Sentinel-1A Data Research Articles

Mapping the water cover of a protected area in the Amazon using Sentinel 1A data

Mapping the water cover of a protected area in the Amazon using Sentinel 1A data

Provision of land use and forest density maps in semi-arid areas of Iran using Sentinel-2 satellite images and vegetation indices

Zagros forests of Iran have several environmental, ecological, and socioeconomic values which provide the unique habitats for many endemic species. However, these ecosystems have severely been destroyed by many anthropogenic and natural factors in recent years. Knowledge of area, distribution, and density of these forests and current natural and human-made land uses inside these ecosystems is necessary for protective management of Zagros forests. This research was performed to obtain the land use and forest density maps in Zagros vegetative area of Fars province in southwestern Iran using Sentinel-2A, vegetation indices, Google Earth, and field data. First the boundary of Zagros forests in Fars province was digitized on Google Earth images. Then, Sentinel-2A satellite images covering the forest region in Fars province were obtained from Copernicus website. A pilot area (16000 ha) in the province was considered to investigate the precision of classification methods. Then, Sentinel-2A satellite image of pilot region was classified by some supervised classification methods (ML: maximum likelihood, MD: minimum distance, MaD: mahalanobis distance, SAM: spectral angle mapper, NN: neural network, SVM: support vector machine, and RF: random forest). In addition, the efficiency of different vegetation indices (NDVI, TNDVI, SAVI, and RVI) was evaluated for classifying the forest density in the pilot region. Furthermore, pilot area was considered to test the precision of Google Earth satellite images in this study. For this purpose, 270 field samples were taken as square plots in nine land uses. Selection of the plots inside each land use was randomly. After initial analysis, all satellite images covering the forest area in Fars province were classified by the most accurate algorithm (SVM) to obtain the land use map. On the other hand, the most accurate vegetation index (SAVI) was used to classify the forest density in the study area. Validation of final maps was performed using several random plots on Google Earth images. The initial results of this research indicated that Google Earth images have an overall accuracy (OA) of 96% compared to ground truth in Fars province. Results of land use map showed that Zagros forest has covered an area of 794651.79 ha in Fars province. The findings of this research demonstrated that SVM algorithm (OA: 92.94%, and k: 0.85) has efficiently classified the land uses in Fars province. In addition, SAVI (r2=0.719, p<0.001) has highly been correlated to the forest density in Zagros vegetative area of Fars province. Therefore, land use mapping using SVM algorithm and Sentinel-2A images, and forest density mapping using SAVI is highly recommended in Zagros forests of Fars province in time series which is essential for protective management of these forests in spatio-temporal scale.

Sentinel-2A Image Reflectance Simulation Method for Estimating the Chlorophyll Content of Larch Needles with Pest Damage

With the development of remote sensing technology, the estimation of the chlorophyll content (CHLC) of vegetation via satellite data has become an important means of monitoring vegetation health, and high-precision estimation has been the focus of research in this field. In this study, we used larch affected by Yarl’s larch looper (Erannis jacobsoni Djak) in the boundary region of Mongolia as the research object, simulated the multispectral reflectance, downscaled Sentinel-2A satellite data, performed mixed-pixel decomposition, analyzed the potential of Sentinel-2A satellite data for estimating the chlorophyll content by calculating the spectral indices (SIs) and spectral derivatives (SDFs) of images, and then extracted sensitive spectral features as the model training set. Spectral features sensitive to the chlorophyll content were extracted to establish the training set, and, finally, the chlorophyll content estimation model for larch was constructed on the basis of the partial least squares algorithm (PLSR). The results revealed that SI and SDF based on simulated remote sensing data were highly sensitive to the chlorophyll content under the influence of pests, with the SAVI and EVI2 spectral indices as well as the D_B2 and D_B5 spectral derivatives being the most sensitive to the chlorophyll content. The estimation models based on simulated data performed significantly better than models without simulated data in terms of accuracy, especially those based on SDF-PLSR. The simulated spectral reflectance well reflected the spectral characteristics of the larch canopy and was sensitive to damaged larch, especially in the green light, red edge, and near-infrared bands. The proposed approach improves the accuracy of chlorophyll content estimation via Sentinel-2A data and enhances the ability to monitor changes in the chlorophyll content under complex forest conditions through simulations, providing new technical means and a theoretical basis for forestry pest monitoring and vegetation health management.

Beyond traditional methods: Innovative integration of LISS IV and Sentinel 2A imagery for unparalleled insight into Himalayan ibex habitat suitability.

