مطالعات جغرافیایی مناطق خشک

مطالعات جغرافیایی مناطق خشک

شناسایی مناطق آسیب‌­پذیر در برابر مخاطره بیابان‌­زایی (مطالعه موردی: استان قم)

نوع مقاله : مقاله پژوهشی

نویسندگان
1 پژوهشکده حفاظت آب و خاک و آبخیزداری، تهران، ایران
2 گروه ژئومورفولوژی، دانشگاه تهران، تهران، ایران
3 گروه ژئومورفولوژی، دانشگاه تبریز، تبریز، ایران
چکیده
هدف: موقعیت جغرافیایی ایران سبب شده است تا بخش زیادی از مناطق آن را مناطق خشک و نیمه خشک دربرگیرد. با توجه به حساسیت بالای این مناطق در برابر تخریب زیست­محیطی، گسترش بیابان­زایی به عنوان یکی از مهم­ترین مخاطرات زیست­محیطی این مناطق محسوب می‌شود. با توجه به اهمیت موضوع، در این پژوهش به شناسایی مناطق در معرض مخاطره بیابان‌­زایی در استان قم پرداخته شده است.
روش و داده: در این تحقیق از تصاویر گوگل ارث، تصاویر ماهواره­‌های MODIS، CHIRPS و مدل رقومی ارتفاعی 30 متر، به عنوان مهم‌­ترین داده­‌های تحقیق استفاده شده است. مهم‌­ترین ابزارهای مورد استفاده در تحقیق شامل سامانه گوگل ارث انجین، ArcGIS و Super Decisions بوده است. همچنین در این تحقیق از شاخص­‌های NDVI و AOD و مدل‌­های ترکیب خطی وزنی (WLC) و تحلیل شبکه‌ای (ANP) استفاده شده است. روش کلی کار در این پژوهش به این صورت بوده است که بر مبنای 9 پارامتر ­(تراکم پوشش­ گیاهی، غلظت گرد و غبار، میانگین بارش، میانگین دما، ضریب خشکی، ارتفاع، شیب، فاصله از جاده اصلی و فاصله از نقاط شهری) و همچنین مدل تلفیقی WLC-ANP، مناطق آسیب­‌پذیر در برابر مخاطره بیابان­‌زایی شناسایی شده است.
یافته‌ها: بر اساس نتایج حاصله، بخش زیادی از مناطق مرکزی استان قم به دلیل تراکم کم پوشش­ گیاهی، غلظت زیاد گرد و غبار، ضریب خشکی بالا، میانگین بارش کم و دمای زیاد، ارتفاع کم و همچنین نزدیکی به نقاط شهری و جاده­‌های اصلی، دارای پتانسیل آسیب­‌پذیری خیلی زیادی در برابر مخاطره بیابان‌­زایی است.
نتیجه‌گیری: مناطق مرکزی استان قم دارای حساسیت بالایی در برابر مخاطره بیابان­‌زایی است. بنابراین باید در برنامه‌­ریزی‌­های محیطی و خصوصاً برنامه­‌های مربوط به تغییرات کاربری اراضی به این مسئله توجه ویژه‌­ای شود.
نوآوری، کاربرد نتایج: نوآوری این تحقیق استفاده ترکیبی از سامانه گوگل ارث انجین و پارامترهای ژئومورفولوژی است و همچنین فقط مناطقی به عنوان محدوده مطالعاتی انتخاب شده است که دارای ضریب NDVI بیش از ۰/۱ بوده‌­اند. از نتایج این تحقیق می­‌توان در برنامه‌ریزی‌­های مختلف محیطی استفاده کرد.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Identification of Areas Vulnerable to Desertification Hazards (Case Study: Qom Province)

نویسندگان English

Maesomeh Asadi 1
Mina Shahjamali 2
Hamid Ganjaeian 2
Atrin Ebrahimi 3
1 Soil and Water Conservation and Watershed Management Research Institute, Tehran, Iran
2 Faculty of Geomorphology, University of Tehran, Tehran, Iran
3 Faculty of Geomorphology, University of Tabriz, Tabriz, Iran
چکیده English

