نشریه سنجش از دور و GIS ایران

شماره جاری

بر اساس شماره‌های نشریه

بر اساس نویسندگان

بر اساس موضوعات

نمایه نویسندگان

نمایه کلیدواژه ها

درباره نشریه

اهداف و چشم انداز

اعضای هیات تحریریه

اصول اخلاقی انتشار مقاله

بانک ها و نمایه نامه ها

پیوندهای مفید

پرسش‌های متداول

فرایند پذیرش مقالات

اطلاعات آماری نشریه

اخبار و اعلانات

حساسیت‌سنجی شاخص خشکی گیاه (VDI) به بازتابش باندهای گوناگون فروسرخ موج کوتاه در مناطق خشک و نیمه‌خشک (مطالعة موردی: استان سیستان و بلوچستان)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استاد گروه جغرافیا، پردیس علوم انسانی و اجتماعی، دانشگاه یزد

2 دانشجوی دکتری گروه جغرافیا، پردیس علوم انسانی و اجتماعی، دانشگاه یزد

3 دانشیار گروه جغرافیا، پردیس علوم انسانی و اجتماعی، دانشگاه یزد،

4 استادیار گروه جغرافیا، پردیس علوم انسانی و اجتماعی، دانشگاه یزد

5 استادیار گروه جغرافیای طبیعی، دانشکدة جغرافیا و برنامه‌ریزی محیطی، دانشگاه سیستان و بلوچستان

چکیده

پایش خشکسالی، به‌منظور هشدار سریع برای خطر خشکسالی، بسیار حیاتی و مهم است. در این پژوهش، سعی شده است که شاخص پایش خشکسالی VDI براساس باندهای متفاوت داده‌های ماهواره‌ای مادیس، با تفکیک مکانی متوسط، توسعه یابد. شاخص VDI به تنش آب در گیاهان می‌پردازد. مطالعات طیفی نشان داده است که بازتابندگی باند فروسرخ موج کوتاه (SWIR) با محتوای آب‌برگ ارتباط منفی دارد و به‌دلیل حساس‌بودن SWIR به محتوای آب‌برگ، در ایجاد شاخص‌های گوناگون سنجش از دور، ازجمله VDI و به‌منظور شناسایی محتوای آب گیاهان، کاربرد گسترده‌ای دارد. این پژوهش نقشه‌های خشکسالی شاخص VDI را براساس میزان حساسیت به رطوبت، با استفاده از بازتابش باند‌های 5 و 6 فروسرخ موج کوتاه SWIR (VDI5 و VDI6) مادیس، ارزیابی کرده است. بدین‌منظور از تصاویر ماهواره‌ای مادیس و داده‌های بارش ماهیانة مدل جهانی GLDAS، در محدودة استان سیستان و بلوچستان در دورة زمانی نوزده‌ساله‌ای (2018-2000) استفاده شد. برای ارزیابی دقت نقشه‌های محاسبه‌شده براساس دو باند، ضریب همبستگی پیرسون به‌کار رفت. نتایج همبستگی بالایی را میان شاخص VDI6 و داده‌های بارش نشان داد و مشخص شد که باند 6 موج کوتاه فروسرخ، در استان سیستان و بلوچستان، به شرایط خشک خاک بیشترین واکنش را نشان می‌دهد؛ ازاین‌رو این مطالعه استفاده از شاخص VDI براساس باند 6 را برای شناسایی زودهنگام و نظارت بر خشکسالی کشاورزی در برنامه‌های عملیاتی مدیریت خشکسالی، پیشنهاد می‌کند.

