امکان‌سنجی خودکارسازی تعیین تغییرات مناطق جنگلی با استفاده از تصاویر ماهواره‌ای (مطالعه موردی: منطقه حفاظت شده البرز مرکزی)

نوع مقاله : مقاله پژوهشی

نویسندگان
امیر ستاری راد 1
بهزاد رایگانی 2
علی جهانی 3
حمید گشتاسب میگونی 4
1 دانشگاه محیط زیست، کرج ، ایران
2 گروه ارزیابی و مخاطرات محیط زیست، پژوهشکده محیط زیست و توسعه پایدار، تهران، ایران
3 دانشیار، گروه ارزیابی و مخاطرات محیط زیست، پژوهشکده محیط زیست و توسعه پایدار، تهران، ایران
4 گروه محیط زیست طبیعی و تنوع زیستی، دانشکده محیط زیست، کرج، ایران.
10.22034/el.2024.450145.1022
چکیده
سابقه و هدف

جنگل‌زدایی یکی از بزرگ‌ترین چالش‌های جهانی محیط‌زیست است، زیرا جنگل‌ها یکی از مهم‌ترین اجزای بوم‌سازگان‌های جهان هستند و نقش کلیدی در تعدیل اقلیم و تنظیم چرخه‌های مختلف بیوشیمیایی دارند. جنگل‌های شمال ایران یکی از باارزش‌ترین اکوسیستم‌های جنگلی ایران به شمار می‌روند و ازنظر تنوع گونه‌های گیاهی جزو جنگل‌های غنی محسوب می‌شوند. بنابراین، تشخیص تغییرات سطوح در جنگل‌ها با کمک داده‌های چندزمانی به ما این امکان را می‌دهد که با شناسایی خودکار این تغییرات از تخریب بیشتر جلوگیری نماییم. هدف اصلی این تحقیق شناسایی آستانه‌ها و اعمال آن‌ها بر روی تصاویر شاخص تفاوت گیاهی نرمال شده (NDVI) در سنجنده مودیس و پایش خودکار مناطق جنگلی است.

مواد و روش‌‌ها

این تحقیق در منطقه حفاظت‌شده البرز مرکزی با وسعت بیش از ۳۹۸ هزار هکتار و پوشش گیاهی بسیار غنی با بیش از ۱۱۰۰ گونه گیاهی انجام شد. در این تحقیق محصول MOD13Q1 سنجنده مودیس و شاخص NDVI با وضوح مکانی ۲۵۰ متر و وضوح زمانی ۱۶ روز و همچنین تصاویر محصول MCD12C1 سنجنده مودیس با تفکیک مکانی 5600 متر و تفکیک زمانی یک‌ساله در سه لایه با طرح‌های طبقه‌بندی برنامه بین‌المللی ژئوسفر – بیوسفر (IGBP)، دانشگاه مریلند (UMD) و طرح مشتق شده مودیس (LAI) استفاده شد. تصاویر NDVI ۱۶ روزه با الگوریتم حداکثر مقدار به تصاویر ماهانه تبدیل شدند و الگوریتم PCA با ۲۵ مؤلفه برای حذف خطا‌ها بر روی آن‌ها اعمال شد. با استفاده از سامانه گوگل ارث، ۵ چندضلعی تصادفی در مناطق جنگلی بکر انتخاب شد تا مقدار پیکسل‌ها تخمین‌زده شده و درنهایت آستانه تعیین شود. همگنی پوشش گیاهی با استفاده از MCD12C1 موردبررسی قرار گرفت. آستانه جنگل با محاسبه میانگین و انحراف معیار مناطق نمونه، طی یک دوره ۱۶ ساله تعیین شد. درنهایت، تغییرات با اعمال آستانه بر روی تصاویر سال‌های ۲۰۰۱ و ۲۰۱۶ و اعمال عملیات بولین با عملگرهای and و or روی آن‌ها شناسایی شد. سپس با استفاده از سامانه گوگل ارث اعتبار سنجی این نقاط انجام شد.

