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عبیات, محمد, عبیات, مرتضی, عبیات, مصطفی. (1402). ارزیابی شدت بیابان‎زایی با استفاده از شاخص‌های طیفی منتج از تصاویر ماهواره‌ای مطالعه موردی: شهرستان بندر ماهشهر. , 55(4), 61-81. doi: 10.22059/jphgr.2023.355751.1007753
محمد عبیات; مرتضی عبیات; مصطفی عبیات. "ارزیابی شدت بیابان‎زایی با استفاده از شاخص‌های طیفی منتج از تصاویر ماهواره‌ای مطالعه موردی: شهرستان بندر ماهشهر". , 55, 4, 1402, 61-81. doi: 10.22059/jphgr.2023.355751.1007753
عبیات, محمد, عبیات, مرتضی, عبیات, مصطفی. (1402). 'ارزیابی شدت بیابان‎زایی با استفاده از شاخص‌های طیفی منتج از تصاویر ماهواره‌ای مطالعه موردی: شهرستان بندر ماهشهر', , 55(4), pp. 61-81. doi: 10.22059/jphgr.2023.355751.1007753
عبیات, محمد, عبیات, مرتضی, عبیات, مصطفی. ارزیابی شدت بیابان‎زایی با استفاده از شاخص‌های طیفی منتج از تصاویر ماهواره‌ای مطالعه موردی: شهرستان بندر ماهشهر. , 1402; 55(4): 61-81. doi: 10.22059/jphgr.2023.355751.1007753

ارزیابی شدت بیابان‎زایی با استفاده از شاخص‌های طیفی منتج از تصاویر ماهواره‌ای مطالعه موردی: شهرستان بندر ماهشهر

پژوهش های جغرافیای طبیعی
مقاله 4، دوره 55، شماره 4، دی 1402، صفحه 61-81    اصل مقاله (1.46 M)
نوع مقاله: مقاله کامل
شناسه دیجیتال (DOI): 10.22059/jphgr.2023.355751.1007753
نویسندگان
محمد عبیات1؛ مرتضی عبیات* 2؛ مصطفی عبیات2
1گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهید چمران اهواز، اهواز، ایران
2گروه جغرافیا و برنامه‌ریزی روستایی، دانشکده علوم جغرافیایی و برنامه‌ریزی، دانشگاه اصفهان، اصفهان، ایران
چکیده
بیابان‌زایی از عوامل تخریب اکوسیستم‌های طبیعی در مناطق خشک جهان به شمار می‌آید. شناخت مناطق در معرض بیابان‌زایی، جهت مبارزه با این پدیده اهمیت فراوانی دارد. سنجش از دور، ابزاری مهم در ارزیابی و پایش تخریب سرزمین و بیابان­زایی است. هدف پژوهش حاضر، ارزیابی شدت بیابان‌زایی در شهرستان بندر ماهشهر براساس شاخص‌های طیفی منتج از تصاویر ماهواره­ای است. ابتدا شاخص‌های NDVI، SAVI، RVI، TGSI و Albedo با کمک نرم‌افزار ENVI از تصویر OLI لندست 8 منطقه استخراج شدند. سپس، برای ارزیابی رابطه همبستگی بین شاخص‌های طیفی از رگرسیون خطی استفاده شد و شدت بیابان‌زایی در منطقه طبقه‌بندی گردید. نتایج نشان داد که ضریب همبستگی بین دو شاخص NDVI و Albedo برابر با 83/0-، بین دو شاخص SAVI و Albedo برابر با 78/0- و بین دو شاخص RVI و Albedo برابر با 77/0- بوده است. ضریب همبستگی بین دو شاخص TGSI و Albedo برابر 86/0 بوده است. همبستگی بیشتر بین دو شاخص TGSI و Albedo، بیانگر مناسب­تر بودن مدل Albedo-TGSI جهت ارزیابی شدت بیابان­زایی در منطقه است. نقشه‌ بیابان‌زایی مدل Albedo-TGSI نشان داد که نواحی دارای شدت بیابان‌زایی کمتر، عمدتاً در قسمت‌های شمالی و شرقی و نواحی دارای شدت بیابان‌زایی بیشتر، عمدتاً در قسمت‌های جنوبی و جنوب غربی منطقه واقع شده‌اند.
