bluuebunny/crossref_metadata_2025 huggingface dataset:
An experimental study of the reactivity of terpinolene and β-caryophyllene with the nitrate radical
Authors: Fouqueau Axel , Cirtog Manuela , Cazaunau Mathieu , Pangui Edouard , Doussin Jean-François , Picquet-Varrault Bénédicte
Published: May 2022

Abstract. Biogenic volatile organic compounds (BVOCs) are intensely emitted by forests and crops into the atmosphere. They can
rapidly react with the nitrate radical (NO3) during the nighttime to form a
number of functionalized products. Among them, organic nitrates (ONs) have
been shown to behave as reservoirs of reactive nitrogen and consequently
influence the ozone budget and secondary organic aerosols (SOAs), which are
known to have a direct and indirect effect on the radiative balance and
thus on climate. Nevertheless, BVOC + NO3 reactions remain poorly understood. Thus,
the primary purpose of this study is to furnish new kinetic and mechanistic
data for one monoterpene (C10H16), terpinolene, and one
sesquiterpene (C15H24), β-caryophyllene, using simulation
chamber experiments. These two compounds have been chosen in order to
complete the few experimental data existing in the literature. Rate
constants have been measured using both relative and absolute methods. They
have been measured to be (6.0 ± 3.8) ×10-11 and (1.8 ± 1.4) ×10-11 cm3 molec.−1 s−1 for
terpinolene and β-caryophyllene respectively. Mechanistic studies
have also been conducted in order to identify and quantify the main reaction
products. Total organic nitrates and SOA yields have been determined. Both
terpenes appear to be major ON precursors in both gas and particle phases
with formation yields of 69 % for terpinolene and 79 % for β-caryophyllene respectively. They are also major SOA precursors, with
maximum SOA yields of around 60 % for terpinolene and 90 % for β-caryophyllene. In order to support these observations, chemical analyses
of the gas-phase products were performed at the molecular scale using
a proton transfer reaction–time-of-flight–mass spectrometer (PTR-ToF-MS) and FTIR. Detected products allowed proposing chemical mechanisms
and providing explanations through peroxy and alkoxy reaction pathways.
Improvement of the soil drainage simulation based on observations from lysimeters
Authors: Sobaga Antoine , Decharme Bertrand , Habets Florence , Delire Christine , Enjelvin Noële , Redon Paul-Olivier , Faure-Catteloin Pierre , Le Moigne Patrick
Published: May 2022

Abstract. Soil drainage is the main source of groundwater recharge and river flow. It is therefore a key process for water resource management. In this study, we evaluate the soil drainage simulated by the Interaction-Soil-Biosphere-Atmosphere (ISBA) land surface model currently used for hydrological applications from the watershed scale to the global scale. This validation is done using seven lysimeters from two long term experiment sites measuring hourly water dynamics between 2009 and 2019 in northeastern France. These 2-meter deep lysimeters are filled with different soil types and are either maintained bare soil or covered with vegetation. The commonly used closed-form equations describing soil-water retention and conductivity curves from Brooks and Corey (1966) and van Genuchten (1980) are tested. The results indicate a good performance by the different experiments in terms of soil volumetric water content and water mass. The drained flow at the bottom of the lysimeter is well modeled using Brooks and Corey (1966) while some weaknesses appears with van Genuchten (1980) due to the complexity of its hydraulic conductivity function. Combining the soil-water curve of van Genuchten (1980) with the hydraulic conductivity function of Brooks and Corey (1966) allow to solve this problem and even to improve the simulation of the drainage dynamic, especially for intense drainage events. The study highlights the importance of the vertical heterogeneity of the soil hydrodynamic parameters to correctly simulate the drainage dynamic, as well as the primary influence of the n and b parameters which characterize the shape of the soil-water retention curve.
Modelling floating riverine litter in the south-eastern Bay of Biscay: a regional distribution from a seasonal perspective
Authors: Ruiz Irene , Rubio Anna , Abascal Ana J. , Basurko Oihane C.
Published: May 2022