The utilization of satellite images in conservation research is becoming more prevalent due to advancements in remote sensing technologies. To achieve accurate classification of wildlife habitats, it is important to consider the different capabilities of spectral and spatial resolution. Our study aimed to develop a method for accurately classifying habitat types of the Himalayan ibex (Capra sibirica) using satellite data. We used LISS IV and Sentinel 2A data to address both spectral and spatial issues. Furthermore, we integrated the LISS IV data with the Sentinel 2A data, considering their individual geometric information. The Random Forest approach outperformed other algorithms in supervised classification techniques. The integrated image had the highest level of accuracy, with an overall accuracy of 86.17% and a Kappa coefficient of 0.84. Furthermore, to delineate the suitable habitat for the Himalayan ibex, we employed ensemble modelling techniques that incorporated Land Cover Land Use data from LISS IV, Sentinel 2A, and Integrated image, separately. Additionally, we incorporated other predictors including topographical features, soil and water radiometric indices. The integrated image demonstrated superior accuracy in predicting the suitable habitat for the species. The identification of suitable habitats was found to be contingent upon the consideration of two key factors: the Soil Adjusted Vegetation Index and elevation. The study findings are important for advancing conservation measures. Using accurate classification methods helps identify important landscape components. This study offers a novel and important approach to conservation planning by accurately categorising Land Cover Land Use and identifying critical habitats for the species.

Regional subsidence monitoring and prediction along high-speed railways based on PS-InSAR and LSTM

The rapid construction of high-speed railways in China has forced more and more routes to pass through slow subsidence zones caused by groundwater extraction or underground mining. It is crucial to carry out deformation monitoring and prediction those areas along high-speed railways to ensure the safe operation. Twenty-nine periods of Sentinel-1A data between Taiyuan South Station and Taigu East Station were first processed using the PS-InSAR technique, and the ground subsidence sequences at ten typical points were selected. Then the Variational modal decomposition (VMD) was combined with the Adaptive Boosting Algorithm (AdaBoost), and the VMD-LSTMAda-LSTMAda model was constructed by combining the long short-term memory (LSTM) model for the prediction of regional ground subsidence along the high-speed railway. The results show that the cumulative subsidence within the 200 m buffer of the line centreline is − 37.58 − 89.19 mm, and the annual average subsidence rate is − 26.57 − 82.63 mm/y. The proposed model can reduce the complexity of the deformation sequence and combines with AdaBoost to improve the prediction accuracy of the LSTM model. It performs well in terms of RMSE, MAE, MAPE, and R2 at the two feature points (3703: RMSE = 0.82 mm, MAE = 0.25 mm, MAPE = 6.31%, and R2 = 0.94; 522: RMSE = 1.32 mm, MAE = 0.46 mm, MAPE = 4.92%, R2 = 0.95). The model improves the accuracy of regional subsidence prediction along the high-speed railway.

Effect of Land Use/Land Cover Maps on CN Method: Case Study over Kahramanmaras

The impact of Land Use/Land Cover (LULC) data obtained from various spatial resolution satellites on the distribution of the Curve Number (CN) across Kahramanmaras using the Soil Conservation Service (SCS)-CN technique was investigated in this research. This study aimed to examine the effect of LULC on runoff due to increasing urbanization in the last century and the conversion of forest areas to agricultural lands. CORINE data with a spatial resolution of 100 m, and Sentinel-2A data with spatial resolutions of 10, 20, and 60 m were utilized in the analysis. The results reveal that only the average CNs of the products are in close proximity, while there are significant discrepancies in the CN variation over the maps as the spatial resolution increases. The Sentinel-2A satellite findings revealed higher CN values compared to the CORINE dataset over the study area where three geographical regions, namely the Mediterranean, Eastern Anatolia, and Southeastern Anatolia. Additionally, the results show that an increase in urbanization triggers surface runoff potential and reduces it in agricultural and forest areas in the city. Thus, the updated and higher spatial resolution of LULC promises accurate flood forecasting and/or more scientific potential drought estimation as well as other hydrological applications.

