Monitoring Soil Salinity and Vegetation Using Multispectral Remote Sensing Data in Interceptor Drain of Salt Marsh in Qazvin Plain

Document Type : Research Paper

Authors

1 Agricultural Engineering Research Department, Qazvin Agricultural and Natural Resources Research and Education Center, AREEO, Qazvin, Iran.

2 Department of Agricultural Engineering Research Institute (AERI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

3 Department of Irrigation and Drainage, Agricultural Engineering Research Institute; Agricultural Research, Education and Extension Organization, Karaj, Iran

4 Department of Irrigation and Drainage Engineering, College of Aburaihan, University of Tehran, Tehran, Iran

Abstract

Soil salinity and soil salinization as one of the problems facing agriculture and natural resources are of great importance which needs to be prevented with proper knowledge. In this regard, it is important to obtain information about soil salinity and vegetation, such as their amount and distribution. The use of satellite data enables extensive study of soil salinity and vegetation. Since vegetation in most arid and semi-arid regions is strongly influenced by soil properties such as salinity, therefore, this study investigated the effects of Interceptor Drain on soil salinity and vegetation changes using remote sensing capabilities in a 15-year interval. Results showed that construction Interceptor Drain in Salt Marsh Qazvin plain had no effect on soil salinity changes and vegetation cover. According to the results of correlation test between measured soil elements and satellite image bands, bands 5 and 7 were highly correlated with soil SAR (Sodium Adsorption Ratio) index prior to drainage construction and thus, the two bands after drainage construction had a significant correlation with soil EC (Electrical Conductivity) index. In fact, indices including red and infrared bands showed a significant relationship with soil salinity parameters. Also the results of correlation test of remote sensing indices and ground data in the salinity area showed that SI (Salinity Index) index had a highly significant correlation with soil salinity data.
Extended Abstract
1-Introduction
One of the methods of salinity monitoring is ground-based data, which can be time-consuming and costly, especially if large-scale monitoring is performed over multiple time periods. Using Remote Sensing method and georeferenced and laboratory data, soil salinity changes over time can be monitored. The spectral reflectance of a variety of salts at the soil surface has been studied in several studies which has been used as a direct indicator in remote sensing. However, when the soil moisture is high or the salt layer is not visible at the soil surface or the salt is mixed with other soil components, the direct salinity detection approach will become more complex. However, vegetation and saline-friendly plants can be used as a sign of soil salinity for indirect detection and identification of saline areas based on spectral reflectance of plants. The purpose of the present study was to investigate the trend of soil salinity and vegetation changes using remote sensing capabilities in two intervals before and after construction of Interceptor Drain in Salt Marsh Qazvin plain. Therefore, the trends of soil salinity and vegetation changes over a 15-year period have been studied.
2-Materials and Methods
The study area is located in a part of Qazvin province, 150 km northwest of Tehran and the major cities adjacent to the area in Qazvin are Takestan  in the west, Abike in the north and Dansfahan in the southwest. Due to the geological conditions of the bedrock and deposition of sediments and groundwater discharge from Qazvin and Hashtgerd Plains, the marsh has been formed which has become saline due to years of severe evaporation. Most saline species have little growth in the salinity range and only in the months when rainfall increases and it is moderately saline, the plants with moderate to low salinity resistance and with a low vegetative period have a short time. Then, with increasing salinity, the soil is seated and dried; only saline-resistant plants in this area survive for the remainder of the year.
In this study, spatial and temporal variations of vegetation and soil salinity were investigated using Landsat 7 satellite images. After calculating salinity and vegetation indices, the spatial variability map of soil salinity and vegetation index was prepared. In this study, Landsat satellite images during years 2004 to 2018 were used to study the trends of soil salinity and vegetation changes in Salt Marsh Qazvin plain. After analyzing the remote sensing indices in the study area, the data from satellite images and georeferenced data were compared. The soil salinity and vegetation indices used in the study included 6 soil salinity indices and 5 vegetation indices. The soil samples used in this study were related to 99 observation wells dug in the Proximity of Interceptor Drain of Salt Marsh Qazvin plain during 2010 and 2012.
3-Results and Discussion
According to the results of correlation test between measured elements in soil and satellite image bands, bands 5 and 7 were highly correlated with soil SAR index before drainage construction, and thus two bands after drainage construction, with soil EC index. The use of different soil salinity and vegetation index equations gives the results the differential preferences to achieve adequate soil salinity and vegetation index estimation on a large scale using remote sensing data. The evaluation of different soil salinity indices was based on the Statistical test. Statistical test results comparing mean at seasonal variations scale showed that remotely sensed indices related to soil salinity monitoring including BI, SI, SI1, SI2 and SI3 indices showed significant response to seasonal changes in surface soil condition. On the other hand, indices related to monitoring of vegetation status showed a significant difference in vegetation status from spring to summer. The results of correlation test of remote sensing indices and ground data expressed that in the salinity range of SI index there was a significant correlation with soil salinity data. Mean correlation results demonstrated that GVI had a high negative correlation with all salinity indices, but there was less correlation between salinity and other vegetation indices. The satellite images used in this study indicated the highest correlation with soil salinity in the dry months of the year and the correlation between the two factors was reduced in the months with precipitation. Soil salinity estimation based on SI index has been investigated in several cases which revealed that this index is accurate in estimating surface soil salinity in arid and semi-arid regions.
4-Conclusion
The present study showed that Interceptor Drain of Salt Marsh Qazvin plain had no effect on soil salinity and vegetation changes in the region, so none of the 11 indices derived from satellite images had significant changes in the period before and after drainage. In other words, soil salinity and percentage of vegetation in the Salt Marsh Qazvin plain have not changed significantly over a period of 15 years. However, due to the dryness of the area and the lack of rainfall as well as the incidence of drought, the fresh water content for natural leaching of adjacent drainage lands is limited and the drainage increases the intensity of groundwater flow and reduces soil salinity by creating a hydraulic gradient. Strengthening of vegetation in the area is affected by drainage. As the Qazvin Plain is one of the agricultural hubs of the country, hence the expansion of saline lands is one of the biggest threats to agriculture in the region. Since deep soil salinity status is one of the important factors in the establishment of vegetation in the region, studies combining remote sensing method, geophysical data and simulation models can lead to a better understanding of the status of the area.
 

