Sentinel-2 Satellite Research Articles

Environmental Monitoring of Changes in the Flooded Areas of the Marshes and Their Relationship to Water Quality Using GIS Techniques

This study aims to examine the relationship between the flooded areas of the marshes in southern Iraq and their water quality, as well as their relationship with climatic factors such as temperatures and rainfall amounts and their impact on the water discharge into the marshes of southern Iraq in the provinces (Basra, Maysan, and Dhi Qar) through the interpretation of maps with remote sensing tools of the wet cover (water and vegetation), descriptive and quantitative analysis of the flooded areas comparing them with the values of salt concentrations and clarifying the monitoring sites of the water quality and water discharge into the marshes through GIS, as well as the relationship of the wet cover with temperatures, rainfall amounts, and evaporation. The data of this study were obtained from environmental field observation data at climate monitoring stations, incoming water discharge stations, and water quality measurement sites in the three southern governorates. As for the areas of vegetation, the data was obtained through the interpretation of the satellites’ visuals (Sentinel-2 satellite) with a resolution of 15 m using GIS programs through a temporal and spatial comparison of the marshland for April -June-October for ten years (2010-2015-2020) respectively, The results showed that there is a direct relationship between the water quality and the wet cover (vegetation and water) through classification as well as the effect of climatic elements in the marshes.

NTL-Unet: A Satellite-Based Approach for Non-Technical Loss Detection in Electricity Distribution Using Sentinel-2 Imagery and Machine Learning.

This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company's coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas.

Exploring Spatial Aggregations and Temporal Windows for Water Quality Match-Up Analysis Using Sentinel-2 MSI and Sentinel-3 OLCI Data

Effective monitoring and management of inland waterbodies depend on reliable assessments of water quality through remote sensing technologies. Match-up analysis plays a significant role in investigating the comparability between in situ and remote sensing data of physical and biogeochemical variables. By exploring different spatial aggregations and temporal windows, we aimed to identify which configurations are most effective and which are less effective for the assessment of remotely sensed water quality data within the context of governmental monitoring programs. Therefore, in this study, remote sensing data products, including the variables of Secchi depth, chlorophyll-a, and turbidity, derived from the Copernicus satellites Sentinel-2 and Sentinel-3, were compared with in situ laboratory data from >100 waterbodies (lakes and reservoirs) in Germany, covering a period of 5 years (2016–2020). Processing was carried out using two different processing schemes, CyanoAlert from Brockmann Consult GmbH and eoapp AQUA from EOMAP GmbH & Co. KG, in order to analyze the influence of different processors on the results. To investigate appropriate spatial aggregations and time windows for validation (the match-up approach), we performed a statistical comparison of different spatial aggregations (1 pixel; 3 × 3, 5 × 5, and 15 × 15 macropixels; and averaging over the whole waterbody) and time windows (same day, ±1 day, and ±5 days). The results show that waterbody-wide values achieved similar accuracies and biases compared with the macropixel variants, despite the large differences in spatial aggregation and spatial variability. An expansion of the temporal window to up to ±5 days did not impair the agreement between the in situ and remote sensing data for most target variables and sensor–processor combinations, while resulting in a marked rise in the number of matches.

Semantic Web Techniques for Extracting and Analyzing of Cropland Abandonment in Hilly Areas

Due to rising rural labor costs, farmland abandonment is common in China's hilly areas. Timely, accurate extraction of its spatiotemporal changes is crucial for sustainable farmland use. This study presents a novel approach for extracting abandoned cropland using NDVI time series from Sentinel-2 satellite images, exploiting the difference in vegetation phenology between abandoned and cultivated croplands. Dynamic Time Warping (DTW) algorithm was used to determine the similarity between NDVI time-series curves of different cropland types. The similarity metrics were used to find the optimal NDVI threshold for abandoned cropland through F1-score evaluation. The method's overall accuracy is 92.4%, higher than comparison methods at 84.60% and 75.6%. The approach captures year-round vegetation changes, expands time dimension data, and improves accuracy. Spatiotemporal analysis revealed decreased patch size, increased shape complexity, and more dispersed distribution of abandoned cropland over time. Major factors were proximity to settlements, roads, water bodies.

