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Author: search.filters.author.Abukhadra, Mostafa R.

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    Integrated landslide risk assessment utilizing open source satellite and ground data in the Himalayas of Northern Pakistan
    (2025-07-01)
    Ahmad, Naseem
    ;
    Shafique, Muhammad
    ;
    Hussain, Mian Luqman
    ;
    Shah, Munawar
    ;
    Jamjareegulgarn, Punyawi
    Landslides have devastating effect on communities, infrastructure, and the environment, making them one of the most recurring and harmful natural hazards globally. This study propose an integrated approach of freely available geospatial data and semi-quantitative techniques to evaluate landslide hazard, vulnerability, and risk in one of the most landslide-prone valleys i.e., Kaghan Valley, northern Pakistan. The Google Earth Pro, high-resolution DEM and satellite images are used to develop a landslide inventory, derived causative factors and assess the landslide hazard and risk assessment in Kaghan Valley. The landslide susceptibility map is then integrated with landslide-triggering factors to derive a landslide hazardindex map. A geospatial database of element-at-risk data of 66,282 building footprints, typological data, road network, population, and land cover are obtained through remote sensing and extensive field surveys. The quantitative analysis revealed that 8.43 km<sup>2</sup> (0.66%) of the total area falls under the very high-risk category, while 271.19 km<sup>2</sup> (21.11%) is classified as high risk, and 80.91 km<sup>2</sup> (6.30%) as moderate risk, establishing a strong basis for risk assessment.
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    Remotely sensed atmospheric anomalies of the 2022 Mw 7.0 Bantay, Philippines earthquake
    (2025-02-15)
    Khan, Sohrab
    ;
    Shah, Munawar
    ;
    Jamjareegulgarn, Punyawi
    ;
    El-Sherbeeny, Ahmed M.
    ;
    Abukhadra, Mostafa R.
    Remote sensing satellites have emerged as invaluable tools for surveilling natural disasters with more inevitable insights at various altitudes in atmosphere for various precursors. Moreover, the methods and satellite data before and after any event need more understanding for predicting the main shock due to the complexity of precursors. This study involves data from multiple sensors to assess how atmospheric parameters change in space and time over the Mw 7.0 Bantay, Philippines epicenter. The methods of statistical analysis, Nonlinear Autoregressive Network with Exogenous Inputs (NARX), and Multilayer Perceptron (MLP) are applied to various atmospheric parameters, including Land Surface Temperature (LST), Air Temperature (AT), Relative Humidity (RH), and Outgoing Longwave Radiation (OLR) to identify abnormal atmospheric patterns associated with earthquakes (EQ). These analyses focus on 3–5 days before the earthquake day. For this purpose, we trained daily average indices of atmospheric parameters for the month leading up to and the 15 days following the main shock. Since variations are irregular, detection can be challenging with classical statistics; therefore, we leveraged supervised machine learning to detect anomalies promptly and minimize the chances of missed detection. Thus, these findings support the lithosphere-atmosphere–ionosphere coupling (LAIC) hypothesis and suggest the need for further investigation in future research.
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    Possible atmospheric-ionospheric precursors of the 2020 Hotan China earthquake from various satellites
    (2024-10-01)
    Hameed, Amna
    ;
    Shah, Munawar
    ;
    Ghaffar, Bushra
    ;
    Riaz, Salma
    ;
    Jamjareegulgarn, Punyawi
    The earthquake (EQ) precursors from satellites data portray an image of the energy propagation from the lithosphere to atmosphere and then to the ionosphere. Previous studies have often presented detailed discussion on different precursors at various altitudes. However, this study aimed to investigate the anomalies at various altitudes associated with the Hotan China EQ (hypocentral depth: 10 km, latitude 35.5°N, longitude 82.4°E). The goal was to identify pre-and post-seismic anomalies statistically in the conjunction with the wavelet transformation. We observed possible precursors in the atmosphere such as variations in aerosol optical depth, tropopause pressure, relative humidity, latent heat flux, and outgoing longwave radiation in a window of 5–10 days before the seismic event. Moreover, the total electron content had precursors during quiet geomagnetic storm conditions (−20 < Dst ≤ − 40 nT, Kp ≤ 3) beyond the bound within 5–10 days. These findings highlight the potential of using atmospheric and ionospheric parameters to detect seismic anomalies as EQ precursors for improved EQ early warning systems.