پایش خشکسالی منطقه ای با استفاده از تصاویر لندست - منطقه مورد مطالعه: شهرستان کرمانشاه

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانش آموخته کارشناسی ارشد آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ایران

2 عضو هیأت علمی گروه مهندسی و فناوری کشاورزی دانشگاه پیام نور، ایران

3 استاد گروه علوم مهندسی آبخیزداری، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ایران

10.22131/sepehr.2019.35643

چکیده

خشکسالی یک خطر جدی با اثرات بسیار گسترده بر روی خاک، اقتصاد و تهدید معیشت و سلامت جوامع محلی میباشد. این بلا به عنوان یک پدیده ناگوار اقلیمی که بطور مستقیم جوامع را از طریق محدودیت در دسترسی به منابع آب تحت تأثیر قرار میدهد، هزینههای اقتصادی، اجتماعی و محیطی زیادی را به همراه دارد. شاخصهای خشکسالی هواشناسی مستقیماً از روی دادههای هواشناسی نظیر بارندگی محاسبه میشوند و در صورت فقدان دادههای مذکور، در پایش خشکسالی مفید واقع نخواهند شد. لذا تکنیک سنجش از دور میتواند ابزاری مفید در پایش خشکسالی به شمار رود. هدف از این مطالعه پایش خشکسالی و سلامت پوشش گیاهی در شهرستان کرمانشاه با استفاده از تصاویر ماهوارهای لندست میباشد. بدین منظور ابتدا با بررسی دادههای بارانسنجی و سینوپتیک ایستگاههای موجود و با استفاده از مدل شاخص بارش استاندارد شده خشکترین سال و یکسال مرطوب به عنوان نمونه انتخاب شد. در این مطالعه دو سال 1394 و 1395 به عنوان سالهای خشک و تر انتخاب شد و سپس پوشش گیاهی منطقه با تصاویر لندست مورد مقایسه قرار گرفت. ابتدا پیشپردازش و پردازشهای لازم همانند تصحیح هندسی و رادیومتریک بر روی تصاویر ماهوارهای انجام شد. سپس شاخصهای شرایط دمایی، شاخص وضعیت پوشش گیاهی و شاخص سلامت پوشش گیاهی برای پایش خشکسالی تهیه گردید. بدین ترتیب در مرحله بعد نتایج این مطالعه نشان داد تصاویر لندست و شاخصهای ساخته شده دارای قابلیت لازم برای پایش خشکسالی میباشد. نتایج این تحقیق میتواند گزینه مناسبی برای تصمیمگیران به منظور بررسی نظارت، بررسی و حل و فصل شرایط خشکسالی مؤثر باشد و ضرورت تعریف نمایهای را دوچندان کند.

کلیدواژه‌ها


عنوان مقاله [English]

Monitoring the regional drought using the LANDSAT images Case Study: The City of Kermanshah

نویسندگان [English]

  • Milad Soltani 1
  • Adel Soltani 2
  • Mahin Kalehhouei 1
  • Karim Solaimani 3
1 M.Sc graduated in watershed management engineering, Sari Agricultural Sciences and Natural Resources University, Iran
2 Faculty of Member, Dept. of Agriculture, Payam Noor University, Iran
3 Professor, Dept.of watershed management, Sari Agricultural Sciences and Natural Resources University
چکیده [English]