Evaluating D-InSAR performance to detect small water level fluctuations in two small lakes in Sweden

Monitoring lake water level fluctuations is critical for managing water resources, predicting the impacts of climatic change, and preserving ecosystem services lakes provide. However, traditional gauging stations are insufficient to monitor all lakes worldwide due to the large number of existing lakes, the challenges of installation and maintenance, and the remote locations of some. Although satellite altimetry is an alternative for measuring water levels, it cannot monitor small lakes effectively. This study evaluates the potential of Differential Interferometric Synthetic Aperture Radar (D-InSAR) for tracking minor water level changes in small lakes, a method more typically used in wetland studies. We investigate two Swedish lakes using Sentinel-1A and Sentinel-1B data from 2019, generating six-day interferograms and filtering out those with in situ water level changes exceeding one phase cycle. Our results show that D-InSAR can detect small water level changes with Lin’s correlations up to 0.63 and 0.89 and RMSE values of approximately 9 and 4 mm, respectively. These results evidence the potential of future L-band SAR missions with larger wavelengths, such as the NASA-ISRO SAR (NISAR) of the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organisation (ISRO), to track water level changes in lakes and aid water tracking missions such as the SWOT (Surface Water and Ocean Topography).

Monitoring and Law Analysis of Secondary Deformation on the Surface of Multi-Coal Seam Mining in Closed Mines

A large number of mines have been closed due to resource depletion, failure to meet safety production requirements, and other reasons. To effectively ensure the safety of the ecological environment above these closed mines along with the safety of engineering construction, it is necessary to monitor the secondary deformation of closed mines. Based on TerraSAR-X, Sentinel-1A data, and InSAR technology, this study obtained high-density secondary surface deformation data on the Jiahe Coal Mine and Pangzhuang Coal Mine in the western Xuzhou area. Combining mining geological data, we analyzed the spatiotemporal variation patterns and mechanisms of secondary deformation in multi-seam mining of closed mines. It was found that when mining multiple seams involves large interlayer spacing, the secondary deformation pattern shows a “W” shape. In this situation, the deformation can be divided into five stages: subsidence, uplift, re-subsidence, re-uplift, and relative stability. This study provides technical support for the evaluation and prevention of secondary deformation hazards in closed mines.

An assessment of recent peat forest disturbances and their drivers in the Cuvette Centrale, Africa

Abstract The largest tropical peatland complex in the Cuvette Centrale, is marked by persistent knowledge gaps. We assessed recent peat forest disturbances and their direct drivers from 2019 to 2021 in Cuvette Centrale, spanning the Republic of Congo (ROC) and the Democratic Republic of Congo (DRC). Utilizing peatland extent and Radar for Detecting Deforestation (RADD) alert data, we analyzed spatial and temporal patterns of disturbances. Further, we examined 2,267 randomly sampled peat forest disturbance events through visual interpretation of monthly Planet and Sentinel 2A data to identify direct drivers. Our findings revealed that between 2019 and 2021, about 91% of disturbances occurred in DRC, with hotspots concentrated in the northwest Sud-Ubangi district. Disturbances predominantly followed a sharp seasonal pattern, recurring during the first half of each year with temporal hotspots emerging between February and May, closely associated with smallholder agriculture activities. Smallholder agriculture accounted for over 88% of disturbances in Cuvette Centrale, representing a leading role both in ROC (~77%) and DRC (~89%). While, small-scale logging contributed 7% to disturbances in the region, it constituted an important driver (18 %) in the ROC. Other drivers included flood, roads and settlements. Approximately 77% of disturbances occurred outside managed forest concessions in Cuvette Centrale, with 40% extending into protected areas. About 90% of disturbances were concentrated within 1 km of peat forest edges and ~76% of the disturbances occurred within 5 km of road or river networks. The insights underscore the crucial need for effective peat forest conservation strategies in Cuvette Centrale and can inform national policies targeting peatland protection, aligning with commitments in the Brazzaville Declaration and Paris Agreement. Further, our findings on direct driver assessment could serve as a reference dataset for machine learning models to automate the visual interpretation and upscale the assessment across the entire region.

Monitoring Mining Impact for Geosites Using Time Series NDVI and Run-off in the Eastern Part of Southern Java Mountains, Indonesia