Keywords

Main Subjects

References

احمدی، مجید؛ رضایی­مقدم، محمدحسین؛ فیضی­زاده، بختیار (1396). بررسی شاخص‌ها و تهیة نقشة شوری خاک با استفاده از داده‌های سنجش ‌از دور (مطالعة موردی: دلتای آجی چای). سنجش ‌از دور و سامانة اطّلاعات جغرافیایی در منابع طبیعی، 8 (1)، 85-96.
اژیرابی، رحیم؛ کامکار، بهنام؛ عبدی، امید (1394). مقایسة شاخص‎های مختلف استخراج­شده از تصاویر ماهوارة لندست برای پهنه‏بندی شوری خاک در مزرعة نمونة ارتش گرگان. مدیریت خاک و تولید پایدار، 5 (1)، 173-186.
اسکندری، لیلا؛ افلاطونی، محمد؛ فولادمند، حسین (1392). ارزیابی مدل PMWIN در پایین­انداختن سطح ایستابی در حاشیة زهکش حائل شوره‌زار قزوین. نشریة آبیاری و زهکشی ایران، 7 (3)، 422-431.
افلاطونی، محمد؛ اسکندری، لیلا؛ دهقانی سانیج، حسین (1393). واسنجی و تحلیل حسّاسیت رفتار هیدرولیکی آبخوان در شبیه‌سازی زهکش حائل دشت قزوین. تحقیقات آب­وخاک ایران، 45 (3)، 283-291.
خدادادی، مارال؛ عسکری، محمدصادق؛ سرمدیان، فریدون؛ رفاهی، حسینقلی؛ نوروزی، علی‌اکبر؛ حیدری، احمد؛ متین‌فر، حمیدرضا (1388). تهیة نقشة خاک‌های تحت‌ تأثیر شوری با استفاده از ‏داده‌های سنجندة ‏ETM+‎‏ در بخشی از دشت قزوین. مهندسی و مدیریت آبخیز، 1(2)، 99-110.
دائم­پناه، راضیه؛ حق­نیا، غلامحسین؛ علیزاده، امین؛ کریمی کارویه، علیرضا (1390). تهیة نقشة شوری و سدیمی خاک سطحی با روش‌های دورسنجی و زمین‌آماری در جنوب شهرستان مه­ولات. آب­وخاک، 25 (3)، 498-508.
دشتکیان، کاظم؛ پاک‌پرور، مجتبی؛ عبدالهی، جلال (1387). بررسی ‌روش‌های ‌تهیة ‌نقشة ‌شوری ‌خاک با استفاده از داده‌های ماهواره‌ای لندست ‌در منطقة ‌مروست. تحقیقات مرتع و بیابان ایران، 15(2)، 139-157.
زینالی، محمد؛ جعفرزاده، علی­اصغر؛ شهبازی، فرزین؛ اوستان، شاهین؛ ولی­زاده کامران، خلیل (1395). ارزیابی شوری خاک سطحی با روش پیکسل­مبنا براساس داده­های سنجندة TM. اطّلاعات جغرافیایی (سپهر)، 25 (99)، 127-139.
ستوده­نیا، عباس؛ جعفری، محدثه؛ دانش کار آراسته، پیمان (1393). نقش زهکش حائل شوره‌زار مرکزی قزوین در کنترل شوری. تحقیقات آب­وخاک ایران، 45 (4)، 447-452.
ستوده­نیا، عباس؛ جعفری، محدثه (1395). بررسی اثر زهکش حائل شوره‌زار قزوین بر سطح ایستابی منطقه با استفاده از مدل Seep/w. تحقیقات آب­وخاک ایران، 47 (2)، 237-245.
شیرازی، میترا؛ زهتابیان، غلامرضا؛ علوی­پناه، سیّد کاظم (1389). امکان‌پذیری استفاده از تصاویر ماهواره­ای IRS در بررسی وضعیّت آب، خاک، پوشش گیاهی منطقة نجم‌آباد ساوجبلاغ. محیط‌زیست طبیعی (منابع طبیعی ایران)، 63 (1)، 33-50.
عبدی، علی (1383). بررسی تهیة نقشة شوری خاک با استفاده از ایجاد همبستگی بین داده­های ماهواره­ای با مقادیر عددی شوری خاک در دشت قزوین. پژوهش و سازندگی، 17 (3)، 33-38.
علوی­پناه، سیّد کاظم (1385). کاربرد سنجش از دور در علوم زمین (علوم خاک). تهران: دانشگاه تهران.
کرم، امیر؛ کیانی، طیبه؛ دادرسی سبزوار، ابوالقاسم؛ داورزنی، زهرا (1398). برآورد شوری خاک با استفاده از داده‌های دورسنجی و آمار مکانی در منطقة سبزوار. پژوهش­های ژئومورفولوژی کمّی، 7 (4)، 31-53.
مرادیان، شیرین؛ نبی‌اللهی، کمال؛ تقی­زاده مهرجردی، روح‌الله (1396). پیش‌بینی شوری خاک با استفاده از رگرسیون درختی و شبکة عصبی مصنوعی در منطقة قروة استان کردستان. مجلّة مدیریت خاک و تولید پایدار، 7 (4)، 115-129.
مسعودی، مسعود (1380). بررسی قابلیت تصاویر ماهواره­ای در طبقه‌بندی خاک‌های تحت تأثیر شوری و قلیائیت. تحقیقات مرتع و بیابان ایران، 8 (4)، 10-21.