Introduction
Drought is a serious danger with very extensive impacts on the soil, economy, and the threat to the livelihood and health of local communities. This disaster as an unpleasant climatic phenomenon that directly affects the communities through restrictions on access to water resources, causes high economic, social and environmental costs. Meteorological drought indicators are calculated directly from meteorological events such as precipitation, and in the absence of these data, drought monitoring will not be useful. Due to the fact that meteorological drought indicators are only valid for a single location and do not have the required spatial resolution and are also dependent on weather station information, and these stations are often distributed distantly, the reliability of these indicators has been questioned. Given the characteristics of satellite data such as spatial and temporal resolution, extensive coverage of studied areas, and direct investigation of vegetation status by satellite indices, many studies have been carried out for drought modeling using this technology and these indicators. Over the past four decades, far-reaching drought monitoring tools have been widely developed and drought monitoring models are widely proposed, which are generally based on vegetation indices, surface temperature, humidity and reflection in the visible and infrared regions. These include leaf water content index, vegetation cover index and temperature - drought - vegetation index. Therefore, remote sensing techniques can be a useful tool in drought monitoring. The purpose of this study is to monitor drought and vegetation health in the city of Kermanshah using LANDSAT satellite imagery. For this purpose, first, by examining the rain-gauging and synoptic data of existing stations and using the standard precipitation index model, the driest year and one wet year were selected as the sample. In this study, two years of 2015 and 2016 were selected as the dry and wet years and then, the vegetation cover of the region was compared with the Landsat images. To use these images, it is first necessary to make sure that there is no geometric error. For this purpose, the road vector layer was used, which was revealed that the images have geometric errors. Images with less than a half-pixel error were corrected geometrically using 21 and 24 auxiliary points. The adaptation of the vector layers with the roads existing in the image indicated the accuracy of the correction. At the next stage, the driest year and one wet year were selected as samples by examining the rain-gauging and synoptic data of the existing stations and by using the standardized model of rainfall index. At the next stage, the Temperature Condition Indices and Vegetation Health Index (VHI) were compared in two wet and drought periods were studied in order to determine the differences of these indices during a dry year and a year with high precipitation. For this purpose, each of the aforementioned indices was built using the LANDSAT-8 imagery, and the stages of building these indices were subsequently presented.  The required pre-processing and processing as well as the geometric and radiometric corrections were first performed on the satellite images. Then, temperature condition indices, vegetation status index and vegetation health index were prepared for drought monitoring. Considering that, the meteorological drought indices are only valid for a single location and lack the required spatial resolution and are also dependent on the information of the meteorological stations, and these stations are often distributed far apart from each other, the reliability of these indexes has been questioned. Satellite data characteristics like high spatial and temporal resolution, extensive coverage of the study areas, and direct survey of the vegetation status by the satellite indexes have led to a large number of studies on drought modeling using this technology, and the confirmation of the use of these indices. The aim of this study is to determine the moisture, heat and health of the vegetation using the LANDST images. Thus, the results of the study in the next stage indicated that the LANDSAT images and the built indices have the required capabilities to monitor drought. The results of this research can be a proper option for decision-makers to effectively supervise, examine and resolve the drought conditions and double the necessity of profile definition. Supplementary studies are suggested for spatial drought monitoring by satellite imagery through ground measurements of the quantitative changes in the coverage and temperature of the earth’s surface. There are limitations in the use of NDVI and satellite thermal bands. These include weather and cloud conditions that should be considered. Using maps obtained from the drought monitoring and evaluating indices can help improve drought management programs and play a significant role in reducing the effects of drought. Using vegetation health status index, it was determined that the vegetation status has had a lot of changes during drought compared to the wet period, hydrological drought has had a major share in the destruction of vegetation and drying of the lakes and, consequently, the abandonment of agricultural lands and the lack of access to alternative water resources, as well as the lack of groundwater resources or the lack of alternative surface water resources have intensified, and it seems that, this part of Iran will face numerous problems if the drought continues in the coming years and the appropriate methods are not used to deal with it. Also, given that the water resources of the region are going to decrease in the coming decades, the necessity of using comprehensive water management methods in all sectors, including the reserve, transfer and distribution sectors seems very essential and inevitable. Finally, it is expected that the trend of destruction of vegetation decreases in the future by applying proper management practices, sustainable water distribution, regional negotiations, methodical agriculture as well as the establishment of optimal hydrological conditions. 