Several geosites in the Eastern Part of the Southern Mountains of Java which are currently being mined are important geosites because they have recorded tectonic processes in the tertiary period. However, mining activities without environmental monitoring could be lead loss of rock outcrops and alteration of mineralscontained in the geosite. This study aims to monitor the vegetation index and run off in karst geosites and gold mined hills. We used Sentinel 2A Imagery on the Google Earth Engine (GEE) to get the vegetation index (NDVI) and run off values temporally. The Sentinel 2A data acquisition pro-cess in both 2018 and 2022 was carried out on the Google Earth Engine with specific steps consisting import data from cloud collection, cloud masking, and customing date acquistion. The results show that there has been a decrease in the vegetation index in the period 2018-2022 which is indicated by the decrease in the vegetation index value in the mined area. Another impact found is that there has been an increase in the run-off value in 2022 in both the Karst Puger Hills and the Tumpang Pitu Hills. The NDVI pattern on mined geosites has also changed significantly due to increasingly intense mining activities. The NDVI fluctuation pattern in the Karst Hills in 2018-2020 ranges from 0.41 to 0.74, while in 2021-2022 the fluctuation pattern is only in the range 0.05 to 0.4, respectively. The NDVI fluctuation pattern in the Tumpang Pitu Gold Hills in 2018-2019 is in the range 0.6 to 0.8, while in 2020-2022 it is in the range 0.38 to 0.52, respectively. Google Earth Engine is able to map the vegeta-tion index more efficiently by using a time series approach. These advancement is different compare to previous studies, where our study shows rapid acquisition during pre-processing, performing NDVI with rapid temporal analysis, and shows numerous degraded land in southern java mountains. There-fore, it can be concluded that there has been a decrease in the vegetation index and an increase in run off which can threaten the rock outcrop on the geosite. Furthermore, this study suggest that GEE should be considered as a main tool to identified degraded land, particularly for geosite conservation.

Soil Organic Carbon Estimation via Remote Sensing and Machine Learning Techniques: Global Topic Modeling and Research Trend Exploration

Understanding and monitoring soil organic carbon (SOC) stocks is crucial for ecosystem carbon cycling, services, and addressing global environmental challenges. This study employs the BERTopic model and bibliometric trend analysis exploration to comprehensively analyze global SOC estimates. BERTopic, a topic modeling technique based on BERT (bidirectional encoder representatives from transformers), integrates recent advances in natural language processing. The research analyzed 1761 papers on SOC and remote sensing (RS), in addition to 490 related papers on machine learning (ML) techniques. BERTopic modeling identified nine research themes for SOC estimation using RS, emphasizing spectral prediction models, carbon cycle dynamics, and agricultural impacts on SOC. In contrast, for the literature on RS and ML it identified five thematic clusters: spatial forestry analysis, hyperspectral soil analysis, agricultural deep learning, the multitemporal imaging of farmland SOC, and RS platforms (Sentinel-2 and synthetic aperture radar, SAR). From 1991 to 2023, research on SOC estimation using RS and ML has evolved from basic mapping to topics like carbon sequestration and modeling with Sentinel-2A and big data. In summary, this study traces the historical growth and thematic evolution of SOC research, identifying synergies between RS and ML and focusing on SOC estimation with advanced ML techniques. These findings are critical to global ecosystem SOC assessments and environmental policy formulation.

Mapping temperate old-growth forests in Central Europe using ALS and Sentinel-2A multispectral data

Old-growth forests are essential to preserve biodiversity and play an important role in sequestering carbon and mitigating climate change. However, their existence across Europe is vulnerable due to the scarcity of their distribution, logging, and environmental threats. Therefore, providing the current status of old-growth forests across Europe is essential to aiding informed conservation efforts and sustainable forest management. Remote sensing techniques have proven effective for mapping and monitoring forests over large areas. However, relying solely on remote sensing spectral or structural information cannot capture comprehensive horizontal and vertical structure complexity profiles associated with old-growth forest characteristics. To overcome this issue, we combined spectral information from Sentinel-2A multispectral imagery with 3D structural information from high-density point clouds of airborne laser scanning (ALS) imagery to map old-growth forests over an extended area. Four features from the ALS data and fifteen from Sentinel-2A comprising raw band (spectral reflectance), vegetation indices (VIs), and texture were selected to create three datasets used in the classification process using the random forest algorithm. The results demonstrated that combining ALS and Sentinel-2A features improved the classification performance and yielded the highest accuracy for old-growth class, with an F1-score of 92% and producer’s and user’s accuracies of 93% and 90%, respectively. The findings suggest that features from ALS and Sentinel-2A data sensitive to forest structure are essential for identifying old-growth forests. Integrating open-access satellite imageries, such as Sentinel-2A and ALS data, can benefit forest managers, stakeholders, and conservationists in monitoring old-growth forest preservation across a broader spatial extent.