نتایج و یافته‌ها

با اعمال PCA روی تصاویر به همراه حفظ اطلاعات اصلی، میزان خطا تا حد مطلوب کاهش یافت. نتایج بررسی همگنی پوشش گیاهی در ۵ منطقه نمونه نشان داد که در سه لایه طبقه‌بندی، همگن بوده و از طبقه جنگل‌های پهن‌برگ خزان‌کننده هستند. نتایج محاسبات انحراف معیار و میانگین ماهانه ارزش جنگل حاکی از آن است که ماه‌های جون، جولای و اوت (۱۱ خرداد الی ۹ شهریور) برای بررسی تغییرات پوشش جنگلی منطقه مورد مطالعه مناسب هستند. آستانه برای این ماه‌ها به ترتیب برابر با 55/8596، ۸۰۰۰ و 51/8497 به دست آمد. با اعمال آستانه‌ها بر روی تصاویر در این سه ماه در سال‌های ۲۰۰۱ و ۲۰۱۶ و کسر این تصاویر از یکدیگر، پیکسل‌هایی که ارزش جنگلی خود را ازدست‌داده بودند، مشخص شدند. درنهایت، پس از انجام عملیات‌ بولین ، ۲۰۰ پیکسل با مساحت تقریبی ۲۳/۱۷۲۸ هکتار به‌عنوان نقاط تغییریافته با کاهش پوشش جنگلی شناسایی شد. پس از بررسی، دقت کلی برای نقاط تعیین‌شده برابر با 5/88 درصد بود. بنابراین با اعمال آستانه‌های تعیین‌شده، امکان تشخیص خودکار تغییرات در عرصه‌های پوشش جنگلی به‌صورت به‌روز و مستمر فراهم است.

نتیجه‌گیری

به‌طورکلی منطقه حفاظت‌شده البرز مرکزی یکی از مراکز مهم جنگلی کشور است و بر اساس نتایج به‌دست‌آمده طی 16 سال به دلایل مختلف، مقدار قابل‌توجهی از جنگل‌ها دچار تخریب شده یا ازدست‌رفته است. همچنین نتایج این تحقیق حاکی از اهمیت استفاده از روش‌های خودکار در شناسایی تغییرات پوشش جنگلی با اعمال آستانه‌های معین بر روی تصاویر شاخص NDVI سنجنده مودیس در سنجش‌ازدور است.

کلیدواژه‌ها

موضوعات

عنوان مقاله English

Feasibility of automating the determination of changes in forest areas using satellite images (Case Study: Central Alborz protected area)

نویسندگان English

Amir Satari Rad 1
Behzad Rayegani 2
Ali Jahani 3
Hamid Goshtasb Meigooni 4
1 College of Environment, Karaj, Iran.
2 Department of Environmental Hazards and Assessment, Institute of Environment and Sustainable Development, Tehran, Iran
3 Associate Professor, Faculty of Assessment and Environment Risks Department, Research Center of Environment and Sustainable Development, Tehran, Iran.
4 Department of Natural Environment and Biodiversity, Faculty of Environment, Karaj, Iran
چکیده English

Objective and Background

Deforestation is one of the biggest global environmental challenges because forests are one of the most important components of the world's ecosystem and play a key role in adjusting the climate and regulating various biochemical cycles. The forests of northern Iran are considered one of the most valuable forest ecosystems in Iran, and they are among the rich forests in terms of the diversity of plant species. Therefore, detecting changes with the help of multi-temporal data in forest levels allows us to prevent further destruction by automatically identifying these changes. The main goal of this research is to identify the thresholds and apply them to the NDVI vegetation index images in MODIS sensors and automatic monitoring of forest areas.