کلیدواژه‌ها [English]

  • Drought Monitoring
  • Remote Sensing
  • Vegetation health index (VHI)
  • Kermanshah

1- ابراهیم‌زاده، س.، بذرافشان، ج. و قربانی، خ. 1392. امکان‌سنجی تشخیص تغییرات پوشش گیاهی مبتنی بر شاخص‌های زمینی و ماهواره‌ای خشکسالی (مطالعه موردی: استان کرمانشاه). مجله هواشناسی کشاورزی، 1 (1): 37 – 48.

2- چنار، ا. 1380. ارزیابی خشکسالی با استفاده از تصاویر NOAA در آذربایجان شرقی، آذربایجان غربی و استان اردبیل. پایان‌نامه کارشناسی ارشد دانشگاه تربیت مدرس. 89 ص.

3- فاضل دهکردی، ل. 1392. هشدار خطر خشکسالی به‌منظور مدیریت بهینه مراتع، پایان‌نامه دکتری، دانشکده منابع طبیعی تهران، 160 ص.

4- متکان، ا.ا.، درویش زاده، ر.، حسینی اصل، ا.، ابراهیمی، م.، ابراهیمی، ز. 1390.  پهنه‌­بندی خطر خشکسالی مناطق خشک با استفاده از روش دانش‌پایه در محیط GIS (مطالعه موردی: حوضه آبخیز شیطور، استان یزد). نشریه پژوهش‌های اقلیم‌شناسی، 2: 103- 116.

5- محمودزاده، ع. 1387. بررسی همبستگی شاخص خشکسالی SPI و شاخص NDVI در منطقه فریدون‌شهر، سومین کنفرانس مدیریت منابع آب ایران. دانشکده مهندسی عمران، دانشگاه تبریز.

6- مؤمن‌زاده، ح.، کمالی، غ.، وظیفه دوست، م. 1390. بررسی تغییرات ماده خشک و عملکرد گندم در دوره‌های خشکسالی و ترسالی با کمک داده‌های ماهواره‌ای مادیس در استان اصفهان، نشریه بوم‌شناسی، 3 (2): 1-13.

7- Anderson, R.P., Peterson, A.T. and Egbert. S.L. 2006. Vegetation-index models predict areas vulnerable to purple loosestrife (lythrum salicaria) invasion in Kansas.  Southwest. Nat. 51: 471–480.

8- Bayarjargal, Y., Karnieli, A., Bayasgalan, M., Khudulmur, S., Gandush, C. and Tucker, C.J. 2006. A comparative study of NOAA–AVHRR derived drought indices using change vector analysis. Remote Sens. Environ. 105: 9–22.

9- Caccamo, G., Chisholm, L.A., Bradstock, R.A. and Puotinen, M.L. 2011. Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sens. Environ, 115: 2626–2639.

10- Cancelliere, A., Mauro. B and Rossi, G. 2007. Drought forecasting using the Standarized Precipition. Journal of Water Resource Management, 21: 801-819.

11- Darwish, T. and G. Faour. 2008. Rangeland degradation in two watersheds of Lebnon. Lebanese Sci. J. 9: 71-80.

12- FAO:http://www.fao.org/docrep/017/aq191e/aq191e.pdf (accessed on 15 June 2015).

13- Ghaleb, F., Mhawej Mario, M. and AbouNajem Sandra, A.N. 2015. Regional Landsat-Based Drought Monitoring from 1982 to 2014. Climate, 3: 563-577 

14- Goddard, L., Mason, S.J., Zebiak, S.E., Ropelewski, C.F., Basher, R. and Cane, M.A. 2001. Current approaches to seasonal to inter annual climate predictions. International Journal of Climatology, 21: 1111–1152.

15- Gurgel, H. C., and Ferreira, N. J., 2003. Annual and inter annual variability of NDVI in Brazil and its connections with climate. International Journal of Remote Sensing, 24(18): 3595–3609.

16- Huang, C., Geiger, E., van Leeuwen W. and Marsh. S. 2009. Discrimination of invaded and native species sites in a semi-desert grassland using MODIS multi-temporal data. Int. J. Remote Sens. 30: 897–917.

