تلفیق داده‌های زمینی و ماهواره‌ای برای پهنه‌بندی خشک‌سالی (مطالعه موردی: دشت ملایر)
محورهای موضوعی :
اکوسیستم ها
احمد اسدی می آبادی
1
,
داوود اخضری
2
,
حمید نوری
3
1 - دانشجوی کارشناسی ارشد مرتعداری، دانشکده منابع طبیعی و محیط زیست، دانشگاه ملایر.
2 - دانشیار گروه مهندسی طبیعت، دانشکده منابع طبیعی و محیط زیست، دانشگاه ملایر. *(مسول مکاتبات)
3 - دانشیار گروه مهندسی طبیعت، دانشکده منابع طبیعی و محیط زیست، دانشگاه ملایر.
تاریخ دریافت : 1398/08/05
تاریخ پذیرش : 1399/10/13
تاریخ انتشار : 1400/04/01
کلید واژه:
دشت ملایر,
نمایه تأثیر خشک‌سالی,
سنجش‌ازدور,
پوشش گیاهی,
چکیده مقاله :
زمینه و هدف: یکی از مهم‌ترین پیامدهای خشک‌سالی، کاهش تراکم پوشش گیاهی است. با کاهش پوشش گیاهی، شرایط محیطی برای بروز مشکلات مختلف نظیر فرسایش خاک، افزایش میزان رواناب سطحی و خطر بروز سیل فراهم می‌شود. بر این اساس، ارزیابی اثرات خشک‌سالی بر روی پوشش گیاهی از اهمیت زیادی برخوردار است. هدف تحقیق حاضر این است که با استفاده از نمایه تأثیر خشک‌سالی (IDI)، امکان بهره‌گیری از داده‌های تلفیقی زمینی و ماهواره‌ای در منطقه مورد مطالعه سنجیده شود.روش بررسی: در این پژوهش شاخصی به نام نمایه تأثیر خشک‌سالی (IDI) استفاده شده است که بیانگر تأثیرات درازمدت شرایط اقلیمی منطقه مطالعاتی، بر وضعیت پوشش گیاهی آن منطقه است. در این مطالعه نمایه IDI با تلفیق داده‌های ایستگاه‌های هواشناسی دشت ملایر برای تهیة نقشه‌های بارندگی و دمای دشت (آمار ۵ ایستگاه سینوپتیک موجود در داخل و خارج منطقه موردمطالعه، با مقیاس 20 ساله) و یک سری از داده‌های ماهواره‌ای سنجنده لندستOLI، TM و ETM+ برای تهیه نقشة پوشش گیاهی NDVI (مشتمل بر 6 تصویر در ماه مه و سال‌های 2000، 2002، 2007، 2009، 2013 و 2015 میلادی) محاسبه و سپس پهنه‌بندی شد. پژوهش حاضر در اردیبهشت سال 1398 انجام شد.یافته ها: نتایج نشان داد ارتباط خوبی بین داده‌های تلفیقی (IDI) و شاخص بارش استاندارد شده (SPI) برقرار است که نشان‌دهندة کارایی بالای داده‌های تلفیقی است. نتایج همبستگی پیرسون نشان داد که بین میانگین شاخص SPI و IDI همبستگی بالایی برابر با 963/0 در سطح معنی‌داری 01/0P< وجود دارد.بحث و نتیجه گیری: تاکنون برای مطالعه خشک‌سالی‌ها از داده‌های زمینی و ماهواره‌ای به‌وفور استفاده‌شده است، اما شاخصی که از تلفیق این داده‌ها به‌دست‌آمده باشد کمتر موردتوجه محققان قرارگرفته است، بنابراین هدف و نوآوری این تحقیق این است که با استفاده از نمایه تأثیر خشک‌سالی (IDI)، امکان بهره‌گیری از داده‌های تلفیقی زمینی و ماهواره‌ای در منطقه موردمطالعه سنجیده شود.
