ارزیابی و مقایسه روش های طبقه بندی نظارت شده جهت تهیه نقشه درجات شوری خاک با استفاده از تصاویر ماهواره ای سنتینل-2

نوع مقاله: مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد سنجش ازدور، دانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده های فنی، دانشگاه تهران

2 استادیار، دانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده های فنی، دانشگاه تهران

3 استادیار، موسسه تحقیقات خاک و آب، کرج

10.22131/sepehr.2019.36608

چکیده

شوری خاک یکی از عوامل گسترش بیابان­زایی و تخریب منابع زیست محیطی در مناطق خشک و نیمه خشک محسوب می­شود. با توجه به روند رو به گسترش شوری­زایی در طی سالیان اخیر و اهمیت حفظ منابع طبیعی، تعیین گستره نواحی تحت تأثیر این پدیده و شدت شوری در این مناطق از اهمیت ویژه­ای برخوردار است. استفاده از پتاسیل تصاویر ماهواره­ای با توان تفکیک مکانی و طیفی بالا و به­ کارگیری تکنیک­های سنجش ­از دوری یکی از راه­های مؤثر در تشخیص این پدیده و تعیین شدت شوری در نواحی آسیب دیده است. براین اساس پژوهش حاضر با نمونه­برداری از خاک منطقه­ای واقع در کوه سفید استان قم که تحت تأثیر شوری است، به تهیه نقشه سطوح مختلف شوری خاک با استفاده از تصاویر ماهواره سنتینل-2 پرداخته است. در این راستا، شاخص­های متنوع شوری از تصاویر ماهواره­ای استخراج شده و در فرآیند طبقه­بندی تصویر به کلاس­های شوری از قبیل خاک بدون شوری، با شوری کم، شوری متوسط، شوری بالا و خاک اشباع از شوری مورد استفاده قرار گرفتند. نتایج طبقه­بندی صورت گرفته از 5 الگوریتم طبقه­بندی نظارت شده شامل حداقل فاصله، ماهالانوبیس، متوازی السطوح، حداکثر احتمال و ماشین­بردارپشتیبان، بیانگر بالاترین دقت به ­دست ­آمده از طبقه­بندی­ کننده ماشینبردار پشتیبان با دقت کلی 218/92 درصد و ضریب کاپای 894/0 در تهیه نقشه­ی کلاس­های شوری بود. ارزیابی نقشه­های به دست­ آمده از کلاس‌‌های شوری همچنین نشان­دهنده­ی شدت شوری بالاتر نواحی شرقی کوه­سفید نسبت به دیگر مناطق بوده که ناشی از مجاورت بیشتر این نواحی نسبت به دریاچه نمک استان قم و کشیده شدن سطوح نمک به زمین­های اطراف می­باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluating and comparing supervised classification algorithms With the aim of mapping soil salinity levels using Sentinel-2 satellite imageries

نویسندگان [English]

  • Mohammad Mahdi Taghadosi 1
  • Mahdi Hasanlou 2
  • Kamran Eftekhari 3
1 Ms.c student of remote sensing in School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran
2 Assistant professor in School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran
3 Assistant professor in Soil & Water Research Institute
چکیده [English]