مومی­پور، مهدی (1397). بررسی تغییرات زمانی و مکانی شوری خاک شهرستان آبادان در بازة 24 ساله با تصاویر ماهواره­ای. جغرافیا و پایداری محیط، 8 (27)، 47-58.
مهندسین مشاور سامان آبراه (1389).مطالعات زهکشی حاشیة شوره‌زار دشت قزوین (جلد 6، مطالعات زهکشی). قزوین: سازمان جهاد کشاورزی استان قزوین.
نوحه‏گر، احمد؛ زارع، غلامرضا (1391). استخراج پهنه‏های شوری خاک در مناطق خشک و نیمه‏خشک با استفاده از داده‏های سنجش از دور (مطالعة موردی: شهرستان داراب). مجلّة جغرافیا و مخاطرات محیطی، 1(1)، 49-64.
نوروزی، علی‌اکبر؛ همائی، مهدی؛ فرشاد، عباس (1392). برآورد شوری خاک سطحی با استفاده از تصاویر ماهواره‌ای لندست: مقایسة آمار کلاسیک با مدل‌های آمار مکانی. مرتع و آبخیزداری، 66 (4)، 609-620.
واعظی، علیرضا (1395). مطالعة تأثیر زهکش حائل بر روی پویایی پوشش گیاهی در شوره‌زار دشت مرکزی قزوین. تهران: مؤسسة تحقیقات جنگل‌ها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی.
References
Abdi, A. (2004). An investigation on preparing of the soil salinity map using correlation method between imagery and soil salinity data in the Qazvin plain. Pajouhesh & Sazandegi, 17 (3), 33-38. (In Persian)
Aflatouni, M., Eskandari, L. & Dehghanisanich, H. (2014). Calibration and Sensitivity Analysis of Hydraulic Behavior in Qazvin Plain Aquifer. Iranian Journal of Soil and Water Research, 45 (3), 283-291. (In Persian)
Ahmadi, M., Rezaei Moghadam, M. & Feizizadeh, B. (2017). Study indexes and mapping of soil salinity using remote sensing data (Case study: Aji Chay river delta). Journal of RS and GIS for Natural Resources, 8 (1), 85-96. (In Persian)
Alavi Panah, S. K. (2006). Application of Remote Sensing in the Earth Sciences (Soil).Tehran: University of Tehran Press. (In Persian)
Alavi Panah, S. K., Goossens, R., Matinfar, H. R., Mohammadi, H., Ghadiri, M., Irannegad, H. & Alikhah Asl, M. (2010). The efficiency of Landsat Tm and Etm+ thermal data for extracting soil information in arid regions. Journal of Agricultural Science and Technology, 10 (5), 439-460.
Alhammadi, M. S. & Glenn, E. P. (2008). Detecting date palm trees health and vegetation greenness change on the eastern coast of the United Arab Emirates using SAVI. International Journal of Remote Sensing, 29 (6), 1745-1765.
Ali, S. M. & Mohammed, M. J. (2013). Gap-filling restoration methods for ETM+ sensor image. Iraqi Journal of Science, 54 (1), 206-214.
Allbed, A., Kumar, L. & Sinha, P. (2018). Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia. Geocarto International, 33 (8), 830-846.
Azhirabi, R., Kamkar, B. & Abdi, O. (2015). Comparison of different indices adopted from Landsat images to map soil salinity in the army field of Gorgan). Journal of Soil Management and Sustainable Production, 5 (1), 173-186. (In Persian)
Cheng, Q. & Wu, X. (2011). Mapping paddy rice yield in Zhejiang Province using MODIS spectral index. Turkish Journal of Agriculture and Forestry, 35 (6), 579-589.
Cruden, B. A., Prabhu, D. & Martinez, R. (2012). Absolute radiation measurement in Venus and mars entry conditions. Journal of Spacecraft and Rockets, 49 (6), 1069-1079.
