A New Dust Index Based on Reflective and Thermal Infrared MODIS Bands to Detect the Spatial Extent of Sand and Dust Storm in Western Asia

Document Type : Research Paper

Authors

1 M.Sc., Remote Sensing, Surveying and Geospatial Engineering, Aerospace Research Institute, Ministry of Science, Research and technology, Tehran. Iran

2 Assistant Professor, Aerospace Research Institute, Ministry of Science, Research and technology, Tehran. Iran

Abstract

Moderate Resolution Imaging Spectroradiometer (MODIS) provides appropriate images for studying of Sand and Dust Storms (SDS). Commonly used MODIS based dust indices, can’t monitor SDS more accurately and properly. In this study, the proposed new dust index is based on reflective and thermal infrared MODIS bands, which consists of a combination of bands 3, 7, 31, and 32. This index is applied to identify two dust events in western Asia ocured July 15 and 16, 2008. The results of applying this index compared to well-known MODIS based dust indices, such as Brightness Temperature Difference (BTD) index between band 32 and band 31 (BTD32-31), and band 20 and 31 (BTD20-31), and Normalize Difference Dust Index (NDDI). The results of this study indicated that the new dust index captured the spatial extent of SDS with high accuracy. Validation of these indices with SDS extracted by MODIS true color image showed that the new dust index detected SDS extent with an overall accuracy more than 88%, which was 7%, 15% and 31% higher than the results derived from BTD20-31, NDDI and BTD32-31, respectively. Also, according to SDS detected by MODIS MCD19A2 Aerosol Optical Depth (AOD) product data, the proposed index identified SDS with an overall accuracy 82%, which was 5%, 8% and 31% higher than the results derived from NDDI, BTD20-31 and BTD32-31, respectively. Our results suggest that the new dust index can effectively capture large-scale SDS and seprate dusty pixels from dust-free areas in western Asia.

