جغرافیا (نشریۀ انجمن جغرافیایی ایران)

جغرافیا (نشریۀ انجمن جغرافیایی ایران)

بررسی روند تغییرات پوشش گیاهی تحت تأثیر عنصرهای اقلیمی دمای سطح زمین، تبخیر و تعرق واقعی و بارندگی در استان هرمزگان

نوع مقاله : مقاله مستخرج از رساله دکتری

نویسندگان
نگار شمسائی 1
رسول مهدوی 2
اسداله خورانی 3
حمید غلامی 2
1 گروه مهندسی منابع طبیعی ، دانشکده کشاورزی و منابع طبیعی، دانشگاه هرمزگان، بندرعباس، ایران.
2 گروه مهندسی منابع طبیعی، دانشکده کشاورزی و منابع طبیعی، دانشگاه هرمزگان، بندرعباس، ایران.
3 گروه جغرافیا، دانشکده علوم انسانی، دانشگاه هرمزگان، بندرعباس، ایران.
10.22034/jiga.2025.2036593.1319
چکیده
تخریب پوشش گیاهی از جمله پارامترهای مهم برای بررسی تغییرات زیست محیطی است. از این‌رو در این مطالعه، با هدف بررسی روند تغییرات پوشش گیاهی (NDVI)، دمای سطح زمین (LST)، تبخیر و تعرق واقعی (ET) و بارندگی استان هرمزگان از داده‌های سنجش از دور حاصل ازسنجنده مودیس و داده‌های ERA5 با روش من-کندال و شیب تخمین‌گرسن در بازه زمانی 2022-2001 استفاده شد. سپس همبستگی بین پوشش گیاهی و دمای سطح خاک، تبخیر و تعرق واقعی و بارندگی در این بازه زمانی محاسبه گردید. بررسی روند تغییرات ماهانه پوشش گیاهی با استفاده از آماره من-کندال نشان داد که پوشش گیاهی در بیش از 80 درصد از مساحت استان افزایشی بوده است، که بررسی شیب تخمین‌گرسن نیز این افزایش را تأیید می‌کند. بررسی روند تغییرات دمای سطح زمین، تبخیر و تعرق واقعی و بارندگی نیز نشان داد که روند تغییرات تبخیر تعرق، دمای سطح زمین و بارندگی در اکثر ماه‌ها افزایشی بوده که به ترتیب این افزایش در بیش از 65، 85 و 61 درصد از استان دیده شده است. بررسی رابطه همبستگی بین پوشش گیاهی با شاخص دمای سطح زمین نشان داد که در ماه‌های سرد این رابطه منفی و قوی می‌باشد. بررسی این رابطه بین پوشش گیاهی و تبخیر و تعرق واقعی نیز نشان داد که در اکثر ماه‌ها این رابطه مثبت و در بیش از 53 درصد از سطح استان دیده شده است. همچنین نتایج بررسی رابطه همبستگی بین پوشش گیاهی و بارندگی بیان داشت که این رابطه در اکثر ماه‌های سال مثبت بوده و در بیش از 54 درصد از استان هرمزگان به خوبی مشهود است. با توجه به این نتایج می‌توان بیان داشت که با استفاده از داده‌های سنجش از دور به خوبی می‌توان روابط بین متغیرهای مختلف با تغییرات پوشش گیاهی و روند تغییرات آن‌ها را در طول زمان مورد بررسی قرار داد.
کلیدواژه‌ها
تخریب پوشش گیاهی
اقلیم
آزمون من-کندال
شیب تخمین‌گرسن

موضوعات

عنوان مقاله English

Investigating the impact of land surface temperature, evaporation, transpiration, and rainfall on changes in vegetation cover in Hormozgan province

نویسندگان English

Negar Shamsaei 1
Rasool mahdavi 2
Asadollah Khoorani 3
Hamid gholami 2
1 Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran
2 Department of Natural Resources Engineering, Faculty of Agriculture and Natural Resources, University of Hormozgan, Bandar Abbas, Iran
3 Department of Geography, Faculty of Humanities, University of Hormozgan, Bandar Abbas, Iran
چکیده English

