Investigation of Vorticity during Prevalent Winter Precipitation in Iran

Rousta, Imán; Javadizadeh, Farshad; Dargahian, Fatemeh; Ólafsson, Haraldur; Shiri-Karimvandi, Amin; Vahedinejad, Sayed Hossein; Doostkamian, Mehdi; Vargas, Edgar Ricardo Monroy; Asadolahi, Anayat

doi:10.1155/2018/6941501

Cited by 24 publications

(12 citation statements)

References 28 publications

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“…The northern and western parts of the study area are located in the pathway of the westerlies. The main factors affecting the atmospheric dynamics are westerlies and the large water bodies (Black, Mediterranean, and Caspian seas), which supply the water vapor; therefore, these areas contain the densest vegetation in the ME [76][77][78][79].…”

Section: Discussionmentioning

confidence: 99%

Anthropogenic Factors Affecting the Vegetation Dynamics in the Arid Middle East

Rousta¹,

Ólafsson²,

Zhang³

et al. 2020

Preprint

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The spatiotemporal variability of vegetation in the Middle East was investigated for the period 2001–2019 using the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day/500 m composites of the Normalized Difference Vegetation Index (NDVI; MOD13A1). The results reveal a strong increase in the NDVI coverage in the Middle East during the study period (R = 0.75, p-value = 0.05). In Egypt, the annual coverage exhibits the strongest positive trend (R = 0.99, p-value = 0.05). In Turkey, both the vegetation coverage and density increased from 2001 to 2019, which can be attributed to the construction of some of the biggest dams in the Middle East, such as the Atatürk and Ilisu dams. Significant increases in the annual coverage and maximum and average NDVI in Saudi Arabia are due to farming in the northern part of the country for which groundwater and desalinated seawater are used. The results of this study suggest that the main factors affecting the vegetation coverage in the Middle East are governmental policies. These policies can have a positive effect on the vegetation coverage in some countries such as Egypt, Saudi Arabia, Qatar, Kuwait, Iran, and Turkey.

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Section: Discussionmentioning

confidence: 99%

Anthropogenic Factors Affecting the Vegetation Dynamics in the Arid Middle East

Rousta¹,

Ólafsson²,

Zhang³

et al. 2020

Preprint

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“…This has been evidenced as a causal factor in decreasing agricultural productivity [5]. The nature of drought is dynamic and causes difficulties in planning, monitoring, and prediction to assist drought-stricken areas [6,7]. Since the 1970s, numerous studies have used satellite-based land observations to monitor land surface dynamics [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Drought on Vegetation Assessed by Vegetation Indices and Meteorological Factors in Afghanistan

et al. 2020

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Drought has severe impacts on human society and ecosystems. In this study, we used data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) sensors to examine the drought effects on vegetation in Afghanistan from 2001 to 2018. The MODIS data included the 16-day 250-m composites of the Normalized Difference Vegetation Index (NDVI) and the Vegetation Condition Index (VCI) with Land Surface Temperature (LST) images with 1 km resolution. The TRMM data were monthly rainfalls with 0.1-degree resolution. The relationship between drought and index-defined vegetation variation was examined by using time series, regression analysis, and anomaly calculation. The results showed that the vegetation coverage for the whole country, reaching the lowest levels of 6.2% and 5.5% were observed in drought years 2001 and 2008, respectively. However, there is a huge inter-regional variation in vegetation coverage in the study period with a significant rising trend in Helmand Watershed with R = 0.66 (p value = 0.05). Based on VCI for the same two years (2001 and 2008), 84% and 72% of the country were subject to drought conditions, respectively. Coherently, TRMM data confirm that 2001 and 2008 were the least rainfall years of 108 and 251 mm, respectively. On the other hand, years 2009 and 2010 were registered with the largest vegetation coverage of 16.3% mainly due to lower annual LST than average LST of 14 degrees and partially due to their slightly higher annual rainfalls of 378 and 425 mm, respectively, than the historical average of 327 mm. Based on the derived VCI, 28% and 21% of the study area experienced drought conditions in 2009 and 2010, respectively. It is also found that correlations are relatively high between NDVI and VCI (r = 0.77, p = 0.0002), but slightly lower between NDVI and precipitation (r = 0.51, p = 0.03). In addition, LST played a key role in influencing the value of NDVI. However, both LST and precipitation must be considered together in order to properly capture the correlation between drought and NDVI.

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“…Land surface temperature (LST) is a principal characteristic for the evaluation of energy exchange between the land and the atmosphere [1][2][3]. This parameter is applicable to numerous sciences such as climatology, hydrology, agriculture, ecology, public health, and the environment [4][5][6][7][8][9][10][11][12][13][14][15]. One of the methods for measuring LST is to use satellite data and thermal remote sensing techniques.…”

Section: Introductionmentioning

confidence: 99%

Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA)

et al. 2018

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Land surface temperature (LST) is a basic parameter in energy exchange between the land and the atmosphere, and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. Time series of satellite LST data have usually deficient, missing, and unacceptable data caused by the presence of clouds in images, the presence of dust in the atmosphere, and sensor failure. In this study, the singular spectrum analysis (SSA) algorithm was used to resolve the problem of missing and outlier data caused by cloud cover. The region studied in the present research included an image frame of the Moderate Resolution Imaging Spectroradiometer (MODIS) with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran, Turkmenistan, and the Caspian Sea. In this study, MODIS LST products (MOD11A1) were used during 2015 with approximately 1 km × 1 km spatial resolution and day/night LST data (daily temporal resolution). On average, the data have 36.37% gaps in each pixel profile with 730 day/night LST data. The results of the SSA algorithm in the reconstruction of LST images indicated a root mean square error (RMSE) of 2.95 Kelvin (K) between the original and reconstructed LST time series data in the study region. In general, the findings showed that the SSA algorithm using spatio-temporal interpolation can be effectively used to resolve the problem of missing data caused by cloud cover.

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Investigation of Vorticity during Prevalent Winter Precipitation in Iran

Cited by 24 publications

References 28 publications

Anthropogenic Factors Affecting the Vegetation Dynamics in the Arid Middle East

Anthropogenic Factors Affecting the Vegetation Dynamics in the Arid Middle East

Impacts of Drought on Vegetation Assessed by Vegetation Indices and Meteorological Factors in Afghanistan

Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA)

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