| تعداد نشریات | 284 |
| تعداد نشریات سازمان | 82 |
| تعداد شماره‌ها | 3,043 |
| تعداد مقالات | 34,036 |
| تعداد مشاهده مقاله | 46,417,003 |
| تعداد دریافت فایل اصل مقاله | 77,482,613 |
مدل‌سازی تغییر کاربری زمین با مدل‌ساز و پیش بینی وضعیت سال 1409 در سرآب آبخیز سیاه‌رود | ||
| پژوهش های آبخیزداری | ||
| مقاله 8، دوره 34، شماره 3 - شماره پیاپی 132، مهر 1400، صفحه 115-132 اصل مقاله (1.72 M) | ||
| نوع مقاله: پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/wmej.2021.128644.1288 | ||
| نویسندگان | ||
| بهنوش جعفری گرزین1؛ عطااله کاویان* 2؛ کریم سلیمانی2 | ||
| 1دانشجوی دوره دکترای علوم و مهندسی آبخیزداری دانشگاه علوم کشاورزی و منابع طبیعی ساری، واحد پردیس | ||
| 2استاد گروه علوم و مهندسی آبخیزداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری | ||
| چکیده | ||
| برنامه ریزی و مدیریت بهینه ی زمین، مستلزم به روزرسانی اطلاعات تغییر کاربری در طول زمان است. کاربرد توانمندی های سنجش ازدور و مدل‌ساز تغییر زمین (ال‌سی‌ام) بستر مناسبی برای شبیه سازی تغییر کاربری زمین ایجاد می‌کند. تغییر کاربری زمین در منطقه ی سرآب آبخیز سیاه‌رود در بازه ی زمانی 1394-1356 با مدل‌ساز ارزیابی، و سپس کاربری زمین برای سال 1409 شبیه سازی شد. نقشه های کاربری زمین سال های 1356، 1367، 1377، 1388 و 1394 با روش های طبقه بندی نظارت نشده و نظارت شده‌ی تصویرهای ماهواره ی لندست تهیه، و مدل‌ساز تغییر زمین برای سه دوره ی زمانی 1367-1356 و 1377-1367 و 1388-1377 در قالب سه مدل اجرا شد. پس از ارزیابی عملکرد این مدل ها، مناسب ترین آن برای پیش بینی تغییر کاربری زمین انتخاب، و با کاربرد داده های دوره ی 1394-1388 پیش بینی برای سال 1409 انجام شد. نتیجه‌ی این پژوهش بیانگر آن است که کاربرد ورودی های مناسب برای مدل‌ساز تغییر زمین، به کارگیری روش های ترکیبی در طبقه‌بندی، و بهره گیری از دانش کارشناسی می تواند در بهبود نتیجه‌ی طبقه بندی تصویرها ماهواره یی و افزایش کارآیی ال‌سی‌ام موثر باشد (ضریب کاپای 0/82 تا 0/91). پیش بینی تغییر کاربریزمین در منطقه ی سرآب آبخیز سیاه‌رود در بازه ی زمانی 1356 تا 1409 بیانگر کاهش 67 % سطح زمین شالیزاری، کاهش 12 % سطح جنگل، 34 برابر شدن وسعت زمین مخلوط زراعت و باغ، و 3 برابر شدن وسعت منطقه‌های مسکونی است. | ||
| کلیدواژه‌ها | ||
| آشکارسازی تغییر؛ تحلیل چندزمانه؛ تصویرهای ماهواره یی؛ رودخانه‌ی سیاه‌رود | ||
| عنوان مقاله [English] | ||
| Modeling LandUse Changes Using Land Change Modeler for Prediction of the 2030 LandUse on the upstream area of the Siahroud River Basin | ||
| نویسندگان [English] | ||
| Behnoush Jafari Gorzin1؛ Ataollah Kavian2؛ Karim Solaimani2 | ||
| 1Ph.D. Student, Watershed Management Science and Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| 2Professor, Department of the Watershed Management Science and Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran | ||
| چکیده [English] | ||
| An optimized land management and the planning requires updating information on the land use changes over time. The application of remote sensing capabilities and land use change modeler (LCM) may provide a suitable platform for simulating land use changes. The LCM, land use changes in the upstream area of the Siahrud River Basin were evaluated in the 1977-2015 period and simulated for 2030. The land use maps of 1977, 1988, 1998, 2009 and 2015 were prepared, using the unsupervised and supervised classification methods of the Landsat satellite images, and the LCM, for the 1977-1988, 1988-1998 and 1998-2009 periods were implemented in the form of three models. After evaluation the performance of these models, the most appropriate one was selected. Using the data for the 2009-2015 periods a prediction was made for the year 2030. The results indicate that the use of appropriate inputs for the LCM, the application of the combined methods in classification, and utilizing expert knowledge, can be effective in improving the classification results and increasing the efficiency of the LCM. (Kappa coefficient 0.82 to 0.91). Predicting land use changes in the Siahroud River Basin during the 1977 to 2030 period indicates a 67% decrease in paddy fields and 12% decrease in forest area and a 34-fold increase in the extent of the mixed agricultural and orchards, and threefold increase in the extent of the residential areas. | ||