Abstract. Although rivers contribute to the flux of litter to the coastal and marine environment, estimates of riverine litter amounts are scarce and the behaviour of riverine litter at river mouths and coastal waters is highly uncertain. This paper provides a comprehensive overview of the seasonal trends of floating riverine litter transport and fate in the south-eastern Bay of Biscay based on riverine litter characterization, drifters and high-frequency radars observations and Lagrangian simulations. Virtual particles were released close to the river mouths as a proxy of litter entering the ocean from rivers and were parameterized with a wind drag coefficient (Cd) to represent their trajectories and fate according to the buoyancy of the litter items. They were forced with numerical winds and measured currents provided by high-frequency radars covering selected seasonal week-long periods between 2009 and 2021. To gain a better insight on the type and buoyancy of the items, samples collected from a barrier placed at Deba river (Spain) were characterized at laboratory. Items were grouped into two categories: low buoyant items (objects not exposed to wind forcing e.g., plastic bags) and highly buoyant items (objects highly exposed to wind forcing, e.g., bottles). Overall, low buoyant items encompassed almost 90 % by number and 68 % by weight. Low buoyant items were parametrized with Cd=0 %, and highly buoyant items with Cd=4 %, this later one as a result of the joint analysis of modelled and observed trajectories of four satellite drifting buoys released at Adour (France), Deba (Spain) and Oria (Spain) river mouths. Results show that all regions in the study area are highly affected by rivers within or nearby the region itself. Simulations of riverine litter parametrized with Cd=4 % showed that particles drifted faster towards the coast by the wind, notably during the first 24 hours. In summer, over the 97 % of particles beached after one week of simulation. In autumn this value fell to 54 %. In contrast, the low buoyant litter items take longer to arrive to the coastline, particularly during Spring with fewer than 25 % of particles beached by the end of the simulations. When comparing coastline concentrations, the highest concentrations of particles (>200 particles/km) were recorded during summer in the French region of Pyrénées-Atlantiques for Cd=4 %. These results coupled observations and a river-by-river modelling approach and can assist policy and decision makers on setting emergency responses to high fluxes of riverine litter arrivals and on defining future monitoring strategies for heavy polluted regions within the study area.
Time varying changes and uncertainties in the CMIP6 ocean carbon sink from global to regional to local scale
Authors: Gooya Parsa , Swart Neil C. , Hamme Roberta C.
Published: May 2022

Abstract. As a major sink for anthropogenic carbon, the oceans slow the increase of carbon dioxide in the atmosphere and regulate climate change. Future changes in the ocean carbon sink, and its uncertainty at a global and regional scale, are key to understanding the future evolution of the climate. Here, we conduct a multimodel analysis of the changes and uncertainties in the historical and future ocean carbon sink using output data from the latest phase of the Coupled Model Intercomparison Project: CMIP6, and observations. We show that the ocean carbon sink is concentrated in highly active regions – 70 percent of the total sink occurs in less than 40 percent of the global ocean. High pattern correlations between the historical and projected future carbon sink indicate that future uptake will largely continue to occur in historically important regions. We conduct a detailed breakdown of the sources of uncertainty in the future carbon sink by region. Scenario uncertainty dominates at the global scale, followed by model uncertainty, and then internal variability. We demonstrate how the importance of internal variability increases moving to smaller spatial scales and go on to show how the breakdown between scenario, model, and internal variability changes between different ocean basins, governed by different processes. Moreover, we show that internal variability changes with time based on the scenario. As with the mean sink, we show that uncertainty in the future ocean carbon sink is also concentrated in the known regions of historical uptake. The resulting patterns in the signal-to-noise ratio have strong implications for observational detectability and time of emergence, which varies both in space and with scenario. Our results suggest that to detect human influence on the ocean carbon sink as early as possible and to efficiently reduce uncertainty in future carbon uptake, modeling and observational efforts should be focused in the known regions of high historical uptake, including the Northwest Atlantic and the Southern Ocean.
Four-century history of land transformation by humans in the United States: 1630–2020
Authors: Li Xiaoyong , Tian Hanqin , Pan Shufen , Lu Chaoqun
Published: May 2022