کلیدواژه‌ها

عنوان مقاله [English]

Sensitivity of Vegetation Dryness Index (VDI) to Reflectance of Different Shortwave Infrared Bands in Arid and Semi-Arid Regions (Case Study: Sistan & Baluchestan Province)

نویسندگان [English]

  • Kamal Omidvar 1
  • massumeh nabavi zadeh 2
  • Ahmad Mazidi 3
  • HamidReza Ghaffarian Malmiri 4
  • Peyman Mahmoudi 5

1 Prof. of Geography, Campus of Humanities and Social Sciences, Yazd University

2 Ph.D. Students, Dep. of Geography, Campus of Humanities and Social Sciences, Yazd University

3 Associate Prof., Dep. of Geography, Campus of Humanities and Social Sciences, Yazd University

4 Assistant Prof., Dep. of Geography, Campus of Humanities and Social Sciences, Yazd University

5 Assistant Prof., Dep. of Natural Geography, Faculty of Geography and Environmental Planning, Sistan

چکیده [English]

Drought monitoring is critical for early warning of drought hazard. This study  is  attempted to develop remote sensing drought monitoring index (VDI), based on Accuracy of different bands of Moderate Resolution Imaging Spectroradiometer data MODIS, VDI focuses about the vegetation water stress.
 Spectral studies have demonstrated that due to the large absorption by leaf water, shortwave infrared reflectance (SWIR) is negatively related to leaf water content. Being sensitive to leaf water content, SWIR is widly utilized to construct various remote-sensing indices for example VDI for reflecting vegetation water content, . In this study, Vegetation Drought Index (VDI) was evaluated Based on the sensitivity rate to moisture by shortwave infrared reflectance bands SWIR 5 and 6 (VDI5 and VDI6). The data included the MODIS sensor images from Terra satellite in a period of nineteen years from 2000 to 2018 and Precipitation data are obtained from the Global Land Data Assimilation System (GLDAS), in Sistan & Balouchestan Province, Pearson correlation coefficient was used to evaluate the accuracy of the Drought spatial distribution maps calculated based on the two bands.
Results indicate high significant correlation rate between VDI6 and Precipitation data . Study also showed that shortwave infrared band 6 (SWIR) is more sensitive to agricultural drought than band 5,in Sistan and Baluchestan province . The study recommends  to use VDI index with and 6 for better early detection and monitoring of agricultural drought in operational drought management programmes.

کلیدواژه‌ها [English]

  • Drought
  • VDI Vegetation Drought Index
  • SWIR Shortwave Infrared Bands
  • GLDAS Global Model Precipitation Data
  • Sistan & Baluchestan province
مراجع
Ahmadalipour, A., Moradkhani, H. & Svoboda, M., 2017, Centennial Drought Outlook over the CONUS Using NASA-NEX Downscaled Climate Ensemble, International Journal of Climatology, 37(5), PP. 2477-2491.
Almamalachy, Y.S., Al-Quraishi, A.M.F. & Moradkhani, H., 2020, Agricultural Drought Monitoring over Iraq Utilizing MODIS Products, In Environmental Remote Sensing and GIS in Iraq (PP. 253-278), Springer, Cham.
Alonso, A., Muñoz-Carpena, R. & Kaplan, D., 2020, Coupling High-Resolution Field Monitoring and MODIS for Reconstruc-ting Wetland Historical Hydroperiod at a High Temporal Frequency, Remote Sensing of Environment, 247, P. 111807.
Anyamba, A. & Tucker, C.J., 2005, Analysis of Sahelian Vegetation Dynamics Using NOAA-AVHRR NDVI Data from 1981–2003, Journal of Arid Environments, 63(3), PP. 596-614.
Bajgain, R., Xiao, X., Wagle, P., Basara, J. & Zhou, Y., 2015, Sensitivity Analysis of Vegetation Indices to Drought over Two Tallgrass Prairie Sites, ISPRS Journal of Photogrammetry and Remote Sensing, 108, PP. 151-160.
 