کلیدواژه‌ها
بیابان‌زایی؛ شاخص‌های طیفی؛ آلبیدوی سطحی؛ لندست؛ بندر ماهشهر
عنوان مقاله [English]
Evaluation of Desertification Intensity using Spectral Indices Resulting from Satellite Images the Case Study of Bandar Mahshahr County
نویسندگان [English]
Mohammad Abiyat1؛ Morteza Abiyat2؛ Mostefa Abiyat2
1Department of Soil Science and Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2Department of Geography and Rural Planning, Faculty of Geographical Sciences and Planning, Isfahan University, Isfahan, Iran
چکیده [English]
ABSTRACT
Desertification is one of the factors in the destruction of natural ecosystems in arid regions of the world. Knowing the areas exposed to desertification is very important to combat this phenomenon. Remote sensing is a practical tool for evaluating and monitoring land degradation and desertification. The current research aims at the desertification intensity evaluation in Bandar Mahshahr County based on the spectral indices derived from satellite images. To begin with, utilized the ENVI software to extract several indices, such as NDVI, SAVI, RVI, TGSI, and Albedo, from the satellite image captured by the Landsat 8 OLI in the region. Then, Linear regression was utilized to determine correlations of spectral indices in the region, and the desertification intensity in the region was classified. The results showed that the correlation coefficient between NDVI and Albedo indices was -0.83, between SAVI and Albedo indices was -0.78, and between RVI and Albedo indices was -0.77. The correlation coefficient between TGSI and Albedo indices was 0.86. The higher correlation between TGSI and Albedo indicates that the Albedo-TGSI model is more appropriate for evaluating the desertification intensity in the region. The desertification map of the Albedo-TGSI model showed that the areas with less desertification intensity are located mainly in the northern and eastern parts, and the areas with more desertification intensity were situated in the southern and southwestern parts of the region
Extended abstract
Introduction
Many arid and semi-arid regions of the world are affected by land degradation and desertification. Climate changes, environmental hazards, and human activities cause desertification. Desertification causes a decrease in land potential due to factors such as loss of vegetation and destruction of soil resources. Controlling desertification is one of the necessities and priorities of natural resources management. Due to spatial and temporal information, remote sensing (RS) and satellite images play an essential role in evaluating and monitoring land degradation and desertification at local, regional, and global scales. Over the last few years, spectral indices have been increasingly utilized to determine land cover. These indicators are particularly beneficial in identifying areas susceptible to environmental hazards. Using spectral indices in creating desertification intensity maps can be an effective tool. By visualizing the areas susceptible to desertification, decision-makers and land managers can prioritize their efforts and resources more effectively. The detailed information provided by these intensity maps allows for targeted interventions and the implementation of appropriate land management and conservation practices to mitigate the effects of desertification. Additionally, by utilizing spectral indices to create intensity maps, stakeholders can better understand the spatial distribution and severity of desertification, leading to more informed decision-making in natural resources management. This, in turn, can facilitate the development and implementation of sustainable land use policies and programs aimed at controlling and reversing the process of desertification. Therefore, these maps serve as effective tools for reducing the impact of land degradation and implementing strategic desertification control measures. This research aims to assess and classify the severity of desertification in Bandar Mahshahr County, located in the southwest of Iran and south of Khuzestan province, by utilizing spectral indices derived from satellite images.
 
Materials and Methods
In this research, all the processes were performed on the OLI sensor image of the Landsat satellite 8 of the region on June 18, 2021, in row 39 and pass 165. The dark Subtraction method was used for the atmospheric corrections of the image. Then, spectral indices of NDVI, SAVI, RVI, TGSI, and Albedo were extracted from the region's image using ENVI 5.6 software. SPSS 22 software was used for statistical analysis, and ArcGIS 10.8 software was used to prepare desertification intensity maps. After extracting the spectral indices, the correlation between them was evaluated. To investigate the relationship between the four indices NDVI, SAVI, RVI, and TGSI with the Albedo index, a linear regression model based on 40 random pixels was used. In order to obtain desertification intensity equations, the slope coefficient of the regression line between the spectral indices was calculated. The natural breaks (Jenks) method in ArcGIS software was used to classify the data value into five degrees of desertification (areas without impact, low intensity, medium intensity, high intensity, and very high intensity). The map of spectral indices was validated using the error matrix and two parameters as Overall Accuracy and Kappa Coefficient.
 
Results and Discussion
Numerical values for the NDVI index, -0.45 to 0.51; for the SAVI index, from -0.91 to 1.03; for the RVI index, from 0.36 to 3.14; and for the TGSI index, from -0.09 to 0.17 were obtained. An Albedo index map was created to assess the relationship between the NDVI, SAVI, RVI, and TGSI indices and the Albedo index. Based on the obtained results, the minimum and maximum values of the Albedo index were 0.127 and 0.415, respectively. The lowest values of the Albedo index were estimated in the northern and eastern regions, and the highest values were estimated in the southern and southwestern regions. The results showed that with an increase in vegetation in the region, the number of the Albedo decreases. The linear regression model results between the indices showed that the three indices, NDVI, SAVI, and RVI, have a negative correlation with the Albedo index. Thus, the Albedo index decreases as the NDVI, SAVI, and RVI indices increase. The correlation coefficient between the two indices NDVI and Albedo is -0.83, between SAVI and Albedo, is .78, and between RVI and Albedo is -0.77. The linear regression model results between the TGSI and Albedo indices showed that these indices have a strong correlation relationship. The correlation coefficient between the TGSI and Albedo indices was 0.86. The study findings indicated that as the TGSI index increases, the Albedo also increases. Previous studies have also shown a significant relationship between desertification processes and Albedo and TGSI indices. Thus, the amount of Albedo is a function of the size of the surface soil particles, and with an increase in the size of the surface soil particles, the amount of Albedo increases. The study of desertification intensity maps in this region showed that the areas with less desertification intensity are located mainly in the northern and eastern parts, and the areas with higher desertification intensity are situated in the southern and southwestern parts of the region. For spectral index map validation, 231 pixels were selected as the ground reality of the study area. More samples were taken from the classes that had more desertified lands. Validation results of the spectral indices showed that the NDVI index had the least accuracy, and the TGSI index had the most accuracy in zoning the desertification intensity in the region.
