- همه نشریات
- نشریات علمی سازمان تحقیقات، آموزش و ترویچ کشاورزی
- مجلات ترویجی سازمان تحقیقات، آموزش و ترویج کشاورزی
- سایر نشریات سازمان تحقیقات، آموزش و ترویج کشاورزی
- نشریات علمی انجمن ها
- علوم آب و خاک
- علوم اقتصاد و ترویج کشاورزی
- علوم جنگل, مرتع و آبخیزداری
- علوم دامی و شیلات
- علوم زراعی و باغی
- علوم گیاهپزشکی
- علوم مهندسی و مکانیزاسیون کشاورزی
- نشریات با زبان انگلیسی
- نشریات با زبان فارسی
- نشریات دانشگاه آزاد اسلامی
- نشریات علمی
پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 283 |
| تعداد نشریات سازمان | 81 |
| تعداد شماره‌ها | 2,726 |
| تعداد مقالات | 31,042 |
| تعداد مشاهده مقاله | 28,113,129 |
| تعداد دریافت فایل اصل مقاله | 16,248,526 |
روند تغییرات کاربری اراضی در منطقه حفاظت شده مانشت و قلارنگ با استفاده از تصاویر ماهوارهای | ||
| تحقیقات حمایت و حفاظت جنگلها و مراتع ایران | ||
| دوره 18، شماره 1 - شماره پیاپی 35، شهریور 1399، صفحه 14-30 اصل مقاله (697.08 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfrpr.2020.341865.1416 | ||
| نویسندگان | ||
| جواد میرزایی* 1؛ ناهید جعفریان2؛ امید کرمی3 | ||
| 1هیات علمی دانشگاه ایلام | ||
| 2دانشگاه ایلام | ||
| 3دانشگاه ساری | ||
| چکیده | ||
| هدف از این پژوهش پیشبینی و بررسی روند تغییرات کاربری اراضی در منطقه حفاظت شده مانشت و قلارنگ در استان ایلام با استفاده از تصاویر ماهوارهای لندست (تصاویر TMسال‌های 1363، 1378 و OLA سال 1393) است. پس از تصحیح هندسی و اتمسفریک در این تصاویر ماهوارهای، چهار کاربری زراعت، باغ، مرتع و جنگل در منطقه مورد مطالعه شناسایی شد. برای طبقهبندی تصاویر ماهوارهای از مقایسه روش‌های حداکثر احتمال و نزدیکترین همسایه و برای شناسایی تغییرات کاربری از روش‌های مقایسه بعد از طبقهبندی، تفاوت شاخص پوشش گیاهی (NDVI) و تفاوت تجزیه مؤلفه‌های اصلی (PCA) استفاده شد. پیشبینی تغییرات کاربری در سال 1403 با استفاده از مدل تلفیقی سلولی خودکار و زنجیره مارکوف انجام شد. به طور کلی طبقهبندی تصاویر مورد استفاده با روش حداکثر احتمال صحت بیشتری را نسبت به روش نزدیکترین همسایه ارائه کرده است. براساس نتایج طبقهبندی طی دوره 1393-1363 کاهش در سطح اراضی مرتعی و جنگل و افرایش در اراضی باغی و زراعی مشاهده شد. در روش مقایسه پس از طبقهبندی بیشترین تغییرات در طی دوره 1378-1363 مربوط به تبدیل جنگل به زراعت، در دوره 1393-1378 مربوط به تبدیل جنگل به مرتع بود. پیشبینی تغییرات برای سال 1403 نشان داد که اراضی زراعی 10/1 درصد، باغی 52/0 درصد افزایش و سطح مراتع و جنگلها به ترتیب 76/0 درصد و 86/0 درصد کاهش می‌یابد. براساس نتایج استفاده از تصاویر لندست در تلفیق با مدل مارکوف در مدلسازی تغییرات کاربری اراضی از صحت و دقت بالایی برخوردار است و می‌تواند در تصمیم‌گیری و مدیریت منطقه مؤثر باشد. | ||
| کلیدواژه‌ها | ||
| سنجش از دور؛ نزدیک ترین همسایه‌ها؛ حداکثر احتمال؛ زنجیره مارکوف؛ ایلام | ||
| عنوان مقاله [English] | ||
| Land Use Changes Trends in Manasht - Ghalarang Protected Area Using satellite imagery | ||
| نویسندگان [English] | ||
| nahid jaafarian2؛ omid karami3؛ | ||
| 2ilam uni | ||
| 3Sari University | ||
| چکیده [English] | ||
| The aim of this study was to predict and investigate land use change trends in Manasht - ghalarang protected area in Ilam province using Landsat satellite imagery (L5 TM-1984, L5TM-1999, L8 OLA-2014). After geometric and atmospheric correction on satellite images, four land uses including agriculture, garden, pasture and forest were identified in the study area. For classification of satellite images used the comparison of maximum likelihood (MLC) and k nearest neighbor (KNN) methods, and for identifying user changes from post-classification comparison methods, the difference of vegetation index (NDVI) and principal component analysis (PCA) were used. Prediction of land use changes in 2024 was performed using the automated fusion cell