Materials and Methods

This research was conducted in the Central Alborz protected area with an area of more than 398 thousand hectares and very rich vegetation with more than 1100 plant species. In this research, the MOD13Q1 product of the MODIS sensor and the normalized vegetation difference index (NDVI) with a spatial resolution of 250 meters and a temporal resolution of 16 days, as well as The MCD12C1 product images of the MODIS sensor with a spatial resolution of 5600 m and a temporal resolution of one year were used in three layers with the classification schemes of the International Geosphere-Biosphere Program (IGBP), University of Maryland (UMD) and the Modis Derived Scheme (LAI). The 16-day NDVI images were converted to monthly images with the maximum value algorithm and the PCA algorithm with 25 components was applied to them to eliminate errors and noise. Using the Google Earth system, 5 random polygons were selected on virgin forest areas, in order to estimate the value of the pixels and finally determine the thresholds. Vegetation homogeneity was checked using MCD12C1 images. Forest thresholds were determined by calculating the mean and standard deviation of the sample areas during 16 years. Finally, the changes were detected by applying a threshold on the images of 2001 and 2016 and applying Boolean operations with the bolean operators on them. Then, using the Google Earth system, these points were validated.

Results

By applying PCA to the images, the amount of noise was reduced to the optimal level while maintaining the original information of the images. The results of investigating the homogeneity of the vegetation in 5 sample areas showed that they are homogeneous in three levels and are deciduous broadleaf forests The results obtained from calculating the standard deviation and monthly average of forest values indicate that the months of June, July, and August are most suitable for assessing changes in the forest cover within the study area. The threshold for these months was obtained as 8596.55, 8000 and 8497.51 respectively. By applying these thresholds to the images captured in these three months in 2001 and 2016, and then subtracting the images from each other, the pixels that experienced a decrease in forest value were identified. Finally, after performing the Boolean operation, 200 pixels with an approximate area of 1728.23 hectares were identified as changed points with a decrease in forest cover. Upon cheching, the overall accuracy for the determined points was found to be 88.5%. Hence, the application of these thresholds enables the automatic detection of changes in forest cover areas in an up-to-date and continuous manner.

Conclusions

In general, the Central Alborz protected area is one of the important forest centers of the country, and according to the results, we have lost a significant number of forests during the 16 years due to various reasons. Also, the results of this research indicate the importance of using automation methods in identifying forest cover changes by applying certain thresholds on the NDVI index images of the Modis sensor in remote sensing.