 
Conclusion
This research used Landsat satellite images to extract spectral indices and prepare a desertification intensity map in Bandar Mahshahr County. The overall accuracy criteria and Kappa coefficient of the produced maps show the reliability of the desertification intensity zoning results. The TGSI index map has been the most accurate in zoning the desertification intensity in the region. The linear regression model results showed that the three spectral indices NDVI, SAVI, and RVI have a negative correlation with the Albedo index, and the TGSI index has a positive and strong correlation with the Albedo index. The strong correlation between TGSI and Albedo indices showed that the Albedo-TGSI model is suitable for evaluating the desertification intensity in the study area according to its climatic conditions. This model can be used in regions with similar climates to determine the desertification intensity. According to the obtained maps of desertification, the southern and southwestern parts of the region have the highest intensity of desertification.
 
Funding
There is no funding support.
 
Authors’ Contribution
All of the authors approved the content of the manuscript and agreed on all aspects of the work.
 
Conflict of Interest
Authors declared no conflict of interest.
 
Acknowledgments
We are grateful to all the scientific consultants of this paper.
کلیدواژه‌ها [English]
Desertification, Spectral Indices, Surface Albedo, Landsat, Bandar Mahshahr
مراجع
  1. احمدی، حمزه؛ اسماعیل‌پور، یحیی؛ مرادی، عباس و غلامی، حمید. (1398). ارزیابی حساسیت اراضی به بیابان‌زایی با استفاده از رویکرد پویایی سیستم در حوضه آبخیز جازموریان. پژوهش‌های حفاظت آب و خاک، 26(2)، 221-224. doi:10.22069/jwsc.2019.15565.3076
  2. ادب، حامد؛ امیراحمدی، ابولقاسم و عتباتی، آزاده. (1393). ارتباط پوشش‌گیاهی با دما و آلبیدو‌‌ی سطحی در دوره گرم سال با استفاده از داده‌های مودیس در شمال ایران. پژوهش‌های جغرافیای طبیعی، 46(4)، 419-434. doi: 10.22059/jphgr.2014.52994
  3. اصغری، صیاد؛ جلیلیان، روح‌اله؛ پیروزی‌نژاد، نوشین؛ مددی، عقیل و یادگاری، میلاد. (1399). ارزیابی شاخص‌های استخراج آب با استفاده از تصاویر ماهواره‌ای لندست؛ مطالعه موردی: رودخانه گاماسیاب کرمانشاه. تحقیقات کاربردی علوم جغرافیایی، ۲۰(۵۸)، ۵۳-۷۰. doi: 10.29252/jgs.20.58.53
  4. ایمانی، جمال؛ ابراهیمی، عطاء الله؛ قلی‌نژاد، بهرام و طهماسبی، پژمان. (1397). مقایسه دو شاخص NDVI و SAVI در سه جامعه گیاهی مختلف با شدت نمونه‌برداری متفاوت؛ مطالعه موردی: مراتع اطراف تالاب چغاخور چهارمحال و بختیاری. تحقیقات مرتع و بیابان ایران، 25(1)، 152-169.  doi: 10.22092/ijrdr.2018.116233
  5. حجازی‌زاده، زهرا؛ طولابی‌نژاد، میثم؛ رحیمی، علیرضا؛ بزمی، نسرین و بساک، عاطفه. (1396). مدلسازی فضایی- زمانی آلبیدو‌‌ در گستره‌ی ایران زمین. تحقیقات کاربردی علوم جغرافیایی، ۱۷ (۴۷)، ۱-۱۷. dor: 20.1001.1.22287736.1396.17.47.6.8
  6. خنیفر، جواد؛ خادم‌الرسول، عطااله و عامری‌خواه، هادی. (1399). مدل‌سازی میانگین وزنی قطر خاکدانه‌ها با استفاده از شاخص‌های پوشش‌گیاهی در کاربری‌های مرتع و جنگل. پژوهش‌های حفاظت آب و خاک، 27(6)، 201-214. doi: 10.22069/jwsc.2021.18202.3383
  7. درخشی، جعفر؛ سبحانی، بهروز و اصغری، صیاد. (1399). ارزیابی روند تغییرات کاربری اراضی و تأثیر آن بر آلبیدوی سطحی و دمای سطح زمین در حوضه آبخیز اهرچای. جغرافیا و آمایش شهری منطقه‌ای، 10(37)، 123-142. doi: 10.22111/gaij.2020.5951