 
Bartholic, J.F., Namken, L.N. & Wiegand, C.L., 1972, Aerial Thermal Scanner to Determine Temperature of Soils and Crop Canopies Differing in Water Stress, Agronomy Journal, 64(5), PP. 603-608.
Braga, P., Crusiol, L. G.T., Nanni, M.R., Caranhato, A.L.H., Fuhrmann, M.B., Nepomuceno, A.L., ... & Mertz-Henning, L.M., 2020, Vegetation Indices and NIR-SWIR Spectral Bands as a Phenotyping Tool for Water Status Determination in Soybean, Precision Agriculture, PP. 1-18.
Chen, D., Huang, J. & Jackson, T.J., 2005, Vegetation Water Content Estimation for Corn and Soybeans Using Spectral Indices Derived from MODIS Near-and Short-Wave Infrared Bands, Remote Sensing of Environment, 98(2-3), PP. 225-236.
Chen, X., Guo, Z., Chen, J., Yang, W., Yao, Y., Zhang, C. & Cao, X., 2019, Replacing the Red Band with the Red-SWIR Band (0.74 Ρred+ 0.26 Ρswir) Can Reduce the Sensitivity of Vegetation Indices to Soil Background, Remote Sensing, 11(7), P. 851.
Cohen, J., 1988, Statistical Power Analysis for the Behavioral Sciences (2nd ed.), Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.
Eriksen, S. & Silva, J.A., 2009, The Vulnerability Context of a Savanna Area in Mozambique: Household Drought Coping Strategies and Responses to Economic Change, Environmental Science & Policy, 12(1), PP. 33-52.
Fensholt, R. & Sandholt, I., 2003, Derivation of a Shortwave Infrared Water Stress Index from MODIS Near-and Shortwave Infrared Data in a Semiarid Environment, Remote Sensing of Environment, 87(1), PP. 111-121.
Gao, B.C., 1996, NDWI - a Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space, Remote Sensing Env., 58, PP. 257-266.
Gu, Y.X., Brown, J.F., Verdin, J.P. & Wardlow, B., 2007, A Five-Year Analysis of MODIS NDVI and NDWI for Grassland Drought Assessment over the Central Great Plains of the United States, Geophysical Research Letters, 34, L06407.
Hashimoto, Y., Ino, T., Kramer, P.J., Naylor, A.W. & Strain, B.R., 1984, Dynamic Analysis of Water Stress of Sunflower Leaves by Means of a Thermal Image Processing System, Plant Physiology, 76(1), PP. 266-269.
Heilman, J.L., Kanemasu, E.T., Rosenberg, N.J. & Blad, B.L., 1976, Thermal Scanning Measurement of Canopy Temperatures to Estimate Evapotranspiration, Remote Sensing of Environment, 5 (1), PP. 137-145.
Idso, S.B., Jackson, R.D., Pinter, P.J., Reginato, R.J. & Hatfield, J.L., 1981, Normalizing the Stress-Degree-Day Parameter for Environmental Variability, Agricultural Meteorology, 24(1), PP. 45-55.
Jackson, R.D., Idso, S.B., Reginato, R.J. & Pinter, P.J., 1981, Canopy Temperature as a Crop Water-Stress Indicator, Water Resources Research, 17(4), PP. 1133-1138.
Jacques, D.C., Kergoat, L., Hiernaux, P., Mougin, E. & Defourny,P., 2014, Monitoring Dry Vegetation Masses in Semi-Arid Areas with MODIS SWIR Bands, Remote Sensing of Environment, 153, PP. 40-49.
Kergoat, L., Hiernaux, P., Dardel, C., Pierre, C., Guichard, F. & Kalilou, A., 2015, Dry-Season Vegetation Mass and Cover Fraction from SWIR1. 6 and SWIR2. 1 Band Ratio: Ground-Radiometer and MODIS Data in the Sahel, International Journal of Applied Earth Observation and Geoinformation, 39, PP. 56-64.
Khosravi, M., 2009, Time-Spatial Analysis of Stable Hamun Lakes, Iran Water Resources Research Journal, No. 3, PP. 68-79.