Extended Abstract
Introduction
In recent decades, land degradation has emerged as a critical global environmental issue that has garnered attention from researchers, planners, and policymakers. It refers to the temporary or permanent decline in land productivity due to physical, chemical, and biological factors impacting agricultural production, ecosystem functions, quality of life, and human livelihood. Vegetation and soil degradation, leading to the loss of high-quality vegetation and soil, are considered significant forms of land degradation worldwide. Remote sensing data has become valuable and reliable for studying land degradation, particularly in assessing long-term ecosystem function decline. Vegetation cover, often measured using the normalized vegetation cover index (NDVI) derived from satellite data, is a crucial parameter for investigating land degradation. Studies have explored its relationship with other factors such as land surface temperature (LST), evapotranspiration, and precipitation. This study aims to analyze changes in vegetation trends in Hormozgan province and its correlation with evapotranspiration, surface temperature, and rainfall using MODIS sensor data from 2001 to 2022.
Methodology
In this study, we used the Normalized Vegetation Index (NDVI) data from the MODIS sensor (MOD13A3) to examine changes in land surface vegetation. The NDVI data was collected monthly with a 1000-meter resolution. Additionally, we utilized MOD16A2 and MOD11A2 products to analyze evaporation, transpiration, and ground surface temperature. These products have spatial resolutions of 500 and 1000 meters and cover 8 days. We downloaded ET, NDVI, and LST data from the United States Geological Survey (USGS) website in HDF format, spanning 2001-2022. Furthermore, we used reanalyzed ERA5 data from the ECMWF database to assess monthly rainfall trends. To prepare the data for analysis, we utilized the geographical longitude and latitude coordinate system and processed it using Acgis10.8 and Terrset software. I analyzed the changes in vegetation cover, evaporation, transpiration, land surface, and rainfall from 2001 to 2022 using the non-parametric Mann-Kendall analysis and Sen’s slope estimation. Initially presented by me and later developed by Kendall, this test is based on data ranks in a time series. It is used to determine trends. Additionally, I used the slope of the age estimator to assess the accuracy and enhance the process. Furthermore, in Terrset software, I performed a correlation analysis to examine the relationship between vegetation and soil surface indicators, evaporation, and rainfall. The correlation coefficient ranges between -1 and +1, where a positive value indicates a direct relationship and a negative value indicates an inverse relationship between the two changes.
Results and Discussion
The analysis of spatial and temporal changes in vegetation cover index (NDVI), evapotranspiration (ET), land surface temperature (LST), and monthly rainfall from 2001 to 2022 indicates that during winter and early spring, the vegetation cover index, evapotranspiration, and precipitation reach their highest values, while the land surface temperature shows its lowest value during these months. Over the 22 years, the average spatial changes in NDVI reveal that the highest vegetation cover is primarily located in the region's northern, northeastern, and northwestern parts, aligning with the monthly rainfall patterns during this period. The examination of the land surface temperature index (LST) indicates that the highest values are observed in the southern parts of Hormozgan province, particularly in the Persian Gulf and Oman Sea. The analysis of changes in the z-kendall index of the NDVI (Normalized Difference Vegetation Index) showed an increase throughout all the months studied, with the increase observed in over 80% of Hormozgan province. The examination of the slope of the monthly estimator confirmed the increase in vegetation cover during these months. Similarly, the z-kendall evapotranspiration index analysis indicated an increase in the evapotranspiration index in most months, occurring in over 54% of the region. The slope of the estimator for evaporation and transpiration index also supported these findings. The z-Kendall LST (Land Surface Temperature) index exhibited increasing trends in the majority of months over the 21 years, encompassing over 61% of the area. The slope statistics of the age estimator
 
for this index confirmed the monthly increase in the Hormozgan province. Lastly, based on the z-Kendall change, the rainfall index over the 21 years increased in most months, covering more than 76% of the province. The slope statistics of the age estimator of this index also supported the monthly increase. From 2001 to 2022, the monthly correlation between the vegetation cover index and the evapotranspiration index in Hormozgan province indicated a strong positive relationship in most months, covering over 80% of the province. Additionally, the vegetation cover index and surface temperature correlation showed a predominantly negative relationship, especially during autumn and winter, covering more than 54% of the area. Furthermore, the correlation between the vegetation cover index and the rainfall index revealed a positive relationship in over 54% of the province for most months over the 21 years.
Conclusion
The analysis of monthly vegetation changes in Hormozgan province using Mann-Kendall statistics and the age estimator slope indicates that there has been an increase in vegetation cover in over 80% of the area. The study by Eskandari Doman et al. in 2019 confirmed that the trend of vegetation changes, as indicated by the NDVI index between 2018 and 2019, showed a significant increase, particularly in the higher classes of the index (exceeding 0.4). Furthermore, examining changes in the earth's surface temperature, evaporation, transpiration, and rainfall revealed that these factors increased in most months, with increases observed in over 65%, 85%, and 61% of the province, respectively. The study also found a correlation between vegetation cover and earth surface temperature, indicating a positive relationship in most areas of the province and both negative and positive correlations between evaporation, transpiration, and rainfall. Given the comprehensive nature of this study, it is recommended to conduct similar research and compare the results with those presented here.