Leveraging deep learning for coastal monitoring: A VGG16-based approach to spectral and textural classification of coastal areas with sentinel-2A data

Coastal ecosystems are vital for the planet's health, providing essential habitats for diverse species and supporting human communities. However, these complex environments face increasing threats from climate change and human activities. Effective monitoring of these areas requires large-scale, efficient, and accurate methods. This study explores the potential of deep learning for automated coastal land cover classification using Sentinel-2A satellite imagery on the Google Earth Engine (GEE) platform. We investigate the impact of transfer learning and spectral band combinations on classification accuracy for five coastal types: artificial, bedrock, sandy, muddy, and vegetation-covered. Our findings demonstrate that a pre-trained VGG16 Convolutional Neural Network (CNN) with transfer learning significantly improves classification accuracy (average 19.3% increase) compared to using default weights. Notably, including the near-infrared (NIR) band in training data leads to superior results, particularly for artificial and bedrock coastlines, where the NIR band's effectiveness in separating land-water boundaries enhances classification accuracy. These results highlight the potential of deep learning for large-scale, automated coastal monitoring, informing applications in sustainable fisheries management, coastal vulnerability assessment, and marine ecosystem conservation.

Ensemble Machine Learning on the Fusion of Sentinel Time Series Imagery with High-Resolution Orthoimagery for Improved Land Use/Land Cover Mapping

In the United States, several land use and land cover (LULC) data sets are available based on satellite data, but these data sets often fail to accurately represent features on the ground. Alternatively, detailed mapping of heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences among scenes, landscape heterogeneity, and computational limitations. Various machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study were to evaluate bagging (Random Forest, RF), boosting (Gradient Boosting Machines [GBM] and extreme gradient boosting [XGB]), and stacking ensemble ML models. We used these techniques on a time series of Sentinel 2A data and NAIP orthoimagery to create a LULC map of a portion of Irion and Tom Green counties in Texas (USA). We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. We then compared accuracy based on random cross-validation without accounting for spatial autocorrelation and target-oriented cross-validation accounting for spatial structures of the training data set. Comparison of random and target-oriented cross-validation results showed that autocorrelation in the training data offered overestimation ranging from 2% to 3.5%. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base learners. We show that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use/land cover classification using geographic object-based image analysis.

A comparative analysis to assess the efficiency of lineament extraction utilizing satellite imagery from Landsat-8, Sentinel-2B, and Sentinel-1A: A case study around Suez Canal Zone, Egypt

Satellite remote sensing data has been extensively utilized in various fields, for example topography, geology, and hydrogeology, to extract lineament information. With notable advancements in remote sensing techniques, the process of lineament extraction and identification can now be performed in a more efficient and accurate manner, surpassing traditional manual methods. This study presents a comparative analysis utilizing Landsat-8, Sentinel-2B, and Sentinel-1A data to automatically extract lineaments. The approach includes ground truth data, an existing geological map, and a Digital Elevation Model (DEM) in addition to the data on satellite images. Through the use of a semi-totally automatic method that combines a line-linking algorithm and an edge-line detection technique, within the study area, we have determined the optimal parameters for automated lineament extraction. It has been demonstrated through further comparison and assessment of the data that using Sentinel-1A data resulted in more efficient restitution of lineaments. This demonstrates how well radar data performs in this kind of investigation when compared to optical data.

Stability monitoring and analysis of submerged landslides in the Three Gorges Reservoir area based on time-series InSAR data and GNSS data

ABSTRACT Precise landslide monitoring is crucial for understanding and mitigating their impact. The Three Gorges Dam has exacerbated landslides due to steep slopes and rising water levels. This study used ALOS-2 PALSAR-2 (2016–2017) and Sentinel-1A (2016–2018) data to monitor the Daping slope. Findings showed deformation rates of −86.3 mm/y (ALOS-2) and −62 mm/y (Sentinel-1A), confirmed by GNSS data. Analysis indicated that rainfall and water level fluctuations drive the Daping landslide, highlighting key factors influencing landslides in the Three Gorges region and their threats to life and property.

Analysis of Devanur and Manamedu Ophiolite Complexes in SGT, India: A detailed examination employing remote sensing techniques and Laboratory Spectral Signature investigations

This study employs advanced satellite imagery from ASTER and Sentinel-2A to conduct detailed lithological mapping of the Devanur and Manamedu ophiolite complexes in the southern Central Shear Zone (CSZ). The primary focus is on the Manamedu Ophiolite Complex (MOC) and the Devanur Ophiolitic Complex (DOC). Image enhancement techniques such as Color composites, Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) were utilized to differentiate various rock types. RGB band combinations derived from PCA and MNF outputs demonstrated effective discrimination of rock units. Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods were employed on ASTER and Sentinel-2A images, yielding classified lithologies that closely matched existing maps from the Geological Survey of India (GSI) and other studies, validating the accuracy of the findings. Additionally, Laboratory Spectral Signature Studies were conducted on 10 rock samples using an ASD FieldSpec Pro® spectroradiometer, providing reflectance spectra from 350 nm to 2500 nm. These spectra, particularly the continuum-removed reflectance, revealed diagnostic absorption features that were corroborated by geochemical analyses. A detailed analysis investigated how elemental compositions and key minerals influenced absorption bands. Major oxide geochemical compositions of DOC and MOC samples were identified using XRF methods. The aim of this research is to characterize DOC and MOC through remote sensing and spectral signature analysis. Sentinel-2A data proved more effective in lithological discrimination compared to ASTER, with spectral signatures indicating the presence of iron (Fe) and magnesium (Mg) contents. Notably, SVM classification of Sentinel-2A MNF + DEM data achieved an overall accuracy of more than 90% when compared with field investigations. This study underscores the efficacy of processing VNIR and SWIR bands from ASTER and Sentinel-2A satellite imagery alongside DEM data and ground surveys for mapping mafic-ultramafic rocks in the DOC and MOC regions of the CSZ.

Agroforestry area mapping using medium resolution satellite data and object-based image analysis

Agroforestry is gaining increased attention within global policy processes and has been promoted as a strategy for working towards better food security, climate change adaptation and mitigation and livelihood resilience. With this gaining importance of agroforestry, site-specific studies are required to delineate different agroforestry systems and estimate the exact area under agroforestry. In this study remote sensing and GIS techniques were used for the estimation of area under agroforestry using medium-resolution satellite (sentinel 2A and 2B) data. Agroforestry area classification and estimation were done in e-Cognition developer software (10.0 version) through multiresolution segmentation and object-based image analysis (OBIA). The agroforestry area estimated in the erstwhile Warangal district was 3753 ha with an overall accuracy of 86% and a kappa coefficient of 0.84. The major agroforestry systems observed in the erstwhile Warangal district were eucalyptus, subabul, malabar neem, teak, sandalwood and red sanders.

Lembang Fault Deformation Study Using Sentinel-1A Image with Ps-Insar Method

Badan Nasional Penanggulangan Bencana (BNPB) states that the earthquakes that have occurred in the territory of Indonesia during 2018 were 11,418. Nearly 95 percent of earthquakes occur in fault areas and plate boundaries. In West Java, especially in the Bandung area there is a fault structure called the Lembang Fault which is mostly occupied by community settlements. The Lembang Fault is an active fault, therefore it is necessary to conduct research related to monitoring the Lembang Fault for disaster mitigation purposes. One of parameter to determine the level of activity of a fault is deformation. The amount of deformation can be analyzed through the remote sensing methods, one of them is the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) method. In this study the main data used is Sentinel-1A image data consist of eight scenes with acquisition time from January 2018 to March 2019. The results of the study concluded that the average deformation that occurred along the Lembang Fault was 2,485 mm with a graph that tends to rise. The area most affected by deformation is Cisarua Subdistrict with Line-of-Sight velocity of deformation of 28.4 - 48.9 mm / year. While the smallest deformation occurred in Lembang District with Line-of-Sight velocity of deformation of -2.3 - 19.2 mm / year.

Characteristics of internal waves in the Bali Sea from Sentinel-1A data and ocean modeling

Abstract Internal waves (IWs) that propagate into the Bali Sea are supposed to originate from the generating sill area in the southern part of the Lombok Strait. This study aims to investigate surface features and mechanism of IWs formation and propagation into the Bali Sea. The datasets are acquired from the Sentinel-1A imageries and CROCO model simulation. The results showed that solitary wave packets are spread over two areas: near Lombok Strait as generating area, and in the western and northern parts of the Bali Sea as propagating area. The length of IWs crests increased as they moved into the propagation area. The model showed that mixed semidiurnal tidal currents near the sill flow northward (southward) alternately during the flood (ebb) tidal period, resulting in the pycnocline deepening down to ~400 m near the northern sill area during the flood tide. Furthermore, the model demonstrated an alternating upward and downward movement of vertical currents components, associated with propagation of IWs’ crests and troughs along IWs pathway.