چکیده:
پژوهش حاضر با هدف پایش روند بیابانزایی در محدودة پیرامونی دریاچة ارومیه در بازة زمانی 2000 تا 2018 میلادی انجام شده است. برای رسیدن به این هدف، نخست هفت فریم از تصاویر سنتینل-2 مربوط به سال 2018 و سه فریم از تصاویر ماهوارة لندست 5 مربوط به سال 2000 میلادی با استفاده از نرمافزار QGIS و ENVI 5.3 پیشپردازش و پردازش، و شاخصهای معرف بیابانزایی در قالب زوج شاخصهای طیفی آلبدو – شاخص پوشش گیاهی تفاضلی نرمالشده، میزان سبزینگی- ضریب روشنایی و میزان رطوبت– ضریب روشنایی استخراج شد. در مرحلة بعد روابط آماری موجود بین زوج شاخصهای یادشده بررسی شد.
براساس نتایج حاصل، زوج شاخصهای میزان سبزینگی– ضریب روشنایی و میزان رطوبت– ضریب روشنایی، با کسب همبستگی منفی بهمثابة زوج شاخصهای معرف بیابانزایی انتخاب و نقشة شدت خطر بیابانزایی برمبنای آنها تهیه شد. برای صحتسنجی نتایج بهدستآمده، الگوریتم بیشترین درجة شباهت به کار رفت. الگوریتم یادشده با کسب درجة صحت 96/91 و ضریب کاپای 95/0 برای سال 2000 میلادی، درجة صحت 25/91 و ضریب کاپای 89/0 در سال 2018 نشاندهندة انطباق مناسب نتایج کسبشده با واقعیتهای زمینی است. برای پایش روند وقوع پدیدة بیابانزایی، تغییر مساحت کلاسهای خطر بیابانزایی در محدودة مطالعهشده بررسی شد. براساس نتایج بهدستآمده، مساحت کلاسهای خطر شدید (01/5 درصد)، نسبتاً شدید (47/11 درصد) و متوسط (12/6 درصد) رشد مثبت و مساحت کلاسهای خطر ضعیف (17/9 درصد) و بدون بیابانزایی (43/13 درصد) رشد منفی دارد؛ بنابراین روند افزایشی درصد مساحت کلاسهای خطر شدید، نسبتاً شدید، متوسط و کاهش مساحت کلاسهای خطر ضعیف و بدون خطر بیابانزایی نشاندهندة روند صعودی وقوع بیابانزایی در محدودة مطالعهشده است. معیار آب زیرزمینی، اقلیم و درصد پوشش گیاهی، مهمترین عوامل مؤثر در وقوع بیابانزایی در محدودة مطالعهشده است.
Extended Abstract Introduction According to the First World Conference on Deserts and Desertification, desertification refers to the destruction and degradation of natural ecosystems in arid, semi-arid, and sub-humid arid regions, which results in lower biomass production and the emergence of soil erosion (Ekhtesasi et al., 2011). Desertification results from natural factors such as climate variables and anthropogenic activities (Binal et al, 2018; Claado et al, 2002) and its impact on ecological processes is enormous and complex. Therefore, counteracting desertification is necessary to maintain long-term soil fertility in arid areas of the world. The present study aimed at evaluating desertification trends in the areas surrounding Lake Urmia in the period from 2000 to 2018. The main objectives of this study were 1) identification of the most suitable spectral index pair of desertification in the study area during the study period, taking into account the statistical relations; 2) mapping the desertification risk for the study period and the assessment of desertification trend in the study area by using the spectral biophysical indices such as normalized difference vegetation index (NDVI), surface albedo, Tasseled cap along with three components of brightness, Wetness, and greenness, and 3) identifying the most important factor that caused desertification in the study area by using the logistic regression model. Methodology In the present study, first, three frames of Landsat 5 TM sensor and seven frames of Sentinel 2 images were downloaded and analyzed by ENVI5.3 and QGIS software for July 2000 and 2018. In the next step, spectral indices of desertification, including the normalized difference vegetation index (NDVI), surface albedo, Tasseled Cap (including three components of brightness coefficient, Wetness, and greenness) were extracted for the study period. Thereafter, using the statistical relations and the determination coefficient, the most suitable spectral index pair of desertification in the study area was identified. After the identification of suitable spectral index pairs, the selected spectral index pair was normalized and the desertification mapping was performed for the years 2000 and 2018 taking into account the obtained gradient by using the linear regression relation. Finally, by applying the statistical change detection method, changes in the class's risk were investigated and using the Logistic Regression model, the most effective factor in the occurrence of desertification was identified. Discussion The normalized difference vegetation index (NDVI), wetness, and greenness were considered as the independent variables and surface albedo and brightness coefficient as dependent variables. The pairs of NDVI-Albedo spectral indicators have a positive correlation, but two spectral index pairs of humidity-brightness coefficient and brightness coefficient-greenness due to having a negative correlation were selected as the desertification index pairs and then normalized in the next step through the relevant relations. After mapping the desertification risk according to the index pairs of brightness