  8. درویش‌زاده، روشنک؛ متکان، علی اکبر؛ حسینی‌اصل، امین و ابراهیمی‌خوسفی، محسن. (1391). تخمین درصد پوشش‌گیاهی منطقه خشک ایران مرکزی با استفاده از تصاویر ماهواره‌ای؛ مطالعه موردی: حوزه شیطور، بافق. خشک‌بوم، 2(1)، 25-38. dor: 20.1001.1.2008790.1391.2.1.3.8
  9. ذوالفقاری، فرهاد و عبداللهی، وحیده. (1401). تعیین مناسب‌ترین شاخص پوشش‌گیاهی برای تهیه نقشه شدت بیابان‌زایی در مناطق خشک به کمک تصاویر ماهواره سنتینل. مدیریت بیابان، 10(1)، 1-14. doi: 10.22034/jdmal.2022.548652.1375
  10. روستایی، شهرام؛ مختاری، داود و خدائی‌قشلاق، فاطمه (1399). بررسی خطر وقوع بیابان‌زایی با استفاده از شاخص‌های طیفی در محدوده‌ی پیرامونی دریاچه‌ی ارومیه. پژوهش‌های ژئومورفولوژی کمی، 9(3)، 1-17. doi: 10.22034/gmpj.2020.122206
  11. سارلی، رضا؛ روشن، غلامرضا و  گرب، استفان. (1398). سنجش و پیش‌بینی تغییرات پوشش‌گیاهی حوزه استان مازندران طی بازه زمانی 2017 تا 2005 با استفاده از زنجیره مارکوف و سیستم اطلاعات جغرافیایی (GIS). اطلاعات جغرافیایی «سپهر»، 28(111)، 149-162. doi: 10.22131/sepehr.2019.37514
  12. عبیات، محمد؛ عبیات، مرتضی و عبیات، مصطفی. (1401). بررسی کارایی روش‌های طبقه‌بندی و شاخص‌های طیفی در برآورد سطح زیرکشت محصولات زراعی شهرستان شوش. آب و خاک، 36(4)، 493-509. doi: 10.22067/jsw.2022.76746.1167
  13. محمدی، پروا؛ ابراهیمی، کیومرث و بذرافشان، جواد. (1402). بررسی تغییرات کاربری اراضی حوزه آبخیز گرگانرود با استفاده از پلتفرم گوگل ارث انجین. علوم و مهندسی آبخیزداری ایران، ۱۷(۶۰)، ۱۱-19. dor: 20.1001.1.20089554.1402.17.60.1.3
  14. نظارات، ناصر. (1393). اصطلاحات، لغات وضرب المثل‌های گویش مردم ماهشهر، هندیجان و روستاهای حومه. چاپ اول، اشراق کویر: یزد.
  15. نوروزی، آذین و نوروزی، الدوز. (1402). کاربرد الگوریتم پنجره مجزا در شناسایی جزایر حرارتی شهرستان یزد. مدل‌سازی و مدیریت آب و خاک. 3(1)، 115-129. doi: 10.22098/mmws.2022.11148.1103
  16. هاشم‌گلوگردی، ساره؛ ولی، عباسعلی و شریفی، محمدرضا. (1400). کاربرد مدل فضای ویژگی TGSI - Albedo در بررسی وضعیت بیابانی شدن مرکز استان خوزستان. مدیریت بیابان، 9(3)، 49-66. doi: 10.22034/jdmal.2021.534364.1341
  17. Abiyat, M., Abiyat, M., & Abiyat, M. (2022). Evaluation of Efficiency between Classification Methods and Spectral Indices in Cropped Area Estimation of Shush County. Water and Soil, 36(4), 493-509. doi: 10.22067/jsw.2022.76746.1167 [In Parsian]
  18. Adab, H., Amir-Ahmadi, A., & Atabati, A. (2014). Relating vegetation cover with land surface temperature and surface Albedo in warm period of year using MODIS imagery in North of Iran. Physical Geography Research Quarterly, 46(4), 419-434. doi: 10.22059/jphgr.2014.52994 [In Parsian]
  19. Ahmadi, H., Esmaeilpour, Y., Moradi, A., & Gholami, H. (2019). Assessment of land sensitivity to desertification hazard using system dynamics approach in the Jazmurian Watershed. Water and Soil Conservation, 26(2), 221-224. doi:10.22069/jwsc.2019.15565.3076 [In Parsian]
  20. Akbari, M., Memarian, H., Neamatollahi, E., Jafari Shalamzari, M., Alizadeh-Noughani, M., & Zakeri, D. (2021). Prioritizing policies and strategies for desertification risk management using MCDM–DPSIR approach in northeastern Iran. Environment, Development and Sustainability, 23, 2503-2523. doi:10.1016/j.sciaf.2019.e00146
  21. Allen, R., Tasumi, M., & Trezza, R. (2002). Surface energy balance algorithms for land. Advanced Training and User’s Manual Idaho Implementation: Washington DC.
