Analysis of Long-Term Mean of Red Band Albedo in Iran | ||
| پژوهش های جغرافیای طبیعی | ||
| Article 6, Volume 52, Issue 1, April 2020, Pages 85-94 PDF (942.64 K) | ||
| Document Type: Full length article | ||
| DOI: 10.22059/jphgr.2020.284888.1007413 | ||
| Authors | ||
| Omid Reza Kefayat Motlagh1; Mahmood Khosravi* 2; Sayed Abolfazl Masoodian3; Mohammad Sadegh Keykhosravi Kiani4; Mohsen Hamidianpour5 | ||
| 1PhD Candidate of Climatology, Department of Physical Geography, University of Sistan and Baluchestan, Zahedan, Iran | ||
| 2Professor of Climatology, Department of Physical Geography, University of Sistan and Baluchestan, Zahedan, Iran | ||
| 3Professor of Climatology, Department of Physical Geography, University of Isfahan, Isfahan, Iran | ||
| 4Postdoctoral Researcher, Department of Physical Geography, University of Isfahan, Isfahan, Iran | ||
| 5Assistant Professor of Climatology, Department of Physical Geography, University of Sistan and Baluchestan, Zahedan, Iran | ||
| Abstract | ||
| Introduction Today, the assessment and control of environmental and climate change at the regional and global levels is of particular importance for monitoring the current situation and for predicting future changes. Albedo (or solar reflectivity) plays an important role in the thermal behavior of pavements and other ground surfaces and their resultant impacts on humans and the environment (Li, 2016). Therefore, the study of its temporal and spatial behavior can be a tool for understanding environmental changes. Albedo, as one of the important components in the Earth's Radiation balance, is the ratio of reflected flux density to incident flux density, referenced to some surface. Albedos commonly tend to be broadband ratios, usually referring either to the entire spectrum of solar radiation or just to the visible portion. More precise work requires the use of spectral albedos, referenced to specific wavelengths. Visible albedos of natural surfaces range from low values of ∼0.04 for calm, deep water and overhead sun, to > 0.8 for fresh snow or thick clouds. Many surfaces show an increase in albedo with increasing solar zenith angle (American Meteorological Society, 2019). Remote sensing is one of the most suitable tools for measuring albedo. Many researchers have used the parameter and also evaluated its algorithm (Jackson et al., 1987; Schaaf et al., 2002; Wang et al., 2018). Data and methods In this study, for the assessment of long-term mean value of albedo in Iran from the first band of MCD43A4, the Modis sensor was employed in the range of 0.62-0.67 microns during the period from 2000/03/20 to 2018/03/20 for 6574 days. The reason for choosing this product is because it has the Bidirectional Reflectance Distribution Function (BRDF). Indeed, BRDF can determine when the radiation energy reaches a certain level (an opaque surface)and it is reflected in the other direction. The data of this sensor is available as separate tiles at 1200 x 1200 km and Iran generally falls into six tiles. Of course, based on the Inpolygon function in MATLAB software, grid points outside of Iran were clipped. Iran was divided into 4 regions based on digital number of pixels. The basis of division is the quart values (first, second, and third quartiles). After zoning, we calculated statistical characteristics including the average, minimum, maximum, variance, range of changes and coefficient of skewness and Kurtosis of the cells located in each region. These statistical characteristics provide an opportunity to compare albedo in the different regions of Iran. Finally, we calculated four seaboard areas in Iran were mapped and its relation with height. Results and discussion The statistical characteristics of the red band albedo over Iran during the study period show alternative shifts (Table 1). The average, minimum, and maximum of the first quartile is 53.15, 1.45 and 85.18, respectively. The coefficient of variation in this class is 21.76%. The highest coefficient of variation is seen in the first quartile, but this may be due to the number of pixels affected. In general, the coefficient of variation shows that the highest coefficient of variation occurs at very low or high levels of Albedo. Quartile Fre. No Data Min Max Average Range Standard deviation Variance Skewness Kurtosis CV % First quartile: the lowest 25% of values 1878729 6646 1.45 18.85 15.53 17.39 3.39 11.44 -1.88 6.45 21.76 Second quartile: between 25.1% and 50% (up to the median) 1881589 3786 18.85 22.2 2.58 3.35 0.95 0.91 -0.06 1.82 4.62 Third