coefficient-greenness and humidity-brightness, the combined map of desertification was obtained using line slope from the normalized relationship of the selected index pair and overlay function for the years 2000 and 2018 in 5 classes of non-desertification, weak, moderate, severe, and relatively severe desertification risks. To verify the results, using the classification algorithm, the Maximum Likelihood Algorithm and the Error Matrix were obtained, and the algorithm, with the accuracy of 91.96 and the kappa coefficient of 0.95 for 2000, and accuracy of 91.25 and a kappa coefficient of 0.89 for 2018 indicated a good correlation between the obtained results and the real-world data. Conclusion The results of this study were as follows: A) The two spectral index pairs of humidity-brightness coefficient and brightness coefficient-greenness were selected as the most suitable desertification indices in the study area, and therefore, the desertification risk maps were obtained through using this spectral index pair, B) The classification algorithm showed the highest degree of similarity with the accuracy of 91.96 and the kappa coefficient of 0.95 for the maps of 2000, and accuracy of 91.25 and a kappa coefficient of 0.89 for the maps of 2018, which indicated a good correlation between the obtained results and the real-world data, C) According to the results of statistical change detection analysis method, the areas of severe, relatively severe, and moderate desertification risk classes were increasing from 2000 to 2018, D) The desertification risk maps of 2000 and 2018 showed that the lands on the eastern coast, and especially on the southeast of the Lake Urmia, and the areas at the marginal edge of Tabriz Plain, overlooking the Lake Urmia were more sensitive to the desertification risk, and showed more severe degradation, compared to those on the west coast of Lake Urmia, F) Indicators such as underground water electric conductivity, chlorine index of underground water, Sodium adsorption ratio, drought index, Percentage of vegetation, had a high impact on the occurrence of desertification. Keywords: Desertification Monitoring, Lake Urmia, ENVI 5.3, Logistic Regression, Maximum Likelihood Algorithm. References: - Binal A., Christian, P. S., & Dhinwa, A. (2018). Long-term Monitoring and Assessment of Desertification Processes Using Medium and High Resolution Satellite Data. Journal of Applied Geography, 97, 10-24. - Boali, A. H., Jafari, R., & Bashari, H. (2016). Boali, A. H., Jafari, R., & Bashari, H. (2017). Analyzing the Effect of Groundwater Quality on Desertification using Bayesian Belief Networks in Segzi Desertification Hotspot. JWSS-Isfahan University of Technology, 21(3), 205-218. - Collado, A. D., Chuvieco, E., & Camarasa, A. (2002). Satellite Remote Sensing Analysis to Monitor Desertification Processes in the Crop-rangeland Boundary of Argentina. Journal of Arid Environments, 52(1), 121-133. - Cui, G., Lee, W. K., Kwak, D. A., Choi, S., Park, T., & Lee, J. (2011). Desertification Monitoring by LANDSAT TM Satellite Imagery. 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خلاصه ماشینی:
مهم ترين اهداف پـژوهش حاضـر بررسي روابط آماري موجود بين شاخص هاي طيفي مطالعه شده (پوشش گياهي تفاضلي نرمـال شـده ٣، آلبـدوي سـطح زمين ٤، تسلدکپ به همراه سه مؤلفۀ ضريب روشنايي ٥، ميزان رطوبـت ٦ و ميـزان سـبزينگي٧)، شناسـايي بهتـرين زوج شاخص طيفي معرف بيابان زايي، تهيۀ نقشۀ خطر وقوع بيابان زايي براي سال هـاي بررسـيشـده ، ارزيـابي رونـد وقـوع پديدٔە بيابان زايي و شناسايي مهم ترين عوامل مؤثر در وقوع بيابان زايي در محدودٔە مطالعه شده است .
در محدودٔە پـژوهش ميـزان پوشش گياهي، ميزان نمناکي و ميزان سبزينگي در بازٔە زماني مدنظر (٢٠٠٠- ٢٠١٨ م ) کاهش و ميزان آلبدو و ضـريب روشنايي افزايش يافته است ؛ به بيان ديگر بر وسعت نواحي داراي ضريب روشنايي يا ميـزان آلبـدوي سـطحي بيشـتر افزوده شده است ؛ بدين ترتيب در مطالعات مرتبط با بيابان زايي که بـا اسـتفاده از تکنيـک هـاي سـنجش از دور انجـام مي پذيرد، با استفاده از تکنيک هاي آمـاري رابطـۀ منفـي موجـود در بـين آنهـا بررسـي و زوج شـاخص طيفـي داراي همبستگي منفي (يعني کاهش متغير مستقل به افزايش مقدار متغير وابسته مـيانجامـد) بـه مثابـۀ زوج شـاخص معـرف بيابان زايي انتخاب و نقشۀ بيابان زايي در محدودٔە پژوهش براساس آن زوج شاخص طيفي تهيه مـي شـود.
براساس نمودارهاي نشان داده شده در شکل ٤ و روابط خطي يادشده در جدول ٢، زوج شاخص هاي ميزان سبزينگي- ضريب روشنايي و ميزان نمناکي- ضريب روشـنايي در سال هاي ٢٠٠٠ و ٢٠١٨ م همبستگي منفي دارند و زوج شاخص آلبدو- پوشش گياهي تفاضـلي نرمـال شـده بـه دليـل داشتن رابطۀ رگرسيوني مثبت معرف خوبي براي بيابان زايي در محدودٔە پژوهش نيست .
Desertification risk map of 2000 and 2018 (Authors, 2019) در مرحلۀ بعد صحت نتايج و به بياني اعتبار و صحت نقشه هاي خطر بيابان زايي براي سال هاي ٢٠٠٠ و ٢٠١٨ م بـا الگوريتم بيشترين درجۀ شباهت ١ بررسي شد.