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Open AccessJournal ArticleDOI

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

TLDR
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, however, there is a huge inter-regional variation in vegetation coverage in the study period with a significant rising trend in Helmand Watershed.
Abstract
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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Citations
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Journal ArticleDOI

Comparison of Multi-Year Reanalysis, Models, and Satellite Remote Sensing Products for Agricultural Drought Monitoring over South Asian Countries

TL;DR: In this paper, the relationship between soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) was evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI).
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Decadal Urban Land Use/Land Cover Changes and Its Impact on Surface Runoff Potential for the Dhaka City and Surroundings Using Remote Sensing

TL;DR: In this paper, a study was conducted to quantify the changes in land use/land cover (LULC) and urban runoff extent based on the Natural Resources Conservation Service (NRCS) Curve Number (CN) during 1978-2018.
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Remote Sensing Indices for Spatial Monitoring of Agricultural Drought in South Asian Countries

TL;DR: In this article, the authors explore the performance of the evaporative stress index (ESI), vegetation health index (VHI), enhanced vegetation index (EVI), and standardized anomaly index (SAI) based on satellite remote sensing data from 2002-2019 for agricultural drought assessment in Afghanistan, Pakistan, India, and Bangladesh.
Journal ArticleDOI

Comparison of Harmonic Analysis of Time Series (HANTS) and Multi-Singular Spectrum Analysis (M-SSA) in Reconstruction of Long-Gap Missing Data in NDVI Time Series

TL;DR: The evaluation of the performance of gap filling capability of HANTS and M-SSA by filling artificially created gaps in data using Landsat and MODIS data presents a favorable option for solving the missing data challenge in NDVI time series.
References
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Journal ArticleDOI

Red and photographic infrared linear combinations for monitoring vegetation

TL;DR: In this article, the relationship between various linear combinations of red and photographic infrared radiances and vegetation parameters is investigated, showing that red-IR combinations to be more significant than green-red combinations.

The relationship of drought frequency and duration to time scales

TL;DR: The definition of drought has continually been a stumbling block for drought monitoring and analysis as mentioned in this paper, mainly related to the time period over which deficits accumulate and to the connection of the deficit in precipitation to deficits in usable water sources and the impacts that ensue.

Monitoring Vegetation Systems in the Great Plains with Erts

TL;DR: In this paper, a method has been developed for quantitative measurement of vegetation conditions over broad regions using ERTS-1 spectral bands 5 and 7, corrected for sun angle, which is shown to be correlated with aboveground green biomass on rangelands.
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The Tropical Rainfall Measuring Mission (TRMM) Sensor Package

TL;DR: In this article, the authors present a reference guide to users wishing to make use of the Tropical Rainfall Measuring Mission data, which covers each of the three primary rainfall instruments: the passive microwave radiometer, the precipitation radar, and the Visible and Infrared Radiometer System on board the spacecraft.
Journal ArticleDOI

A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter

TL;DR: In this article, the Savitzky-Golay filter was used to smooth out noise in NDVI time-series, specifically that caused primarily by cloud contamination and atmospheric variability.
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