بررسی خطر وقوع بیابان‌زایی با استفاده از شاخص‌های طیفی در محدوده‌ی پیرامونی دریاچه‌ی ارومیه

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استاد گروه ژئومورفولوژی دانشگاه تبریز

2 استاد گروه ژئومورفولوژی ،دانشگاه تبریز

3 دانشجوی دکتری ژئومورفولوژی دانشگاه تبریز.

10.22034/gmpj.2020.122206

چکیده

پژوهش حاضر با هدف بررسی وقوع بیابان­زایی در محدوده­ی پیرامون دریاچه­ی ارومیه انجام شده است. بدین منظور در ابتدا، تصاویر ماهواره­ی سنتینل-2 با استفاده از نرم­افزار QGIS مورد پیش پردازش قرار گرفته و پس از انجام تصحیحات اتمسفری، اقدام به استخراج شاخص­های طیفی نشانگر بیابان­زایی (پوشش گیاهی تفاضلی نرمال شده (NDVI)، آلبدوی سطحی، میزان نمناکی (Wetness)، ضریب روشنایی (Brightness)، میزان سبزینگی (Greenness) شد. پس از استخراج شاخص­های طیفی مذکور و در جهت شناسایی مناسب­ترین زوج شاخص­های طیفی، میزان همبستگی و رابطه­ی رگرسیونی موجود بین شاخص­های مورد مطالعه با استفاده از تحلیل­های آماری صورت پذیرفته در نرم­افزارSPSS(22)  بررسی شد. بر طبق نتایج حاصل، میزان همبستگی برای زوج شاخص­ (میزان سبزینگی - ضریب روشنایی­ (برابر با 9/4- و برای زوج شاخص­ (میزان نمناکی - ضریب روشنایی) برابر با 33/0- می­باشد. در مرحله­ی بعد نقشه­ی خطر بیابان­زایی بر اساس دو زوج شاخص مذکور تهیه و با استفاده از الگوریتم  Jenks Natural Break  در محیط نرم­افزار ARC-GIS 10.6 در پنج کلاس خطر شدید، نسبتا شدید، متوسط، ضعیف و بدون خطر بیابان­زایی، طبقه­بندی شد. نتایج نشان داد که 89/9 درصد از کل مساحت محدوده­ی مورد مطالعه در کلاس خطر شدید، 60/30 درصد در کلاس خطر نسبتا شدید، 48/37 درصد در کلاس خطر متوسط، 42/12 درصد در کلاس خطر ضعیف و 61/9 درصد در کلاس خطر بدون بیابان­زایی قرار دارد. نتایج به دست آمده با استفاده از مشاهدات میدانی و ماتریس خطا (­Confusion Matrix using Ground truth ROI­) ارزیابی و با کسب ضریب کاپا 95/0 و درجه­ی صحت 51/90 درصد مورد صحت­سنجی قرار گرفت.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluating the risk of desertification using the spectral indices in the surrounding area of Lake Urmia

نویسندگان [English]

  • shahram rostaei 1
  • davoud Mokhtari 2
  • fatemeh khodaei gheshlagh 3
1 Tabriz university
2 Tabriz university
3 Tabriz university
چکیده [English]