Estimation of Coastal Wetland Vegetation Aboveground Biomass by Integrating UAV and Satellite Remote Sensing Data

Aboveground biomass (AGB) serves as a crucial indicator of the carbon sequestration capacity of coastal wetland ecosystems. Conducting extensive field surveys in coastal wetlands is both time-consuming and labor-intensive. Unmanned aerial vehicles (UAVs) and satellite remote sensing have been widely utilized to estimate regional AGB. However, the mixed pixel effects in satellite remote sensing hinder the precise estimation of AGB, while high-spatial resolution UAVs face challenges in estimating large-scale AGB. To fill this gap, this study proposed an integrated approach for estimating AGB using field sampling, a UAV, and Sentinel-2 satellite data. Firstly, based on multispectral data from the UAV, vegetation indices were computed and matched with field sampling data to develop the Field–UAV AGB estimation model, yielding AGB results at the UAV scale (1 m). Subsequently, these results were upscaled to the Sentinel-2 satellite scale (10 m). Vegetation indices from Sentinel-2 data were calculated and matched to establish the UAV–Satellite AGB model, enabling the estimation of AGB over large regional areas. Our findings revealed the AGB estimation model achieved an R2 value of 0.58 at the UAV scale and 0.74 at the satellite scale, significantly outperforming direct modeling from field data to satellite (R2 = −0.04). The AGB densities of the wetlands in Xieqian Bay, Meishan Bay, and Hangzhou Bay, Zhejiang Province, were 1440.27 g/m2, 1508.65 g/m2, and 1545.11 g/m2, respectively. The total AGB quantities were estimated to be 30,526.08 t, 34,219.97 t, and 296,382.91 t, respectively. This study underscores the potential of integrating UAV and satellite remote sensing for accurately assessing AGB in large coastal wetland regions, providing valuable support for the conservation and management of coastal wetland ecosystems.

A New and Robust Index for Water Body Extraction from Sentinel-2 Imagery

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method and confusion matrix along with the derived accuracy evaluation indicators are used to provide a threshold reference when extracting water bodies and evaluate the accuracy of the water body extraction results, respectively. The SMBWI and eight other commonly used water indices are qualitatively and quantitatively compared through vision and accuracy evaluation indicators, respectively. Here, the SMBWI is proven to be the most effective at suppressing interference of buildings and their shadows, cultivated lands, vegetation, clouds and their shadows, alpine terrain with bare ground and glaciers when extracting water bodies. The overall accuracy in all tests was consistently greater than 96.5%. The SMBWI is proven to have a high ability to identify mixed pixels of water and non-water, with the lowest total error among nine water indices. Most notably, better results are obtained when extracting water bodies under interfering environments of cover features. Therefore, we propose that our novel and robust water index, the SMBWI, is ready to be used for mapping land surface water with high accuracy.

SwinDefNet: A Novel Surface Water Mapping Model in Mountain and Cloudy Regions Based on Sentinel-2 Imagery

Surface water plays a pivotal role in the context of climate change, human activities, and ecosystems, underscoring the significance of precise monitoring and observation of surface water bodies. However, the intricate and diverse nature of surface water distribution poses substantial challenges to accurate mapping. The extraction of water bodies from medium-resolution satellite remote sensing images using CNN methods is constrained by limitations in receptive fields and inadequate context modeling capabilities, resulting in the loss of boundary details of water bodies and suboptimal fusion of multi-scale features. The existing research on this issue is limited, necessitating the exploration of novel deep-learning network combinations to overcome these challenges. This study introduces a novel deep learning network combination, SwinDefNet, which integrates deformable convolution and Swin Transformer for the first time. By enhancing the effective receptive field and integrating global semantic information, the model can effectively capture the diverse features of water bodies at various scales, thereby enhancing the accuracy and completeness of water extraction. The model was evaluated on Sentinel-2 satellite images, achieving an overall accuracy of 97.89%, an F1 score of 92.33%, and, notably, an accuracy of 98.03% in mountainous regions. These findings highlight the promising potential of this combined approach for precise water extraction tasks.