  22. Asghari, S., Jalilyan, R. A., Pirozineghad, N., Madadi, A., & Yadeghari, M. (2020). Evaluation of water extraction indices using landsat satellite images: Case study of Gamasiab River in Kermanshah. Applied Researches in Geographical Sciences, 20(58), 53-70. doi: 10.29252/jgs.20.58.53 [In Parsian]
  23. Bagan, H., & Yamagata, Y. (2012). Landsat analysis of urban growth: How Tokyo became the world's largest megacity during the last 40 years. Remote sensing of Environment, 127, 210-222. doi:10.1016/j.rse.2012.09.011
  24. Barone, P. M., Matsentidi, D., Mollard, A., Kulengowska, N., & Mistry, M. (2022). Mapping decomposition: A preliminary study of non-destructive detection of simulated body Fluids in the Shallow Subsurface. Forensic Sciences, 2(4), 620-634. doi: 10.3390/forensicsci2040046
  25. Carns, R. C., Light, B., & Warren, S. G. (2016). The spectral albedo of sea ice and salt crusts on the tropical ocean of Snowball Earth: II. Optical modeling. Geophysical Research: Oceans, 121, 5217–5230. doi: 10.1002/2016JC011804
  26. Chen, A., Yang, X., Guo, J., Xing, X., Yang, D., & Xu, B. (2021). Synthesized remote sensing-based desertification index reveals ecological restoration and its driving forces in the northern sand-prevention belt of China. Ecological Indicators, 131, 108230. doi: 10.1016/j.ecolind.2021.108230
  27. Chen, B., Yang, Y., Xu, D., & Huang, E. (2019). A dual band algorithm for shallow water depth retrieval from high spatial resolution imagery with no ground truth. Photogrammetry and Remote Sensing, 151, 1-13. doi: 10.1016/j.isprsjprs.2019.02.012
  28. Chu, H., Venevsky, S., Wu, C., & Wang, M. (2019). NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang river basin from 1982 to 2015. Total Environment, 650, 2051-2062. doi: 10.1016/j.scitotenv.2018.09.115
  29. Clark, M. L., Roberts, D. A., & Clark, D. B. (2005). Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment, 96, 375-398. doi: 10.1016/j.rse.2005.03.009
  30. Correia, W. L. F., De Barros Santiago, D., De Oliveira-Júnior, J. F., & Da Silva Junior, C. A. (2019). Impact of urban decadal advance on land use and land cover and surface temperature in the city of Maceió, Brazil. Land use policy, 87, 104026. doi: 10.3390/su14116935
  31. Darvishzadeh, R., Matkan, A. A., Hosseiniasl, A. H., & Ebrahimi-Khusefi, M. (2012). Estimation of vegetation fraction in the Central arid region of Iran using satellite images: Case study of Sheitoor basin, Bafgh. Arid Biome, 2(1), 25-38. dor: 20.1001.1.2008790.1391.2.1.3.8 [In Parsian]
  32. Demarez, V., Gastellu-Etchegorry, J. P., Mougin, E., Marty, G., Proisy, C., Dufrêne, E., & Dantec, V. L. (1999). Seasonal variation of leaf chlorophyll content of a temperate forest. Inversion of the PROSPECT model. International Journal of Remote Sensing, 20, 879-894. doi: 10.1080/014311699212975
  33. Derakhshi, J., Sobhani, B., & Asghari, S. (2020). Evaluation of land use change trend and its impact on surface Albedo and land surface temperature in Aharchai watershed. Geography and Territorial Spatial Arrangement, 10(37), 123-142. doi: 10.22111/gaij.2020.5951
  34. Duanyang, X., Xiaogang, Y., Chunlin, X. (2019). Assessing the spatial-temporal pattern and evolution of areas sensitive to land desertification in North China, Ecological Indicators, 97, 150-158. doi: 10.1016/j.ecolind.2018.10.005
  35. Fathizad, H., Ardakani, M. A. H., Mehrjardi, R. T., & Sodaiezadeh, H. (2018). Evaluating desertification using remote sensing technique and object-oriented classification algorithm in the Iranian central desert. African Earth Sciences, 145, 115-130. doi: 10.1016/j.jafrearsci.2018.04.012
  36. Feng, K., Wang, T., Liu, S., Kang, W., Chen, X., Guo, Z., & Zhi, Y. (2022). Monitoring desertification using machine-learning techniques with multiple indicators derived from MODIS images in Mu Us Sandy Land, China. Remote Sensing, 14(11), 2663. doi: 10.3390/rs14112663
  37. Gillespie, T. W., Ostermann-Kelm, S., Dong, C., Willis, K. S., Okin, G. S., & Mac-Donald, G. M. (2018). Monitoring changes of NDVI in protected areas of southern California. Ecological Indicators, 88, 485-494. doi:10.1016/j.ecolind.2018.01.031
  38. Gonzalez, M., Zvoleff, A., Noon, M., Liniger, H., Fleiner, R., Harari, N., & Garcia, C. (2019). Synergizing global tools to monitor progress towards land degradation neutrality: Trends, earth and the world overview of conservation approaches and technologies sustainable land management database. Environmental Science & Policy, 93, 34-42. doi: 10.1016/j.envsci.2018.12.019
  39. Gutman, G., Skakun, S., & Gitelson, A. (2021). Revisiting the use of red and near-infrared reflectances in vegetation studies and numerical climate models. Science of Remote Sensing, 4, 100025. doi: 10.1016/j.srs.2021.100025
  40. Han, L., Zhang, Z., Zhang, Q., & Wan, X. (2015). Desertification assessments in the Hexi corridor of northern China’s Gansu Province by remote sensing. Natural Hazards, 75(3), 2715-2731. doi: 10.1007/s11069-014-1457-0
  41. Hartomo, K. D., Nataliani, Y., & Hasibuan, Z. A. (2022). Vegetation indices’ spatial prediction based novel algorithm for determining tsunami risk areas and risk values. PeerJ Computer Science, 8, 935. doi: 10.7717/peerj-cs.935
