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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.
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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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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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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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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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Unleashing the potential of UAVs in agriculture: ASEAN and Thailand’s rice production industry improvements: Review article
(2023-09-01)
Mikhaylov, D.
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Song, J. J.
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Mitrokhin, M.
Agriculture is vital for economic sustainability and structural transformation in ASEAN. This paper explores the agricultural landscape in ASEAN, focusing on Thailand as a prominent rice producer. Challenges such as rural-urban migration, the ageing population employed in agriculture, and need to implement innovative solutions to strengthen regional food security are discussed. The primary focus is the innovative use of unmanned aerial vehicles (UAVs) in ASEAN agriculture. UAVs revolutionize data collection through aerial photography, providing real-time insights into terrain, vegetation health, and soil composition. They enable informed decisions, optimize resource allocation, and streamline processes, resulting in cost savings. Equipped with sensors, UAVs precisely monitor crop health, irrigation efficiency, and early pest detection. Integration of advanced software and geographical information systems enhances data analysis and visualization. UAVs facilitate high-resolution mapping, offering detailed information on crop density, weed infestation, and disease outbreaks. This enables targeted interventions, reducing input costs and optimizing resource allocation. Multispectral or hyperspectral sensors provide insights into plant health, chlorophyll content, and water stress, enabling site-specific management strategies for improved sustainability. UAVs are affordable, versatile, and continuously advancing. They can potentially improve productivity, efficiency, and resource management in agriculture. In rice production, UAVs offer benefits like crop monitoring, precise spraying, and uniform seed spreading. They provide real-time information on crop health and pest infestations, enabling optimized management. Spray drones accurately apply pesticides, while seed-spreading drones enhance crop growth and save costs. UAV drones offer significant advantages in ASEAN agriculture, addressing workforce challenges and enhancing productivity. Supportive measures will lead to efficient UAV integration, enhancing sustainability and farmer livelihoods.
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Semantic Segmentation on Medium-Resolution Satellite Images Using Deep Convolutional Networks with Remote Sensing Derived Indices
(2018-09-06)
Chantharaj, Sirinthra
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Pornratthanapong, Kissada
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Chitsinpchayakun, Pitchayut
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Panboonyuen, Teerapong
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Vateekul, Peerapon
Semantic Segmentation is a fundamental task in computer vision and remote sensing imagery. Many applications, such as urban planning, change detection, and environmental monitoring, require the accurate segmentation; hence, most segmentation tasks are performed by humans. Currently, with the growth of Deep Convolutional Neural Network (DCNN), there are many works aiming to find the best network architecture fitting for this task. However, all of the studies are based on very-high resolution satellite images, and surprisingly; none of them are implemented on medium resolution satellite images. Moreover, no research has applied geoinformatics knowledge. Therefore, we purpose to compare the semantic segmentation models, which are FCN, SegNet, and GSN using medium resolution images from Landsat-8 satellite. In addition, we propose a modified SegNet model that can be used with remote sensing derived indices. The results show that the model that achieves the highest accuracy RGB bands of medium resolution aerial imagery is SegNet. The overall accuracy of the model increases when includes Near Infrared (NIR) and Short-Wave Infrared (SWIR) band. The results showed that our proposed method (our modified SegNet model, named RGB-IR-IDX-MSN method) outperforms all of the baselines in terms of mean F1 scores.
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An enhanced deep convolutional encoder-decoder network for road segmentation on aerial imagery
(2018-01-01)
Panboonyuen, Teerapong
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Vateekul, Peerapon
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Jitkajornwanich, Kulsawasd
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Lawawirojwong, Siam
Object classification from images is among the many practical examples where deep learning algorithms have successfully been applied. In this paper, we present an improved deep convolutional encoder-decoder network (DCED) for segmenting road objects from aerial images. Several aspects of the proposed method are enhanced, incl. incorporation of ELU (exponential linear unit)—as opposed to ReLU (rectified linear unit) that typically outperforms ELU in most object classification cases; amplification of datasets by adding incrementally-rotated images with eight different angles in the training corpus (this eliminates the limitation that the number of training aerial images is usually limited), thus the number of training datasets is increased by eight times; and lastly, adoption of landscape metrics to further improve the overall quality of results by removing false road objects. The most recent DCED approach for object segmentation, namely SegNet, is used as one of the benchmarks in evaluating our method. The experiments were conducted on a well-known aerial imagery, Massachusetts roads dataset (Mass. Roads), which is publicly available. The results showed that our method outperforms all of the baselines in terms of precision, recall, and F1 scores.

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