Kogan, F.N., 1990, Remote-Sensing of Weather Impacts on Vegetation in Nonhomogeneous Areas, International Journal of Remote Sensing, 11(8), PP. 1405-1419.
Kokhakhan, M.S., Fordoie, A.R., Mousavi, S.H. & Vali, A., 2018, Assessment of Drought Dynamics in Sistan & Baluchistan Province Using MODIS Satellite Data (2000-2015), Iran Pasture and Desert Research Quarterly, 26th year, 76(3) (Autumn 2018), PP. 754-771.
Li, L., Vrieling, A., Skidmore, A., Wang, T., Muñoz, A.R. & Turak, E., 2015, Evaluation of MODIS Spectral Indices for Monitoring Hydrological Dynamics of a Small, Seasonally-Flooded Wetland in Southern Spain, Wetlands, 35(5), PP. 851-864.
Linsheng, H., Yansheng, D., Dongyan, Z., Wenjiang, H. & Dong, L., 2012, Using Temperature Vegetation Drought Index for Monitoring Drought Based on Remote Sensing Data, Trans Tech Publications Switzerland, 356, PP. 2854-2859.
Liu, W.T.H., Massambani, O. & Nobre, C.A., 1994, Satellite Recorded Vegetation Response to Drought in Brazil, International Journal of Climatology, 14(3), PP. 343-354.
Mahmoudi, P., Shirazi, S.A., Firoozi, F., Jahanshahi, S.M.A. & Mazhar, N., 2020, Detection of Land Cover Changes in Baluchistan (Shared between Iran, Pakistan, and Afghanistan) Using the MODIS Land Cover Product, Arabian Journal of Geosciences, 13(23), PP. 1-14.‏
Maki, M., Ishiahra, M. & Tamura, M., 2004, Estimation of Leaf Water Status to Monitor the Risk of Forest Fires by Using Remotely Sensed Data, Remote Sensing of Environment, 90(4), PP. 441-450.
Malo, A.R. & Nicholson, S.E., 1990, A Study of Rainfall and Vegetation Dynamics in the African Sahel Using Normalized Difference Vegetation Index, Journal of Arid Environments, 19(1), PP. 1-24.
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), PP. 246-263.
Niazi, Y., Talebi, A., Mokhtari, M.H., Vazife Dost, M., 2017, Evaluation of the efficiency of plant drought indices (VDI) and temperature (TDI) based on satellite images in central Iran, two quarterly journals Khoshk Boom Scientific-Research Journal, Volume 7, Number 1, pp. 79-94.
Nikpi, H. M., Moameni, M., 2019, Influence of  Climatology and altitude zoning on correlation of drought indexes of remote sensing by rainfall data and introducing local  indexes, Magazine of Remote Sensing & GIS of Iran,  (2),47-62.
Nurul Fatin, M., Mohd Razali, S., Ahmad, A.A. & Mohd Shafri, Z.M., 2019, Oil Palm Dry Season Analysis Based on Moderate-Resolution Imaging Spectroradiometer (MODIS) Satellite Indices, International Journal of Remote Sensing, 40(19), PP. 7663-7678.
Omasa, K., Hashimoto, Y. & Aiga, I., 1981, A Quantitative Analysis of the Relationship between O3 Sorption and Its Acute Effect on Plant Leaves Using Image Instru-mentation, Environmental Control in Biology, 19(3), PP. 85-92.
Piri, H., Rahdari, V. & Maleki, S., 2013, Review and Omparison of the Efficiency of Four Meteorological Drought Profiles in the Management of Drought Risk in Sistan & Baluchistan Province, Irrigation and Water Engineering Research Quarterly, 3rd year, 11th issue, PP. 96-114.
Rahimzadeh-Bajgiran, P., 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), PP. 1086-1096.