Aim: Iran's geographical location has caused a large part of its regions to include arid and semi-arid regions. Given the high sensitivity of these areas to environmental degradation, the spread of desertification is considered one of the most important environmental hazards in these areas. Given the importance of the issue, this study aims to identify areas at risk of desertification in Qom province
Materials & Methods: In this research, Google Earth images, MODIS satellite images, CHIRPS, and 30-meter digital elevation model have been used as the most important research data. The most important tools used in the research include Google Earth Engine, ArcGIS, and Super Decisions. Also, NDVI, weighted linear combination (WLC), and analytical network (ANP) models have been used. The general method of work in this research has been based on 9 parameters (vegetation density, dust concentration, average precipitation, average temperature, aridity coefficient, elevation, slope, distance from main road, and distance from urban areas), as well as the combined WLC-ANP model. Areas vulnerable to desertification risk have been identified.
Finding: Based on the results, a large part of the central areas of Qom province is affected by low vegetation density, high dust concentration, high aridity coefficient, low average rainfall, high temperature, low altitude, and proximity to urban areas and main roads. It has a very high potential for vulnerability to desertification.
Conclusion: The overall results of this study have shown that the central regions of Qom province are highly sensitive to the risk of desertification, so special attention should be paid to this issue in environmental planning, especially in plans related to land use changes.
Innovation: The innovation of this research is the combined use of Google Earth Engine and geomorphological parameters, and only areas with an NDVI coefficient greater than 0.1 were selected as study areas. The results of this research can be used in various environmental planning.

کلیدواژه‌ها English

Desertification
Google Earth Engine
WLC
Qom Province

Extended Abstract

1. Introduction

Desertification refers to the degradation of land in arid, semi-arid, and semi-humid areas, which is influenced by various human and natural factors and is often considered a significant threat to human societies. The phenomenon of desertification today affects many areas, particularly in arid and semi-arid regions. Arid and semi-arid areas are highly sensitive to the destruction of vegetation cover. In these areas, various natural factors, such as climate change and drought, as well as human factors, like land use changes, can contribute to the destruction of vegetation cover and the spread of desertification. The importance of the issue of desertification has made it a significant challenge for many countries. Given that approximately two-thirds of Iran's territory is comprised of arid and semi-arid areas, it can be said that a significant portion of Iran's land is vulnerable to desertification. In fact, Iran's location has caused a large part of its central and eastern regions to be at risk of desertification. Among the areas exposed to this phenomenon is Qom province. The climatic conditions of Qom province and its human activities have put pressure on natural resources, leading to the destruction of pastures and, consequently, the spread of desertification. Given that the risk of desertification is one of the leading threats in Qom province, it is crucial to study and investigate this phenomenon in detail and identify areas vulnerable to its spread, which is addressed in this research.

2. Materials and Methods

In this study, Google Earth images, MODIS satellite images, CHIRPS, and a 30-meter digital elevation model were utilized as the primary research data. The most important tools used in the study included the Google Earth Engine system (to prepare maps of dust concentration, vegetation density, average temperature, average precipitation, and aridity coefficient), ArcGIS (to prepare the desired maps), and Super Decisions (weighting the parameters). In this study, various indices were also employed, the most important of which were the AOD index (used to create the dust concentration map of the region) and the NDVI index (used to create the vegetation density map of the region). Additionally, this study employed the integrated WLC-ANP model to create the final map of desertification-prone areas. Considering the desired objectives, this study was carried out in three general stages. In the first stage, the parameters were selected, and the main study area was determined. In the second stage, information layers related to the desired parameters were prepared. In the third stage, using the integrated ANP-WLC model, the final map of areas vulnerable to desertification risk was prepared.

3. Results and Discussion

The geographical location of Iran has led to a significant portion of its regions being arid and semi-arid. Given the high sensitivity of arid and semi-arid regions to environmental degradation, the loss of vegetation cover and the spread of desertification are considered one of the most significant environmental hazards in these regions. Among the regions facing the challenge of desertification is Qom province. The results of this study have shown that a significant portion of this province consists of land without vegetation cover. In fact, the results obtained from the NDVI coefficient have shown that a large part of the eastern half of Qom Province lacks vegetation cover, and as a result, only parts of Qom Province with an NDVI coefficient of more than 0.1 have been studied. The results obtained from the evaluation of climatic parameters have shown that due to the significant difference in altitude between the western regions and the central and eastern regions of Qom province, in a general trend, the western regions have more precipitation, lower temperature, lower aridity coefficient, and lower dust concentration coefficient than the central and eastern regions of this province. Due to climatic differences, topographical variations, and varying levels of human activity in the studied area, the potential for desertification also varies across different regions of Qom Province.