Keywords

References

-Ackerman, Steven A. (1997). "Remote sensing aerosols using satellite infrared observations." Journal of Geophysical Research: Atmospheres 102, no. D14: 17069-17079.
-Ackerman, Steven A. (1989). "Using the radiative temperature difference at 3.7 and 11 μm to tract dust outbreaks."Remote Sensing of Environment 27, no. 2: 129-133.
-Albugami, S., Palmer, S., Meersmans, J., & Waine, T. (2018). Evaluating MODIS dust-detection indices over the Arabian Peninsula. Remote Sensing, 10(12), 1993.
-Baddock, M. C., Bullard, J. E., & Bryant, R. G. (2009). Dust source identification using MODIS: a comparison of techniques applied to the Lake Eyre Basin, Australia. Remote Sensing of Environment, 113(7), 1511-1528.
-Bahl, T. (2003). Encyclopaedia of Muslim world. Anmol publ.
-Bin Abdulwahed, A., Dash, J., & Roberts, G. (2019). An evaluation of satellite dust-detection algorithms in the Middle East region. International journal of remote sensing, 40(4), 1331-1356.
-Boroghani, M., Pourhashemi, S., Zanganeh Asadi, M., Moradi, H. (2017). Dust Source Identification in the Middle East by Using Remote Sensing. Journal of Natural Environmental Hazards, 6(11), 101-118. doi: 10.22111/jneh.2017.2959, (In Persiani).
-Boschetti, L., Flasse, S. P., & Brivio, P. A. (2004). Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: The Pareto Boundary. Remote sensing of environment, 91(3-4), 280-292.
-Broomandi, P., & Bakhtiar Pour, A. (2017). Dust source identification using physical-chemical characterization and numerical modeling in Masjed Soleyman. Iranian Journal of Health and Environment, 9(4), 517-526, (In Persiani).
-Cao, H., Amiraslani, F., Liu, J., & Zhou, N. (2015). Identification of dust storm source areas in West Asia using multiple environmental datasets. Science of the Total Environment, 502, 224-235.
-Cao, H., Liu, J., Wang, G., Yang, G., & Luo, L. (2015). Identification of sand and dust storm source areas in Iran. Journal of Arid Land, 7(5), 567-578.
-Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics, 21(1), 1-13.
-Dayan, U., Heffter, J., Miller, J., & Gutman, G. (1991). Dust intrusion events into the Mediterranean basin. Journal of Applied Meteorology, 30(8), 1185-1199.
-Delgado, R., & Tibau, X. A. (2019). Why Cohen’s Kappa should be avoided as performance measure in classification. PloS one, 14(9), e0222916.
-Esmaili, O., Tajrishy, M., & Arasteh, P. D. (2006). Evaluation of dust sources in Iran through remote sensing and synoptical analysis. In Atlantic Europe conference on remote imaging and, spectroscopy (pp. 136-43).
-Fallah Zazuli, M., Vafaeinezhad, A., Kheirkhah Zarkesh, M., Ahmadi Dehka, F. (2014). Monitoring and Synoptic Analysis of Dust Haze Phenomenon using Remote Sensing and GIS (Case study: June 18, 2012 Dust haze). Scientific- Research Quarterly of Geographical Data (SEPEHR), 23(91), 69-80. doi: 10.22131/sepehr.2014.12863, (In Persiani).
-Farajzadeh, M., and Alizadeh, A. (2011). Temporal and spatial analysis of dust storms in Iran. The Journal of Spatial Planning, 22(1), 65-84, (In Persiani).
-Han, L., Tsunekawa, A., Tsubo, M., & Zhou, W. (2013). An enhanced dust index for Asian dust detection with MODIS images. International journal of remote sensing, 34(19), 6484-6495.
 -Keikhosravi, G., and Haseli, M. (2017). Trajectory simulation of some examples of severe dust storms in Kermanshah province from the perspective of the companion and the HYSPLIT model. Physical Geography Quarterly, 10(37), 59-82, (In Persiani).
-Kermani M, Taherain E, Izanloo M. Analysis of dust and dust storms in Iran, Investigation Internal and external origin of dust storms in Iran using satellite images and Control methods. rsj. 2016; 2 (1) :39-51, (In Persiani).
-Khamooshi, S., Panahi, F., Vali, A., & Mousavi, S. H. (2016). Dust storm monitoring using HYSPLIT model and NDDI (Case study: Southern cities of Shiraz, Bushehr and Fasa, Iran). Ecopersia, 4(4), 1603-1616.
-Lyapustin, A., Wang, Y., Korkin, S., & Huang, D. (2018). MODIS collection 6 MAIAC algorithm. Atmospheric Measurement Techniques, 11(10), 5741-5765.
-Lyapustin, A., & Wang, Y. (2018). MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) Data User’s Guide. NASA: Greenbelt, MD, USA.
 -Mehrabi, S., Jafari, R., Soltani, S. (2015). Investigating the Performance of NDDI index for dust mapping of arid lands (Khuzestan Province), Desert Ecosystem Engineering Journal, 4(8): 1-10, (In Persiani).
-Moridnejad, A., Karimi, N., & Ariya, P. A. (2015). A new inventory for middle east dust source points. Environmental monitoring and assessment, 187(9), 1-11.
-Nabavi, S. O., Haimberger, L., & Samimi, C. (2016). Climatology of dust distribution over West Asia from homogenized remote sensing data. Aeolian Research, 21, 93-107.
-Naeimi, M., Yousefi, M., Khosroshahi, M., Zandifar, S., Ebrahimikhusfi, Z. (2020). Climatic factors affecting dune mobility in the west of Khorasan Razavi Province, Iran. The Journal of Geographical Research on Desert Areas, 7(2), 25-45, (In Persiani).
-Omidvar, K., Alizadeh, M., & Mehr Afarin, R. (2015). Synoptic Analysis of Transfer Patterns of the Atmospheric Dust to Khuzestan Province. The Journal of Geographical Research on Desert Areas, 3(2), 73-96, (In Persiani).
-Omidvar, K., Omidi, Z. (2013). The Analysis of Dust Phenomenon in the Southern and Central Fars Province. The Journal of Geographical Research on Desert Areas, 1(1), 85-114, (In Persiani).
-Qu, J. J., Hao, X., Kafatos, M., & Wang, L. (2006). Asian dust storm monitoring combining Terra and Aqua MODIS SRB measurements. IEEE Geoscience and remote sensing letters, 3(4), 484-486.
-Samadi, M., Boloorani, A. D., Alavipanah, S. K., Mohamadi, H., & Najafi, M. S. (2014). Global dust Detection Index (GDDI); a new remotely sensed methodology for dust storms detection. Journal of environmental health science and engineering, 12(1), 1-14.
 -Shahraiyni, H. T., Karimi, K., Nokhandan, M. H., & Moghadas, N. H. (2015). Monitoring of dust storm and estimation of aerosol concentration in the Middle East using remotely sensed images. Arabian Journal of Geosciences, 8(4), 2095-2110.
-Strandgren, J., Mei, L., Vountas, M., Burrows, J. P., Lyapustin, A., & Wang, Y. (2014). Study of satellite retrieved aerosol optical depth spatial resolution effect on particulate matter concentration prediction. Atmospheric Chemistry and Physics Discussions, 14(18), 25869-25899.
-Taghavi, F., Owlad, E., & Ackerman, S. A. (2017). Enhancement and identification of dust events in the south-west region of Iran using satellite observations. Journal of Earth System Science, 126(2), 28.
-Tavousi, T., Khosravi, M., Raispour, K. (2010). Dust phenomenon is the most important environmental crisis in Khuzestan province. Political and Economic Ettela’at, No. 273, pp. 166-177, (In Persiani).
-Yue, H., He, C., Zhao, Y., Ma, Q., & Zhang, Q. (2017). The brightness temperature adjusted dust index: An improved approach to detect dust storms using MODIS imagery. International journal of applied earth observation and geoinformation, 57, 166-176.
-Zeinali, B., Asghari, S. (2018). Evaluation of some indicators to identify and monitor dust storm (case study: dust storms 10 August 2008 East the Iran). Geography and Planning, 22(65), 205-222, (In Persiani).
-Zoljoodi, M., Didevarasl, A., & Saadatabadi, A. R. (2013). Dust events in the western parts of Iran and the relationship with drought expansion over the dust-source areas in Iraq and Syria.
 
The Journal of Geographical Research on Desert Areas
Volume 9, Issue 2
October 2021
Pages 159-180

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