Satellite-based interferometric synthetic aperture radar (InSAR) is an invaluable technique in the detection and monitoring of changes on the surface of the earth. Its high spatial coverage, weather ...friendly and remote nature are among the advantages of the tool. The multi-temporal differential InSAR (DInSAR) methods in particular estimate the spatio-temporal evolution of deformation by incorporating information from multiple SAR images. Moreover, opportunities from the DInSAR techniques are accompanied by challenges that affect the final outputs. Resolving the inherent ambiguities of interferometric phases, especially in areas with a high spatio-temporal deformation gradient, represents the main challenge. This brings the necessity of quality indices as important DInSAR data processing tools in achieving ultimate processing outcomes. Often such indices are not provided with the deformation products. In this work, we propose four scores associated with (i) measurement points, (ii) dates of time series, (iii) interferograms and (iv) images involved in the processing. These scores are derived from a redundant set of interferograms and are calculated based on the consistency of the unwrapped interferometric phases in the frame of a least-squares adjustment. The scores reflect the occurrence of phase unwrapping errors and represent valuable input for the analysis and exploitation of the DInSAR results. The proposed tools were tested on 432,311 points, 1795 interferograms and 263 Sentinel-1 single look complex images by employing the small baseline technique in the PSI processing chain, PSIG of the geomatics division of the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC). The results illustrate the importance of the scores—mainly in the interpretation of the DInSAR outputs.
The increasing availability of Synthetic Aperture Radar (SAR) images facilitates the generation of rich Differential Interferometric SAR (DInSAR) data. Temporal analysis of DInSAR products, and in ...particular deformation Time Series (TS), enables advanced investigations for ground deformation identification. Machine Learning algorithms offer efficient tools for classifying large volumes of data. In this study, we train supervised Machine Learning models using 5000 reference samples of three datasets to classify DInSAR TS in five deformation trends: Stable, Linear, Quadratic, Bilinear, and Phase Unwrapping Error. General statistics and advanced features are also computed from TS to assess the classification performance. The proposed methods reported accuracy values greater than 0.90, whereas the customized features significantly increased the performance. Besides, the importance of customized features was analysed in order to identify the most effective features in TS classification. The proposed models were also tested on 15000 unlabelled data and compared to a model-based method to validate their reliability. Random Forest and Extreme Gradient Boosting could accurately classify reference samples and positively assign correct labels to random samples. This study indicates the efficiency of Machine Learning models in the classification and management of DInSAR TSs, along with shortcomings of the proposed models in classification of nonmoving targets (i.e., false alarm rate) and a decreasing accuracy for shorter TS.
Displacement time series (TS) provides temporal and spatial information related to ground deformation. This study aims to investigate temporal behavior of ground deformation TS, including ...classification of displacement trends and periodicity evaluation, which ease the interpretation of movements. To this end, we propose several modifications to an existing automatic classification workflow of Persistent Scatterers Interferometry (PSI) TS using new tests to classify ground deformations into seven main trends: Stable, Linear, Quadratic, Bilinear, Phase Unwrapping Errors (PUE), Discontinuous with constant and different velocities. We illustrate our approach over 1500 km
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of the Granada region and the metropolitan area of Barcelona, which were monitored using Sentinel-1 images and a PSI technique. This study provided the spatial distribution of different ground movement types and was useful to detect several TS anomalies due to PUE. The proposed approach also identified stable targets, which were wrongly classified as moving scatterers by the existing classification method. A periodicity analysis was finally performed using the Welch's power spectral density estimator to investigate seasonal and yearly fluctuations. The method was validated using simulated data, where the classified TSs characterized by probable phase unwrapping errors were verified by PSI experts. The overall classification accuracy was 77.8%, indicating that the proposed method has a considerable TS classification potential.
In the modern era where technology usage is a tradition of the generation, integrating the teaching and learning with mediums that could catch up and satisfy pupils’ interest is noteworthy. In line ...with this, the contributions of GeoGebra in the teaching-learning of mathematics: as a tool to foster students’ interest and achievement, and as an environment to flourish different learning styles are explored in this study. Besides, the cautions to consider before implementing a GeoGebra integrated lesson with the challenges, limitations and areas of future development are indicated. Among these: the belief and technology fluency of users and the student class ratio are found to be among the challenges for effective integration of GeoGebra in mathematics lessons. The difficulty of some commands in the input bar especially for students and teachers with no prior programming experience are considered among the limitations of GeoGebra.
Early alarm systems can activate vital precautions for saving lives and the economy threatened by natural hazards and human activities. Interferometric synthetic aperture radar (InSAR) products ...generate valuable ground motion data with high spatial and temporal resolutions. Integrating the InSAR products and forecasting models make possible to set up early alarm systems to monitor vulnerable areas. This study proposes a technical support to early warning detection tools of ground instabilities using machine learning and InSAR time series that is capable of forecasting regions affected by potential collapses. A long short-term memory (LSTM) model is tailored to predict ground movements in three forecast ranges (i.e., SAR observations): 3, 4, and 5 multistep. A contribution of the proposed strategy is utilizing adjacent time series to decrease the possibility of falsely detecting safe regions as significant movements. The proposed tool offers ground motion-based outcomes that can be interpreted and utilized by experts to activate early alarms to reduce the consequences of possible failures in vulnerable infrastructures, such as mining areas. Three case studies in Spain, Brazil, and Australia, where fatal incidents happened, are analyzed by the proposed early alert detector to illustrate the impact of chosen temporal and spatial ranges. Since most early alarm systems are site dependent, we propose a general tool to be interpreted by experts for activating reliable alarms. The results show that the proposed tool can identify potential regions before collapse in all case studies. In addition, the tool can suggest an optimum selection of InSAR temporal (i.e., number of images) and spatial (i.e., adjacent measurement points) combinations based on the available SAR images and the characteristics of the study area.