17- Hunt, E.R., Rock, B.N. and Nobel, P.S. 1987. Measurement of leaf relative water content by infrared reflectance. Remote Sensing of Environment, 22 (3): 429–435.

18- Jian, W. and Shuo, L. 2005. Effect of climatic change on snowmelt runoffs in mountainous regions of Inland River of northwestern china. Earth science, 49 (8): 881-888.

19- Kogan, F.N., Stark, R., Gitelson, A., Jargalsaikhan, L., Dugrajav, C. and Tsooj, S. 2004. Derivation of pasture biomass in Mongolia from AVHRR-based vegetation health indices. Int. J. Remote Sens, 25: 2889–2896.

20- Kogan, F.N. 1993. United States droughts of late 1980’s as seen by NOAA polar orbiting satellites. International Geoscience and Remote Sensing Symposium, 1: 197-199.

21- Kogan, F.N. 1995. Droughts of the late 1980s in the United States as derived from NOAA polar orbiting satellite data. Bulletin of the American Meteorological Society, 76 (5): 655–668.

22- Lin, M.L., Chu, Ch.M. and Tsai, B.W. 2011. Drought Risk Assessment in Western InnerMongolia, Int. J. Environ. Res. 5(1): 139-148.

23- Manandhar, R., Odeh, I.O.A. and Ancev, T. 2009. Improving the Accuracy of land use and land cover classification of Landsat data using post- classification enhancement. Remote Sensing, 1: 330-344pp.

24- Mladenova, I.E., Jackson, T.J., Njoku, E., Bindlish, R., Chan, S., Cosh, M.H., Holmes, T.R.H., de Jeu, R.A.M., Jones, L. and Kimball, J. 2014. Remote monitoring of soil moisture using passive microwave-based techniques—Theoretical basis and overview of selected algorithms for AMSR-E. Remote Sens. Environ. 144: 97–213.

25- Murthy, C.S., Sai, M.V.RS., Chandrasekar, K. and Roy, P.S. 2009. Spatial and temporal responses of different crop-growing environments to agricultural drought: a study in Haryana state, India using NOAA AVHRR data. International Journal of Remote Sensing, 30 (11): 2897-2914.

26- Parida, B.R. 2006. Analyzing the effect of severity and duration of agricultural drought on crop performance using Terra/MODIS satellite data and meteorological data, Indian Institute of Remote Sensing.

27- Rhee, J., Im, J. and Carbone, G.J. 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sens. Environ. 114: 2875–2887.

28- Rojas, O., Vrieling, A. and Rembold, F. 2011. Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery. Remote Sens. Environ, 115: 343–352.

29- Rouse, J.W., Haas, R.H., Schell, J.A. and Deering, D.W. 1973. Monitoring vegetation systems in the Great Plains with ERTS, Third ERTS Symposium, NASA, SP-351 I, 309-317.

30- Silva. V.P.R. 2003. On Climate Variability in Northeast Brazil, Journal of Arid Environment, 54 (2): 256-367.

31- Singh, R.P., Roy, S. and Kogan. F. 2003. Vegetation and Temperature Condition Indices from NOAA AVHRR Data for Drought Monitoring over India. International Journal of Remote Sensing, 24 (22): 4393–4402.

32- Tong S.S. and Lan, P. T. 2009. Land cover change analysis using change vector analysis method in Duy Tien district, Ha Nam province in Vietnam. 7th FIG Regional Conference Spatial Data Serving People: Land Governance and the Environment Vietnam, 19-22 October.

33- Wan, Z., Wang, P. and Li, X.  2004. Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, USA. Int.J. Remote Sens. 25(1): 61-72.

34- Weng, Q. 2001. A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta. China. International Journal of Remote Sensing, 22 (10): 1999-2014.

35- Wilhite, D.A. and Glantz, M.H. 1985. Understanding the drought phenomenon، the role of definitions، Water International, 10 (3):111–120.

36- Zhang, A. and Jia, G. 2013. Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data. Remote Sens. Environ, 134: 12–23.