Extended Abstract
Introduction: Soil salinity is considered to be a major cause of desertification and destruction of environmental resources in arid and semi-arid regions. Due to the importance of conserving natural resources and also the increasing trend of soil salinity during the last few years, determining the extent of salinity spread and its severity in affected areas is especially important. Using the potential of high resolution satellite imagery and remote sensing techniques is one of the most effective ways for detecting salinity in salt affected regions. Among different satellite sensors, satellites which provide large scale multispectral satellite imageries with high spatial and spectral resolution have a high potential for assessing salinization and mapping soil salinity in study regions.
Materials and Methods: Accordingly, this paper aims to map different salinity levels in an area in Kuh-Sefid district (Qom Province), which is highly affected by salinity, using Sentinel-2 recent imageries. For this purpose, field study was conducted and salinity level was measured for several soil samples randomly collected from the site. Different salinity indicators, like salinity and vegetation indices, LST, and Digital Elevation Model of the site produced based on SRTM elevation data were also extracted from corresponding satellite images. These indicators were then used for mapping salinity levels in Kuh-Sefid district. Principal Component Analysis was used to gain the largest amount of available information and reduce the dimensionality of data cube. Based on the performed analysis, different supervised classification algorithms were used to map salinity levels and divide the site into five distinct salinity classes - normal, slightly saline, moderately saline, highly saline, and extremely saline. 
Results and Discussion: Data was analyzed based on five supervised classification algorithms, including Minimum Distance, Mahalanobis, Parallelepiped, Maximum Likelihood, and Support Vector Machine (SVM). Results indicated that the best accuracy in mapping salinity classes was obtained from SVM classifier, with overall accuracy of 92.218 and Kappa coefficient of 0.894. The results also revealed that Maximum Likelihood Classifier with overall accuracy of 90.718 has a high potential for discriminating saline surfaces and producing salinity map. In addition, more than 62% of soil types in this region are categorized in moderate, high and extreme salinity classes, which indicates that the area is highly at risk. 
Conclusions: Evaluating the results of salinity classes shows that the eastern areas of Kuh-Sefid are relatively more severely affected by salinity. This is due to vicinity of Qom Salt Lake and drawing of saline soil into surrounding areas. If this process continues, it will lead to loss of soil fertility and crop productivity in this region over the next few years. Due to their potential in detecting soil salinity and providing large-scale maps of salinity levels, multispectral Sentinel-2 imageries are considered to be a powerful tool in soil reclamation programs and land management studies.

کلیدواژه‌ها [English]

  • Salinization – Multispectral Satellite Imagery – Salinity Indicators – Supervised Classification – Salinity Levels

1- فلاحی، ش.، م. ح. بنائی، ی. اسکندرزاده. 1362. گزارش مطالعات خاکشناسی نیمه تفصیلی منطقه قم مسیله. سازمان تحقیقات کشاورزی و منابع طبیعی مؤسسه تحقیقات خاک و آب وزارت کشاورزی و عمران روستایی. نشریه فنی شماره 628.

2- Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433–459.

3- Allbed, A., & Kumar, L. (2013). Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Advances in remote sensing, 2(04), 373.

4- Allbed, A., Kumar, L., & Aldakheel, Y. Y. (2014). Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region. Geoderma, 230–231, 1–8.

5- Asfaw, E., Suryabhagavan, K. V., & Argaw, M. (2016). Soil salinity modeling and mapping using remote sensing and GIS: the case of Wonji sugar cane irrigation farm, Ethiopia. Journal of the Saudi Society of Agricultural Sciences.

6- Avdan, U., & Jovanovska, G. (2016). Algorithm for Automated Mapping of Land Surface Temperature Using LANDSAT 8 Satellite Data. Journal of Sensors, 2016, 1–8.

7- Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5), 893–903.

8- Cooley, T., Anderson, G. P., Felde, G. W., Hoke, M. L., Ratkowski, A. J., Chetwynd, J. H., Berk, A. (2002). FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation. In Geoscience and Remote Sensing Symposium, 2002. IGARSS’02. 2002 IEEE International, Vol. 3, pp. 1414–1418.

9- Elhaddad, A., & Garcia, L. (2006). Detecting soil salinity levels in agricultural lands using satellite imagery. In Proceedings of the American Society for Photogrammetry and Remote Sensing Annual Conference.

10- Elnaggar, A. A., & Noller, J. S. (2009). Application of Remote-sensing Data and Decision-Tree Analysis to Mapping Salt-Affected Soils over Large Areas. Remote Sensing, 2(1), 151–165.