کلیدواژه‌ها English

Remote Sensing
Deforestation
MODIS Sensor
NDVI Index
Google Earth System
  1. Abbasnezhad Alchin, A., & Darvishsefat, A. A. 2023. Investigating the trend of vegetation changes (greening and browning) using MODIS-NDVI time series in Mazandaran province. Journal of Wood and Forest Science and Technology, 30(1) (in Persian).
  2. Ahl, D.E., Gower, S.T., Burrows, S.N., Shabanov, N.V., Myneni, R.B., Knyazikhin, Y. 2006. Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS. Remote Sens. Environ, 104: 88–95.
  3. Alonso, Laura., Rodríguez, Andrés., Picos, Juan ., Armesto, Julia., 2022. Challenges in automatic forest change reporting through land cover mapping. Forestry: An International Journal of Forest Research, 96(2): 155–169.
  4. Amarnath, G., Babar, S., & Murthy, M. S. R. 2017. Evaluating MODIS-vegetation continuous field products to assess tree cover change and forest fragmentation in India – A multi-scale satellite remote sensing approach. Egyptian Journal of Remote Sensing and Space Science 20 (2), 157–168.
  5. Amiri, Mojtaba., D., Dargahi., Habashi, Hashem., D., Azadfar.,  Nikanm, Solaymani. 2009. Comparision of regeneration density and species diversity in managed and natural stands of Loveh Oak Forest. Journal of Agricultural Sciences and Natural Resources, 15: 37-48 (in Persian).
  6. Bao, Nisha ; Wu, L. & Liu, Shan-jun & Li, Na. 2016. Scale parameter optimization through high-resolution imagery to support mine rehabilitated vegetation classification. Ecological Engineering, 97: 130-137.
  7. Barka, I., Bucha, T., Molnár, T., Móricz, N., Somogyi, Z., & Koreň, M., 2019. Suitability of MODIS-based NDVI index for forest monitoring and its seasonal applications in Central Europe. Central European Forestry Journal,65(3-4), 206–217.
  8. Cano, Emmanuelle., Denux, Jean-Philippe., Bisquert, Mar., Hubert-Moy, Laurence., Chéret, Véronique. 2017. Improved forest-cover mapping based on MODIS time series and landscape stratification. International Journal of Remote Sensing, 38(7): 1865-1888.
  9. Chakraborty, Abhishek., Seshasai,  M.V.R., Reddy, C. Sudhakar.,   Dadhwal, V.K.  2018. Persistent negative changes in seasonal greenness over different forest types of India using MODIS time series NDVI data (2001–2014). Ecological Indicators, 85: 887-903.
  10. Choudhary, K., Shi, W., Boori, M.S., Corgne, S.  2019. Agriculture Phenology Monitoring Using NDVI Time Series Based on Remote Sensing Satellites: A Case Study of Guangdong, China. Optical Memory and Neural Networks, 28: 204–214.
  11. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E.  2004. Digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25, 1565−1596.
  12. Dallimer, M., Tang, Z., Bibby, P.R., Brindley, P., Gaston, K.J., Davies, Z.G.  2011. Temporal changes in greenspace in a highly urbanized region. Biology Letters, 7: 763–766.
  13. Darvishsefat, A. A.  2007. Atlas of protected area of Iran. Department of the Environment Press, Tehran (in Persian).
  14. Ellis, E. C., Gauthier, N., Klein Goldewijk, K., Bliege Bird, R., Boivin, N., Díaz, S., Fuller, D. Q., Gill, J. L., Kaplan, J. O., Kingston, N., Locke, H., McMichael, C. N. H., Ranco, D., Rick, T. C., Shaw, M. R., Stephens, L., Svenning, J.-C., & Watson, J. E. M. 2021. People have shaped most of terrestrial nature for at least 12,000 years. Proceedings of the National Academy of Sciences, 118 (17).
  15. Ferchichi, Aya ., Abbes, Ali ., Barra, Vincent ., Farah, Imed.  2022. Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review. Ecological Informatics. 68.
  16. Garcia, C.L., Teich, I., Gonzalez-Roglich, M., Kindgard, A.F., Ravelo, A.C., Liniger, H.  2019. Land degradation assessment in the Angentinean Puna: Comparing expert knowledge with satellite-derived information. Environmental Science & Policy, 91: 70–8.
  17. Gholamalifard, M., Joorabian, S.S ., Hosseini, H.K., Mirzaei ,M.  2013. Land Cover Change Modeling of Coastal Areas of Mazandaran Province Using LCM in a GIS Environment. Journal of Environmental Studies, 38(4): 109-124 (in Persian).
  18. Ghorbani, M., Azarnivand, H., Mehrabi, A., Bastani, S., Jafari, M., & Nayebi, H.  2012. Social network analysis: A new approach in policy-making and planning of natural resources co-management. Journal of Range and Watershed Managment, 65(4):553-568 (in Persian).