Extended Abstract
Introduction
Desertification should be considered as the destruction of the fragile balance generating human, plant, and animal life in arid, semi-arid, and sub-humid arid areas.
During the recent decade, Urmia catchment and the surrounding area of Lake Urmia have encountered imbalance by experiencing severe environmental fluctuations.
Thus, due to the ecological and eco-systemic significance of this area, studying the occurrence of desertification seems essential. Thus, the present study including:1. the evaluation of the occurrence of desertification in the surrounding area of Lake Urmia using the spectral indices of vegetation, Albedo, tasseled cap, 2) Identifying a base pixel relationship between different biophysical indices (normalized vegetation difference, greenery rate, humidity rate, surface Albedo, brightness degree) and detection are among the best study pairs for evaluating the status of desertification due to the highest negative correlation among them.
The studied area
The studied is part of Urmia catchment located on northwestern Iran with geographical coordinates of 44 degrees and 0 minute to 47 degrees and 0-minute east longitude and also 37 degrees and 0 minute to 38 degrees and 20 minutes’ north latitude and has an area of 14395 square kilometers.
Methodology
In the first step, Satellite images of Sentinel-2 (Level-1C) were downloaded from Copernicus Open Access Hub (https://scihub.copernicus.eu). The images were acquired on July 2018. The cloud- free images were selected on July because it is the period when natural and annual vegetation is minimal and crops are harvested. Desertification during this period is best assessed to avoid confusion with seasonal vegetation. In the second step the images were pre-processed and processed using QGIS software. Then, ArcGIS 10.3, SAGA-GIS, and SPSS (22) software were used for the statistical regression analysis between vegetation and Albedo (the first pair of spectral index), greenery and brightness coefficient (the second pair of spectral index), as well as humidity rate and brightness coefficient (the third pair of spectral index) were used to identify the pair of spectral indices with higher negative correlation.
Results and discussion
Based on the results, the high rate of NDVI index and greenery rate are related to the points like the Alluvial fans located at the foot of Misudagh hillside.
However, the low rate of the above-mentioned indices is related to the areas like aquatic zones (Lake Urmia). The Albedo rate in the studied area is between -0.01 to 0.9.
The low values of Albedo index are related to the areas full of vegetation and aquatic bodies. The high values of Albedo and brightness rate are related to bright soils.
The high rates of humidity are related to aquatic bodies, the areas with vegetation, and the humid lands around Lake Urmia while its low values are related to the soils with bright texture, the land without vegetation, and the lands and salt marsh without humidity resulted from the retrogression of Lake Urmia and poor soils without organic materials.
In order to evaluate the regression relationship between the spectral indices, the SAGA-GIS was used. The analysis showed a strong negative correlation between the spectral index pair of humidity rate and brightness coefficient (r=0.37).
After the spectral index pair of brightness coefficient-humidity rate, the spectral index pair of normalized difference Vegetation-Albedo is equal to r=0=1.14.
The negative correlation is between the spectral index rate of brightness coefficient- greenery rate (r=4.9) allocating higher rate to itself than the previous spectral index pair.
Thus, first the spectral index pair of brightness coefficient and humidity rate as well as normalized difference vegetation and Albedo was normalized.
Conclusion
After normalizing the two above-mentioned index pairs, the correlation coefficient for normalized vegetation -Albedo is equal to -0.34 and for humidity rate-brightness coefficient is equal to -0.31. Due to the close correlation obtained for the above-mentioned indices, the map for desertification risk was prepared based on the two above-mentioned pairs and then verified using the Confusion Matrix using Ground truth ROI. Kappa coefficient obtained for the map is equal to 0.89 and the obtained accuracy rate is equal to 90.1. The achieved desertification map was classified in five categories of without risk, poor, average, relatively severe, and severe. From the total 14350.81 square kilometers of the studied area, 557.391 square kilometers (3.9%) is at the severe risk of desertification, 2015.330 square kilometers (14.04$) is at the relatively severe risk, 4007.073 square kilometers (27.92%) is at the average risk, 3660.534 square kilometers (25.50%) is at the poor risk and 4110.473 square kilometers (28.64%) is at the class of no risk for desertification .Based on the above-mentioned issues, any effort for exploiting the lands located on the studied area should be made with full cation and based on sufficient knowledge on the conditions of these lands.

کلیدواژه‌ها [English]