Climate change and coastal erosion hotspots in West Africa: The case of Togo

Global warming is a worldwide phenomenon with disastrous implications ranging from ocean warming and glacier melting to sea level rise and coastal damage. This research assesses climate change trends, investigates coastal erosion hotspots and forecasts shoreline dynamics in West Africa. It also includes information on coastal erosion impacts and current coastal defence measures. The data set includes annual mean sea level anomaly, eddy kinetic energy, wave energy data and monthly climate data, such as precipitation, temperature, wind speed, and sunshine, from 1988 to 2023. Additionally, field survey data and Sentinel-2 satellite images were employed from 2015 to 2023 for coastal erosion monitoring. The Mann-Kendall test and Sen's slope test have been employed for climate trend analysis, while the DSAS tools have been used to identify coastal erosion hotspots and forecast future trends. The results revealed a significant increasing trend in annual sea level rise anomaly, annual wave energy, annual eddy kinetic energy, monthly temperature, and monthly sunshine. According to the shoreline dynamics investigation on Togolese coastal area, the Aflao-Port transect had significant accretion rates (up to 15 m/years), whereas the Port-Aneho transect experienced coastal erosion (up to −12 m/years). Climate change may increase wave energy intensity in certain regions, while coastal development can alter littoral transport and shoreline changes. Although there are soft and hard stabilisation solutions to counteract coastal erosion, such as groynes, sandbags, reforestation, and a series of nozzles in Togolese coast, the phenomenon of coastal erosion has evolved in recent years. Future projections in 2033 show that coastal erosion could move from 50 to 250 m inland. It is recommended that climate trend indicators, shoreline dynamics, and coastal stabilisation solutions be incorporated into all coastal and marine policies for sustainable management, with a view to ensuring minimal disruption to the ecosystem services provided by the sea and coast to people.

FLOOD SUSCEPTIBILITY MAPPING USING MACHINE LEARNING IN KENING RIVER, SUB WATERSHED OF BENGAWAN SOLO, TUBAN

Floods are natural occurrences with the potential to cause damage to ecosystems and pose significant threats to human life, resulting in the destruction of property, infrastructure, and socioeconomic challenges. In recent times, flooding in the Sub-Watershed of Bengawan Solo has been linked to the overflowing Kening River in Tuban County. Aim: This study aims to produce a flood susceptibility map to mitigate the frequency of flood occurrences as well as facilitate effective planning for flood disaster risk management. Methodology and results: Flood data is collected from 2016 to 2023 through field surveys, Sentinel-1 satellite imagery, and data from the Development Planning Agency, Tuban County. Integrating remote sensing data from satellite imagery (PlanetScope, Sentinel-2), geographic information systems (GIS), and spatial modeling techniques, a flood susceptibility map is developed for the Kening River catchment. The occurrence of floods in the Kening River area is associated with various factors (11 variables) assessed through the frequency ratio approach, including profil curvature, LS factor, aspect, rainfall, river distance, road distance, building density 100 m, road density 100 m, vegetation type, normalized difference water index (NDWI), and soil adjusted vegetation index (SAVI). The results show flood susceptibility maps utilizing frequency ratio (FR) and convolutional neural network (CNN) techniques. The flood susceptibility map obtained through the CNN method demonstrates a notably high AUC value. The model development generated a validation AUC value of 0.857 for training and 0.856 for testing. Conclusion, significance and impact study: This research provides an understanding into the factors that influence the occurrence of floods in the Kening River catchment area. It also emphasizes the benefit of advanced machine learning approaches in mapping the susceptibility of floods. Furthermore, this study has the potential to be helpful in guiding regional policy decisions and result in enhanced flood risk management measures in Tuban County.

Assessment of the applicability of atmospheric correction algorithms for Type I and Type II water bodies based on in-situ data - a case study using sentinel data

ABSTRACT The application of remote sensing technology in the retrieval of marine biophysical and physical parameters is becoming increasingly widespread. Accurate atmospheric correction is an essential prerequisite for subsequent retrieval of biophysical and physical parameters of water bodies. However, different atmospheric correction algorithms exhibit significant regional applicability variations across various water body types, requiring researchers to select suitable atmospheric correction algorithms for their study areas. In this study, utilizing Sentinel-2 and Sentinel-3 satellite data sources, global publicly available datasets, and in-situ synchronous measured reflectance data for Type I and Type II water bodies, we evaluated the accuracy of atmospheric correction using three types of algorithms based on radiative transfer, dark spectrum fitting, and neural networks. We analysed the sources of algorithm errors and their applicability from both algorithm principles and water body properties, providing criteria for selecting suitable atmospheric correction algorithms for different characteristic water bodies and proposing recommendations for improving correction accuracy.