| کلیدواژه‌ها [English] | ||
| Change detection, multi-temporal analysis, satellite images, Siahroud River | ||
| مراجع | ||
|
Afifi ME. 2020. Modeling land use changes using Markov chain model and LCM model. Journal of Applied Researches in Geographical Sciences. 20(56):141–158. (In Persian). http://jgs.khu.ac.ir/article-1-3243-en.html Amaral e Silva A, Braga M, Quintão Ferreira J, Juste dos Santos V, do Carmo Alves S, de Oliveira JC, Calijuri ML. 2020. Anthropic activities and the Legal Amazon: Estimative of impacts on forest and regional climate for 2030. Remote Sensing Applications. (18):1–24. doi: https://10.1016/j.rsase.2020.100304 Azizi Ghalati S, Rangzan K, Taghizadeh A, Ahmadi Sh. 2014. LCM Logistic regression modelling of land-use changes in Kouhmare Sorkhi, Fars province. Iranian Journal of Forest and Poplar Research. 22(4): 585–596. (In Persian). Babur M, Shrestha S, Bhatta B, Datta A, Ullah H. 2020. Integrated assessment of extreme climate and landuse change impact on sediment yield in a mountainous transboundary watershed of India and Pakistan. Journal of Mountain Science. 17(3): 624–640. https://doi.org/10.1007/s11629-019-5547-z. Chaudhary BS, Kumar S. 2017. Use of RS and GIS for land use/land cover mapping of K-J Watershed. India. Int. Journal of Advances in Remote Sensing and GIS. 5(1): 85–92. Dastorani MT, Afkhami H. 2011. Application of artificial neural networks on drought prediction in Yazd (Central Iran). Desert. 16(1): 39–48. https://10.22059/jdesert.2011.23021. Department of Watershed Management-Mazandaran Jihad Agriculture Organization. 2002. Report of feasibility-executive studies of Siahroud Watershed. 790 p. (In Persian). Eastman RJ. 2012. IDRISI selva manual. IDRISI Tutorial. s.l. Clark University, Worcester. www.clarklabs.org Congalton RG, Green K. 2019. Assessing the accuracy of remotely sensed data: Principles and Practices, 3nd ed. CRC Press: 347 p. Falahatkar S, Hoseini SM, Salman Mahini A, Ayoubi SH. 2016. Landuse change prediction using LCM. Environment Reaserch. 7(13): 163–174. (In Persian). Gounaridis D, Chorianopoulos I, Symeonakis E, Koukoulas S. 2019. A random forest-cellular automata modelling approach to explore future land use/cover change in Attica (Greece), under different socio-economic realities and scales. Science of the Total Environment. 646: 320–335. https://10.1016/j.scitotenv.2018.07.302. Guan D, Zhao Z, Tan J. 2019. Dynamic simulation of land use change based on logistic-CA-Markov and WLC-CA-Markov models: A case study in three gorges reservoir area of Chongqing, China. Environmental Science and Pollution Research. 26(20): 20669–20688. https://doi.org/10.1007/s11356-019-05127-9. Gupta R, Sharma LK. 2020. Efficacy of spatial land change modeler as a forecasting indicator for anthropogenic change dynamics over five decades: A case study of Shoolpaneshwar Wildlife Sanctuary, Gujarat, India. Ecological Indicators. 112(106171):1–19 https://doi.org/10.1016/j.ecolind.2020.106171. Jagarnath M, Thambiran T, Gebreslasie M. 2019. Modelling urban land change processes and patterns for climate change planning in the Durban metropolitan area, South Africa. Journal of Land Use Science. 14(1):81–109. https://doi.org/10.1080/1747423X.2019.1610809. Jande JA, Nsofor GN, Abdulkadir A. 2020. Modelling of urban growth with land change modeler in Otukpo Metropolis of Benue State, Nigeria. International Journal of Engineering Applied Sciences and Technology. 4(9): 26-40. https://10.33564/IJEAST.2020.v04i09.004. Hasan S, Wenzhong Shi, Xiaolin Zhu, Sawaid Abbas, Ahmed Khan HU. 2020. Future simulation of land use changes in rapidly urbanizing south China based on land change modeler and remote sensing data. Sustainability. 12(11):1–24; https://doi.org/10.3390/su12114350. Kavyan A, Zargosh Z, Jaffaryan jolodar Z, Darabi H. 2017. Land use changes modelling using logistic regression and Markov Chain in the Haraz Watershed. Journal of Natural Environment. 