Jamjareegulgarn, Punyawi

Preferred name

Alternative Name

Jamjareegulgarn, P.

Main Affiliation

KMITL Prince of Chumphon Campus

punyawi.ja@kmitl.ac.th

Now showing 1 - 10 of 17

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Remotely sensed atmospheric anomalies of the 2022 Mw 7.0 Bantay, Philippines earthquake
(2025-02-15)
Khan, Sohrab
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Shah, Munawar
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Jamjareegulgarn, Punyawi
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El-Sherbeeny, Ahmed M.
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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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Exploring vegetation health in Southern Thailand under climate stress from temperature and water impacts between 2000 and 2023
(2025-12-01)
Mehmood, Kaleem
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Anees, Shoaib Ahmad
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Shahzad, Fahad
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Muhammad, Sultan
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Liu, Qijing
This study provides a detailed spatiotemporal analysis of vegetation health in Southern Thailand from 2000 to 2023, focusing on the impacts of temperature and water stress on vegetation degradation. Using high-resolution Landsat-derived kernel Normalized Difference Vegetation Index (kNDVI) and Land Surface Temperature (LST), alongside precipitation (PPT), soil moisture (SM), vapor pressure deficit (VPD), and solar radiation (SR), several key indices were derived such as Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). The study offers a robust framework to monitor vegetation health under climate stress by integrating satellite-based indices with detailed climate datasets. Our findings reveal significant temperature-induced stress during critical years like 2005 and 2016, with over 60% of the region experiencing vegetation degradation. Long-term trend analysis indicates that while 22.5% of forested areas show signs of recovery, 3.6% continue to degrade, primarily due to persistent temperature extremes and water stress. Soil moisture emerged as a critical driver during the dry season, positively influencing 11.16% of the region, while solar radiation exhibited mixed effects depending on moisture availability. These insights highlight the complex interplay of climatic drivers on vegetation dynamics, particularly in tropical ecosystems. The study underscores the need for adaptive management strategies to enhance resilience against climate extremes, providing valuable guidance for sustainable land management in Southern Thailand.
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Exploring Electromagnetic Wave Propagation Through the Ionosphere Over Seismic Active Zones
(2025-03-01)
Eshkuvatov, Husan
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Ahmedov, Bobomurat
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Shah, Munawar
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Begmatova, Dilfuza
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Jamjareegulgarn, Punyawi
This study presents an analytical solution for the electric current formation in the lower ionosphere as a result of charged aerosols being ejected from the ground before the earthquakes. The impact of ionosphere-related processes on radio wave propagation through the atmosphere is explored by investigating the resulting energy losses of electromagnetic waves traversing this ionospheric layer. Theoretical considerations suggest that these processes may generate detectable electromagnetic signals, offering insights into seismic precursors. The effects of electron density inhomogeneities in the upper ionospheric layers on electromagnetic wave properties such as group delay, Faraday rotation, and Doppler frequency shift are examined. Understanding these effects aims to improve ionospheric monitoring techniques to detect pre-earthquake disturbances. To validate the theoretical findings, a comparison is made with the empirical data from various sources, including VLF transmitters and GPS-TEC measurements. This comparative analysis underscores the potential of electromagnetic phenomena as credible indicators of impending seismic events.
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Spatiotemporal analysis of surface Urban Heat Island intensity and the role of vegetation in six major Pakistani cities
(2025-03-01)
Anees, Shoaib Ahmad
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Mehmood, Kaleem
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Raza, Syed Imran Haider
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Pfautsch, Sebastian
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Shah, Munawar
The Urban Heat Island (UHI) phenomenon exacerbates thermal discomfort in urban areas and significantly contributes to urban overheating when combined with climate change. This study investigates the spatiotemporal patterns of Surface Urban Heat Island Intensity (SUHII) in six major cities of Pakistan, focusing on the interplay between urban expansion, vegetation cover, and SUHII. To quantify SUHII dynamics, the impact of urban sprawl and vegetation changes was analyzed. The study offers critical insights into the implications for urban planning and policymaking in Pakistan. Using remote sensing data from Landsat satellites, analyzed with Geographic Information Systems (GIS) techniques, estimates of SUHII, urban expansion, and vegetation cover were derived. Specifically, imagery from Landsat-5 (2010−2013) and Landsat-8 (2014–2022), obtained from the US Geological Survey (USGS), was employed. Statistical analyses, including Pearson's correlation and linear regression, were conducted to assess relationships between these variables from 2010 to 2022. SUHII was found to increase annually by 0.18 °C in Islamabad and 0.19 °C in Peshawar, with corresponding urban expansion rates of 8.07 km2 (8967.75 pixels) and 1.67 km2 (1860.42 pixels) per year, respectively. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC) were inversely correlated with SUHII, explaining up to 50 % of the variance in Peshawar. However, weaker correlations in Lahore suggest the presence of additional factors influencing SUHII. A distinct spatial relationship between increased vegetation and cooler areas was observed. For instance, Islamabad has greater vegetation cover and cool zones over 41.5 km2. In contrast, Lahore's hot spots spanned 127.1 km2, compared to Abbottabad's 10.4 km2, underscoring the thermal impact of reduced vegetation. The findings emphasize the effectiveness of urban greening, particularly in Islamabad's neutral thermal regions, in mitigating SUHII. This study offers important insights for urban planners in developing sustainable, climate-resilient cities within similar urban contexts. While the results are specific to Pakistani cities, the role of vegetation in mitigating SUHII may hold broader relevance for urban planning strategies in comparable settings.
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Atmospheric precursors from multiple satellites associated with the 2020 Mw 6.5 Idaho (USA) earthquake
(2024-01-01)
Qasim, Muhammad
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Shah, Munawar
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Shahzad, Rasim
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Jamjareegulgarn, Punyawi
Remote sensing has became a powerful tool for identifying lithosphere and atmosphere anomalies associated with the impending Earthquakes (EQs) in the vicinity of seismic breeding zone. In this paper, Land Surface Temperature (LST) of both the daytime and nighttime from the Moderate Resolution Imaging Spectroradiometer (MODIS) along with Air Temperature (AT), Relative Humidity (RH), Air Pressure (AP) and Outgoing Longwave Radiations (OLR) are studied for the 2020 Idaho (USA) EQ of Mw 6.5. We found the EQ induced surface and atmospheric parameters anomalies just one day prior to EQ main shock using statistical analysis. Moreover, we observed a sharp increment in LST and AT followed by the drop in both AP and RH, which are responsible for cooling the hot gases emitted from epicenter during preparation period. Also, we observed a large increase in OLR on the same day confirming these anomalous variations to be related with the main shock. Furthermore, these abrupt variations are also confirmed using neural networks (nonlinear autoregressive network with exogenous inputs (NARX), multilayer perceptron (MLP) and continuous wavelet transformation (CWT). These multi-parameter and multi-technique analyses can contribute to assist in the main shock forecasting in future with an enhanced cluster of satellite observations.
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Hemispheric responses of ionosphere-thermosphere to intense geomagnetic storms over the East Asian-Australian sector
(2025-11-15)
Tahir, Afnan
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Wu, Falin
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Shah, Munawar
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Jamjareegulgarn, Punyawi
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Ameen, Muhammad Ayyaz
The irregularities of huge geomagnetic storms impact the satellite communication by enforcing large delays in ray path. This work investigates the hemispheric asymmetries in ionosphere-thermosphere responses to three large geomagnetic storms (Dst ∼−170 nT), occurring in different seasons and storm phases beginning in different local times, over the middle and low latitudes of East Asian-Australian longitude sector. The variations have been studied by critical frequency of F2 layer (foF2) and multi-satellites observables at ±60° conjugate latitudes along ∼115° E. The enhanced equatorial ionization anomaly (EIA) is associated to the strong eastward electric fields, trigger significant part in main phases of the December 2015 and August 2018 storms during dayside. The main phase covering the night side during March 2023 storm, exhibited no significant total electron content (TEC)/foF2 variations except small-scale ionospheric irregularities. The unexpected ionospheric-thermospheric asymmetry on 26 August 2018 is mainly influenced by the northward neutral winds as opposed to the seasonal winds. Moreover, the recovery phases of the December 2015 and March 2023 commenced around morning hours activate a classic seasonal response at middle latitudes along with disturbance dynamo effect at low latitudes. On contrary, the ionospheric responses at the recovery phase during August 2018 storm is not dominated by expected seasonal flow and disturbance dynamo. At different phases of the three storms, E region electric fields, thermospheric composition and neutral winds played a major role for ionospheric disturbances.
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Atmospheric precursors associated with two Mw > 6.0 earthquakes using machine learning methods
(2024-06-01)
Khalid, Zaid
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Shah, Munawar
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Riaz, Salma
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Ghaffar, Bushra
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Jamjareegulgarn, Punyawi
The advancements in remote sensing (RS) satellite applications have revolutionized natural disaster surveillance and prediction in the earthquake monitoring by delineating various precursors at the Earth’s surface and in atmosphere. In this paper, the earthquake precursors comprising land surface temperature, outgoing longwave radiations, relative humidity, and air temperature for both the daytime and nighttime are investigated for two Mw > 6.0 events in USA. Interestingly, we noticed surface and atmospheric parameters anomalies in 6–8 days window prior to both the events by using standard deviation method. Moreover, these abrupt deviations are also validated by the recurrent neural networks like autoregressive network with exogenous inputs and long short-term memory inputs. The findings of this study demonstrate the potential of using modern analysis tools to further develop our knowledge of the linked dynamics of the lithosphere and atmosphere preceding seismic occurrences. This study implements substantially the developing of natural hazard surveillance and earthquake prediction capabilities for future researches as a valuable addition of reference in the field of RS.
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Multi-satellite based possible precursory signals detection linked to the 2024 Mw 7.5 Noto Peninsula Japan earthquake
(2025-06-15)
Shahzad, Rasim
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Shah, Munawar
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Nabi, Imtiaz
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Jamjareegulgarn, Punyawi
Satellite-based anomaly detection can provide substantial precursory information linked to impending earthquakes (EQ). The strong EQs are followed by some complex precursory signals both before and after the main shock. For this, different methods and datasets are employed to monitor these disastrous events. In our study, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite to evaluate land surface temperature (LST), the Global Navigation Satellite System (GNSS) to observe total electron content (TEC) variations, Swarm satellites to monitor spatial variations in electron density, and Cosmic satellites were used to measure variations in the vertical profile of electron density to look for the complex precursors of Noto Peninsula Japan EQ of 7.5 Mw (occurred on 1st January 2024). Our objective was to observe both the pre- and post-EQ induced anomalies within 25 days and 10 days of the main shock by integrating the statistical, nonlinear autoregressive network with exogenous inputs (NARX) and continuous wavelet transformation (CWT) methods. We found synchronized and co-located pre-seismic anomalies on December 25 in LST, TEC and electron density. Which was further confirmed using NARX and CWT as well. Additionally, we found some potential post-seismic anomalies. There was an anomalous enhancement in daytime LST, TEC, and electron density on January 2nd with the exception of nighttime LST which showed abrupt increments on the night of the main shock (i.e., January 1st). These findings point towards the strong EQ-induced energy into the atmosphere and ionosphere for more prominent proof of lithosphere-atmosphere–ionosphere coupling (LAIC).
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Possible atmospheric-ionospheric precursors of the 2020 Hotan China earthquake from various satellites
(2024-10-01)
Hameed, Amna
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Shah, Munawar
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Ghaffar, Bushra
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Riaz, Salma
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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.
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Integrated analysis of atmospheric and ionospheric precursors using SARIMAX, NARX, and LSTM approaches for the 2024 Mw 7.4 Taiwan earthquake
(2026-08-01)
Tahreem, Azka
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Shah, Munawar
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Jamjareegulgarn, Punyawi
Earthquakes are among the most damaging natural hazards, highlighting the need for improved monitoring frameworks and rigorous analysis of potential precursory signals. The Mw 7.4 Taiwan earthquake provides a relevant case for evaluating methodologies to identify and interpret atmospheric and ionospheric anomalies in seismically vulnerable regions. In this study, satellite-based Remote Sensing (RS) products and Global Navigation Satellite System (GNSS) observations are integrated to examine candidate precursors, including Outgoing Longwave Radiation (OLR), Relative Humidity (RH), Air Temperature (AT), Air Pressure (AP), and Total Electron Content (TEC). Using statistical approaches, including the standard deviation (STDEV) method and the Seasonal AutoRegressive Integrated Moving Average with Exogenous Variables (SARIMAX) model, together with machine-learning frameworks such as the Nonlinear AutoRegressive model with eXogenous inputs (NARX) and Long Short-Term Memory (LSTM) networks, this study identified synchronized anomalies approximately 5–6 days prior to the event. In addition, geomagnetic perturbations were observed approximately nine days before the event, coinciding with a pronounced geomagnetic storm (Kp > 8; Dst < −120 nT; ap > 225 nT). To limit the influence of background variability and potential false alarms, a historical comparative analysis was performed using atmospheric parameters from the same region and comparable time window across the preceding five years, which further supported the robustness of the observed anomalies. By integrating statistical detection, spatial screening, and time-series forecasting models, this work contributes to a more detailed understanding of atmospheric–ionospheric signals associated with seismic activity and highlights the value of multi-parameter monitoring for seismic hazard assessment and risk-reduction planning.

Jamjareegulgarn, Punyawi

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