Daempanah, R., Haghnia, G., Alizadeh, A. & Karimi, A. (2011). Mapping Salinity and Sodicity of Surface Soil by Remote Sensing and Geostatistic Methods in South Side of Mah Valat County. Journal of Water and Soil, 25 (3), 498-508. (In Persian)
Dashtekian, K., Pakparvar, M. & Abdollahi, J. (2008). Study of soil salinity preparing methods by using landsat images in Marvast. Iranian journal of Rangeland and Desert Research, 15(2), 139-157. (In Persian)
Dehni, A. & Lounis, M. (2012). Remote sensing techniques for salt affected soil mapping: application to the Oran region of Algeria. Procedia Engineering, 33, 188-198.
Elhag, M. (2016). Evaluation of Different Soil Salinity Mapping Using Remote Sensing Techniques in Arid Ecosystems, Saudi Arabia. Sensors, 3, 1-8.
Engdawork Asfaw, K., Suryabhagavan, V. & Argaw, M. (2018). 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, 17 (3), 250-258.
Ennajia, W., Barakata, A., Karaouib, I., Baghdadia, M. E. & Ariouab, A. (2018). Remote sensing approach to assess salt-affected soils in the north-east part of Tadla plain, Morocco. Geology, Ecology, and Landscapes, 2 (1), 22-28.
Eskandari, L., Aflatouni, M. & Fuladmand, H. (2014). Evaluation of PMWIN Model for Lowering Water Level in Qazvin Saline Drainage Margin. Iranian Journal of Irrigation and Drainage, 7 (3), 422-431. (In Persian)
Farifteh, J., Farshad, A. & George, R. J. (2005). Assessing salt-affected soils using remote sensing, Solute Modeling and Geophysics. Geoderma, 130 (3-4), 191-206.
Huete, A. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing Environment, 25 (3), 295-309.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X. & Ferreira, L.G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83 (1-2), 195-213.
Karam, A., Kiani, T., Dadrasi sabzevar, A. & Davarzani, Z. (2019). Estimating soil salinity by using of remote sensing data and spatial statistic in sabzevar region. Quantitative Geomorphological Research, 7 (4), 31-53. (In Persian)
Khan, N.M., Rastoskuev, V. V., Sato, Y. & Shiozawa, S. (2005). Assessment of hydro saline land degradation by using a simple approach of remote sensing indicators. Agriculture Water Manage, 77 (1-3), 96-109.
Khodadadi, M., Askari, M., Sarmadian, F., Gholi Refahi, H., Norouzi, A., Heidari, A. & Matinfar, H. (2009). Saline soil mapping using ETM+ data in the Qazvin plain.Watershed Engineering and Management, 1 (2), 99-110. (In Persian)
Masudi, M. (2001). Investigation of satellite imagery capability for classification of salt and sodium affected soils. Iranian journal of Rangeland and Desert Research, 8 (4), 10-21. (In Persian)
Metternicht, G. I. & Zinck, J. A. (2003). Remote sensing of soil salinity: potentials and constraints. Journal of Remote Sensing of Environment, 85 (2), 1-20.
Moradian, S., Nabiollahi, K. & Taghizadeh, R. (2018). Prediction of soil salinity using tree regression and artificial neural network in Ghorveh soils, Kurdistan Province. Journal of Soil Management and Sustainable Production, 7 (4), 115-129. (In Persian)