چکیده انگلیسی:
Background and Objective: One of the most important consequences of drought is reducing the amount of vegetation. Reducing vegetation and environmental conditions lead various problems such as soil erosion, increased runoff levels and flood risk. Accordingly, evaluation of the effects of drought on vegetation has a great importance. The purpose of this study is to use the Drought Effect Index (IDI), remotely sensed data and terrestrial data in the study area.Method: The IDI index indicates the long-term effects of climate conditions in the study area on the vegetation cover in area. In this study, the IDI index combines data of the meteorological stations of Malayer Plain to prepare rainfall and temperature maps (information from 5 synoptic stations inside and outside of study area with a 19-years scale) and a series of Landsat TM satellites and ETM + were calculated for the NDVI vegetation mapping (including 6 images in May and 2000, 2002, 2007, 2009, 2013 and 2015). The zonation map was prepared based on this information. This investigation has been done in May 2019.Findings: The results showed that there is a meaningful correlation between aggregate data (IDI) and SPI index, indicates the efficiency of combined data. Results of Pearson correlation showed that there is a significant correlation between the mean SPI and IDI of 0.963 at a significant level of P<0.01.Discussion and Conclusions: So far, land and satellite data have been widely used for the study of droughts, but the index derived from the integration of these data has received little attention from researchers, so the aim and innovation of this research is to make it possible to use drought impact index (IDI). Combine terrestrial and satellite data in the study area.
منابع و مأخذ:
Matkan, A.A., Darvishzadeh, R., Hosseiniasl, A., Ebrahimi Khosofi, M., & Ebrahimi Khosofi Z. (2011). Drought risk zoning of arid regions using GIS based knowledge methods (Case study: Sheithour Watershed, Yazd). Journal of Climate Research 2(5-6), 103-116.
Kaim, D., Kozak, J., Kolecka, N., Ziółkowska, E., Ostafin, K., Ostapowicz, K., Gimmi, U., Munteanu, C., & Radeloff, V.C. (2016). Broad scale forest cover reconstruction from historical topographic maps. Applied Geography 67: 39-48.
Alavipanah, S.K. (2003). The Use of Remote Sensing in Geosciences (Soil Sciences). Tehran, Tehran University Press 496 p.
Moran, M.S., Clarke, T.R., Inoue, Y., & Vidal, A. (1994). Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote sensing of environment 49(3):246-263.
Tanriverdi, C. (2003). Available water effects on water stress indices for irrigated corn grown in sandy soils. UMI.
Fatemi, S.B., & Rezaee, Y. (2006). Basics of Remote Sensing. Azadeh Publications 257 p.
Pei, F., Wu, C., Liu, X., Li, X., Yang, K., Zhou, Y., Wang, K., Xu, L., & Xia, G. (2018). Monitoring the vegetation activity in China using vegetation health indices. Agricultural and Forest Meteorology 248: 215-227.
Zarch, M.A., Sivakumar, B., & Sharma, A. (2015). Droughts in a warming climate: A global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI). Journal of Hydrology 526: 183-195.
Sobral BS, Oliveira‐Júnior JF, de Gois G, Pereira‐Júnior ER.2018. Spatial variability of SPI and RDIst drought indices applied to intense episodes of drought occurred in Rio de Janeiro State, Brazil. International Journal of Climatology, 38(10): 3896-3916.
Karimi, M., & Shahedi, K. (2018). Investigation of meteorological, hydrological and agricultural droughts using drought indices (Case Study: Gharasu Watershed). Remote sensing and GIS in natural resources 9(2), 1-16.
Bajgiran, P. R., Darvishsefat, A. A., Khalili, A., & Makhdoum, M. F. (2008). Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran. Journal of Arid Environments, 72(6), 1086-1096.
Kogan, FN. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in space research, 15(11) :91-100.