  42. Hashem-Geloogerdi, S., Vali, A., & Sharifi, M. R. (2021). Application of TGSI - Albedo feature space model in assessing of desertification status in the center of Khuzestan province. Desert Management, 9(3), 49-66. doi: 10.22034/jdmal.2021.534364.1341 [In Parsian]
  43. Hejazizadeh, Z., Toulabi-Nejad, M., Rahimi, A., Bazmi, N., & Bosak, A. (2017). Modeling of spatio-temporal of albedo over Iran. Applied researches in Geographical Sciences, 17(47), 1-17. dor: 20.1001.1.22287736.1396.17.47.6.8 [In Parsian]
  44. Hou, J., Gao, Y., Fan, T., Wang, P., Wang, Y., Wang, J., & Lu, W. (2023). Tsunami risk change analysis for qidong County of China based on land use classification. Marine Science and Engineering, 11(2), 379. doi: 10.3390/jmse11020379
  45. Houssa, R., Pion, J.C., & Yésou, H. (1996). Effects of granulometric and mineralogical composition on spectral reflectance of soils in a Sahelian Area. Photogrammetry and Remote Sensing, 51, 284-298. doi: 10.1016/S0924-2716(96)00023-8
  46. Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2020). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Forestry Research, 32(1), 1-6. doi: 10.1007/s11676-020-01155-1
  47. Imani, J., Ebrahimi, A., Gholonejad, B., & Tahmasebi, P. (2018). Comparison of NDVI and SAVI in three plant communities with different sampling intensity: Case study of Choghakhour Lake Rangelands in Chaharmahal and Bakhtiari Province. Range and Desert Research, 25(1), 152-169. doi: 10.22092/ijrdr.2018.116233 [In Parsian]
  48. Izadi, R., & Allahverdi, A. (2022). An overview of methods and materials for sandy soil stabilization: emerging advances and current applications. Ecopersia, 10(4), 333-347. dor: 20.1001.1.23222700.2022.10.4.7.6
  49. Kalyan, S., Sharma, D., & Sharma, A. (2021). Spatio-temporal variation in desert vulnerability using desertification index over the Banas River Basin in Rajasthan, India. Geosciences, 14: 1-13. doi: 10.1007/s12517-020-06417-0
  50. Karmaoui, A., El Jaafari, S., Chaachouay, H., & Hajji, L. (2021). The socio-ecological system of the Pre-Sahara zone of Morocco: A conceptual framework to analyse the impact of drought and desertification. GeoJournal, 87, 4961-4974. doi:10.1007/s10708-021-10546-8
  51. Karunaratne, S., Thomson, A., Morse-McNabb, E., Wijesingha, J., Stayches, D., Copland, A., & Jacobs, J. (2020). The fusion of spectral and structural datasets derived from an airborne multispectral sensor for estimation of pasture dry matter yield at paddock scale with time. Remote Sensing, 12(12), 2017. doi: 10.3390/rs12122017
  52. Khanifar, J., Khademalrasoul, A., & Amerikhah, H. (2021). Modeling mean weight-diameter of aggregates based on vegetation indices in rangeland and forest land uses. Water and Soil Conservation, 27(6), 201-214. doi:10.22069/jwsc.2021.18202.3383 [In Parsian]
  53. Kong, Z. H., Stringer, L., Paavola, J., & Lu, Q. (2021). Situating China in the global effort to combat desertification. Land, 10(7), 702. doi: 10.3390/land10070702
  54. Lal, R. (2006). Encyclopedia of soil science, Second Edition, Marcel Dekker: New York. doi: 10.1017/S0014479703341523
  55. Lamamri, M., Lghabi, N., Ghazi, A., El Harchaoui, N., Adnan, M. S. G., & Shakiul Islam, M. (2022). Evaluation of desertification in the Middle Moulouya Basin (North-East Morocco) using sentinel-2 images and spectral index techniques. Earth Systems and Environment, September 19, 1-20. doi: 10.1007/s41748-022-00327-9
  56. Lamchin, M., Lee, W. K., Jeon, S. W., Lee, J. Y., Song, C., Piao, D., Lim, C. H., Khaulenbek, A. & Navaandorj, I. (2017). Correlation between desertification and environmental variables using remote sensing techniques in Hogno Khaan, Mongolia. Sustainability, 9(4), 581. doi:10.3390/su9040581
  57. Lamqadem, A. A., Saber, H., & Pradhan, B. (2018). Quantitative assessment of desertification in an arid oasis using remote sensing data and spectral index techniques. Remote Sensing, 10(12), 1862. doi: 10.3390/rs10121862
  58. Li, X., & Shi, F. (2021). Effects of evolving salt precipitation on the evaporation and temperature of sandy soil with a fixed groundwater table. Vadose Zone Journal, 20(3), 1-12. doi: 10.1002/vzj2.20122
  59. Liang, S. (2001). Narrowband to broadband conversions of land surface albedo I Algorithms. Remote Sensing of Environment, 76 (2): 213-238. doi: 10.1016/S0034-4257(00)00205-4
  60. Liang, X., Li, P., Wang, J., Shun Chan, F. K., Togtokh, C., Ochir, A., & Davaasuren, D. (2021). Research progress of desertification and its prevention in mongolia. Sustainability, 13(12), 6861. doi:10.3390/su13126861
  61. Meng, X., Gao, X., Li, S., Li, S., & Lei, J. (2021). Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020. Ecological Indicators, 129: 107908. doi: 10.1016/j.ecolind.2021.107908
  62. Mohammadi, P., Ebrahimi, K., & Bazrafshan, J. (2023). Investigation of land use changes in Gorganrood catchment using Google Earth Engine platform. Watershed Management Science and Engineering, 17(60), 11-19. dor: 20.1001.1.20089554.1402.17.60.1.3 [In Parsian]
  63. Newcomer, M., Chen Hsu, W., Justice, E., Guild, L., Rogoff, D. & Skiles, J. (2011). Prototype Application of NASA Missions to Identify Patterns of Wetland Vegetation Development within the South San Francisco Bay Salt Ponds. ASPRS 2011 Annual Conference, Milwaukee: Wisconsin.