quartile: 51% to 75% (above the median) 1880826 4550 22.2 25.82 23.9 3.62 1.03 1.07 0.11 1.83 4.31 Fourth quartile: the highest 25% of values 1883848 1528 25.82 61.08 29.33 35.25 3.13 9.82 1.67 7.08 10.67 A long-term albedo map was developed for the time period (2000-2018) based on remote sensing data. After preparing the aforementioned map based on the thresholds obtained from the quartile method, Iran was divided into four distinct areas in terms of albedo. The first area covers about 2% of the total area of the country where it is most commonly found in the south-east of the country, the Caspian coast and the water areas within the land, such as Lake Urmia. The second zone covers the range of 22-19 percent and is seen in different parts of the country. The third zone is in the range of 26-22% and is further dispersed in the center of the country. The fourth region has a range of 62-26%, which shows the highest albedo. In this area, snow-covered mountains such as the Alborz and Zagros heights and the zones that have been stained with white evaporation deposits over time are seen. Larger parts of this area, due to permanent snow or evaporation deposits, have albedo more than the average planet Earth (24%). There is a good relationship between albedo and height, although this is a completely nonlinear relationship. Up to 1200 m altitude, with increasing altitude, albedo decreased and at an altitude from 1400 to 1200 m it is varied from 12 to 36% due to different land use. From a height of 1,400 meters, the strong link is seen between Albedo and the altitude of the sea level in Iran. As Albedo increased with altitude due to lower temperature and also snowfall, it reaches nearly 60% at Altitude 4000 meters. In general, it can be said that this relative relationship between Albedo and altitude of sea level in Iran is due to the complexity of topography and land use. As a result, the relationship is straight above altitudes of 1,400 meters, and with increasing altitude, the albedo is elevated, and there is a decrease in albedo at altitudes less than 1000 meters indirectly with increasing altitude. Conclusion The MODIS sensor produces albedo in the surface of the earth continuously on a global scale with low spatial resolution and provides free access to the public. In this study, for measuring the average long-term albedo of Iran, the daily data of Albedo in the region of Iran was extracted from the MODIS website during the period from 2000/03/20 to 2018/03/20 for 6574 days. Then, based on nearly 45 billion cells, the long-term average of Iran's albedo was calculated. The results showed that albedo of Iran with an average of 21% is close to the albedo of the planet average which depends on latitude and topography and land surface conditions in Iran. The relationship between albedo and altitude from sea level was studied. The results of this section indicate that this relationship is a completely nonlinear relationship. Thus, in the first altitudes up to 1200 meters, the albedo has a decreasing behavior, and between the altitudes of 1200 and 1400 meters there is a steady trend; from 1400 to higher the albedo behavior is quite increasing. The increasing behavior of albedo well illustrates the behavior of snow cover in the highlands. In general, it can be concluded that this relationship is due to the diversity of topography and the type of the earth's surfaces. For this reason, this relationship is direct in elevations above 1400 meters. As the altitude increases, the albedo is increased, and at elevations less than 1000 meters the relations are inversed (by increasing the elevation, a decrease is observed in albedo). | ||
| Keywords | ||
| Iran; Quartile; Albedo; MODIS; Long-term Mean | ||
| References | ||
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حاجیزاده، ز. (1396). بررسی اثر تغییرات پوشش گیاهی بر تغییرات آلبدو و دمای سطح زمین با استفاده از تصاویر ماهوارهای در شهرستان مشکینشهر، پایاننامة کارشناسی ارشد رشتة سنجش از دور و GIS، دانشگاه تبریز. سلطانی اکمل، ف. (1397). آبوهواشناسی سپیدایی ایران به کمک دادههای CDR، پایاننامة کارشناسی ارشد رشتة آبوهواشناسی، دانشگاه اصفهان. سُنبلی، ز. (1389). تحلیل فضایی تابش در ایران، پایاننامة کارشناسی ارشد رشتة آبوهواشناسی، دانشگاه تربیت معلم. عساکره، ح. (1390). مبانی اقلیمشناسی آماری، زنجان: انتشارات دانشگاه زنجان. فرخخان طُرقی، ا. (1392). بررسی نقش مدیریت مناطق حفاظتشده در بهبود وضعیت خاک و پوشش گیاهی با استفاده از تصاویر ماهوارهای (مطالعة موردی: منطقة حفاظتشدة ارسباران)، پایاننامة کارشناسی ارشد رشتة سنجش از دور و GIS، دانشگاه تبریز. قبادی، ا.