  19. Guan, Yanjun., Wang, Juan., Wei, Zhou., Bai, Zhongke., Cao, Yingui.  2022. Identification of land reclamation stages based on succession characteristics of rehabilitated vegetation in the Pingshuo opencast coal mine. Journal of Environmental Management, 305.
  20. Hasani, A, andBastenegar, M. 2015.  Economic evaluation of natural tourism sites, a case study: Central Alborz protected area in Tehran province. The first international strategic scientific conference on the development of tourism in the Islamic Republic of Iran, challenges and prospects (2015). Mashhad.
  21. Hostert, Patrick., Griffiths, Patrick., van der Linden, Sebastian., Pflugmacher, Dirk.,  2015. Time Series Analyses in a New Era of Optical Satellite Data. Remote Sensing Time Series, 25-41.
  22. Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. 2020. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32 (1) 1–6. 
  23. Javamiripour, M., Karami, J ., Henareh Khalyani. J ., Karimi, L., Niazifar, H, and Babakhani, N. 2020 .Determining and identifying ecosystem uses and forecasting their changes in the forests of northern Iran (Case study: Dohezar and Sehezar Basin, Central Alborz Forest). Scientific and Research Electronic Journals.11(2),34.1-15(in Persian).
  24. John, Ranjeet., Chen, Jiquan., Lu, Nan., Guo, Ke., Liang, Cunzhu., Wei, Yafen., Noormets, Asko., Ma, Keping., Han, Xing-Guo.  2008. Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia. Remote Sensing of Environment, 112:2018-2032
  25. Jong, Rogier., Verbesselt, Jan., Zeileis, Achim., Schaepman, Michael.,  2013. Shifts in Global Vegetation Activity Trends. Remote Sensing, 5: 1117-1133.
  26. Kalpoma, K.; Rahman, A.  2021. Web-based monitoring of boro rice production using improvised NDVI threshold of modis MOD13Q1 and MYD13Q1 images. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, Brussels, Belgium, 6877-6880.
  27. Kennedy, Robert., Cohen, Warren., Schroeder, Todd.  2007. Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment, 110: 370-386.
  28. Kiapasha, K., Darvishsefat, A., Zargham, N., Attarod, P., Nadi, M., & Schaepman, M. 2017. Greening trend in the Hyrcanian forests using NOAA NADVI time series during 1981-2012. Forest and Wood Products, 70(3) (in Persian)..
  29. Klisch, A., Atzberger, C. 2016) Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series. Remote Sensing, 8.
  30. Li, Xuejian., Du, Huaqiang., Zhou, Guomo., Mao, Fangjie., Zhang, Meng., Han, Ning., Fan, Weiliang., Liu, Hua., Huang, Zihao., He, Shaobai., Mei, Ting-Ting. 2021. Phenology estimation of subtropical bamboo forests based on assimilated MODIS LAI time series data. ISPRS Journal of Photogrammetry and Remote Sensing, 173: 262-277.
  31. Maroufzade, E., & Attarod, P. 2021. Are variations of forest vegetation consistent with trends of meteorological parameters in the northern Zagros region of Iran?. Iranian Journal of Forest, 12(4) (in Persian).
  32. Moon, M., Seyednasrollah, B., Richardson, A.D., Friedl, M.A.  2021. Using time series of MODIS land surface phenology to model temperature and photoperiod controls on spring greenup in North American deciduous forests. Remote Sensing of Environment, 260.
  33. Nejad, Meysam., Rayegani, Behzad., Jahani, Ali., Nezami, Bagher.  2018. Proposing an Early-Warning System for Optimal Management of Protected Areas (Case Study: Darmiyan Protected Area, Eastern Iran). Journal for Nature Conservation. 46: 79-88 (in Persian).
  34. Novo-Fernández, A., Franks, S., Wehenkel, C., López-Serrano, P. M., Molinier, M., & López-Sánchez, C. A. 2018. Landsat time series analysis for temperate forest cover change detection in the Sierra Madre Occidental, Durango, Mexico. International Journal of Applied Earth Observation and Geoinformation, 73, 230–244.
  35. Olokeoguna, O.S., Iyiolab,O.F ., Iyiola.K.  2014. Application of remote sensing and GIS in land use/land cover mapping and change detection in Shasha forest reserve, Nigeria. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL (8): 613-616.
  36. Potapov, P., Hansen, M. C., Laestadius, L., Turubanova, S., Yaroshenko, A., Thies, C., Smith, W., Zhuravleva, I., Komarova, A., Minnemeyer, S., & Esipova, E. 2017. The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Science Advances, 3 (1). 