and Markov chain model. The classification of images used with the MLC method is higher than KNN method. According to the classification results, during the period 1984–2014, decreases in rangelands and forests and increase in gardens and agricultural were observed. In the comparative method after classification most changes happened in period of 1984-1999 were related to forest conversion to agriculture, while in period of 1999-2014 were to forest to pasture. Forecasts of changes for 2024 showed that agricultural land-uses increased by 1.10%, garden by 0.52% and rangelands and forests decreased by 0.76% and 0.86 %, respectively. Based on the results, the use of Landsat images in combination with the Markov model in modeling land use change, had high accuracy and can be effective in decision-making and management of the region. | ||
| کلیدواژه‌ها [English] | ||
| Remote Sensing, Nearest Neighbors, Maximum likelihood, Markov Chain, Ilam | ||
| مراجع | ||
|
-Abdollah Pour Hassan Kiyade, A., Khan Mohammadi, M., Naderi, S. and Jahdi, R. 2016. Environmental Monitoring of Temporal Changes - Land Use Location in Boujak National Park Using Remote Sensing and GIS. M.Sc. thesis, University of Guilan, 88p. -Aci, M., Inan, C. and Avci, M. 2010. A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm. Expert Systems with Applications, 37(7): 5061-5067. -Akbari, A., Zanganeh Asadi, M.A. and Toghi Moghaddam, A. 2016. Monitoring Land Use Changes Using Different Methods of Statistical Education Theory in Neyshabur. Journal of Geographical Space Preparation, Golestan University Journal, 6(20): 35-50 (In Persian). -Adedeji, O.H., Tope-Ajayi, O.O. and Abegunde, O.L. 2015. Assessing and Predicting Changes in the Status of Gambari Forest Reserve, Nigeria Using Remote Sensing and GIS Techniques. Journal of Geographic Information System, 7:301-318. -Amini Parsa, V. and Nandnejadi, A. 2016. Application of CA-Markov Model in Predicting Dynamics of Protected Areas Land Structure (Case Study: Deismar Conservation Area). Natural Geography Research, 48(4): 661-674 (In Persian). -Alban, J.D.T., Connette, J. M., Oswald, R. and Webb, E.L. 2018. ICombined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection inDynamic Tropical Landscapes. Remote Sens, 10(306): 1-28, https://doi:10.3390/rs10020306D. -Alam, A., Bhat, M.S. and Maheen, M. 2019. Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley, GeoJournal, https://doi.org/10.1007/s10708-019-10037-X. -Arkhi, S. and neyazi, E. 2009. Evaluation of Different Remote Sensing Methods for Monitoring Land Use Change (A Case Study of Ilam darashar. Iranian Journal of Rangeland and Wildlife Research, 17(1): 74-93 (In Persian). -Arkhi, S. and Fathi Zadeh, H. 2013. Comparison of Different Methods of Land Use Change Detection in Dehloran Desert Area of Ilam Province. Journal of Desert Ecosystem Engineering, 2(1): 65-80 (In Persian). -Arkhi, S. and Isfahani, M. 2017. Video Training of Idrissi Selva Software. Golestan University Press, 336p. -Brown, D.G., Pijanowski, B. C. and Duh, J.D. 2000. Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA. Journal of Environmental Management, 59: 247-263. -Coppin, P. and Bauer, M. 1996. Digital Change Detection in Forest Ecosystems with Remote Sensing Imagery. Remote Sensing, 13:207-234. -Fathizad, H., zare, M., Karimi, H. and Khanamani, A. 2018. Spatio-temporal Modeling of Landscape Changes using Markov Chain Compilation Model and Automated Cells (Case Study: Arid and Semi-Arid Area Dehloran). Arid Biome Scientific and Research Journal, 8(1): 11-26 (In Persian). -Falahatkar, S., Hosseini, S.M., Salman Mahini, AS. and Ayubi, Sh. 2016. Forecasting Land Use Changes Using the LCM Model. Journal of Environmental Research, 7(13): 163-174 (In Persian). -Farajollahi, A., Askari, H.R., Awnak, M., Mahboobi, M.R. and Salman Mahini, AS.R. 2015. Monitoring and Forecasting of Spatial and Temporal Changes in Land Use / Land Coverage (Case Study: Maraveh Tapeh Region, Golestan). Journal of Remote Sensing and Geographic Information Systems in Natural Resources, 6(4): 1-14 (In Persian). -Feizizadeh, B. 2018. Modeling Land Use Changes and Its Impacts on Erosion System in Alavian Dam Area Using Remote Sensing and GIS Techniques. Journal of Hydrogeomorphology, 11: 21-38. -Griffiths, P., Kuemmerle, T., Baumann, M., Radeloff, V.C., Abrudan, I.V., Lieskovsky, J., Munteanu, C., Ostapowicz, K. and Hostert, P. 2014. Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites. Remote Sensing of Environment, 151: 72-88. -Guan, D., Li, H.; Inohae, T.; Su, W.; Nagaie, T. and Hokao, K. 2011. Modeling urban land use change by the integration of cellular automaton and Markovmodel.Ecological Modelling, 222(20–22): 3761-3772. -Hao, C., Zhang, J., Li, H., Yao, F., Huang, H. and Meng, W. 2015. ”Integration of Multinomial-Logistic and Markov-Chain Models to Derive Land-Use Change Dynamics. Journal of Urban Planning and Development, 141(3), 05014017. -Hathout, S. 2002. The use of GIS for monitoring and predicting urban growth in East and West St Paul, Winnipeg, Manitoba, Canada. Journal Environmental Management, 66: 229-238. -Heidarizadi, H. and Mohamadi, A. 2016. Predicting the Land Use Change Using Markov- Cellular automata Model in Mehran Plain. Desert ecosystem engineering journal, 5(10): 57-68 (In Persian). -Jafari, M. 2005. Environmental Image of Manasht and Gharangang Protected Area, Ilam Publications Office of Environmental Protection. -Jafari, M. 2007. Environmental Image of Manasht and Gharang Protected Area. Ilam Publications Office of Environmental Protection Publications, Ilam. -Lu, D., Mausel, P., Brondi´zio, E. and Moran, E. 2004. Change detection techniques. International Journal of Remote Sensing, 25(12): 2365–2407. -Lubowski, R.N., Vesterby, M., Bucholtz, S., Baez, A. and Roberts, M.J. 2006. Major Uses of Land in the United States, 2002. Economic Information Bulletin. -Macleod, R.D. and Congalton, R. 1998. A Quantitative comparison of change-detection algorithms for monitoring Eelgrass from Remotely Sensed data, Photogrammetric Engineering and Remote Sensing, 64(3): 207-216. -Mazaheri, M.R., Esfandiari, M., Masih Abadi, M.H. and Kamali, A. 2013. Detecting temporal land use changes using remote sensing and GIS techniqes (Case study: Jiroft, Kerman Province). Journal of Applied RS and GIS Techniques in Natural Resource Science, 4(2) (In Persian). -Mitsova, D., Shuster, W. and Wang, X. 2011. A cellular automata model of land cover change to integrate urban growth with open space conservation, Landscape and Urban Planning, 99(2): 141-153. -Mubea, T., Ngigi, G., Mundia Jomo, C.N. and W Kenyatta, K. 2010. Assessing Application of Markov chain Analysis in Municipality. University of agriculture and technology jagst, 12(2): markov analysis126144. -Mohammadzadeh Asl, R., Rasouli, A.S. and korshed dost, A. M. 2010. Survey of Land Use Change in Ghar Ghashlagh Wetland Using Satellite Image Processing Technique. M.Sc., Remote Sensing and GIS, Faculty of Humanities and Social Sciences, University of Tabriz, 100p. -Mohammadi, M.E., Amiri, M. and dastoran, C. 2015. Modeling Land Use Changes in Ramian County in Golestan Province. Journal of