70(2): 397–411. http://10.22059/jne.2017.116904.850. Khoi DD, Murayama Y. 2010. Forecasting areas vulnerable to forest conversion in the Tam Dao National Park Region, Vietnam. Remote Sensing. 2(5): 1249–1272. https://doi.org/10.3390/rs2051249. Li K, Feng M, Biswas A, Su H, Niu Y, Cao J. 2020. Driving factors and future prediction of land use and cover change based on satellite remote sensing data by the LCM Model: A case study from Gansu Province, China. Sensors. 20(10):1–20. https://doi.org/10.3390/s20102757. Lillesand TM, Kiefer RW, Chipman JW. 2015. Remote sensing and image interpretation. John Wiley & Sons, Inc. 770 p. Losiri C, Nagai M, Ninsawat S, Shrestha RP. 2016. Modeling urban expansion in Bangkok Metropolitan Region using demographic–economic data through cellular automata-markov chain and multi-layer perceptron-markov Chain Models, Sustainability. 8(7):1–23. https://doi.org/10.3390/su8070686. Megahed Y, Cabral P, Silva J, Caetano M. 2015. Land cover mapping analysis and urban growth modelling using remote sensing techniques in greater Cairo Region-Egypt. ISPRS International Journal of Geo-Information. 4(3):1750–1769. https://doi.org/10.3390/ijgi4031750. Meteorology Administration of Mazandaran Province, 2020, Meteorological data for the period 2006 to 2020. Mishra VN, Rai PK, Mohan K. 2014. Prediction of land use changes based on land change modeler (LCM) using remote sensing: A case study of Muzaffarpur (Bihar), India. Journal of the Geographical Institute Jovan Cvijic, SASA. 64 (1):111–127. https://doi.org/10.2298/IJGI1401111M. Nath B, Wang Z, Ge Y, Islam K, Singh RP, Niu Z. 2020. Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA Model and analytical hierarchy process. International Journal of Geo-Information. 9(134): 1–25. https://doi.org/10.3390/ijgi9020134. Saadani S, Laajaj R, Maanan M, Rhinane H, Aaroud A. 2020. Simulating spatial–temporal urban growth of a Moroccan metropolitan using CA–Markov model. Spatial Information Research volume. 28(5):609–621. https://doi.org/10.1007/s41324-020-00322-0. Shade C, Kremer P. 2019. Predicting land use changes in Philadelphia following green infrastructure Policies. Land. 8(28): 1–19. https://doi:10.3390/land8020028. Shooshtari, SJ, Shayesteh K, Gholamalifard M, Azari M, López-Moreno JI. 2018. Land cover change modelling in Hyrcanian forests, northern Iran: A landscape pattern and transformation analysis perspective. Cuadernos de Investigación Geográfica. 44(2):743–761. https://doi.org/10.18172/cig.3279. Tanhapour M, Banihabib ME. 2018. Artificial neural network model of multilayer perceptron for prediction of daily discharge suspended sediment load and assessment of factors affecting sediment estimation. Watershed Engineering and Management. 10(2): 249–255. (In Persian). https://10.22092/ijwmse.2017.108115.1215. Watershed Management Department of Mazandaran Jihad Sazandegi Organization, 2002, Justification and Implementation studies report of Siahroud Watershed, 757 p. Yousefi M, Pajoohesh M, Honarbakhsh A. 2020. Modeling trends land use changes local by using LCM Model based on artificial neural networks and Markov Chain analysis (Case study: BeheshtAbad Watershed). Journal of Watershed Management Research. 11(21):129–142. (In Persian). http://jwmr.sanru.ac.ir/article-1-977-en.html. Zabihi M, Moradi HR, Gholamalifard M, Khaledi Darvishan A. 2019. Effects of land use/land cover change scenarios on landscape metrics on the Talar Watershed. Watershed Management Research. 32(1):84–99. (In Persian). Zare Garizi A, Sheikh V, Sadoddin A, Mahiny S. 2012. Simulating the spatiotemporal changes of forest extent for the Chehelchay watershed (Golestan province), using integrated CA-Markov model. Iranian Journal of Forest and Poplar Research. 20(2): 273–285. (In Persian). | ||
|
آمار تعداد مشاهده مقاله: 863 تعداد دریافت فایل اصل مقاله: 339 |
||