Thenkabail PS, Enclona EA, Ashton MS, Legg C, Jean De Dieu M. 2004.The Use of Remote Sensing Data for Drought Assessment and Monitoring in Southwest Asia, International Water Management Institute, PO Box 2075, Colombo, Sri Lanka
Liu L, Liao J, Chen X, Zhou G, Su Y, Xiang Z, Wang Z, Liu X, Li Y, Wu J, Xiong X. 2017. The Microwave Temperature Vegetation Drought Index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010). Remote Sensing of Environment, 199:302-320.
Bento VA, Gouveia CM, DaCamara CC, Trigo IF. A. 2018. A climatological assessment of drought impact on vegetation health index. Agricultural and Forest Meteorology, 259: 286-295.
Akhzari, D., & Asadi Meyabadi, A. (2016). Soil salinity mapping using spectral analysis of OLI and field data (Case Study: South of Malayer Plain). Remote sensing and GIS in natural resources 7(2), 87-100.
Tamasoki, E., Khourani, A., Darvishi Bolourani, A., & Nohegar, A. (2015). Monitoring and prediction of dust storms using telescope data, spatial information system and terrestrial data based on vegetation and climate change surveys (Case Study: South and Southeast of Iran). Iranian Journal of Remote Sensing and GIS 7(4), 27-44.
Pourmohammadi, S., Rahimian, M., Kalantar, M., & Pourmohammadi, S. (2012). Mapping of Drought Impacts on Vegetation Cover in Yazd-Ardakan Plain Using Remote Sensing. Journal of Natural Geography Research 44(2), 125-140.
Akhtari, R., Mahdian, M. H., & Morid, S. (2006). Spatial analysis of SPI and EDI indices in Tehran province. Iranian Water Resources Research Journal 2(3), 27-38.
Raziee, T., Daneshkar Arasteh, P., Akhtari, R., & Saghifian, B. (2007). Study of meteorological drought in Sistan and Baluchestan province using SPI index and Markov chain model. Iranian Water Resources Research Journal 3(1), 25-35.
Yaghoubi, S., Faramarzi, M., Karimi, H., & Sarvarian, J. (2016). Evaluation of artificial neural network performance in desertification prediction using GIS (Case study: Dehloran Plain, Ilam). Remote Sensing and GIS in Natural Resources 7(3), 61-77.
Guttman NB. 1999. Accepting the standardized precipitation index: A calculation algorithm1. JAWRA Journal of the American Water Resources Association, 35(2): 311-322.
Liu D, You J, Xie Q, Huang Y, Tong H. 2018. Spatial and Temporal Characteristics of Drought and Flood in Quanzhou Based on Standardized Precipitation Index (SPI) in Recent 55 Years. Journal of Geoscience and Environment Protection, 6(08): 25-37.
Lloyd Hughes B, Saunders MA. 2002. A drought climatology for Europe. International journal of climatology, 22(13): 1571-1592.
Mishra AK, Singh VP. 2010. A review of drought concepts. Journal of hydrology, 391(1-2): 202-216.
Mukherjee S, Mishra A, Trenberth KE. 2018. Climate change and drought: a perspective on drought indices. Current Climate Change Reports, 4: 145-163.
Tsakiris G, Pangalou D, Vangelis H .2007. Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water resources management, 21(5): 821-833.
Wu H, Hayes MJ, Wilhite DA, Svoboda MD. 2005. The effect of the length of record on the standardized precipitation index calculation. International journal of climatology, 25(4): 505-520.
Yang L, Wylie BK, Tieszen LL, Reed BC.1998. An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the US northern and central Great Plains. Remote Sensing of Environment, 65(1): 25-37.
Wang J, Price KP, Rich PM. 2001. Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains. International journal of remote sensing, 22(18): 3827-3844.
Richard Y, Poccard I. 1998. A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa. International Journal of Remote Sensing, 19(15): 2907-2920.
Li J, Lewis J, Rowland J, Tappan G, Tieszen LL. 2004. Evaluation of land performance in Senegal using multi-temporal NDVI and rainfall series. Journal of Arid Environments, 59(3): 463-480.
Ji L, Peters AJ. 2004. A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains. International Journal of Remote Sensing, 25(2): 297-311.