کلیدواژه‌ها English

degradation of vegetation
climate
Mann-Kendall test
estimated slope
  1. اژدری، زهرا؛ بذرافشان، ام البنین؛ بذرافشان، جواد؛ شکاری، مرضیه و زمانی، حسین (1399). پایش خشکسالی هواشناسی در استان هرمزگان مبتنی بر شاخص‌های چند متغیره آماری و احتمالاتی. خشک بوم (2)10، 17-1.https://doi.org/10.29252/aridbiom.2021.15258.1821
  2. اسکندری دامنه، حامد؛ زهتابیان، غلامرضا؛ خسروی، حسن؛ آذرنیوند، حسین و براتی، علی اکبر (1401). بررسی روند تغییرات پوشش گیاهی متأثر از خشکسالی در مناطق خشک و نیمه‌خشک با استفاده از تکنیک سنجش از دور (مطالعۀ موردی: استان هرمزگان) . مهندسی اکوسیستم بیابان، (28)، 28-13. https://doi.org/10.22052/deej.2020.9.28.11
  3. اسکندری دامنه، هادی، غلامی، حمید، مهدوی، رسول، خورانی، اسداله و لی، جوران (1400). ارزیابی تخریب اراضی با استفاده از شاخص کارایی مصرف آب و خشکسالی (مطالعۀ موردی: استان فارس). نشریه علمی - پژوهشی مرتع و آبخیزداری، (1)، 120-103. https://doi.org/10.22059/jrwm.2021.314310.1550
  4. پرون، صدیقه؛ یاوری، غلامرضا و رضازاده، مریم (1397). پهنه بندی اقلیمی استان هرمزگان با استفاده از روشهای کلاسیک. فصلنامه جغرافیا (برنامه ریزی منطقه ای) (33)8، 115-127. https://doi.org/20.1001.1.22286462.1397.9.1.8.8
  5. جوادی، شیما؛ رنجبرفردویی، ابوالفضل؛ خسروی، حسن و اسکندری‌دامنه، هادی (1402). بررسی اثرات خشکسالی بر کارایی مصرف آب در اقلیم‌ و کاربری‌‌های مختلف (مطالعه موردی: استان تهران).خشک بوم، (2)، 15-1. https://doi.org/10.29252/aridbiom.2024.20742.1965
  6. درخشنده، علی؛ خورانی، ا و رضازاده، مریم (1402). روندیابی بارش در ایران بر اساس داده‌‌های فیزیک زمین و فضا (10)49، 683-669 . http//doi.org/10.22059/jesphys.2023.350125.1007465
  7. رحیم زاده، فاطمه؛ هدایت دزفولی، اکرم و پوراصغریان، آرزو (1390). ارزیابی روند و جهش نمایه‌های حدی دما و بارش در استان هرمزگان . نشریه جغرافیا و توسعه (21)9، 116-97. https://doi.org/10.61186/jsaeh.10.2.149
  8. روح‌الله نژاد، محمد؛ سلامتی هرمزی، وحید؛ رمضانی، راحله و خان سالاری، سکینه (1401). پیش‌یابی کمیت‌های دما و بارش در دوره آماری 2080-2021 در استان هرمزگان جهت استخراج خشکسالی و ریزمقیاس نمایی آن توسط نرم‌افزار. فیزیک زمین و فضا،  (3)، 712-693. https://doi.org/10.22059/jesphys.2022.334358.1007383
  9. ناطقی، سعیده، گوهردوست، آزاده و سلیمانی ساردو، فرشاد (1401). واکاوی اثر پوشش گیاهی بر وقوع پدیده گرد و غبار (مطالعه موردی: استان هرمزگان). پژوهش‌های فرسایش محیطی، (۲)12، 60-43. https://doi.org/20.1001.1.22517812.1401.12.2.6.3
  10. Ahmadaali, K., Eskandari Damaneh, H., Ababaei, B. & Eskandari Damaneh, H. (2021). Impacts of droughts on rainfall use efficiency in different climatic zones and land uses in Iran. Arabian Journal of Geosciences, 14 (2), 126. https://doi.org/10.1007/s12517-020-06389-1
  11. Bakhtiari, M., Darvishi Boloorani, A., Abdollahi Kakroodi, A., Rangzan, K. & Mousivand, A. (2021). Land degradation modeling of dust storm sources using MODIS and meteorological time series data. Journal of Arid Environments, 190, 104507. https://doi.org/10.1016/j.jaridenv.2021.104507
  12. Dawelbait, M. & Morari, F. (2012). Monitoring desertification in a Savannah region in Sudan using Landsat images and spectral mixture analysis. Journal of Arid Environments, 80, 45-55. https://doi.org/10.1016/j.jaridenv.2011.12.011
  13. Eskandari Dameneh, H., Gholami, H., Telfer, M. W., Comino, J. R., Collins, A. L. & Jansen, J. D. (2021). Desertification of Iran in the early twenty-first century: assessment using climate and vegetation indices. Scientific Reports, 11(1), 20548. https://doi.org/10.1038/s41598-021-99636-8
  14. Eskandari, H., Borji, M., Khosravi, H. & Mesbahzadeh, T. (2016). Desertification of forest, range and desert in Tehran province, affected by climate change. Solid Earth, 7(3), 905-915. https://doi.org/10.5194/se-7-905-2016, 2016