This study proposes modifications to an existing automatic classification method of Persistent Scatterers Interferometry (PSI) time series (TS) and a new procedure to classify ground movements into ...seven classes. We also represent a technique to detect TSs affected by phase unwrapping errors and a reclassification part to detect stable points, which are incorrectly classified as moving points using the original method. Around 60 km2 of Catalunya were classified using Sentinel-1 images and a PSI technique. The proposed method classified 78359 PS TS. This study provided the spatial distribution of ground movement classes and detected several time series anomalies.
Abstract
The coronavirus disease-2019 (COVID-19) pandemic has posed a significant multifaceted threat to the global community. Ethiopia, as a Sub-Saharan African country, is suffering from chronic ...food insecurity, and the emergence of such a pandemic will exacerbate the situation. As a result, this study investigated the spatial variation of non-resilience to food insecurity, its relationship with COVID-19, and household coping strategies to become resilient in the long run among households in the East Gojjam Zone of Northwest Ethiopia. From September 22 to December 24, 2020, an agro-ecological-based cross-sectional study of 3532 households was conducted to assess the spatial distribution and associated factors of non-resilience to household food insecurity. The enumeration areas (EAs) and households were chosen using a multistage sampling technique. Data were gathered using a semi-structured questionnaire and checklist using an Android device loaded with an Open Data Kit (ODK) template. Binary logistic regression was used to identify the specific factors associated with household non-resilience to food insecurity. A thematic analysis was conducted to investigate the opportunities and challenges of resilience for household food insecurity. Nearly two-thirds (62.5%) of the households were farmers, 67.9% lived in rural areas, and nearly three-quarters (73.8%) earned less than or equal to ETB 2100 per month. Males headed more than four-fifths of the households (81.7%). We found that nearly two-thirds of the households (60.02%), 95% CI 58.40, 61.64) were food insecure. After bivariate logistic regression, we found that households who were divorced (AOR = 2.54 (1.65, 3.87)), daily laborers (AOR = 2.37 (1.15, 4.87)), government employees (AOR = 2.06 (1.05, 4.05)), residents of highland and hot areas (AOR = 11.5 (5.37, 16.77)) and lowland areas (AOR = 1.35 (1.02, 3.15)) were frustrated by COVID-19 (AOR = 1.23 (1.02, 1.50)) and price inflation (1.89 (AOR = 1.42, 2.56))) were at higher odds of being non-resilient to household food insecurity at a 95% confidence level. Geospatial hot spot analysis revealed that Kurar kebele (the lowest government administrative unit) in Dejen District and Debre Markos town were the red-hotspot areas of household non-resilience to food insecurity. Less than a quarter of the households attempted to cope with food insecurity by adjusting their food consumption, while more than 60% of the households chose none of the coping strategies tested. According to the thematic analysis, the degree of poverty (lack of asset ownership), the COVID-19 pandemic, farm decreased variety, and low crop productivity were identified as challenges to coping with the hardship of resilience to food insecurity. During the COVID-19 pandemic and public emergency, the proportion of households that were unprepared for food insecurity reached its peak. It was recognized that a segment of the population with low economic capacity was more vulnerable to food insecurity and less resilient. Tough developmental gains will be undermined in this case. As a result, each responsible body and stakeholder should develop and implement solid corrective plans for the local context.
Informed consent in healthcare services is a legal, ethical, and professional requirement on the part of all treating health providers and providing person-centered care. The methods of requesting ...consent during childbirth have not been extensively studied. In Ethiopia, there is not at all a single study done.
The purpose of this study is to determine associated factors among mothers who gave birth at health institutions in the South Wollo Zone, Amhara region, Ethiopia in 2022.
Mothers who gave birth at South Wollo Zone public health institutions, from 01 March to 30 April 2022 participated in a multi-center institutional-based cross-sectional study design. Systematic random sampling was used to select 423 study participants. A validated questionnaire was used for data collection, and the data were collected through face-to-face interviews. Data were entered into Epi-Data version 4.6 and exported to SPSS version 23 for analysis. A multivariable logistic regression analysis was performed to identify factors associated with the outcome variable. The level of significance was determined using an adjusted odds ratio with a 95% confidence interval.
The study had 416 participants in total, with a response rate of 98.3%. Out of the 416 respondents interviewed, 67.1% of the women received consented care. The age group of 30-34, complications during childbirth, intended pregnancy, merchant, and primary and referral hospital were significantly associated with consented care.
The level of non-consented care during delivery was high compared with other literature reflecting substantial mistreatment. Therefore, stakeholders should strengthen monitoring and assessment systems to prevent abuse, and further study is required to look for practical ways to make improvements. Key elements of consented care have also been included in Basic and Emergency Obstetric Care training sessions and given to health providers.
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