  64. Nezarat, N. (2013). Idioms, words and proverbs of the people dialect of Mahshahr and Handijan and the villages in the suburbs. First Edition, Ishraq Kavir: Yazd. [In Parsian]
  65. Norouzi, A., & Norouzi, U. (2023). Application of split-window algorithm to study urban heat island in Yazd county. Water and Soil Management and Modelling, 3(1), 115-129. doi: 10.22098/mmws.2022.11148.1103 [In Parsian]
  66. Othman, B. A., Marto, A., Uzuoka, R., Ueda, K., & Mohd Satar, M. H. (2022). Liquefaction resistance of Sand-Kaolin mixtures: Effect of sand sizes. IOP Conference Series: Earth and Environmental Science, Volume 1103, Natural Disaster Seminar 2019, Kuala Lumpur: Malaysia.
  67. Piña, R., Díaz-Delgado, C., Mastachi-Loza, C. A., & González-Sosa, E. (2016). Integration of remote sensing techniques for monitoring desertification in Mexico. Human and Ecological Risk Assessment, 22(6), 1323-1340. doi: 10.1080/10807039.2016.1169914
  68. Qi, G., Song, J., Li, Q., Bai, H., Sun, H., Zhang, S., & Cheng, D. (2022). Response of vegetation to multi-timescales drought in the qinling mountains of China. Ecological Indicators, 135, 108539. doi: 10.1016/j.ecolind.2022.108539
  69. Rey, F., Bifulco, C., Bischetti, G. B., Bourrier, F., De Cesare, G., Florineth, F., Graf, F., Marden, M., Mickovski, S.B., Phillips, C., Peklo, K., Poesen, J., Polster, D., Preti, F., Rauch, H.P., Raymond, P., Sangalli, P., Tardio, G., & Stokes, A. (2019). Soil and water bioengineering: Practice and research needs for reconciling natural hazard control and ecological restoration. Total Environment, 648, 1210-1218. doi: 10.1016/j.scitotenv.2018.08.217
  70. Rostaei, S., Mokhtari, D., & Khodaei-Gheshlagh, F. (1401). Evaluating the risk of desertification using the spectral indices in the surrounding area of Lake Urmia. Quantitative Geomorphological Research, 9(3), 1-17. doi: 10.22034/gmpj.2020.122206 [In Parsian]
  71. Salunkhe, S. S., Bera, A. K., Rao, S. S., Venkataraman, V. R., Raj, U., & Murthy, Y. K. (2018). Evaluation of indicators for desertification risk assessment in part of Thar Desert Region of Rajasthan using geospatial techniques. Earth System Science, 127, 1-24. doi: 10.1007/s12040-018-1016-2
  72. Sarli, R., Roshan, G., & Grab, S. (2019). Evaluation and prediction of vegetation changes of Mazandaran, Iran from 2005 to 2017 using Markov chain method and geographical information systems (GIS). Geographical Data (Sepehr), 28(111), 149-162. doi: 10.22131/sepehr.2019.37514 [In Parsian]
  73. Sebbah, B., Alaoui, O. Y., Wahbi, M., Maâtouk, M., & Achhab, N. B. (2021). QGIS-Landsat Indices plugin (Q-LIP): Tool for environmental indices computing using landsat data. Environmental Modelling & Software, 137, 104972. doi: 10.1016/j.envsoft.2021.104972
  74. Sirera, À. P, Antichi, D., Warren Raffa, D., & Rallo, G. (2021). Application of remote sensing techniques to discriminate the effect of different soil management treatments over rainfed vineyards in Chianti Terroir. Remote Sensing, 13(4), 716. doi: 10.3390/rs13040716
  75. Somvanshi, S. S., & Kumari, M. (2020). Comparative analysis of different vegetation indices with respect to atmospheric particulate pollution using sentinel data. Applied Computing and Geosciences, 7, 100032. doi: 10.1016/j.acags.2020.100032
  76. Syahindra, K. D., Ma’arif, S., Widayat, A. A., Fauzi, A. F., & Setiawan, E. A. (2021). Solar PV system performance ratio evaluation for electric vehicles charging stations in transit oriented development (TOD) areas. E3S Web of Conferences, 231, 02002. doi: 10.1051/e3sconf/202123102002
  77. Tervonen, T., Sepehr, A., & Kadziński, M. (2015). A multi-criteria inference approach for anti-desertification management. Journal of Environmental Management, 162, 9-19. doi: 10.1016/j.jenvman.2015.07.006
  78. Torres, L. K., Martínez, D. W., & Saba, M. (2023). The widespread use of remote sensing in asbestos, vegetation, oil and gas and geology applications. Atmosphere, 14(1), 172. doi: 10.3390/atmos14010172