  15. Fiorillo, E., Maselli, F., Tarchiani, V. & Vignaroli, P. (2017). Analysis of land degradation processes on a tiger bush plateau in South West Niger using MODIS and landsat TM/ETM+ data. International Journal of Applied Earth Observation and Geoinformation, 62, 56-68. https://doi.org/10.1016/j.jag.2017.05.010
  16. Gao, W., Zheng, C., Liu, X., Lu, Y., Chen, Y., Wei, Y. & Ma, Y. (2022). NDVI-based vegetation dynamics and their responses to climate change and human activities from 1982 to 2020: A case study in the Mu Us Sandy Land, China. Ecological Indicators, 137, 108745. https://doi.org/10.1016/j.ecolind.2022.108745
  17. Ge, W., Deng, L., Wang, F. & Han, J. (2021). Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016. Science of The Total Environment, 773, 145648. https://doi.org/10.1016/j.scitotenv.2021.145648
  18. Hereher, M. & El-Kenawy, A. (2022). Assessment of Land Degradation in Northern Oman Using Geospatial Techniques. Earth Systems and Environment, 6(2), 469-482. https://doi.org/10.1007/s41748-021-00216-7
  19. Huang, Y., Zhu, B., Zhu, Z., Zhang, T., Gong, W., Ji, Y. & Chen, D. (2019). Evaluation and comparison of MODIS collection 6.1 and collection 6 dark target aerosol optical depth over mainland China under various conditions including spatiotemporal distribution, haze effects, and underlying surface. Earth and Space Science, 6(12), 2575-2592. https://doi.org/10.1029/2019EA000809 2
  20. Huete, A. (2016). Vegetation's responses to climate variability. Nature, 531(7593), 181-182. https://doi.org/10.29252/aridbiom.2024.20742.1965
  21. Kendall, M. Rank correlation methods . (1948). https://doi.org/10.1007/978-1-4684-6683-6_9
  22. Kumar, B. P., Babu, K. R., Anusha, B. N. & Rajasekhar, M. (2022). Geo-environmental monitoring and assessment of land degradation and desertification in the semi-arid regions using Landsat 8 OLI / TIRS, LST, and NDVI approach. Environmental Challenges, 8, 100578. https://doi.org/10.1016/j.envc.2022.100578
  23. Liu, F., Chen, Y., Lu, H. & Shao, H. (2017). Albedo indicating land degradation around the Badain Jaran Desert for better land resources utilization. Science of The Total Environment, 578, 67-73. https://doi.org/10.1016/j.scitotenv.2016.06.171
  24. Liu, X., Yin, T., Zhang, Y., Huang, D., Wu, P., Wang, N. & Wang, R. (2023). Water use strategies of Robinia pseudoacacia and Quercus acutissima vary among seasons and planting methods. Plant and Soil, 483(1), 199-207. https://doi.org/10.1007/s11104-022-05736-3
  25. Liu, Z., Si, J., He, X., Jia, B., Zhou, D., Wang, C., Zhu, X., Qin, J., Ndayambaza, B. & Bai, X. (2024). The impact of desertification on soil health stability in Semi-Arid alpine Regions: A case study of the Qilian Mountains in the northeastern Tibetan Plateau, China. Ecological Indicators, 163, 112098. https://doi.org/10.1016/j.ecolind.2024.112098
  26. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society,13(3), 245-259. https://doi.org/10.2307/1907187
  27. Mehmood, K., Anees, S. A., Rehman, A., Tariq, A., Zubair, M., Liu, Q. & Luo, M. (2024). Exploring spatiotemporal dynamics of NDVI and climate-driven responses in ecosystems: Insights for sustainable management and climate resilience. Ecological Informatics, 80, 102532. https://doi.org/10.1016/j.ecoinf.2024.102532
  28. Meshesha, K. S., Shifaw, E., Kassaye, A. Y., Tsehayu, M. A., Eshetu, A. A. & Wondemagegnehu, H. (2024). Evaluating the relationship of vegetation dynamics with Rainfall and Land Surface Temperature using geospatial techniques in South Wollo zone, Ethiopia. Environmental Challenges, 15, 100895. https://doi.org/10.1038/s41598-024-62464-7