Yang W, Yang L, Merchant JW. 1997. An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, USA. International Journal of Remote Sensing, 18(10): 2161-2180.
Hao F, Zhang X, Ouyang W, Skidmore AK, Toxopeus AG. 2012. Vegetation NDVI linked to temperature and precipitation in the upper catchments of Yellow River. Environmental Modeling & Assessment, 17(4): 389-398.
Jun L, Richa W, Shan H, Yuhai Y, Yayeh BD. 2018. Relationship between vegetation change and extreme climate indices on the Inner Mongolia Plateau, China, from 1982 to 2013. Ecological indicators.
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Matkan, A.A., Darvishzadeh, R., Hosseiniasl, A., Ebrahimi Khosofi, M., & Ebrahimi Khosofi Z. (2011). Drought risk zoning of arid regions using GIS based knowledge methods (Case study: Sheithour Watershed, Yazd). Journal of Climate Research 2(5-6), 103-116.
Kaim, D., Kozak, J., Kolecka, N., Ziółkowska, E., Ostafin, K., Ostapowicz, K., Gimmi, U., Munteanu, C., & Radeloff, V.C. (2016). Broad scale forest cover reconstruction from historical topographic maps. Applied Geography 67: 39-48.
Alavipanah, S.K. (2003). The Use of Remote Sensing in Geosciences (Soil Sciences). Tehran, Tehran University Press 496 p.
Moran, M.S., Clarke, T.R., Inoue, Y., & Vidal, A. (1994). Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote sensing of environment 49(3):246-263.
Tanriverdi, C. (2003). Available water effects on water stress indices for irrigated corn grown in sandy soils. UMI.
Fatemi, S.B., & Rezaee, Y. (2006). Basics of Remote Sensing. Azadeh Publications 257 p.
Pei, F., Wu, C., Liu, X., Li, X., Yang, K., Zhou, Y., Wang, K., Xu, L., & Xia, G. (2018). Monitoring the vegetation activity in China using vegetation health indices. Agricultural and Forest Meteorology 248: 215-227.
Zarch, M.A., Sivakumar, B., & Sharma, A. (2015). Droughts in a warming climate: A global assessment of Standardized precipitation index (SPI) and Reconnaissance drought index (RDI). Journal of Hydrology 526: 183-195.
Sobral BS, Oliveira‐Júnior JF, de Gois G, Pereira‐Júnior ER.2018. Spatial variability of SPI and RDIst drought indices applied to intense episodes of drought occurred in Rio de Janeiro State, Brazil. International Journal of Climatology, 38(10): 3896-3916.
Karimi, M., & Shahedi, K. (2018). Investigation of meteorological, hydrological and agricultural droughts using drought indices (Case Study: Gharasu Watershed). Remote sensing and GIS in natural resources 9(2), 1-16.
Bajgiran, P. R., Darvishsefat, A. A., Khalili, A., & Makhdoum, M. F. (2008). Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran. Journal of Arid Environments, 72(6), 1086-1096.
Kogan, FN. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in space research, 15(11) :91-100.
Thenkabail PS, Enclona EA, Ashton MS, Legg C, Jean De Dieu M. 2004.The Use of Remote Sensing Data for Drought Assessment and Monitoring in Southwest Asia, International Water Management Institute, PO Box 2075, Colombo, Sri Lanka
Liu L, Liao J, Chen X, Zhou G, Su Y, Xiang Z, Wang Z, Liu X, Li Y, Wu J, Xiong X. 2017. The Microwave Temperature Vegetation Drought Index (MTVDI) based on AMSR-E brightness temperatures for long-term drought assessment across China (2003–2010). Remote Sensing of Environment, 199:302-320.
Bento VA, Gouveia CM, DaCamara CC, Trigo IF. A. 2018. A climatological assessment of drought impact on vegetation health index. Agricultural and Forest Meteorology, 259: 286-295.
