تعداد نشریات | 161 |
تعداد شمارهها | 6,477 |
تعداد مقالات | 70,016 |
تعداد مشاهده مقاله | 122,924,691 |
تعداد دریافت فایل اصل مقاله | 96,137,430 |
شناسایی مناطق آسیبپذیر پوشش گیاهی به خشکسالی با استفاده از سنجش از دور (مطالعه موردی: استان بوشهر) | ||
نشریه علمی - پژوهشی مرتع و آبخیزداری | ||
مقاله 4، دوره 71، شماره 2، شهریور 1397، صفحه 341-354 اصل مقاله (741.11 K) | ||
نوع مقاله: مقاله پژوهشی | ||
شناسه دیجیتال (DOI): 10.22059/jrwm.2018.231348.1112 | ||
نویسندگان | ||
فاطمه بحرینی* 1؛ فاطمه پناهی2؛ محمد جعفری3؛ آرش ملکیان4 | ||
1دانشجوی دکترای دانشکده منابع طبیعی و علوم زمین دانشگاه کاشان، ایران | ||
2استادیار دانشکده منابع طبیعی و علوم زمین، دانشگاه کاشان، ایران | ||
3استاد دانشکده منابع طبیعی، دانشگاه تهران، ایران | ||
4دانشیار دانشکده منابع طبیعی، دانشگاه تهران، ایران | ||
چکیده | ||
به منظور درک بهتر تأثیر خشکسالی بر روی پوشش گیاهی در منطقه خشک بردخون واقع در جنوب غرب ایران، آنالیز تصاویر ماهوارهای MODIS با فاصله زمانی16روزه، طی سالهای 2000 - 2015 با استفاده از شاخصهای پوشش گیاهی NDVI، EVI، SAVI، روش SPI، نمونهبرداری میدانی و سیستم اطلاعات جغرافیایی در طول فصل رشد انجام گردید. در تحقیـق حاضر، نقشه واقعیت زمینی با روش نمونهگیری و پیمایشهای میدانی تهیه و سپس اطـلاعـات مربوط به پوشش متعلق به 290 پلات در قالب 29 واحد نمونه برداری جمعآوری گردید. سپس میزان همبستگی بین شاخصهای گیاهی و دادههای میدانی محاسبه، و برای هر شاخص، مدل پوشش گیاهی بدست آمد. به منظور بررسی اثر خشکسالی بر پوشش گیاهی، خشکسالی با استفاده از روش SPIاز دادههای بارندگی 14 ایستگاه هواشناسی درون و اطراف منطقه مورد مطالعه، در بازه زمانی مشابه با تصاویر ماهوارهای استخراج گردید. نتایج تحقیق نشان داد که شاخص NDVI بیشترین همبستگی (R2=0.56) را بین شاخصها دارد و جهت تهیه نقشه درصد پوشش گیاهی انتخاب گردید. بررسی بین مقادیر شاخص NDVI با شاخص خشکسالی در بازههای زمانی مختلف نشان داد که بیشترین همبستگی بین شاخص پوشش گیاهی با SPIشش ماهه وجود دارد. بر اساس آنالیز شاخص خشکسالی مشخص شد که منطقه مورد مطالعه در سال 2012 شدیدترین خشکسالی و سال 2004 بهترین وضعیت ترسالی را تجربه کرده است. همین روند تغییرات در پوشش گیاهی بر اساس شاخص NDVI مشاهده شد. مقایسه تصاویر طبقهبندی شده بین سالهای 2012 و 2004 (با تغییر 42 درصدی پوشش گیاهی ضعیف) نشاندهنده اثر خشکسالی بر روی پوشش گیاهی در منطقه مورد مطالعه است. نتایج نشان داد، همبستگی بین SPI و شاخص پوشش گیاهی میتواند برای شناسایی خشکسالی کشاورزی مفید باشد. | ||
کلیدواژهها | ||
بردخون؛ خشکسالی؛ شاخص پوشش گیاهی؛ همبستگی؛ MODIS | ||
عنوان مقاله [English] | ||
Identification of Vegetation-Vulnerable Areas to Drought Using Remote Sensing (Case study: Boushehr Province) | ||
نویسندگان [English] | ||
Fatemeh bahreini1؛ Fatemeh Panahi2؛ Mohammad Jafari3؛ Arash Malekian4 | ||
1university of Tehran | ||
2Assis. Prof. of Faculty of Natural Resources and Earth Sciences, university of Kashan, Iran | ||
4Faculty of Natural Resources, university of Tehran. Iran | ||
چکیده [English] | ||
The complexity of drought phenomenon hinders our full understanding of its impact. Field sampling, Geographic Information Systems, SPI and NDVI, EVI and SAVI indices derived from 16-day interval MODIS images during 2000-2015 were used to better understand the effects of drought on vegetation In recent study, ground true map was prepared by sampling and field surveys and vegetation cover data was obtained from 32 sampling units in 320 plots over the entire study area. Then, the correlation between field sampling data and vegetation indices was estimated and vegetation cover models were produced for different indices. In this study, precipitation data of 14 stations within and around the study area were used and SPI was calculated at the same time scales with the vegetation indices to study the effect of drought on vegetation. The results showed that NDVI has had the highest correlation coefficient (R2=0.56) amongst the indices so it was selected for vegetation cover percentage mapping. Investigating NDVI rates and drought index in different temporal periods, 9-month SPI was found to have the best correlation with NDVI. On the basis of SPI analysis, it was found that the study area had the most severe drought in 2012 and the best wet condition in 2004. The similar trend was observed in NDVI. The comparison of classified images between 2004 and 2012 (with 42 % changes in poor vegetation) indicates the effect of drought on vegetation in the study area. | ||