4. Conclusions

The results of this study have shown that a large part of the central regions of Qom Province has a very high potential for desertification due to low vegetation density, high dust concentration, a high aridity coefficient, low average rainfall, high temperatures, low altitude, and proximity to urban areas and main roads. Additionally, the western regions of this province have a low potential for desertification due to their location within the mountain unit, low impact of human activities, higher rainfall, and lower temperatures compared to the central and eastern regions of Qom province, as well as their high altitude and low dust concentration. The overall results of this study indicate that the central regions of Qom province are highly susceptible to desertification, so special attention should be paid to this issue in environmental planning, particularly in land-use change plans. Additionally, it is possible to provide solutions to increase vegetation density, particularly in sandy areas and in areas without vegetation adjacent to the cities of Qom province.

5. Acknowledgment & Funding

The manuscript did not receive a grant from any organization.

6. Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

بیات، رضا؛ فاصل­­ایرانمنش، رحیم. (1400). بررسی تاثیر طوفان‌های گرد و غبار بر پوشش گیاهی تالاب شادگان. محیط­زیست و مهندسی آب، 7(1)، 13-1.
خالدی، کوهسار. (1392). زیان­های اقتصادی طوفان گرد و غبار بر استان­های غربی ایران (مطالعه موردی: ایلام, خوزستان و کرمانشاه). مدلسازی اقتصادی، 7(3)، 125-105.
خدائی، فاطمه؛ روستایی، شهرام؛ مختاری، داود. (1400). پهنه‌بندی شدت بیابان­زایی بر اساس معیار تخریب منابع آب‌های زیرزمینی با استفاده از مدل بیابان‌زایی مدالوس (مطالعه موردی: محدوده‌ی پیرامون دریاچه ارومیه)، هیدروژئومورفولوژی، 8­(27)، 79-59.
صادقی­روش، محمدحسن. (1403). پایش وضعیت بالفعل بیابان‌زایی با استفاده از مدل‌های امتیازی وی پی ام و واسپاس. جغرافیا و پایدار محیط، 14­(2)، 120-101.
صالحی، اصغر؛ کرباسی، پریسا. (1400). نقش عوامل انسان ساخت در بیابان­زایی شرق اصفهان. برنامه­ریزی فضایی، 11­(3)، 24-1.
صفری­نامیوندی، مهدی؛ گنجائیان، حمید؛ ابراهیمی، عطرین؛ عبادی­نژاد، سیدعلی. (1402). ارزیابی پتانسیل مخاطره بیابان­زایی با استفاده از مدل  DVI(مطالعه موردی: مناطق شمالی استان سمنان)، مطالعات جغرافیایی مناطق خشک، 14­(53)، 17-1.
کاظمی­نیا، عبدالرضا؛ رنگزن، کاظم؛ محمودآبادی، مهدی. (1396). بررسی شدت بیابان‌زایی با استفاده از مدل مدالوس (مطالعه موردی: اراضی غرب اهواز). سنجش‌ازدور و سامانه اطلاعات جغرافیایی در منابع طبیعی، 8­(2)، 126-111.
کرامت­زاده، مژده؛ فتحی، احمد؛ معاضد، هادی. (1401). بررسی روند بیابان­زایی منطقه جنوب شرق اهواز به روش IMDPA  و تأکید بر دو معیار اقلیم و پوشش­گیاهی، علوم و مهندسی آبیاری، 45­(1)، 166-153.
گنجائیان، حمید؛ محمدیان، کلثوم؛ جاودانی، مهناز؛ صفری نامیوندی، مهدی. (1402). برآورد توان بیابان‌زایی استان یزد با بهره‌گیری از شاخص‌های DVI، مدیریت بیابان، 11­(2)، 48-35.
مقصودی، مهران؛ گنجائیان، حمید؛ حسینی، سیدجواد­. (1397). ارزیابی کارایی روش­های طبقه­بندی نظارت­شده و نظارت­نشده در پایش ریگزارها (مطالعه موردی: ریگ جازموریان). مطالعات جغرافیایی مناطق خشک، 9­(32)، 92-81.
منجزی، نسیم؛ رکن‌الدین­افتخاری، عبدالرضا­. (1403). برآورد خسارات اقتصادی پدیده گرد و غبار بر بخش کشاورزی (مطالعه موردی: دهستان‌های شهرستان مسجدسلیمان). مطالعات علوم محیط­زیست، 9(2)، 8354-8345.
Capozzi, F., Di Palma, A., De Paola, F., Giugni, M., Iavazzo, P., & Topa, M. E., Giordano, S. 2018. Assessing desertification in sub-Saharan peri-urban areas: case study applications in Burkina Faso and Senegal. Geochemical Exploration, 190, 281-291. https://doi.org/10.1016/j.gexplo.2018.03.012
D’Odorico, P., Bhattachan, A., Davis, K.F., Ravi, S., & Runyan, C.W., 2013. Global desertification: drivers and feedbacks. Adv Water Res, 51, 326–344. https://doi.org/10.1016/j.advwatres.2012.01.013
De Paola, F., Ducci, D., & Giugni, M., 2013. Desertification and erosion sensitivity: a case study in southern Italy: the Tusciano River catchment. Environmental Earth Sciences, 70(5),1-12. http://dx.doi.org/10.1007/s12665-013-2294-2
Flores, E.S., & Yool, S.R., 2007. Sensitivity of change vector analysis to land cover change in an arid ecosystem. International Journal of Remote Sensing, 28(5), 1069–1088. https://doi.org/10.1080/01431160600868482
Huang, J., Zhang, G., Zhang, Y., Guan, X., Wei, Y., Guo. R., 2020. Global desertification vulnerability to climate change and human activities. Land Degradation & Development, 31(11), 1380-1391. http://dx.doi.org/10.1002/ldr.3556
Lamchin, M., Lee, W.K., Jeon, S.W., Lee, J.Y., Song, C., & Piao, D.,  2017. Correlation between desertification and environmental variables using remote sensing techniques in Hogno Khaan, Mongolia. Sustainability, 9, 519. https://doi.org/10.3390/su9040581
Ma, Z., Xie, Y., Jiao, J., Li, L., & Wang, X., 2011. The construction and application of an Aledo-NDVI based desertification monitoring model. Procedia Environmental Sciences, 10(C), 2029–2035. https://doi.org/10.1016/j.proenv.2011.09.318
Roberto, J.F., 2002. Do humans create deserts? Trends Ecology & Evolution, 17, 6–7. https://doi.org/10.1016/S0169-5347(01)02366-7
Tan, N., Zhang, C., Wu, Y.Y., & Wang, Z.T., 2024. Assessment of desertification sensitivity using an improved MEDALUS model in Northern China. Research in Cold and Arid Regions, 16(3), 141-148. https://doi.org/10.1016/j.rcar.2024.07.003
Vorovencii, L., 2017. Applying the change vector analysis technique to assess the desertification risk in the south-west of Romania in the period 1984–2011. Environmental Monitoring and Assessment, 189(524). https://doi.org/10.1007/s10661-017-6234-6
Wang, J., Wang, Y., & Xu, D., 2024. Desertification in northern China from 2000 to 2020: The spatial–temporal processes and driving mechanisms. Ecological Informatics, 82. https://doi.org/10.1016/j.ecoinf.2024.102769
Zhang, C., Wang, X., Li, J., & Huaa, T., 2020. Identifying the effect of climate change on desertification in northern China via trend analysis of potential evapotranspiration and precipitation. Ecological Indicators 112(1), 106-141. http://dx.doi.org/10.1016/j.ecolind.2020.106141.
Zhao, S., Ding, J., Wang, J., Ge, X., Han, L., Wang, L., & Qin, S., 2024. Central Asia's desertification challenge: Recent trends and drives explored with google earth engine. Cleaner Production, 460. https://doi.org/10.1016/j.jclepro.2024.142595
دوره 16، شماره 62 - شماره پیاپی 62
(در حال انتشار)
زمستان 1404
صفحه 21-36

  • تاریخ دریافت 24 آذر 1403
  • تاریخ بازنگری 08 اسفند 1403
  • تاریخ پذیرش 27 اسفند 1403
  • تاریخ انتشار 01 بهمن 1404