  • Desertification
  • Sentinel-2
  • NDVI
  • surface Albedo
  • tasseled cap transformation (TCT)
جهاد کشاورزی، انتشارات موسسه آب و خاک (1342) ، نقشه خاکشناسی ایران، مقیاس 2500000/1
خیرخواه زرکش، میر مسعود؛ محبوبیان، عادل؛ حصاری، همایون، 1391، مقایسه­ی مقادیر برآوردی آلبدوی سطحی به دست آمده از تصاویر لندست و مودیس، مجله­ی کاربرد سنجش از دور و GIS، سال سوم، شماره­ی 3، صص.
داوری، سرور؛ راشکی، علیرضا؛ اکبری، مرتضی؛ طالبان­فرد، علی اصغر، 1397، پایش تغییرات زمانی – مکانی شاخص­های مؤثر در بیابان­زایی مناطق خشک جنوب استان خراسان رضوی، سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، سال نهم، شماره 2، ص 17-32.
رحیمی، محمد؛ دماوندی، علی اکبر؛جعفریان، وحید، 1392، بررسی کاربردهای سنجش از دور در ارزیابی و پایش تخریب سرزمین و بیابان­زایی، فصلنامه اطلاعات جغرافیایی(سپهر)، دوره­ی 22، شماره 87، صص 115-128.
سلطانیان، محمود؛ حلبیان، امیر حسین، 1397، کاربرد سنجش از دور در علوم محیطی (روش­های پردازش تصویر  در ENVI)، چاپ اول،انتشارات جهاد دانشگاهی اصفهان، اصفهان.
کردوانی، پرویز، (1382)، جغرافیای خاک­ها، تهران، چاپ هشتم، موسسه انتشارات و چاپ دانشگاه تهران، تهران.
-کوه بنانی، حمید رضا؛ دشتی امیر آباد، جمال؛ نیکو، شیما؛ تایا، علی، 1396، پهنه­بندی شدت بیابان­زایی با استفاده از رویکرد منطق فازی (مطالعه­ی موردی: دیهوک طبس)، پژوهش­های فرسایش محیطی، سال هفتم، دوره­ی اول، شماره­ی 25،ص 35-49.
Afrasinei, G. M .and Melis, M.T.and Buttau, C. and Arras, C. and Zerrim, A.and Guied, M. and Ouessar, M. Essifi, B.and Zaied, M.B and Jlali, A., 2017, Classification Methods for Detecting and Evaluating Changes in Desertification-Related Features in Arid and Semi- Arid Environments. Euro-Mediterr. J. Environ. Integr,2 (14), pp.1-19
Lamqadem, A.A. and Hafid, S. and Biswajeet, P., 2018, Quantitative Assessment of Desertification in an Arid, Oasis Using Remote Sensing Data and Spectral, Remote Sens., 10 (1862), pp. 1-18.
Becerril-Piña, R. and Díaz-Delgado, C. and Mastachi-Loza, C.A.and González-Sosa, E. 2016, Integration of remote sensing techniques for monitoring desertification in Mexico. Hum. Ecol. Risk Assess: An International Journal., 22 (1323–1340), pp. 1-38
Duanyang, X.and Xiaogang, Y.and Chunlin X., 2019, Assessing the spatial-temporal pattern and evolution of areas sensitive to land desertification in North China, Ecological Indicators, 97 (150-158).
Guang, Y. Dong, C. and Xinlin, H.and Aihua, L.and  Mingjie, Y.and Xiaolong ., 2017, Land use change characteristics affected by water saving practices in Manas River Basin, China using Landsat satellite images, Int J Agric & Biol Eng,10 (6), pp,123–133.
Guodong, Z. and Hongmin, Z. and Changjing, W. and Huazhu, X. and Jindi, W. and Huawei, W., 2019, Time Series High-Resolution Land Surface Albedo estimation based on the Ensemble Kalman Filter Algorithm, Remote Sens, 11(753), pp. 1-24
Huang,S. and Siegert. F., 2006, Land cover classification optimized to detect areas at risk of desertification in North China based on SPOT vegetation imagery, Journal of Arid Environments 67 () , pp.308–327
Jackson, ­R.D. and Sherwood, B. I, and J. Otterman, 1975, Surface Albedo and desertification, Science, 189(­4207), pp. 1012-1015
Kassas, Mohamed., 1999, Rescuing drylands: a project for the world, Futures, 31(9), pp. 949–958.
Lamchin, M. and Lee, J. Y and Lee,W. K and Lee, E. J and Kim, M. and Lim, C.H and Choi, H .A. and Kim, S.R., 2016, Assessment of land cover change and desertification using remote sensing technology in a local region of Mongolia. Advances in Space Research,57(1), 64–77
Li, C., 2008, research on monitoring the changes of desertification based on remote sensing, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7, pp.1009-1012
Liangliang, J., and Guli, J., and Anming, B. and Alishir, K. and  Hao, G., and Guoxiong Z. Philippe De, M., 2019, Monitoring the long-term desertification process and assessing the relative roles of its drivers in Central Asia, Ecological Indicators,104 (2019),pp. 195-208
Ma, Z., Xie, Y., Jiao, J., li, L., Wang, X., The Construction and Application of an Aledo-NDVI Based Desertification Monitoring Model. Procedia Environ. Sci., 10(part c) pp.2029–2035
Masoudi, M., Parviz, J., Biswajeet, P., 2017, A new approach for land degradation and desertification assessment using geospatial techniques, Nat. Hazards Earth Syst, Sci., 18(, pp. 1133–1140
Book Middleton, N., Thomas, D.S.G. World Atlas of Desertification. Arnold. pp. 182, 1997.
Moharana, P.C., and Shalu, S., and Bhatt, R.K., 2013, NDVI based assessment of desertification in Jaisalmer district of Rajasthan in referenced to regional climate variability, Conference: XXXIII INCA International Congress, At Jodhpur, Volume: 33, pp. 1-9.   
Naegeli, K., Damm, A., Huss, M., and Wulf, H., and Schaepman, M., Hoelzle, M., 2017, Cross-comparison of albedo products for glacier surfaces derived from airborne and satellite (Sentinel-2 and Landsat 8) optical data. Remote Sens, 9(2), pp. 1-22.
Pan, J., and Tianyu L., 2013, Extracting desertification from Landsat TM imagery based on spectral mixture analysis and Albedo- Vegetation feature space, Nat Hazards, , pp.1-13.
Qiang, G. and Bihong, F. and Pilong, S. and Cudahy, T. Jing, Z. Huan, X., 2017, Satellite Monitoring the Spatial-Temporal Dynamics of Desertification in Response to Climate Change and Human Activities across the Ordos Plateau, China, Remote Sens, 9(525), 2-20
Journal: Tasumi, M., 2013, Progress in operational estimation of regional evapotranspiration using satellite imagery. ProQuest Dissertations and Theses: Thesis (Ph.D.)-University of Idaho, Source: Dissertation Abstracts International. Vol. 64-12, Section. B, p. 25
P, Thomas. and Higginbottom, E. and Symeonakis, A., 2014, Assessing Land Degradation and Desertification Using, Vegetation Index Data: Current Frameworks, and Future Directions, Remote Sens, 6 (2072-4292), pp. 9552-9575.
Tsunekawa, A., ­2000, Methodologies of desertification monitoring and assessment. In: Workshop of the Asia regional Thematic Programme Network on Desertification Monitoring and Assessment (TPN1) (provisional edition), 28–30 June 2000, UNU, Tokyo, Japan, pp. 44–55.