Akhzari, D., & Asadi Meyabadi, A. (2016). Soil salinity mapping using spectral analysis of OLI and field data (Case Study: South of Malayer Plain). Remote sensing and GIS in natural resources 7(2), 87-100.
Tamasoki, E., Khourani, A., Darvishi Bolourani, A., & Nohegar, A. (2015). Monitoring and prediction of dust storms using telescope data, spatial information system and terrestrial data based on vegetation and climate change surveys (Case Study: South and Southeast of Iran). Iranian Journal of Remote Sensing and GIS 7(4), 27-44.
Pourmohammadi, S., Rahimian, M., Kalantar, M., & Pourmohammadi, S. (2012). Mapping of Drought Impacts on Vegetation Cover in Yazd-Ardakan Plain Using Remote Sensing. Journal of Natural Geography Research 44(2), 125-140.
Akhtari, R., Mahdian, M. H., & Morid, S. (2006). Spatial analysis of SPI and EDI indices in Tehran province. Iranian Water Resources Research Journal 2(3), 27-38.
Raziee, T., Daneshkar Arasteh, P., Akhtari, R., & Saghifian, B. (2007). Study of meteorological drought in Sistan and Baluchestan province using SPI index and Markov chain model. Iranian Water Resources Research Journal 3(1), 25-35.
Yaghoubi, S., Faramarzi, M., Karimi, H., & Sarvarian, J. (2016). Evaluation of artificial neural network performance in desertification prediction using GIS (Case study: Dehloran Plain, Ilam). Remote Sensing and GIS in Natural Resources 7(3), 61-77.
Guttman NB. 1999. Accepting the standardized precipitation index: A calculation algorithm1. JAWRA Journal of the American Water Resources Association, 35(2): 311-322.
Liu D, You J, Xie Q, Huang Y, Tong H. 2018. Spatial and Temporal Characteristics of Drought and Flood in Quanzhou Based on Standardized Precipitation Index (SPI) in Recent 55 Years. Journal of Geoscience and Environment Protection, 6(08): 25-37.
Lloyd Hughes B, Saunders MA. 2002. A drought climatology for Europe. International journal of climatology, 22(13): 1571-1592.
Mishra AK, Singh VP. 2010. A review of drought concepts. Journal of hydrology, 391(1-2): 202-216.
Mukherjee S, Mishra A, Trenberth KE. 2018. Climate change and drought: a perspective on drought indices. Current Climate Change Reports, 4: 145-163.
Tsakiris G, Pangalou D, Vangelis H .2007. Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water resources management, 21(5): 821-833.
Wu H, Hayes MJ, Wilhite DA, Svoboda MD. 2005. The effect of the length of record on the standardized precipitation index calculation. International journal of climatology, 25(4): 505-520.
Yang L, Wylie BK, Tieszen LL, Reed BC.1998. An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the US northern and central Great Plains. Remote Sensing of Environment, 65(1): 25-37.
Wang J, Price KP, Rich PM. 2001. Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains. International journal of remote sensing, 22(18): 3827-3844.
Richard Y, Poccard I. 1998. A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa. International Journal of Remote Sensing, 19(15): 2907-2920.
Li J, Lewis J, Rowland J, Tappan G, Tieszen LL. 2004. Evaluation of land performance in Senegal using multi-temporal NDVI and rainfall series. Journal of Arid Environments, 59(3): 463-480.
Ji L, Peters AJ. 2004. A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains. International Journal of Remote Sensing, 25(2): 297-311.
Yang W, Yang L, Merchant JW. 1997. An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, USA. International Journal of Remote Sensing, 18(10): 2161-2180.
Hao F, Zhang X, Ouyang W, Skidmore AK, Toxopeus AG. 2012. Vegetation NDVI linked to temperature and precipitation in the upper catchments of Yellow River. Environmental Modeling & Assessment, 17(4): 389-398.
Jun L, Richa W, Shan H, Yuhai Y, Yayeh BD. 2018. Relationship between vegetation change and extreme climate indices on the Inner Mongolia Plateau, China, from 1982 to 2013. Ecological indicators.