کلیدواژهها [English] | ||
Bordekhun, Drought, Vegetation indices, Correlation, MODIS | ||
مراجع | ||
[1] Arzani,H., Baseiri, M., Dehdari, S. and Zarie chahoki,M.A. (2009). Relationships between canopy cover, foliage cover and basal cover with production. Iranian journal of natural resources, 61(3), 773-763. [2] Abrams, M. D., Ruffuer, M. C. and Morgan, T. A. (1998). Tree-ring responses to drought across species and contrasting sites in the ridge and valley of central Pennsylvania, Forest Science, 44, 550–558. [3] Abrams, M. D., Schultz, J. C. and Kleiner, K.W. (1990). Ecophysiological responses in mesic versus xeric hardwood species to an early-season drought in central Pennsylvania. Forest Science, 36, 970–981. [4] Arshad S, Morid S., Reza Mobasheri.M, and Agha Alikhani.M.( 2008). Development of agricultural drought risk assessment model for Kermanshah province (Iran), using satellite data and intelligence methods. Option Mediterrianeennes, Series A, 80. [5] Asghari Tabrizi, A., Khalili, D., Kamgar-Haghigh, A. A. and Zand-Parsa, Sh. (2010). Utilization of time based meteorological droughts to investigate occurrence of stream flow droughts. Water Resources Management, 24, 4287-4306. [6] Baaghideh, M., Alijani, B. and Ziaian, P. (2012). Evaluating the possibility of using the NDVI index to analyze and monitor droughts in Esfahan Province. Arid regions Geographic Studies, 1 (4),1-6. [7] Brian D.W., Martha, C. A. and James, P.V. (2012). Remote sensing of drought. Taylor & Francis Group, 6, 9552–9575. [8] Bhuiyan, C. (2008). Desert vegetarian during droughts: Response and sensitivity. The International archives of the Photogrammetry. Remote Sensing and Spatial Information Science, XXXVII Part B8, 907-912. [9] Bhuiyan, C., Singh, R.P. and Kogan, F.N. (2006). Monitoring drought dynamics in the Aravalli region (India) using different indices based on ground and remote sensing data. International Journal of Applied Earth Observation and Geo information, 8, 289–302. [10] Carreiras, J. M. B., Pereira, J. M. C. and Pereira, J. S. (2006). Estimation of tree canopy cover in evergreen Oak woodlandsu remote sensing. Forest Ecology and Management, 223, 45-53. [11] Chakraborthy, A., Sehgal, V.K. (2010). Assessment of agricultural drought using MODIS derived normalized difference water index. Journal of Agricultural Physics, 10, 28-36. [12] Dabrowska-Zielinska K., Kogan F., Ciolkosz A., Gruszczynska M. & Kowalik W. (2002). Modelling of crop growth conditions and crop yield in Poland using AVHRR based indices. International Journal of Remote Sensing, 23(6), 1109-1123. [13] Franklin, J. and Hiernaux, P.H.Y. (1991). Estimating foliage and woody biomass in Sahelian and Sudanian [14] Gouveia, C.M., Trigo, R.M., Beguería, S. and Vicente-Serrano, S.M. (2017). Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators. Global and Planetary Change, 15, 15–27. [15] Hielkema, J. U., Prince, S. D. and Astle, W. L. (1986). Rainfall and vegetation monitoring in the Savanna zone of Democratic Republic Sudan using NOAA advanced very high resolution radiometer. International Journal of Remote Sensing, 7, 1499 1514. [16] Hanson, P. J. and Weltzin, J. F. (2000). Drought disturbance from climate change: response of United States forests. Science Total Environment, 262, 205–220. [17] Hasan, M. and Saiful Islam, A. K. M. (2011). Drought assessment using remote sensing and GIs In North-West region of Bangladesh, 3rd International Conference on Water & Flood Management ICWFM-2011,pp.1-8. [18] Jafari, M., Zehtabian, G.H., Ehsani, A.H. and Menbari, S. (2013). The study of land cover condition using landsat satellite (ETM+) data. Iranian Journal of Range and Desert Reseach, 20(2), 285-297. [19] Justice, C., Townshend, J.R.D. and Chaudhary B.J. (1989). Comparision of AVHRR and SMMR data for monitoring vegetation phenology on the continental scale. International journal Remote Sensing, 14,603–608. [20] Ji, L. and Peters, A.J. (2003). Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing Environment, 87, 85–98. [21] Khosravi, H., Haydari, E., Shekoohizadegan, S. and Zareie, S. (2017). Assessment the effect of drought on vegetation in desert area using Landsat data. The Egyptian Journal of Remote Sensing and Space Sciences, 20, S3–S12. [22] Kozlowski, T. T., Kramer, P. J. and Pallardy, S. G. (1991). The Physiological ecology of woody plants. Academic Press, San Diego. [23] McCoy RM. (2005). Field methods in remote sensing, Guilford,159. [24] Mohammadi Golrang, B., Gazanchian, A. Gh., Ramzani Moghadam, R. and Falahati, H. (2009). Estimation of forage yields of some range plant species by plant height and diameter measurements. Iranian Journal of Range and Desert Research, 15 (2), 178-158. [25] Narasimhan, B. and Srinivasan, R. (2005). Development and evaluation of soil moisture deficit index and evapotranspiration deficit index for agricultural drought monitoring. Agricultural and Forest Meteorology, 133, 69-88. [26] Pang, G., Wang, X. and Yang, M. (2016). Using the NDVI to identify variations in, and responses of, vegetation to climate change on the Tibetan Plateau from 1982 to 2012. Quaternary International, xxx, 1-10. [27] Rezaeimoghadam, M.H., Valizadeh Kamran, Kh., Rostamzadeh, H. and Rezaee A.(2012). Evaluating the Adequacy of MODIS in the Assessment of Drought (Case Study: Urmia Lake Basin). Journal of Geography and Environmental Sustainability, 25 (5), 37-52. [28] Rahdari, V. and Maleki Najaf abadi, S. (2011). Compression of Vegetation Indices for Vegetation Cover Mapping in Arid and Semi-arid Environment Using Satellite Data (case study: Mouteh Wild Life Sanctuary). Remote sensing and Geographic information system, 1(1).79-87. [29]Sergio, M, V. (2007). Evaluating the Impact of drought using remote sensing in a Mediterranean semi-arid region. Natural Hazards, 40,173–208. [30]Srivastava, S.K., Jayaraman, V., Nageswar Rao, P.P., Manikiam, B. and Chandraeskhar, M.G. (1994). Agro climatic zonal characterization using NOAA AVHRR and meteorological data, IAF-94-B.5.107. Proceeding of the 45th Congress of International Astronotical Federation 9–14 October, Jerusalem, Isarel. [31] Srut.,S. and Aslam, M.A.M.(2015).Agricultural drought analysis using the NDVI and land surface temperature data; a case study of Raichur district. Aquatic Procedia, 4, 1258 – 1264. [32] Thenkabail P.S., Enclona E.A., Ashton M.S., Legg, C. and Jean De Dieu, M. (2004). The use of remote sensing data for drought assessment and monitoring in south west Asia. International Water Management Institute, PO Box 2075,Colombo, Sri Lanka. [33] Vianas, O. and Baulies, X. (2004). 1:250000 Land use map Landsat TM data. International Journal of Remote sensing. 16(1), 129-146. [34] Wilhelmi, O. V. and Wilhite, D. A. (2002), Assessing vulnerability to agricultural drought: a Nebraska case study. Natural Hazards, 25, 37–58. [35] Wu, H. and Wilhite, D. A. (2004), an operational agricultural drought risk assessment model for Nebraska, USA, Natural Hazards, 33, 1–21. | ||
آمار تعداد مشاهده مقاله: 637 تعداد دریافت فایل اصل مقاله: 516 |