  29. Mu, Q., Heinsch, F. A., Zhao, M. & Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111(4), 519-536. https://doi.org/10.1016/j.rse.2007.04.015
  30. Mu, Q., Zhao, M. & Running, S. W. (2011). Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sensing of Environment, 115(8), 1781-1800. https://doi.org/10.1016/j.rse.2011.02.019.
  31. Niu, H., Marquer, L., Sack, D., Gao, G., Wang, J., Meng, M. & Jie, D. (2023). Middle to late Holocene plant cover variation in relation to climate, fire, and human activity in the Songnen grasslands of northeastern China. Frontiers in Plant Science, 13, 1071273. https://doi.org/10.3389/fpls.2022.1071273
  32. Roy, P., Pal, S. C., Chakrabortty, R., Chowdhuri, I., Saha, A., Ruidas, D. & Islam, A. (2024). Climate change and geo-environmental factors influencing desertification: a critical review. Environmental Science and Pollution Research, 1-14. https://doi.org/10.1007/s11356-024-32432-9
  33. Running, S. W., Mu, Q., Zhao, M. & Moreno, A. (2019). MODIS global terrestrial evapotranspiration (ET) product (MOD16A2/A3 and year-end gap-filled MOD16A2GF/A3GF) NASA Earth Observing System MODIS Land Algorithm (for collection 6). National Aeronautics and Space Administration, Washington, DC, USA [data set],3-37. https://doi. org/10.5067/MODIS/MOD16A2, 6
  34. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall's tau. Journal of the American Statistical Association, 63(324), 1379-1389. https://doi.org/10.1080/01621459.1968.10480934
  35. Shao, Y., Jiang, Q. o., Wang, C., Wang, M., Xiao, L. & Qi, Y. (2020). Analysis of critical land degradation and development processes and their driving mechanism in the Heihe River Basin. Science of The Total Environment, 716, 137082. https://doi.org/10.1016/j.scitotenv.2020.137082
  36. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote sensing of Environment, 8(2), 127-150. https://doi.org/10.1016/0034-4257(79)90013-0
  37. Tucker, C. J. & Sellers, P. J. (1986). Satellite remote sensing of primary production. International journal of remote sensing, 7(11), 1395-1416. https://doi.org/10.1080/01431168608948944
  38. Wang, J., Wei, H., Cheng, K., Ochir, A., Davaasuren, D., Li, P., Shun Chan, F. K. & Nasanbat, E. (2020). Spatio-Temporal Pattern of Land Degradation from 1990 to 2015 in Mongolia. Environmental Development, 34, 100497. https://doi.org/10.1016/j.envdev.2020.100497
  39. Yang, Y., Wang, Z., Li, J., Gang, C., Zhang, Y., Zhang, Y., Odeh, I. & Qi, J. (2016). Comparative assessment of grassland degradation dynamics in response to climate variation and human activities in China, Mongolia, Pakistan and Uzbekistan from 2000 to 2013. Journal of Arid Environments, 135, 164-172. https://doi.org/10.1016/j.jaridenv.2016.09.004
  40. Yin, T., Zhai, Y., Zhang, Y., Yang, W., Dong, J., Liu, X. & Wang, R. (2023). Impacts of climate change and human activities on vegetation coverage variation in mountainous and hilly areas in Central South of Shandong Province based on tree-ring. Frontiers in Plant Science, 14, 1158221. https://doi.org/10.3389/fpls.2023.1158221
  41. Zhang, H., Li, L., Zhao, X., Chen, F., Wei, J., Feng, Z. & Hu, M. (2024). Changes in Vegetation NDVI and Its Response to Climate Change and Human Activities in the Ferghana Basin from 1982 to 2015. Remote Sensing, 16(7), 1296. https://doi.org/10.3390/rs16071296
  42. Zhang, R., Ouyang, Z. T., Xie, X., Guo, H. Q., Tan, D. Y., Xiao, X. M. & Zhao, B. (2016). Impact of climate change on vegetation growth in arid northwest of China from 1982 to 2011. Remote Sensing, 8(5), 364. https://doi.org/10.3390/rs8050364
جغرافیا (نشریۀ انجمن جغرافیایی ایران)
دوره 22، شماره 83
زمستان1403، صفحۀ 200-1.
زمستان 1403
صفحه 91-121