ا. (1395). تبیین و تحلیل زمانی- مکانی پدیدة جزیرة گرمایی شهر کرج با تأکید بر مدیریت آلبدو و مدلسازی خرد اقلیم محلی، رسالة دکتری رشتة آبوهواشناسی، دانشکدة جغرافیا و برنامهریزی محیطی، دانشگاه سیستان و بلوچستان. لطفی، ح. (1390). برآورد تابش خالص خورشیدی با کاربرد دادههای سنجندة MODIS، پایاننامة کارشناسی ارشد رشتة هواشناسی کشاورزی، دانشگاه شیراز. موقری، ع.ر. (1394). بررسی تأثیر تغییرات مکانی منطقة همگرایی میان حارهای و نوسان مادن- جولین بر گردش عمومی جو منطقه و اقلیم ایران، رسالة دکتری رشتة آبوهواشناسی، دانشکدة جغرافیا و برنامهریزی محیطی، دانشگاه سیستان و بلوچستان. American Meteorological Society, cited 2019: Climatology. Glossary of Meteorology. [Available online at http://glossary.ametsoc.org/wiki/Albedo]. Asakareh, H. (2011). Fundamentals of statistical climatology, 1 ed. Zanjan: Publisher Zanjan university. Benas, N. and Chrysoulakis, N. (2015). Estimation of the land surface albedo changes in the broader Mediterranean area, based on 12 years of satellite observations, Remote Sensing, 7(12): 16150-16163. Farokhkhan Toroghi, A. (2013). Assessment the Role of Protected Areas Management to Improving the Soil and Vegetation Cover Status Using Satellite Images (Case Study: Arasbaran Protected Area), MS Thesis in RS and GIS, University of Tabriz. Ghobadi, A. (2016). The Spatial- Temporal analysis of urban heat island phenomenon with emphasis on local microclimate modeling and albedo management Case Stady: Karaj, Ph.D Thesis in Climatology, Sistan and Baluchestan University, Zahedan. Govaerts, Y. and Lattanzio, A. (2008). Estimation of surface albedo increase during the eighties Sahel drought from Meteosat observations. Global and planetary change, 64(3-4): 139-145. Hajizadeh, Z. (2017). Evaluting the impact of Vegetation Changes on Albedo and Land Surface Temperature Changes by the Use of Satellite Images in Meshkin-Shahr County, MS Thesis in RS and GIS, university of Tabriz. https://ladsweb.nascom.nasa.gov/data/search.html. Hummel, J.R. and Reck, R.A. (1979). A global surface albedo model, Journal of Applied Meteorology, 18(3): 239-253. Jackson, R.D.; Moran, M.S.; Gay, L.W. and Raymond, L.H. (1987). Evaluating evaporation from field crops using airborne radiometry and ground-based meteorological data, Irrigation Science, 8(2): 81-90. Kukla, G. and Robinson, D. (1980). Annual cycle of surface albedo, Monthly Weather Review, 108(1): 56-68. Lotfi, H. (2011). Estimation of solar net Radiation using MODIS Data, Mohammad Jafar Nazemosadat and Rashid Fallah Shamsi, MS Thesis in Agrometeorology, University of Shiraz. Meng, X.; Lyu, S.; Zhang, T.; Zhao, L.; Li, Z.; Han, B.; … and Luo, S. (2018). Simulated cold bias being improved by using MODIS time-varying albedo in the Tibetan Plateau in WRF model, Environmental Research Letters, 13(4): 044028. Moaghari, A.R. (2015). Investigating the effect of spatial variations of the intertropical convergence zone and Madden-Julian oscillation on the atmospheric general circulation region and Iran, Ph.D Thesis in Climatology, Sistan and Baluchestan University, Zahedan. Nicodemus, F.E.; Richmond, J.C.; Hsia, J.J.; Ginsberg, I.W. and Limperis, T. (1977). Geometrical considerations and nomenclature for reflectance, Natl. Bur. Stand. Rep., NBS MN-160, 1(2). Pinty, B.; Lattanzio, A.; Martonchik, J.V.; Verstraete, M.M.; Gobron, N.; Taberner, M. ... and Govaerts, Y. (2005). Coupling diffuse sky radiation and surface albedo, Journal of the Atmospheric Sciences, 62(7): 2580-2591. Schaaf, C.B.; Gao, F.; Strahler, A.H.; Lucht, W.; Li, X.; Tsang, T. ... and Lewis, P. (2002). First operational BRDF, albedo nadir reflectance products from MODIS, Remote sensing of Environment, 83(1-2): 135-148. Sellers, W.D. )1965.( Physical climatology. University of Chicago Press, 272 pp Sonboli, Z. (2011). The Spatial Analysis of Solar Radiation in Iran, Mohammad Saligeh, MS Thesis in Climatology, Tarbit Moalem University, Tehran. Strahler, A.H.; Muller, J.P.; Lucht, W.; Schaaf, C.; Tsang, T.; Gao, F.; ... and Barnsley, M. J. (1999). MODIS BRDF/albedo product: algorithm theoretical basis document version 5.0. MODIS documentation, 23(4): 42-47. Wang, Z.; Schaaf, C.B.; Sun, Q.; Shuai, Y. and Román, M.O. (2018). Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products, Remote sensing of environment, 207: 50-64. Zhang, X.; Liang, S.; Wang, K.; Li, L. and Gui, S. (2010). Analysis of global land surface shortwave broadband albedo from multiple data sources, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 3(3): 296-305. | ||
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