Mumipour, M. (2018). Temporal and Spatial Variation of Soil Salinity Variations in a 24-year Period in Abadan District Using Satellite Images. Geography and Sustainability of Environment, 8 (27), 47-58. (In Persian)
Nohegar, A. & Zare, G. (2012). Extraction of soil salinity zone in arid and semi-arid regional using of remote sensing data (case study: Darab Township). Geography and Environmental Hazards, 1 (1), 49-64. (In Persian)
Noroozi, A., Homaee, M. & Farshad, A. (2014). Estimating Topsoil Salinity from LANDST Data: A Comparison between Classic and Spatial Statistics. Journal of Range and Watershed Management, 66 (4), 609-620. (In Persian)
Nuarsa, I. W., Nishio, F. & Hongo, C. H. (2012). Rice yield estimation using Landsat ETM+ data and field observation. Journal of Agricultural Science, 4 (3), 45-56.
Rouse, J. R.J., Haas, R. H., Schell, J. A. & Deering, D. W. (1974).Monitoring vegetation systems in the Great Plains with ERTS. USA: NASA special publication.
Safdar, H., Amin, A., Shafiq, Y., Ali, A., Yasin, R. & Sarwar, M. I. (2019). A review: Impact of salinity on plant growth. Nature and Science, 17 (1), 34-40.
Saman Abrah Consulting Engineers. (2010). Drainage studies of the Qazvin plain saline margin (Vol. 6, Drainage studies). Qazvin: Qazvin Agricultural Jihad Organization. (In Persian)
Shirazi, M., Zehtabian, G. & Alavipanah, S. (2010). Applicability of IRS Satellite Images for Surveying Water, Soil and Vegetation Cover Condition of Najm Abad Region, Savojbolagh. Journal of Natural Environment, 63(1), 33-51. (In Persian)
Singh, G., Bundela, D. S., Sethi, M., Lal, K. & Kamra, S. K. (2010). Remote Sensing and Geographic Information System for Appraisal of Salt-Affected Soils in India. Environmental Quality, 39 (1), 5-15.
Siyal, A.A., Dempewolf, J. & Becker-Reshef, I. (2015). Rice yield estimation using Landsat ETM+ Data. Journal of Applied Remote Sensing, 9, 1-16.
Sotoodehnia, A., Jafari, M. & Daneshkar Arasteh, P. (2014). The Role of Qazvin Central Marsh Interceptor Drain in Controlling Shallow Groundwater Salinity. Iranian Journal of Soil and Water Research, 45 (4), 447-452. (In Persian)
Sotoodehnia, A. & Jafarei, M. (2016). Investigation of Qazvin Marshland Interceptor Drain Effects on Water Table Using Seep/w Model. Iranian Journal of Soil and Water Research, 47 (2), 237-245. (In Persian)
Vaezi, A. R. (2016). Study of the effect of Interceptor Drain on vegetation dynamics in the central salinity of Qazvin plain. Tehran: National Institute of Forests and Rangelands Research, Agricultural Research, Education and Promotion Organization. (In Persian)
Zeinali, M., Jaafarzadeh, A., Shahbazi, F., Oustan, S. & Valizadeh Kamran, K. (2016). Evaluating Surface Soil Salinity by pixel-based method based on TM Sensor Data (Case study: Eastern Lands of Khoy). Scientific- Research Quarterly of Geographical Data (SEPEHR), 25 (99), 127-139. (In Persian)

Files

Share

How to cite

Statistics