Landscape and Urban Planning. (93): 10-19.
Pontius Jr, R. G & Pacheco, P. 2004. Calibration and validation of model of forest disturbance in the western ghats, India 1920- 1990. Geo Journal. Vol. 61: 325-334.
Galdavi, S., Mohammadzadeh, M., Salman Mahini, A. & Najafi Nejad, A. 2013. Urban Change Detection Using Multi-Temporal Remotely Sensed imagery (Case study: Gorgan Area, Northern Iran). Environment & Urbanization ASIA. Vol 4: 339-348.
Andaryani, S., Sloan, S., Nourani, V. & Keshtkar, H. 2021. The utility of a hybrid GEOMOD-Markov Chain model of land-use change in the context of highly water-demanding agriculture in a semi-arid region. Ecological Informatics. Vol 64: 1-12.
Abdollahi, S. & Nasiri, V. 2021. Forest change Detecting and predicting in Gilan province using satellite images and geomed. Environmental research and technology. Vol 7. No 5: 141-151. (In Persian)
Nasiri, V. Darvishsefat, A. A. Shirvani, A. & Avatefi Hemmat. 2019. Forest change detecting and modeling in Arsbaran using regression Logistic, Markov chain and geomed model. Vol 19. No 65: 171-189.
Landis, J. & Koch, G. 1977. The measurement of observer agreement for categorical data. Biometrics. Vol 33: 159–174.
Heidarizadi, Z. & Mohammadian Behbahani, A. 2019. Performance comparison of Geomod and LCM models to predict land use changes (case study: Abughovair plain, Ilam province). Iranian Journal of Range and Desert Research. Vol 26. No 3: 660-674. (In Persian)
Yang, Ch., Wu, G., Chend, J., Lia, Q., Dinge, K., Wangf, G & Zhanga, Ch. 2019. Simulating and forecasting spatio-temporal characteristic of land-use/cover change with numerical model and remote sensing: a case study in Fuxian Lake Basin, China. European journal of remote sensing. VOl 52. NO 1: 374–384.
Thiam, S., Ariel, A., Salas, Houngue, N. R., Santos Almoradie, A. D., Verleysdonk, S., Adounkpe, J. & Komi, K. 2022. Modelling Land Use and Land Cover in the Transboundary Mono River Catchment of Togo and Benin Using Markov Chain and Stakeholder’s Perspectives. Sustainability. Vol 14: 1-22.