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy

In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area. Since traditional procedures to collect water quality data are time-consuming, labor-intensive, and costly, water resources management has shifted from gathering field measurement data to utilizing remote sensing (RS) data. WDD has been threatened by various driving forces in recent years, such as contamination from different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, and microbial contamination. Therefore, this study aimed to retrieve and map WQIs, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a) of the Wadi Dayqah Dam (WDD) reservoir from Sentinel-2 (S2) satellite data using a new procedure of weighted averaging, namely Bayesian Maximum Entropy-based Fusion (BMEF). To do so, the outputs of four Machine Learning (ML) algorithms, namely Multilayer Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVRs), and XGBoost, were combined using this approach together, considering uncertainty. Water samples from 254 systematic plots were obtained for temperature (T), electrical conductivity (EC), chlorophyll-a (Chl-a), pH, oxidation–reduction potential (ORP), and dissolved oxygen (DO) in WDD. The findings indicated that, throughout both the training and testing phases, the BMEF model outperformed individual machine learning models. Considering Chl-a, as WQI, and R-squared, as evaluation indices, BMEF outperformed MLR, SVR, RFR, and XGBoost by 6%, 9%, 2%, and 7%, respectively. Furthermore, the results were significantly enhanced when the best combination of various spectral bands was considered to estimate specific WQIs instead of using all S2 bands as input variables of the ML algorithms.

The application of Sentinel-2 satellite imagery to construct a model to estimate the concentration of Chlorophyll-a in surface water in the Hinh River basin, Vietnam

The application of Sentinel-2 satellite imagery to construct a model to estimate the concentration of Chlorophyll-a in surface water in the Hinh River basin, Vietnam

Centering Amah Mutsun voices in the analysis of a culturally important, fire-managed coastal grassland.

Indigenous communities throughout California, USA, are increasingly advocating for and practicing cultural fire stewardship, leading to a host of social, cultural, and ecological benefits. Simultaneously, state agencies are recognizing the importance of controlled burning and cultural fire as a means of reducing the risk of severe wildfire while benefiting fire-adapted ecosystems. However, much of the current research on the impacts of controlled burning ignores the cultural importance of these ecosystems, and risks further marginalizing Indigenous knowledge systems. Our work adds a critical Indigenous perspective to the study of controlled burning in California's unique coastal grasslands, one of the most biodiverse and endangered ecosystems in the country. In this study, we partnered with the Amah Mutsun Tribal Band to investigate how the abundance and occurrence of shrubs, cultural plants, and invasive plants differed among three adjacent coastal grasslands with varying fire histories. These three sites are emblematic of the state's diverging approaches to grassland management: fire suppression, fire suppression followed by wildfire, and an exceedingly rare example of a grassland that has been repeatedly burned approximately every 2 years for more than 30 years. We found that Danthonia californica was significantly more abundant on the burned sites, whereas all included shrub species (Baccharis pilularis, Frangula californica, and Rubus ursinus) were significantly more abundant on the site with no recorded fire, results that have important implications for future cultural revitalization efforts and the loss of coastal grasslands to shrub encroachment. In addition to conducting a culturally relevant vegetation survey, we used Sentinel-2 satellite imagery to compare the relative severities of the two most recent fire events within the study area. Critically, we used interviews with Amah Mutsun tribal members to contextualize the results of our vegetation survey and remote sensing analysis, and to investigate how cultural burning contrasts from typical Western fire management approaches in this region. Our study is a novel example of how interviews, field data, and satellite imagery can be combined to gain a deeper ecological and cultural understanding of fire in California's endangered coastal grasslands.

A dual-path model merging CNN and RNN with attention mechanism for crop classification

Rapid and accurate crop classification is essential for estimating crop information and improving cropland management. The application of deep learning models for crop classification using time-series data has become the most promising method. However, most approaches rely on single models for data processing result in lower classification accuracy and poor stability. Therefore, this study proposes a dual-path approach with attention mechanisms (DPACR) to promote the performance of this model architecture in crop classification using time series data. Specifically, the model comprises two branches, the Recurrent neural network (RNN) branch with bidirectional gated recurrent units (GRU) with a self-attention mechanism, and the convolutional neural network (CNN) branch based on SE-ResNet. Crop classification is accomplished by a main classifier, supported by auxiliary classifiers from the two branches. Using the Google Earth Engine and the Sentinel-2 satellite data, DPACR was tested in the Hetao irrigation district in Inner Mongolia, China. The comparison experiment demonstrated that the DPACR achieved the highest overall accuracy (OA = 0.959) and Kappa coefficient (Kappa = 0.941) compared to other five models (MLP, SE-ResNet, Bi-At-GRU, SVM, and RF). DPACR excelled in classifying six crops, maintaining high accuracy across multiple classes. Compared to attention mechanisms, auxiliary classifiers can significantly improve classification performance. This study highlights the effective combination of cloud computing and deep learning for large-scale crop classification, providing a practical method for agricultural monitoring and management.