Rahimzadeh-Bajgiran, P., Shimizu, Y., Hosoi, F. & Omasa, K., 2009, MODIS Vegetation and Water Indices for Drought Assessment in Semi-Arid Ecosystems of Iran, Journal of Agricultural Meteorology, 65(4), PP. 349-355.
Raziei, T., Dansh P. & Thaqfian, B., 2007, Investigation of Temporal and Spatial Pattern of Meteorological Droughts in Sistan & Baluchistan Province, Scientific Journal of Agriculture, 3(1), PP. 85-99.
Renier, C., Waldner, F., Jacques, D.C., Babah Ebbe, M.A., Cressman, K. & Defourny, P., 2015, A Dynamic Vegetation Senescence Indicator for Near-Real-Time Desert Locust Habitat Monitoring with MODIS, Remote Sensing, 7(6), PP. 7545-7570.
Sandholt, I., Rasmussen, K. & Andersen, J., 2002, A Simple Interpretation of the Surface Temperature/Vegetation Index Space for Assessment of Surface Moisture Status, Remote Sensing of Environment, 79(2-3), PP. 213-224.
Sow, M., Mbow , C., Hély , C., Fensholt , R. & Sambou , B., 2013, Estimation of Herbaceous Fuel Moisture Content Using Vegetation Indices and Land Surface Temperature from MODIS Data, Remote Sens. 2013, 5(2617-2638).
Sun, H., Zhao, X., Chen, Y., Gong, A. & Yang, J., 2013, A New Agricultural Drought Monitoring Index Combining MODIS NDWI and Day–Night Land Surface Temperatures: A Case Study in China, International Journal of Remote Sensing, 34(24), PP. 8986-9001.
Tucker, C.J., Holben, B.N., Elgin, J.H. & Mcmurtrey, J.E., 1981, Remote-Sensing of Total Dry-Matter Accumulation in Winter-Wheat, Remote Sensing of Environment, 11(3), PP. 171-189.
Vincente-Serrano, S.M., Pons-Fernandez, X. & Cuadrat-Prats, J.M., 2004, Mapping Soil Moisture in the Central Ebro River Valley with Landsat and NOAA Satellite Imagery: A Comparison with Meteorological Data, International Journal of Remote Sensing, 25(20), PP. 4325-4350.
Wang, L. & Qu., J.J., 2007, NMDI: A Normalized Multi-Band Drought Index for Monitoring Soil and Vegetation Moisture with Satellite Remote Sensing, Geophysical Research Letters, 34, L20405.
Wang, J., Price, K.P. & Rich, P.M., 2001, Spatial Patterns of NDVI in Response to Precipitation and Temperature in the Central Great Plains, International Journal of Remote Sensing, 22(18), PP. 3827-3844.Wang, L., Qu, J.J., Hao, X. & Zhu. Q., 2008, Sensitivity Studies of the Moisture Effects on MODIS SWIR Reflectance and Vegetation Water Indices, December 2008, International Journal of Remote Sensing, 29(24), P. 7065-7075.
Wang, W., Chen, X., Cao, X. & Chen, J., 2020, A Novel Spectral Linear Transformation to Estimate Non-Photosynthetic Vegetation Coverage in North Asian Steppe, The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, PP. 221-225.
Wilhite, D.A., 2000, Drought as a Natural Hazard, In: Wilhite, D.A. (Editor), Drought: A Global Assessment, Routledge, London.
Yilmaz, M.T., Hunt Jr. E.R. & Jackson, T.J., 2008, Remote Sensing of Vegetation Water Content from Equivalent Water Thickness Using Satellite Imagery, Remote Sensing of Environment, 112(5), PP. 2514-2522.
Zare Abianeh, H., Sabzi Paror, A.A., Maroufi, S., Qiami, F., Mir Masoudi, S.S. & Kazemi, A. 2014, Meteorological Drought Analysis and Monitoring of Sistan & Baluchistan Region, Environmental Science and Technology Quarterly, 17(1), PP. 49-61.
نشریه سنجش از دور و GIS ایران
دوره 14، شماره 4 - شماره پیاپی 56
دی 1401
صفحه 103-118
فایل ها
اشتراک گذاری
ارجاع به این مقاله
آمار