  37. Pradhan, P., Costa, L., Rybski, D., Lucht, W., Kropp, J.P.  2017. A systematic study of sustainable development goal (SDG) interactions. Earth’s Future, 5: 1169–1179.
  38. Rayegani B., Sharifi A., Barati S., Nasri M., Saati M. 2011. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. Egyptian Journal of Remote Sensing and Space Science, 14(1): 49-56 (in Persian).
  39. Rayegani, B., Barati Gi, Susan., Meigooni, H., Sarkheil, H., Ramezani, J. 2019. An effective approach to selecting the appropriate pan-sharpening method in digital change detection of natural ecosystems. Ecological Informatics, 53 (in Persian).
  40. Rayegani, B., Goshtasb, H. 2019. vegetation change detection using multi-temporal remotly sensed data during recent three decades by artificial intelligence technique (Case study: protected area of Bashgol). Journal of Plant Ecosystem Conservation , 7 (14): 253-274 (in Persian).
  41. Rayegani, B., Jahani, A., Satari Rad, A., Shoghi, N. 2018. Predicting of Land Use Changes for 2030 Using Remote Sensing and Landsat Multi-Temporal Images (Case study: Mashhad. Town and Country Planning, 10(2): 249-269 (in Persian).
  42. Rebelo, Lisa-Maria ., Finlayson, Max., Strauch, Adrian., Rosenqvist, Ake., Perennou, Christian., Tøttrup, Christian., Hilarides, Lammert., Paganini, Marc., Ballhorn, Uwe., Navratil, Peter., Davidson, Nick., Franke, Jonas. 2018. The use of Earth Observation for wetland inventory, assessment and monitoring: An information source for the Ramsar Convention on Wetlands. Ramsar Technical Report No:10. Gland, Switzerland: Ramsar Convention Secretariat.
  43. Rhif, Manel & Abbes, Ali & Martinez, Beatriz & Jong, Rogier & Sang, Yanfang & Farah, Imed.  2022. Detection of trend and seasonal changes in non-stationary remote sensing data: Case study of Tunisia vegetation dynamics. Ecological Informatics, 69.
  44. shiravand, H., Khaledi, S., Behzadi, S., & sanjabi, H. A. 2020. Monitoring and assessing the changes in the coverage and decline of oak forests in lorestan province using satellite images and bfast model. Journal of Applied researches in Geographical Sciences, 20 (57),265–280 (in Persian).
  45. Szostak, Marta. 2020. Automated Land Cover Change Detection and Forest Succession Monitoring Using LiDAR Point Clouds and GIS Analyses. Geosciences, 10(8): 321.
  46. Tariq, A., Jiango, Y., Li, Q., Gao, J., Lu, L., Soufan, W., Almutairi, K. F., & Habib-ur-Rahman, M. 2023. Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data. Heliyon, 9 (2)
  47. Tian, F., Wang, Y., Fensholt, R., Wang, K., Zhang, L., Huang, Y. 2013. Mapping and Evaluation of NDVI Trends from Synthetic Time Series Obtained by Blending Landsat and MODIS Data around a Coalfield on the Loess Plateau. Remote Sensing , 5(9): 4255-4279.
  48. Vafaei, S., Maleknia, R., Naghavi, H., & Fathizadeh, O. 2022. Estimation of Forest Canopy Using Remote Sensing and Geostatistics (Case Study: Marivan Baghan Forests). Journal of Environmental Science and Technology, 24(1): 71-82 (in Persian).
  49. Venter, O., Sanderson, E. W., Magrach, A., Allan, J. R., Beher, J., Jones, K. R., Possingham, H. P., Laurance, W. F., Wood, P., Fekete, B. M., Levy, M. A., & Watson, J. E. M. 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications. Vol. 7, Issue 1.
  50. Wang, H., Q. Li., Z. Gao., B. Sun., X. Du. 2016. Assessment of land degradation using time series trends analysis of vegetation indicators in Beijing-Tianjin dust and sandstorm source region. 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 753-756
  51. Winkler, K., Fuchs, R., Rounsevell, M., & Herold, M. 2021. Global land use changes are four times greater than previously estimated. Nature Communications, 12 (1).
  52. Woodcock, Curtis., Loveland, Thomas., Herold, Martin., Bauer, Marvin. 2019. Transitioning from change detection to monitoring with remote sensing: A paradigm shift. Remote Sensing of Environment, 238.
  53. Zewdie, W., Csaplovics, E., & Inostroza, L. 2017. Monitoring ecosystem dynamics in northwestern Ethiopia using NDVI and climate variables to assess long term trends in dryland vegetation variability. Applied geography, 79, 167-178.
اکولوژی انسانی
دوره 2، شماره 4
پاییز 1402
صفحه 278-292