Space Planning and Preparation, 19(4): 141-158 (In Persian). -Mombini, M. and Asgari, h.R. 2019. Monitoring, Investigation and Forecasting of Spatial Changes in Land/Land Use Using Markov Chain Model (Case Study: Shooshtar-Khuzestan). Journal of Geographical Information, 27(105): 35-47 (In Persian). -Mosayebi, M. and Maleki. M. 2014. Change detection in land use using remote sensing data and GIS (Case study: Ardabil county). RS and GIS for Natural Resources, 5(1) (In Persian). -Mousavi, S.A. 2011. Evaluation of vegetation change trends using remote sensing data in the Hamoun Wetland. M.Sc., Desertification, Faculty of Natural Resources, University of Zabol, 152p. -Naghibi, S.J., H.Habibian, S. and Habibian, S.M.R., 2009. Determination of Optimum Vegetation Indices for Vegetation Cover Percentage Modelling, Using Satellite Spectral Reflectance. Journal of Plant Ecophysiology, Azad University of Arsanjan Branch, 1(3): 62-74 (In Persian). -Nazari Samani, A.S.A., Heravi, H., Panahi M. and Jafari Shalamzar, M. 2013. Impact of Land Use Change and Precipitation Changes on Sediment Production in Taleghan Watershed. Rangeland and Watershed Management, Iranian Journal of Natural Resources, 66(1): 157-165 (In Persian). -Noori, S. 2004. Determination of the best vegetation indexes for assessing Mazandaran rangeland, Master thesis, Tarbiat Modarres University, 128p. -Noori, Q. 2005. Determination of best vegetation indices for rangeland assessment of Mazandaran, M.Sc., Tarbiat Modares University, 128p. -Pandya, M., Navalgund, R. and Dadhwal, V.K. 2000. Effect of WiFS viewing geometry on crop reflectance: asimulation study using SAIL model. International Journal of Remote Sensing, 21(9): 1931-1938. -Rahi, Gh., Kavian, A., Soleimani, K., Nazari Samani, A.A. and Pourghasemi, H.R. 2017. Land use Changes Modelling using Satellite Data and Markov Chain (case study: Samal Watershed in Bushehr province). Jornal of Desert Ecosystem Engineer Research, 6(17): 15-26. -Singh, A. 1989. Digital change detection techniques using remotely sensed data. International Journal of Remote Sensing, 10: 989–1003. -Taheri, F., Rahnama, M.R., Kharazmi, A.A. and Chickpeas, B. 2019. Survey and Forecasting of Land Use Changes Using Multi-Time Satellite Data in Shandiz City (1984-2009). Journal of Geography and Development, 127: 50-142 (In Persian). -Virk, R. and King, D. 2006. Comparison of techniques for forest change mapping using Landsat data in Karnataka, India. Geocarto International, 21(4): 9p. -Wang, D., Gong, J., Chen, L., Zhang, L., Song, Y. and Yue, Y. 2013. Comparative analysis of land use/cover change trajectories and their driving forces in two small watersheds in the western Loess Plateau of China. International journal of applied earth observation and geoinformation, 21: 241-252. -Yaghobi, S., Faramarzi, M., Karimi, H. and Sarvarian, J. 2019.Simulation of land-use changes in relation to changes of groundwater level in arid rangeland in western Iran. International Journal of Environmental Science and Technology, 16(3): 1637–1648. -Zhang, L., Wu, j., Zhen, Y. and Shu, J. 2004. A GIS-bassed gradient analysis ofurban landscape pattern of Shanghai metropolitan area, China. Journal of landscape and Urban planning, 69: 1-16. -Zhou, D.; Lin, Z. and Liu, L. 2012. Regional land salinization assessment and simulation through cellular automaton-Markov modeling and spatial pattern analysis. Science of the Total Environment, 439: 260-274. | ||
|
آمار تعداد مشاهده مقاله: 435 تعداد دریافت فایل اصل مقاله: 256 |
||
سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب