Satellite Images Research Articles

Hydrothermally altered mineral mapping of the Malanjkhand copper mineralization in India using Landsat satellite data

ABSTRACT The study evaluates how Landsat-5 Thematic Mapper (TM), Landsat-7 Enhanced Thematic Mapper Plus (ETM+), and Landsat-8 Operational Land Imager (OLI) satellite data can be used to detect and map hydrothermally altered minerals in the Malanjkhand copper mines of Madhya Pradesh, India. By employing band ratio and principal component analysis (PCA) methods, the study generates detailed spatial distributions of hydrothermally altered rocks linked to porphyry copper mineralization in the region. The study also finds that the band ratio combinations and the PCA method effectively distinguished hydrothermally altered minerals, vegetation, and iron oxides in the satellite images. Furthermore, the research reveals that Landsat-8 OLI data outperformed Landsat-5 TM and Landsat-7 ETM+ data in detecting hydrothermally altered minerals using the PCA technique. The correlation of identified minerals with the satellite images from Landsat sensors, supported by spectroscopic and petrographic studies, underscores the comprehensive approach taken in this study to enhance the understanding of hydrothermal alteration zones and improve the efficiency of mineral exploration. In conclusion, the research emphasizes the suitability of Landsat-8 OLI data for visual interpretation of hydrothermal alteration mapping, highlighting its higher radiometric resolution and reduced interference between vegetation and altered minerals.

Stereo vision from space to track tree fall incident

AbstractForests are essential for regulating the climate, enhancement of air quality, and the preservation of biodiversity. However, tree falls pose significant risks to infrastructure, particularly powerlines, leading to widespread blackouts and substantial damage. Traditional methods for monitoring tree fall risks, such as field surveys, are often costly, time-consuming, and lack real-time capabilities. While airborne Light Detection and Ranging (LiDAR) provides precise data for monitoring tree fall risks, it still faces challenges related to frequency of data acquisition and high costs. In response to the European Space Agency's call for more cost-effective monitoring approaches, this study investigates the potential of using very high-resolution optical satellite data, specifically from Pléiades satellite imagery, for assessing tree fall risks to powerlines. Key forest structure metrics such as canopy complexity using the Rumple Index, canopy height, as well as distance to powerlines were analyzed across four study sites in Finland and Switzerland. Sites with simpler canopy structures exhibited stronger correlations between stereo and LiDAR height measurements (R2 values up to 0.64). Stereo-based measurements can overall provide acceptable accuracy (ca. 96.57%) in detecting trees compared with LiDAR data. The results demonstrated that the Rumple Index can identify areas with simpler canopy structures, where stereo-based height measurements yield high accuracy. These findings suggest the potential of hybrid approaches that integrate both stereo imagery and airborne LiDAR data, tailored to site-specific characteristics, for accurate risk assessments. This study contributes to the ongoing efforts in developing an understanding of vegetation management along powerlines, to inform decision-makers in their endeavors to identify and mitigate risks associated with tree falls.

Assessment of Green Space Dynamics Under Urban Expansion of Senegalese Cities: The Case of Dakar

Senegalese cities have experienced rapid urbanisation, leading to profound landscape changes. Dakar, one of Senegalese’s fastest-growing cities, is experiencing rapid urban expansion, significantly reducing green spaces. These green spaces, essential for urban sustainability and resilience, have become increasingly scarce, affecting the city’s environment and the quality of life for its residents. This study aims to assess the spatiotemporal changes in Dakar’s green spaces from 1990 to 2022. Using satellite imagery, this study produces land use maps to quantify green space coverage over the years. The results show a gradual decline in green spaces in Dakar between 1990 and 2022. In 1990, green spaces covered an estimated 13.36% of Dakar’s area, which decreased significantly to 9.54% by 2022. In contrast, other land uses, such as built-up areas, increased significantly over this period, rising from 19.23% in 1990 to 39.34% in 2022. Moreover, built-up areas are not the sole contributor to the reduction of green spaces in Dakar. The study revealed that, between 1990 and 2022, 5.49% of green spaces were converted into bare soil due to excessive tree cutting. This pattern highlights the growing challenge of green space availability as built-up areas expand rapidly, particularly when growth is unplanned. This study underscores the importance of sustainable urban planning that integrates the protection and conservation of Dakar’s vegetation to preserve vital ecosystem services.

Application of a Modified Ecological Quality Monitoring Method in the Southeastern Hilly Region of China

The southeastern hilly region of China is ecologically significant but highly vulnerable to climate change and human activities. This study developed a Modified Remote Sensing Ecological Index (MRSEI) using satellite imagery and Human Footprint data to assess ecological quality across 14 cities surrounding the Wuyi Mountains. We applied Sen’s slope analysis, the Mann–Kendall test, and spatial autocorrelation to evaluate spatiotemporal ecological changes from 2000 to 2020, and used partial correlation analysis to explore the drivers of these changes. The main findings are as follows: (1) Ecological quality generally improved over the study period, with significant year-to-year fluctuations. The eastern region, characterized by higher altitudes, consistently exhibited better ecological quality than the western region. The area of low-quality ecological zones significantly decreased, while Ji’an, Ganzhou, Heyuan, and Meizhou saw the most notable improvements. In contrast, urban areas experienced a marked decline in ecological quality. (2) The region is undergoing warming and wetting trends. Increased precipitation, especially in the western and northern regions, improved ecological quality, except in urban areas, where it heightened flood risks. Rising temperatures had mixed effects: they enhanced ecological quality in high-altitude areas (~516 m) but negatively impacted low-altitude regions (~262 m) due to intensified heat stress. (3) Although industrial restructuring reduced environmental pressure, rapid population growth and urban expansion created new ecological challenges. This study provides an innovative method for the ecological monitoring of hilly regions, effectively integrating human activity and climatic factors into ecological assessments. The findings offer valuable insights for sustainable development and ecological management in similar sensitive regions.

Machine Learning and Deep Learning for Wildfire Spread Prediction: A Review

The increasing frequency and intensity of wildfires highlight the need to develop more efficient tools for firefighting and management, particularly in the field of wildfire spread prediction. Classical wildfire spread models have relied on mathematical and empirical approaches, which have trouble capturing the complexity of fire dynamics and suffer from poor flexibility and static assumptions. The emergence of machine learning (ML) and, more specifically, deep learning (DL) has introduced new techniques that significantly enhance prediction accuracy. ML models, such as support vector machines and ensemble models, use tabular data points to identify patterns and predict fire behavior. However, these models often struggle with the dynamic nature of wildfires. In contrast, DL approaches, such as convolutional neural networks (CNNs) and convolutional recurrent networks (CRNs), excel at handling the spatiotemporal complexities of wildfire data. CNNs are particularly effective at analyzing spatial data from satellite imagery, while CRNs are suited for both spatial and sequential data, making them highly performant in predicting fire behavior. This paper presents a systematic review of recent ML and DL techniques developed for wildfire spread prediction, detailing the commonly used datasets, the improvements achieved, and the limitations of current methods. It also outlines future research directions to address these challenges, emphasizing the potential for DL to play an important role in wildfire management and mitigation strategies.

Evaluating ecological impacts through geospatial analysis of land use land cover dynamics in Jarmet wetland ecosystems in Western Ethiopia

Biodiversity is crucial for maintaining ecological balance and fostering complex relationships among plant and animal species. The global decline in wetlands due to human activities significantly impacts biodiversity and ecosystem stability, highlighting the urgent need for effective conservation. This study utilizes satellite remote sensing to monitor land use changes in the Jarmet wetlands over 29 years, analyzing their effects on ecosystem dynamics. It examines the relationship between biodiversity and the ecological stability of wetland ecosystems, aiming to identify key factors behind land use and cover changes (LULCC) and their impact on biodiversity. To investigate the impact of LULC dynamics on wetlands, this study employed Landsat imagery with a spatial resolution of 30 m. The dataset included Thematic Mapper (TM) imagery from 1986, Enhanced Thematic Mapper Plus (ETM+) images from 2000 and 2005, and Operational Land Imager (OLI) data from 2015. Using multi-source satellite imagery, maximum likelihood algorithms, and advanced spatial tools within a Geographic Information System (GIS), LULCC was mapped. The analysis indicates a substantial 45.9% increase in agricultural areas and a 4.1% rise in built-up and plantation areas, signifying habitat degradation and altered hydrological patterns resulting from drainage and land use changes. Conversely, grasslands, forested areas, wetlands, and shrublands experienced reductions of 21.3%, 14.7%, 7.2%, and 6.8%, respectively. These LULCCs, driven primarily by human activities, threaten the ecological integrity of the Jarmet wetland. The study offers recommendations for sustainable conservation and management, emphasizing the need for comprehensive environmental policies to prevent further ecological degradation.

Mapping typical LULC classes using spatiotemporal analysis and the thresholds of spectral optical satellite imagery indices: a case study in Algiers city.

Land use and land cover (LULC) dynamics have a substantial impact on human-environment interactions. Nowadays, remote sensing imagery has emerged as a useful tool for mapping and tracking LULC changes. Spectral optical indices derived from remote sensing data can provide insightful information about vegetation health, urban expansion, water bodies, deforestation patterns, and many other applications. The present study examines the use of popular optical spectral indices: vegetation index (NDVI), water indices (NDWI and MNDWI), urban indices (UI and NDBI), and bare land index (MNDBI) in threshold-based classification for LULC mapping using Algiers (Algeria) as a case study, and assesses the potential impacts of their spatiotemporal (at a seasonal and annual temporal scales) variations associated with natural seasonal changes and/or the evolution of the city's fabric. Here, a geo-statistical analysis of the values of the selected spectral indices at the level of each LU-class is conducted, threshold values (that account for seasonal variations) are proposed, and a classification approach (making use of best performing indices) is proposed and tested. Although fast and easy to implement, the proposed threshold-based LULC classification approach was successfully used for mapping LULC for the study zone with a high accuracy (an overall accuracy of 90.20 and a kappa of 0.84 for the demonstration year of 2017). The outcomes of the study heighten the potential and the limitations of the use of spectral indices for LULC mapping practices and consequent applications in environmental and urban studies.

Interannual spatio-temporal evolution of the supraglacial lakes on the Belvedere Glacier between 2000 and 2023

Understanding of the formation and evolution of supraglacial lakes in high mountain regions is crucial for accurately assessing their impact on glacier behaviour, hydrology, and potential hazards such as outburst floods. This article examines the annual spatio-temporal evolution of supraglacial lakes on the Belvedere Glacier between 2000 and 2023. Very high-resolution aerial photography and high-resolution satellite imagery were used to identify supraglacial lakes as small as 37 m2 and narrow bands of ice-marginal lakes. The mapping revealed that the well-known Lake Effimero is stable in its position but unstable in size, with variations from 428 m2 to 99.7 × 103 m2. These changes are potentially due to snowmelt or glacier dynamics. In 2002, the area of Effimero was at its largest extent observed during the study period. The first appearance of the Lake Effimero was revelated by the Landsat imagery on 27 May 2001, which differed from the findings of other studies. New lakes were observed to form in a manner independent of Effimero formation, exhibiting a consistent annual occurrence with nearly linear area growth up to 9.7 × 103 m2 in 2023. The formation of the lakes is shown to be influenced by their morphological characteristics.

Depth estimation from multispectral satellite imagery: a comparison of conventional and machine learning-based approaches- case study: Kish Island, Persian Gulf

Depth estimation from multispectral satellite imagery: a comparison of conventional and machine learning-based approaches- case study: Kish Island, Persian Gulf

New and Improved (!) Terrain and Landscape Representations in the Atlas of Yellowstone, Second Edition

Abstract. Creating a second edition of the Atlas of Yellowstone ten years after the first edition provided opportunities to improve existing maps and create new ones using a variety of innovative terrain methodology techniques. These included creating shaded relief with Blender (an open-source 3D software); using illuminated contour shading; and the incorporation of satellite imagery and other environmental datasets to create maps that tell rich stories of the Yellowstone landscape. Thematic maps from the Atlas are used to discuss these techniques along with cartographic design considerations.

GF-1 WFV satellite images based forest cover mapping in China supported by open land use/cover datasets

The United Nations sustainable development agenda emphasizes the importance of forests. China’s forests cover 5% of the world’s forest area, significantly influencing global climate and ecology. In recent decades, China’s forests have undergone notable changes. Accurate forest cover maps are crucial for understanding forest distribution, conducting ecological research and sustainable management. However, there is a lack of forest cover maps satisfying the criteria. To this issue, this study focuses on developing a precise 16-m resolution forest cover map of China. For this purpose, we propose a forest classification framework based on weakly supervised deep learning and prior knowledge from open datasets. Utilizing this framework and GF-1 WFV satellite images, we generated China’s forest cover map in 2020 named FCM16. The FCM16 is evaluated using 136,385 sample points, achieving an overall accuracy of 94.64 ± 0.12%, producer’s accuracy of 91.12 ± 0.27% and user’s accuracy of 87.31 ± 0.34%. Additionally, FCM16 was compared with existing forest-related datasets, demonstrating its reliability. In general, FCM16 effectively represents China’s forest cover in 2020, providing a valuable resource for social and ecological analysis.

FlightScope: An Experimental Comparative Review of Aircraft Detection Algorithms in Satellite Imagery

Object detection in remotely sensed satellite pictures is fundamental in many fields such as biophysical and environmental monitoring. While deep learning algorithms are constantly evolving, they have been mostly implemented and tested on popular ground-taken photos. This paper critically evaluates and compares a suite of advanced object detection algorithms customized for the task of identifying aircraft within satellite imagery. The goal is to enable researchers to choose efficiently from algorithms that are trainable and usable in real time on a deep learning infrastructure with moderate requirements. Using the large HRPlanesV2 dataset, together with rigorous validation with the GDIT dataset, this research encompasses an array of methodologies including YOLO versions 5, 8, and 10, Faster RCNN, CenterNet, RetinaNet, RTMDet, DETR, and grounding DINO, all trained from scratch. This exhaustive training and validation study reveals YOLOv5 as the pre-eminent model for the specific case of identifying airplanes from remote sensing data, showcasing high precision and adaptability across diverse imaging conditions. This research highlights the nuanced performance landscapes of these algorithms, with YOLOv5 emerging as a robust solution for aerial object detection, underlining its importance through superior mean average precision, recall, and intersection over union scores. The findings described here underscore the fundamental role of algorithm selection aligned with the specific demands of satellite imagery analysis and extend a comprehensive framework to evaluate model efficacy. This aims to foster exploration and innovation in the realm of remote sensing object detection, paving the way for improved satellite imagery applications.

The Price of Conflict: Devastation of Cultural Heritage in War and Political Strife, the West Bank as a Case Study

ABSTRACT The recent wave of armed conflict in and around the Gaza Strip, which began on October 7, 2023, has resulted in an unprecedented scale of looting and destruction of archaeological sites in the West Bank, with the aim of extracting valuable artefacts for personal financial gain. The antiquities looters used both traditional hand tools and heavy machinery in their plundering activities. This armed conflict (still ongoing as of this writing), which has led to the destruction of numerous archaeological sites in the Palestinian Territories, motivates this research. The primary objective of this study is to identify the impact of the recent political crisis on Palestinian archaeological sites, specifically focusing on the case studies of Khirbet Qusātīn, Khirbet Sīmiyā, and Khirbet ‘Abda, all located in Hebron governorate. The methodology employs a multidisciplinary approach, including field surveys, photography using cameras and drones, literature review, and analysis of available satellite imagery.

Dynamics of Spatio-temporal Occupation of Biological Soils Crusts Using RS and GIS Approach in the Saria Catchment, West-central Region of Burkina Faso

In the semi-arid environments of sub-Saharan Africa, the combined action of climatic factors and human activities degrades the vegetation cover, resulting in soils that are vulnerable to the risk of erosion. This is when micro-organisms of various kinds, known as biological crusts, colonise the soil surface, playing a part in the pedogenetic processes and combating soil degradation. The aim of this study is to investigate the spatio-temporal occupation dynamics of biological crusts in the Saria catchment using remote sensing and geographic information systems. The methodology is based on a geomatics approach that integrates the use of Landsat-8 OLI/TIRS satellite images, geographic information systems and fieldwork. The processing of Landsat-8 OLI satellite images and the pedomorphological and environmental characterization of soils have made possible to map mainly two types of soils i.e. leached tropical ferruginous soils and low-humidity hydromorphic soils with surface pseudogley. After this step, the field work enabled us to identify the land cover units in our study area from which the in-situ observations of soils surfaces revealed that biological crusts are more developed on the soils of the fallow plots (30% - 95%) than the bare (5% - 15%) and field soils (5% - 10%). The results of this study showed that the level of development of soils biological crusts is linked to the plots disturbance and its ages. In conclusion the use of remote sensing and geographic information systems is an effective and less expensive technique for mapping soil types and land cover units covered by biological crusts.

Unified Detection and Feature Extraction of Ships in Satellite Images

The increasing importance of maritime surveillance, particularly in monitoring dark ships, highlights the need for advanced detection models that go beyond simple ship localisation. Current approaches largely focus on either detection or feature extraction, leaving a gap in unified methods capable of providing detailed ship characteristics. This study addresses this gap by developing a unified model for ship detection and characterisation from Synthetic Aperture Radar images, estimating features such as true length, true breadth, and heading. The model is designed to detect ships of varying sizes while simultaneously estimating their characteristics, and experimental results show a high detection accuracy, with a recall of 87.7% and an F1-score of 93.5%. The model also effectively estimates ship dimensions, with mean errors of 1.4 ± 16.2 m for length and 1.5 ± 4.5 m for breadth. Estimating the heading proved challenging for smaller ships, but was accurate for larger ships. A total of 50% of the heading estimates were within 15 degrees of error. This unified approach offers practical benefits for maritime operations. It is especially useful in situations where both ship detection and detailed information are needed, such as predicting future ship positions or identifying ships.

Color-Distortion Correction for Jilin-1 KF01 Series Satellite Imagery Using a Data-Driven Method

Color distortion is a common issue in Jilin-1 KF01 series satellite imagery, a phenomenon caused by the instability of the sensor during the imaging process. In this paper, we propose a data-driven method to correct color distortion in Jilin-1 KF01 imagery. Our method involves three key aspects: color-distortion simulation, model design, and post-processing refinement. First, we investigate the causes of color distortion and propose algorithms to simulate this phenomenon. By superimposing simulated color-distortion patterns onto clean images, we construct color-distortion datasets comprising a large number of paired images (distorted–clean) for model training. Next, we analyze the principles behind a denoising model and explore its feasibility for color-distortion correction. Based on this analysis, we train the denoising model from scratch using the color-distortion datasets and successfully adapt it to the task of color-distortion correction in Jilin-1 KF01 imagery. Finally, we propose a novel post-processing algorithm to remove boundary artifacts caused by block-wise image processing, ensuring consistency and quality across the entire image. Experimental results show that the proposed method significantly eliminates color distortion and enhances the radiometric quality of Jilin-1 KF01 series satellite imagery, offering a solution for improving its usability in remote sensing applications.

The Oil Resplendence: Evolution of the Oil Extraction Areas in Ecuador through the Night Luminosity

This article assesses a frequently overlooked impact, namely, the artificial illumination emanating from infrastructures associated with oil extraction installations from 2012 to 2019 located in the Amazon region, one of the places with the greatest biodiversity in the world. The primary dataset utilized in this study is derived from VIIRS nighttime satellite imagery. The significance of this research lies not only in examining light pollution but also in presenting a method for utilizing these images. The analysis focuses on Ecuador, one of the principal oil-producing nations in Latin America. The findings suggest a discernible correlation between the evolution of luminosity and the volume of crude oil production, although the luminosity shows a much higher growth rate than crude oil production. Additionally, it is observed that the elevated radiance levels in oil-rich areas yield luminous impacts comparable to those generated by major Ecuadorian cities. Consequently, this study's approach contributes to elucidating the territorial repercussions of the evolving patterns in oil consumption in a non-artificialized territory with high biodiversity. The results obtained herein aspire to instigate discourse on the environmental conditions engendered by extractive activities in their respective habitats. Furthermore, this study seeks to underscore the potential inherent in the selected data source and methodology.

Temporal Estimation of Snow Line Altitude in Glaciers of the Southern Patagonian Icefield Using Google Earth Engine

Abstract. The study analyzes the variability over time of the Snow Line Altitude (SLA) and its correspondence with the Equilibrium Line Altitude (ELA) in four glaciers of the Southern Patagonian Icefield (SPI): Perito Moreno, Upsala, Viedma and De los Tres. Using Google Earth Engine and Landsat satellite imagery, an automated algorithm based on the Otsu image segmentation method was developed to analyze the 1985–2023 time series to obtain the Snow Cover Ratio (SCR) and subsequently estimate the SLA using the ALOS World 3D DSM. The study employed the near infrared (NIR) and shortwave infrared (SWIR) bands of Landsat images, with the SCR and subsequently the SLA determined using only the NIR band, as well as a band ratio between the NIR/SWIR bands. The results indicated that the Viedma glacier showed a statistically significant positive trend in SLA elevation, while the other three glaciers showed a stationary behavior with high variability. The SLA calculated using the NIR/SWIR bands tended to be higher compared to the NIR band calculation, especially for the Viedma and Upsala glaciers. Comparisons with previous studies (De Angelis, 2014; Popovnin et al., 1999) and recent glaciological measurements from the Inventario Nacional de Glaciares (2015–2022) showed that the SLA derived from the NIR bands aligned more closely with the aforementioned works.

Glacier velocity and surge detection in the Karakoram region, Pakistan: using remotely sensed data with cross-correlation feature tracking

ABSTRACT This research addresses the knowledge gap regarding glacier surging detection (GSD) and their potential downstream implications for Glacier Lake Outburst Floods (GLOFs) risk. This study investigates GSD across the Karakoram region, Pakistan, focusing on 30 glaciers from 2013 to 2023. The increasing occurrence of irregular and rapid glacial surges poses significant threats to downstream communities, the potential formation of ice dam lakes, and subsequent outburst floods. High-resolution satellite imagery, DEM, temperature, and precipitation data quantified glacier velocity rate (GVR) using feature-tracking, surge detection, and correlations. Findings indicate substantial movement ≥42 m/y during 2015–19 and 2021–22, peaking at 56.98 m/y in 2018–19; with significant Z-Scores, surges were recorded in KG-01, KG-06, KG-07, KG-19, KG-29, and KG-30. The region witnessed significant elevation changes, with a positive change at the terminus of KG-01, KG-06, KG-07, and KG-29 and a negative at the glacier’s accumulation zones. Furthermore, climatic factors, including rising average temperatures (17°C to 20°C) and precipitation (14 to 30 mm/y), significantly influenced glacier velocity, affecting glaciers by 20-43% and 38-59%, respectively. These findings underscore the pivotal role of surge velocities in shaping glacier outlets and influencing lake drainage, potentially leading to GLOFs.

New Evidence of Holocene Faulting Activity and Strike-Slip Rate of the Eastern Segment of the Sunan–Qilian Fault from UAV-Based Photogrammetry and Radiocarbon Dating, NE Tibetan Plateau

The eastern segment of the Sunan-Qilian Fault (ES-SQF) is located within the seismic gap between the 1927 M8.0 Gulang earthquake and the 1932 M7.6 Changma earthquake in China. It also aligns with the extension direction of the largest surface rupture zone associated with the 2022 Mw6.7 Menyuan earthquake. Understanding the activity parameters of this fault is essential for interpreting strain distribution patterns in the central–western segment of the Qilian–Haiyuan fault zone, located along the northeastern margin of the Tibetan Plateau, and for evaluating the seismic hazards in the region. High-resolution Google Earth satellite imagery and UAV (Unmanned Aerial Vehicle)-based photogrammetry provide favorable conditions for detailed mapping and the study of typical landforms along the ES-SQF. Combined with field geological surveys, the ES-SQF is identified as a continuous, singular-fault structure extending approximately 68 km in length. The fault trends in the WNW direction and along its trace, distinctive features, such as ridges, gullies, and terraces, show clear evidence of synchronous left lateral displacement. This study investigates the Qingsha River and the Dongzhong River. High-resolution digital elevation models (DEMs) derived from UAV imagery were used to conduct a detailed mapping of faulted landforms. An analysis of stripping trench profiles and radiocarbon dating of collected samples indicates that the most recent surface-rupturing seismic event in the area occurred between 3500 and 2328 y BP, pointing to the existence of an active fault from the Holocene epoch. Using the LaDiCaoz program to restore and measure displaced terraces at the study site, combined with geomorphological sample collection and testing, we estimated the fault’s slip rate since the Holocene to be approximately 2.0 ± 0.3 mm/y. Therefore, the ES-SQF plays a critical role in strain distribution across the central–western segment of the Qilian–Haiyuan fault zone. Together with the Tuolaishan fault, it accommodates and dissipates the left lateral shear deformation in this region. Based on the slip rate and the elapsed time since the last event, it is estimated that a seismic moment equivalent to Mw 7.5 has been accumulated on the ES-SQF. Additionally, with the significant Coulomb stress loading on the ES-SQF caused by the 2016 Mw 5.9 and 2022 Mw 6.7 Menyuan earthquakes, there is a potential for large earthquakes to occur in the future. Our results also indicate that high-resolution remote sensing imagery can facilitate detailed studies of active tectonics.