پایش تغییرات کاربری اراضی و پوشش گیاهی در حوزه آبخیز دامغان

محورهای موضوعی : منابع طبیعی و مدیریت زیست محیطی

شیما نیکو ^{1 *} , پیمان اکبرزاده ²

1 - گروه بیابانزدایی، دانشکده کویرشناسی، دانشگاه سمنان
2 - گروه بیابانزادیی، دانشکده کویرشناسی، دانشگاه سمنان

تاریخ دریافت : 1401/03/31 تاریخ پذیرش : 1401/09/14 تاریخ انتشار : 1404/04/31

کلید واژه: تغییرات کاربری اراضی, تغییرات پوشش گیاهی, حوزه آبخیز دامغان سنجش از دور, NDVI,

چکیده مقاله :

پایش تغییرات عوامل محیطی در طی زمان به منظور درک روابط متقابل بین انسان و پدیده‌های طبیعی به‌منظور تصمیم‌گیری بهتر درباره مدیریت پایدار سرزمین از اهمیت بالایی برخوردار است. با توجه به دگرگونی و تغییرات گسترده در کاربری اراضی و پوشش گیاهی، بهره گیری از تکنولوژی سنجش از دور به یک ابزار مهم در بحث بررسی پایش تغییرات پوشش گیاهی/کاربری اراضی تبدیل شده است. در مطالعه حاضر در بازه زمانی 2020-2000 در حوزه آبخیز دامغان، تغییرات پوشش گیاهی با استفاده از شاخص نرمال شده اختلاف پوشش گیاهی (NDVI) و تغییرات کاربری اراضی با استفاده از تصاویر ماهواره لندست 8، 7 و 5 سنجنده های OLI، ETM+ و TM ، نرم افزار eCognition و سیستم اطلاعات جغرافیایی بررسی و از نرم افزار R . نیز برای بررسی تغییرات استفاده شد. نتایج بررسی تغییرات کاربری اراضی نشان داد که مساحت اراضی باغی 6624 هکتار، مناطق شهری 635 هکتار، اراضی بدون پوشش (بایر) 4/54757 هکتار و مساحت منابع آب سطح ناشی از احداث سد 15/453 هکتار افزایش یافته و مساحت اراضی با کاربری مراتع 25/12976 هکتار، جنگل 44/40438 هکتار و اراضی کشاورزی 62/9055 هکتار کاهش داشته است. بیشترین مقدار شاخص NDVI مربوط به سال 2020 و 2000 میلادی به ترتیب با مقدار 598/0 و 481/0 است و کمترین میزان این شاخص مربوط به سال 2010 و 2020 به ترتیب با مقدار 406/0 و 359/0 است. سطح عرصه های با پوشش گیاهی فقیر 3/163798 هکتار افزایش و مناطق دارای پوشش گیاهی متوسط و خوب به ترتیب 4/111001 و 9/52796 هکتار کاهش یافته است. بررسی تغییرات با استفاده از نرم افزار R نیز نشان داد که در 227754 هکتار از حوزه میزان پوشش گیاهی کاهش یافته، در 11/358327 هکتار پوشش گیاهی بدون تغییر و ثابت بوده و نهایتا در سطح 89/8146 هکتار از حوزه آبخیز دامغان میزان پوشش گیاهی افزایش پیدا کرده است.

چکیده انگلیسی:

Vegetation and land use due to natural and human factors change over time and affect the functioning of the ecosystem. Monitoring of environmental factors changes over time is important to understand the interrelationships between humans and natural phenomena in order to make better decisions about sustainable land management. Remote sensing multispectral images are very useful for gaining a better understanding of the environment. Due to widespread changes in land use and vegetation, the use of remote sensing technology has become an important tool for monitoring changes in vegetation / land use. In the present study, changes in vegetation by NDVI and land use changes in 2000-2020 in Damghan watershed in Semnan province using Landsat 8, 7 and 5 satellite images -OLI, ETM + and TM sensors, eCognition software and GIS identified. The results showed that the area of gardens, urban areas, barren land, and the area of surface water resources due to the construction of the dam have increased 6624, 635, 54757.4 and 453.15 hectares respectively. Also the area of rangeland, forest and agricultural lands have decreased 12976.25, 40438.44 and 9055.62 hectares respectively. The highest values of NDVI are related to 2020 and 2000 with the values of 0.598 and 0.481, respectively, and the lowest values of NDVI related to 2010 and 2020 are 0.406 and 0.359, respectively. The results of NDVI vegetation index during the study period showed that the area of lands with low vegetation has increased by 163798.3 hectares and the area of lands with medium and good vegetation has decreased by 11101.4 and 52796.9 hectares, respectively. Then these changes were evaluated with R software and the results showed that vegetation on 227754 hectares of the study area has decreased, on 358327.11 hectares has been unchanged and finally on 8146.89 hectares has increased.

منابع و مأخذ:

1. Ahmadi A.M. Akbari I. Al-Muhammad M. Jamalabadi J. 2022. The effect of land use and vegetation on the formation of thermal islands Case study: Qainat city. Journal of Applied Research in Geographical Sciences, 64: 878-94. doi:https://doi.org/10.52547/jgs.22.64.79. )In Persian(.
2. Ajai Arya A.S. Dhinwa P.S. Pathan S.K. Ganesh Raj K. 2009. Desertification/land degradation status mapping of India. Current Science, 97(25): 1478–1483.
3. Binal A. Christiana P.S. Dhinwab A. 2018. Long term monitoring and assessment of desertification processes using medium & high resolution satellite data. Applied Geography, 97:10–24. doi:https://doi.org/10.1016/j.apgeog.2018.04.010
4. Brabant P. 2019. A land degradation assessment and mapping methodology, standard guideline proposal. Comite’ Scientifique Francais De la desertification les dossiers thematiques. Agropolis International, 8. 56pp.
5. Department of Natural Resources and Watershed Management of Semnan Province, Department of Engineering Studies. 2021. detailed-executive studies of watersheds of Semnan province (In Persian(.
6. Hadeel A S. Mushtak T.J. CHEN Xi. 2011. Remote Sensing and GIS Application in the Detection of Environmental Degradation Indicators. Geo-spatial Information Science, 14(1): 39-47. doi:https://doi.org/10.1007/s11806-011-0441-z
7. Harciníková T. Stankova H. 2014. The possibilities to support ZB GIS® database update using object-based image analysis in eCognition developer software. Geodesy and Cartography, 40(3): 122-132. doi:https://doi.org/10.3846/20296991.2014.962733
8. Hernández-Guzmán R. Ruiz-Luna A. González C. 2019. Assessing and modeling the impact of land use and changes in land cover related to carbon storage in a western basin in Mexico. Remote Sensing Applications: Society and Environment, 13: 318-327. doi:https://doi.org/10.1016/j.rsase.2018.12.005.
9. Javier Tomasellaa A. Rita M. Silva Pinto V. Alexandre A. Barbosab Daniel A. Rodriguezc M, Oliveira S, Marcelo F. 2018. Sestinib Desertification trends in the Northeast of Brazil over the period 2000–2016. International Journal of Applied Earth Observation and Geoinformation, 73:197–206. doi: https://doi.org/10.1016/j.jag.2018.06.012.
10. Lamichhane S. Narendra Man S. 2019. Alteration of groundwater recharge areas due to land use/cover change in Kathmandu Valley. Nepal. Journal of Hydrology: Regional Studies, 26: 100635. doi: https://doi.org/10.1016/j.ejrh.2019.100635.
11. Longley P. Donnay J and Barnsley M. 2001. Remote Sensing and Urban Analysis. London: Taylor and Francis.
12. Matlhodi B, Kenabatho PK, Parida BP, Maphanyane JG. 2019. Evaluating land use and land cover change in the Gaborone dam catchment, Botswana, from 1984–2015 using GIS and remote sensing. Sustainability, 11(19): 5174. doi:https://doi.org/10.3390/su11195174.
13. McCloy KR. 1995. Resource management information system: Process and Practice. London: Taylor and Francis.
14. Mirhosseini S. M. Jamali A. A. Hosseini S. Z. 2016. Investigating and Predicting the Extension of Dunes Using Land Change Modeler (LCM) in the North West of Yazd, Iran. Desert, 21(1): 76-90. doi: https://10.22059/jdesert.2016.58321.
15. Mishra N. Kumar S. 2015. Impact of Land Use Change on Groundwater Recharge in Haridwar District. 20th International Conference on Hydraulics. Water Resources and River Engineering, IIT Roorkee, India, 17-19 December, P8.
16. Munkhnasan L. Sonam Wangyel W, Chul-Hee L. 2020. Understanding global spatio-temporal trends and the relationship between vegetation greenness and climate factors by land cover during 1982e2014. Golbal Ecology and Conservation, 24: 780-801. doi: https://doi.org/10.1016/j.gecco.2020.e01299.
17. Parker DC. Manson SM Janssen MA. Hoffmann MJ. Deadman MJ. 2003. Multi agent systems for the simulation of land use and land cover change: A Review. Annals of the Association of American Geographers, 93(2): 314–337. doi:https://doi.org/10.1111/1467-8306.9302004.
18. Prince SD. Becker-Reshef I. Rishmawi K. 2009. Detection and mapping of long-term land degradation using local net production scaling: Application to Zimbabwe. Remote Sensing of Environment, 113(5): 1046-1057.
19. Rana M. Kharel S. 2019. Feature extraction for urban and agricultural domains using ecognition developer. International Archives of the Photogrammetry. Remote Sensing & Spatial Information Sciences, XLII-3/W6: 609-615. doi: https://doi.org/10.5194/isprs-archives-XLII-3-W6-609-2019.
20. Rawat JS. Biswas V. Kumar M. 2013. Changes in land use/cover using geospatial techniques: A case study of Ramnagar town area, district Nainital, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Sciences, 16: 111-117. doi: https://doi.org/10.1016/j.ejrs.2013.04.002.
21. Rouhani N, Moradi Faraj A, Mojaradi B, Rajaei T, Jabbari E. 2021. Investigation of land use change in Qom province along with climatic parameters using satellite remote sensing technology. Journal of RS and GIS for Natural Resources, 12 (4): 28-46. doi: http://dorl.net/dor/20.1001.1.26767082.1400.12.4.2.9. ( In Persian(.
22. Salarian F. Tatian M.R. Qangarmeh A. Tamratash R. 2021. Modeli ng land cover changes in Golestan province using land change modeling (Land Change Modeler). Journal of RS and GIS for Natural Resources, 12 (4): 47-70. doi: http://dorl.net/dor/20.1001.1.26767082.1400.12.4.3.0. ( In Persian(.
23. Soltani N. Mohammadnejad V. 2021. The efficiency of Google Earth Engine (GEE) system in assessing land use changes and predicting it with the Markov model (Case study of Urmia plain). Journal of RS and GIS for Natural Resources, 12 (3): 101-114. doi: http://dorl.net/dor/20.1001.1.26767082.1400.12.3.6.1. (In Persian).
24. Wang R. Murayama Y. 2017. Change of land use/cover in Tianjin city Based on the Markov and Cellular Automata models. ISPRS International Journal of Geo-Information, 6: 150. doi:https://doi.org/10.3390/ijgi6050150
25. Wang SW. Gebru B M. Lamchin M. Kayastha RB. Lee WK. 2020. Land use and land cover change detection and prediction in the Kathmandu district of Nepal using remote sensing and GIS. Sustainability, 12(9):3925. doi:https://doi.org/10.3390/su12093925
26. Youjia L. Lijun L. Shizuka H. 2020. Spatiotemporal analysis of trends in vegetation change across an artificial desert oasis, Northwest China, 1975–2010. Arabian Journal of Geosciences, 13(742). https://doi.org/10.1007/s12517-020-05707-x.