  79. Uzuner, Ç., & Dengiz, O. (2020). Desertification risk assessment in Turkey based on environmentally sensitive areas. Ecological Indicators, 114, 106295. doi: 10.1016/j.ecolind.2020.106295
  80. Wang, G., Liu, S., Liu, T., Fu, Z., Yu, J., & Xue, B. (2018). Modelling above-ground biomass based on vegetation indexes: a modified approach for biomass estimation in semi-arid grasslands. Remote Sensing, 40(10), 3835-3854. doi: 10.1080/01431161.2018.1553319
  81. Wang, J., Han, P., Zhang, Y., Li, J., Xu, L., Shen, X., Yang, Z., Xu, S., Li, G., & Chen, F. (2022). Analysis on ecological status and spatial–temporal variation of Tamarix chinensis forest based on spectral characteristics and remote sensing vegetation indices. Environmental Science and Pollution Research, 29(25), 37315-37326. doi: 10.1007/s11356-022-18678-1
  82. Wang, J., Liu, D., Ma, J., Cheng, Y., & Wang, L. (2021). Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin. Arid Land, 13, 40-55. doi: 10.1007/s40333-021-0052-y
  83. Wang, L. C., Hoang, D. V., & Liou, Y. A. (2022). Quantifying the impacts of the 2020 flood on Crop production and food security in the middle reaches of the Yangtze river, China. Remote Sensing, 14(13), 3140. doi: 10.3390/rs14133140
  84. Wang, M., He, G., Zhang, Z., Wang, G., Wang, Z., Yin, R., Cui, S., Wu, Z. & Cao, X. (2019(. A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data. Applied Earth Observation and Geoinformation, 76, 204-217. doi: 10.1016/j.jag.2018.11.015
  85. Wei, H., Wang, J., & Han, B. (2020). Desertification information extraction along the China–Mongolia railway supported by multisource feature space and geographical zoning modeling. Selected Topics in Applied Earth Observations and Remote Sensing, 13, 392-402. doi:10.1109/jstars.2019.2962830
  86. Wei, H., Wang, J., Cheng, K., Li, G., Ochir, A., Davaasuren, D., & Chonokhuu, S. (2018). Desertification information extraction based on feature space combinations on the Mongolian plateau. Remote Sensing, 10(10), 1614. doi: 10.3390/rs10101614
  87. Xiao, F., Liu, Q., Li, S., Qin, Y., Huang, D., Wang, Y., & Wang, L. (2023). A Study of the Method for Retrieving the Vegetation Index from FY-3D MERSI-II Data. Remote Sensing, 15(2), 491. doi: 10.3390/rs15020491
  88. Xiao, J., Shen, Y., Tateishi, R., Bayaer, W. (2006). Development of topsoil grain size index for monitoring desertification in arid land using remote sensing. Remote Sensing, 27(12), 2411–2422. doi:10.1080/01431160600554363
  89. Yang, C., Wu, G., Ding, K., Shi, T., Li, Q., & Wang, J. (2017). Improving land use/land cover classification by integrating pixel unmixing and decision tree methods. Remote Sensing, 9(12), 1222. doi: 10.3390/rs9121222
  90. Zhang, T., Xu, X., Jiang, H., Qiao, S., Guan, M., Huang, Y., & Gong, R. (2022). Widespread decline in winds promoted the growth of vegetation. The Total Environment, 825, 153682. doi: 10.1016/j.scitotenv.2022.153682
  91. Zolfaghari, F., & Abdollahi, V. (1401). Determining the desertification intensity based on spectral indices using Sentinel-2 images: Case study of Sistan and Baluchestan province. RS and GIS for Natural Resources, 13(1), 108-126. Doi: 10.22034/jdmal.2022.548652.1375 [In Persian]
  92. Zongfan, B., Ling, H., Xuhai, J., Ming, L., Liangzhi, L., Huiqun, L., & Jiaxin, L. (2022). Spatiotemporal evolution of desertification based on integrated remote sensing indices in Duolun County, Inner Mongolia. Ecological Informatics, 70, 101750. doi: 10.1016/j.ecoinf.2022.101750
  93. Zuo, X., Zhao, H., Zhao, X., Guo, Y., Yun, J., Wang, Sh., & Miyasaka, T. (2009). Vegetation pattern variation, soil degradation and their relationship along a grassland desertification gradient in Horqin sandy land, Northern China. Environmental Geology, 58, 1227-1237. doi: 10.1007/s00254-008-1617-1
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