Mapping vertical and horizonal deformation of the newly reclaimed third runway at Hong Kong International Airport with PAZ, COSMO-SkyMed, and Sentinel-1 SAR images

In order to meet long-term air transportation needs, Hong Kong International Airport (HKIA) has been constructing the third runway system (3RS) through land reclamation projects since 2016. However, the third runway and taxiway, which were paved in September 2021, suffered from severe surface settlement. Therefore, we attempted to reveal the settlement pattern within the 3RS and analyze the factors contributing to this displacement. Employing the multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique, we utilized PAZ, COSMO-SkyMed, and Sentinel-1 satellite images to extract ground displacements along the radar line of sight. The results show that, compared with COSMO-SkyMed and Sentinel-1 images, PAZ demonstrates superior performance in the deformation monitoring of the HKIA, with more deformation details and larger displacements. The maximum settlement rate derived by PAZ on the 3RS exceeds 110 mm/year. To ensure the reliability of the derived displacements along the LOS, we validated them through cross-validation, comparing the outcomes from different SAR data with data from GPS stations. In order to gain a detailed deformation information, we derived the vertical and horizontal (ground range direction) displacements by utilizing deformation measurements along the LOS of multiple SAR satellites and the geometric information, and the maximum values recorded for vertical and E-W displacements reached 88.31 mm/y and 41.91 mm/y, respectively. Furthermore, our investigation revealed that the significant local deformation observed on the taxiway and third runway was predominantly attributed to various factors including the creep of soft soil, consolidation of filling materials, characteristics of road surface materials, and distinct stages of construction.

Comparative analysis of different supervised methods for satellite-based land-use classification: a case study of Reyhanlı

Satellite-based land-use classification plays a crucial role in various Earth observation applications, ranging from environmental monitoring to disaster management. This study presents a comparative analysis of machine learning techniques applied to land cover classification using Landsat-9 and Sentinel-2 satellite imagery in the Reyhanlı district in southern Türkiye. Three different classification algorithms, Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood Classification (MLC), were evaluated for their ability to distinguish different land cover classes. High resolution multispectral satellite imagery processed under the same conditions using Geographic Information System (GIS) software was utilized in this study. Visual inspection and statistical evaluation, including overall accuracy and kappa coefficient, were employed to assess classification performance. The classification of Sentinel-2 and Landsat-9 satellite imagery using different machine learning algorithms resulted in the highest overall accuracy (OA = 0.911, Kappa = 0.879) for Sentinel 2 imagery with the RF algorithm. These findings highlight the importance of satellite image selection and algorithm optimization for accurate land cover mapping. This study provides valuable insights for local planners and authorities and underscores the potential of Sentinel-2 imagery combined with machine learning techniques for effective land-use classification and monitoring.

Response and resilience to drought in northern forests revealed by Sentinel-2

ABSTRACT Enhancing forest drought resilience is important for preserving ecosystem services in the face of climate change, but operational management methods for boreal forests aiming to preserve ecosystem services under drought are still largely missing. This study explores the use of satellite remote sensing to monitor vegetation stress phases related to drought progression and to quantify both short- and long-term impacts on forest growth. Data from the Sentinel-2 satellite mission were used to calculate the response of vegetation indices across six forest types, using the time period from 2015 to 2017 as a reference dataset. The percentual difference between the drought year of 2018 and the reference period was used to represent disturbance intensity and the system’s capability to resist it. Additionally, the recovery time was taken into consideration. Breakpoint detection with daily temporal resolution was used to quantify the response time of each forest type in relation to the onset date of meteorological drought. Results indicate that Sentinel-2 data can be used to monitor vegetation stress associated with drought progression and estimate the characteristics of forest resilience. High temporal resolution observations should be prioritized over annual maximum vegetation index values to determine the intensity of disturbance. Consistent severe impacts were observed in areas with limited soil moisture availability. The recovery time of forests took up to 4 years. Drought exposure during consecutive years could be especially damaging for species requiring a longer recovery time than a single growing season. Sentinel-2-based monitoring approach could benefit decision-support systems for forest management aiming to enhance drought resilience.