11- Garcia, L., Eldeiry, A., & Elhaddad, A. (2005). Estimating soil salinity using remote sensing data. In Proceedings of the 2005 Central Plains Irrigation Conference (Vol. 110).

12- George, J., & Kumar, S. (2015). Hyperspectral Remote Sensing in Characterizing Soil Salinity Severity using SVM Technique -A Case Study of Alluvial Plains. International Journal of Advanced Remote Sensing and GIS, 2015, 1344–1360461.

13- Gorji, T., Sertel, E., & Tanik, A. (2017). Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey. Ecological Indicators, 74, 384–391.

14- Hamzeh, S., Naseri, A. A., Panah, S. A., Mojaradi, B., Bartholomeus, H. M., & Herold, M. (2012, October). Mapping salinity stress in sugarcane fields with hyperspecteral satellite imagery. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV (Vol. 8531, p. 85312B). International Society for Optics and Photonics.

15- Iqbal, S., & Mastorakis, N. (2015). Soil salinity detection using RS data. In Advances in environmental science and energy planning.

16- Katawatin, R., & Kotrapat, W. (2005, April). Use of LANDSAT-7 ETM+ with ancillary data for soil salinity mapping in Northeast Thailand. In Third International Conference on Experimental Mechanics and Third Conference of the Asian Committee on Experimental Mechanics (Vol. 5852, pp. 708-717). International Society for Optics and Photonics.

17- Khan, N. M., Rastoskuev, V. V., Sato, Y., & Shiozawa, S. (2005). Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators. Agricultural Water Management, 77(1), 96–109.

18- Koohafkan, P., & Stewart, B. A. (2008). Water and cereals in drylands. Rome: Food and Agriculture Organization of the United Nations.

19- Lobell, D. B., Lesch, S. M., Corwin, D. L., Ulmer, M. G., Anderson, K. A., Potts, D. J., Baltes, M. J. (2010). Regional-scale assessment of soil salinity in the Red River Valley using multi-year MODIS EVI and NDVI. Journal of Environmental Quality, 39(1), 35–41.

20- Meng, J., & Wu, B.-F. (2008). Study on the crop condition monitoring methods with remote sensing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37.

21- Metternicht, G. I., & Zinck, J. A. (2003). Remote sensing of soil salinity: potentials and constraints. Remote Sensing of Environment, 85(1), 1–20.

22- Morshed, M. M., Islam, M. T., & Jamil, R. (2016). Soil salinity detection from satellite image analysis: an integrated approach of salinity indices and field data. Environmental Monitoring and Assessment, 188(2), 119.

23- Mueller-Wilm, U., Devignot, O., & Pessiot, L. (2016). MPC Sen2Cor Configuration and User Manual. S2-PDGS-MPC-L2A-SUM-V2.3 Issue: 01

24- Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572.

25- Richards, L. A. (1954). Diagnosis and Improvement of Saline and Alkali Soils. Soil Science, 78(2), 154.

26- Shirazi, M., Zehtabian, G. R., Matinfar, H. R., & Alavipanah, S. K. (2012). Evaluation of LISS-III Sensor Data of IRS-P6 Satellite for Detection Saline Soils (Case Study: Najmabad Region). Desert, 17(3), 277–289.

27- Soil Salinity Class Ranges (2018). Retrieved February 24, 2018, from http://vro.agriculture.vic.gov.au/dpi/vro/vrosite.nsf/pages/water_spotting_soil_salting_class_ranges.

28- Taghadosi, M. M., & Hasanlou, M. (2017). Trend Analysis of Soil Salinity in different land cover types using LANDSAT Time Series data (case Study Bakhtegan Salt Lake). International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42.

29- Young, N. E., Anderson, R. S., Chignell, S. M., Vorster, A. G., Lawrence, R., & Evangelista, P. H. (2017). A survival guide to Landsat preprocessing. Ecology, 98(4), 920–932.