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Sultan Qaboos University (2021)

Drought Monitoring Using Remote Sensing-Based Indices and Rainfall Data in Dhofar Mountains.

Al Nadabi Mohammed

Titre : Drought Monitoring Using Remote Sensing-Based Indices and Rainfall Data in Dhofar Mountains.

Auteur : Al Nadabi Mohammed

Université de soutenance : Sultan Qaboos University

Grade : Master of Science ( MS) 2021

Résumé
Drought is a period of time when an area or region experience below-normal precipitation. It is considered to be the most complicated and least understood phenomenon. Drought can have environmental, economic, and social impacts. Therefore, detecting, monitoring, and assessing drought severity using satellite-derived information and rainfall station data is essential to manage and reduce its damages. Several remotely sensed drought indices, including duration, intensity, severity, and spatial extent, have been developed and applied. This study aims to employ remote sensing capabilities to conduct drought monitoring and evaluate satellite-based indices and Standardized Precipitation Index (SPI) to detect, assess, and monitor the spatial and temporal extent of the Dhofar Mountains’ drought condition period between 2000 and 2019. In this research, the Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) were determined from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The VCI was derived from Normalized Difference Vegetation Index (NDVI) that was determined with near-infrared and visible red band reflectance. The TCI was derived from the Land Surface Temperature (LST) product. The VCI and TCI were then combined to create Vegetation Health Index (VHI). Also, three-month Standardized Precipitation Index (SPI) data from rainfall stations in the study area was calculated. The maps were utilized to evaluate the intensity of drought in the Dhofar Mountains from 2000 to 2019. The results show that years of normal condition (least drought) were 2000, 2005, 2006, 2007, 2011, and 2016. However, all indices recorded a drought with different classes for 2003, 2009, and 2012 can be considered unusual conditions. The simple linear regression correlation between SPI and other remote sensing indices showed insignificant correlation. Findings from this study concluded that traditional methods, which depend on rainfall data, have various limitations. Whereas, to classify droughts’ temporal and spatial magnitude on a regional scale, the use of indices derived from remotely sensed data can be constructive. For future research, extended historical satellite images of at least three images should be used for each year. Also, other variables such as vegetation, amount of production of crops and animal wealth, soils, temperature, rate of evaporation, water availability, and other biophysical variables can help in defining a drought more accurately.

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Page publiée le 20 avril 2022