Malak Henchiri

Dr. Malak Henchiri

School of Automation, Qingdao University, Qingdao 266071, China

Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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25 November 2020 in Remote Sensing
Verified
Journal Article
Open access
PDF file
MHMalak Henchiri
Malak Henchiri
QLQi Liu
Qi Liu
BEBouajila Essifi
Bouajila Essifi
Tehseen JavedTehseen Javed
Tehseen Javed
SZSha Zhang
Sha Zhang
Jiahua ZhangJiahua Zhang
Jiahua Zhang
Studying the significant impacts of drought on vegetation is crucial to understand its dynamics and interrelationships with precipitation, soil moisture, and temperature. In North and West Africa regions, the effects of drought on vegetation have not been clearly stated. Therefore, the present study aims to bring out the drought fluctuations within various types of Land Cover (LC) (Grasslands, Croplands, Savannas, and Forest) in North and West Africa regions. The drought characteristics were evaluated by analyzing the monthly Self-Calibrating Palmer Drought Severity Index (scPDSI) in different timescale from 2002 to 2018. Then, the frequency of droughts was examined over the same period. The results have revealed two groups of years (dry years and normal years), based on drought intensity. The selected years were used to compare the shifting between vegetation and desert. The Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Precipitation Condition Index (PCI), and the Soil Moisture Condition Index (SMCI) were also used to investigate the spatiotemporal variation of drought and to determine which LC class was more vulnerable to drought risk. Our results revealed that Grasslands and Croplands in the West region, and Grasslands, Croplands, and Savannas in the North region are more sensitive to drought. A higher correlation was observed among the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Tropical Rainfall Measuring Mission (TRMM), and Soil Moisture (SM). Our findings suggested that NDVI, TRMM, and SM are more suitable for monitoring drought over the study area and have a reliable accuracy (R2 > 0.70) concerning drought prediction. The outcomes of the current research could, explicitly, contribute progressively towards improving specific drought mitigation strategies and disaster risk reduction at regional and national levels.