3D deformation velocity field analysis and TEM method to detect the tectonic influence on the land subsidence in Zamora, Mexico

The city of Zamora is situated in a tectonic basin and, according to previous interferometric studies, has been experiencing sinking up to 13 cm/yr (2007-2011). Although the reported subsidence pattern (WNW-ESE) is similar to the orientation of the regional fault system, there is a lack of detailed studies to establish the connection between this phenomenon, the thickness of sediments, and the basement geometry. Therefore, this paper presents a 3D deformation velocity field analysis for the period of 2014–2021, using 130 images from the Sentinel-2 satellite to calculate the vertical and east-west components of the subsidence. Likewise, a campaign of 28 Transient Electromagnetic soundings was carried out around the city to determine the thickness of sediments and bedrock geometry. The results of the interferometric analysis reveal a WNW-ESE sinking pattern with the maximum vertical velocities observed near the Zamora Museum, amounting to 108 mm/yr (2014–2017) and 102 mm/yr (2018–2021). Meanwhile, the horizontal component has a heterogeneous behavior in both periods with rates reaching up to 1 cm/year. The Transient Electromagnetic survey results show the presence of two grabens, a horst, and a series of half-grabens. The thickness of sediments varies between 94 and 314 m, with the latter corresponding to the main graben located beneath the city. The axis of this structure and the largest sediment deposits are linked to the area of maximum sinking and the spatial pattern of subsidence.

Integration of Microtremor and PS-INSAR Analysis to Investigate the Susceptible Area in the Pronojiwo District (Indonesia) Following the 2021 East Java M6.1 Earthquake

The East Java M = 6.1 Earthquake struck on April 10, 2021, at the bottom of the Indian Ocean, about 96 km south of Kepanjen District, Malang Regency, Indonesia. The neighbouring Pronojiwo District in Lumajang Regency suffered significant damages and loss of lives. The earthquake aftermath underscored the necessity of examining susceptibility factors in the Pronojiwo area for future mitigation efforts. This research focuses on assessing earthquake vulnerability in Pronojiwo District using two methodologies: microtremor analysis and PS-InSAR. Microtremor data were directly collected, while PS-InSAR data were acquired from the Sentinel-1 satellite and processed using the ranging from 0.579 × 10 to 24.840 × 10 s /cm, in Pronojiwo District, with notably higher values found in areas with Semeru deposits, characterized by soft sediments. PS-InSAR analysis indicates concentrated negative shifts in residential zones, signaling structural damage. Regions with elevated vulnerability index values and negative PS points are considered more prone to earthquakes and subsequent building damages.

CVTNet: A Fusion of Convolutional Neural Networks and Vision Transformer for Wetland Mapping Using Sentinel-1 and Sentinel-2 Satellite Data

Wetland mapping is a critical component of environmental monitoring, requiring advanced techniques to accurately represent the complex land cover patterns and subtle class differences innate in these ecosystems. This study aims to address these challenges by proposing CVTNet, a novel deep learning (DL) model that integrates convolutional neural networks (CNNs) and vision transformer (ViT) architectures. CVTNet uses channel attention (CA) and spatial attention (SA) mechanisms to enhance feature extraction from Sentinel-1 (S1) and Sentinel-2 (S2) satellite data. The primary goal of this model is to achieve a balanced trade-off between Precision and Recall, which is essential for accurate wetland mapping. The class-specific analysis demonstrated CVTNet’s proficiency across diverse classes, including pasture, shrubland, urban, bog, fen, and water. Comparative analysis showed that CVTNet outperforms contemporary algorithms such as Random Forest (RF), ViT, multi-layer perceptron mixer (MLP-mixer), and hybrid spectral net (HybridSN) classifiers. Additionally, the attention mechanism (AM) analysis and sensitivity analysis highlighted the crucial role of CA, SA, and ViT in focusing the model’s attention on critical regions, thereby improving the mapping of wetland regions. Despite challenges at class boundaries, particularly between bog and fen, and misclassifications of swamp pixels, CVTNet presents a solution for wetland mapping.