Abstract. The land of the conterminous United States (CONUS) has been transformed dramatically by humans over the last four centuries through land clearing, agricultural land expansion and intensification, and urban sprawl. Spatial-temporal data on long-term historical changes in land use and land cover (LULC) across the CONUS is essential for understanding and predicting the dynamics of coupled natural-human systems. A few efforts have focused on reconstructing historical databases to characterize changes in cropland and urban extent in the CONUS. However, the high-resolution and long-term trajectories of multiple land use types remain unclear. By integrating multi-source data, such as high-resolution remote sensing image-based LULC data, model-based LULC products, and historical census data, we reconstructed LULC history at an annual time scale and 1 km x 1 km spatial resolution for the CONUS in the past 390 years (1630–2020). The results show widespread expansion of cropland and urban land associated with rapid loss of natural vegetation. Newly reclaimed croplands are mainly converted from forest, shrubland, and grassland, especially in the Great Plains and North Central. Forest planting and regeneration accelerated the forest recovery in the Northeast and Southeast since the 1920s. The geospatial and long-term historical land use data from this study can be applied to assess the LULC impacts on regional climate, hydrology, carbon cycle, and greenhouse gas emissions. The datasets are available at https://doi.org/10.5281/zenodo.6469247 (Li et al., 2022).
Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)
Authors: Zhang Wentao , Shi Xiangjun , Lu Chunsong
Published: May 2022

Abstract. The impacts of the ice crystal size distribution shape parameter (μi) were considered in the two-moment bulk cloud microphysics scheme of the Community Atmosphere Model Version 6 (CAM6). The calculating formulas of statistical mean radii indicate that, under the same mass (qi) and number (Ni) of ice crystals, the ratios of the mass-weighted radius (Rqi, not related to μi) to other statistical mean radii (e.g., effective radiative radius) are completely determined by μi. Off-line tests show that μi has a significant impact on the cloud microphysical processes owing to the μi-induced changes in ice crystal size distribution and statistical mean radii (excluding Rqi). Climate simulations show that increasing μi would lead to higher qi and lower Ni in most regions, and these impacts can be explained by the changes in cloud microphysical processes. After increasing μi from 0 to 5, the longwave cloud radiative effect increases (stronger warming effect) by 5.58 W m−2 (25.11 %), and the convective precipitation rate decreases by −0.12 mm day−1 (7.64 %). In short, the impacts of μi on climate simulations are significant and the main influence mechanisms are also clear. This suggests that the μi-related processes deserve to be parameterized in a more realistic manner.
LEVERAGING DYNAMIC OBJECTS FOR RELATIVE LOCALIZATION CORRECTION IN A CONNECTED AUTONOMOUS VEHICLE NETWORK
Authors: Yuan Y. , Sester M.
Published: May 2022

Abstract. High-accurate localization is crucial for the safety and reliability of autonomous driving, especially for the information fusion of collective perception that aims to further improve road safety by sharing information in a communication network of Connected Autonomous Vehicles (CAV). In this scenario, small localization errors can impose additional difficulty on fusing the information from different CAVs. In this paper, we propose a RANSAC-based (RANdom SAmple Consensus) method to correct the relative localization errors between two CAVs in order to ease the information fusion among the CAVs. Different from previous LiDARbased localization algorithms that only take the static environmental information into consideration, this method also leverages the dynamic objects for localization thanks to the real-time data sharing between CAVs. Specifically, in addition to the static objects like poles, fences, and facades, the object centers of the detected dynamic vehicles are also used as keypoints for the matching of two point sets. The experiments on the synthetic dataset COMAP show that the proposed method can greatly decrease the relative localization error between two CAVs to less than 20cm as far as there are enough vehicles and poles are correctly detected by both CAVs. Besides, our proposed method is also highly efficient in runtime and can be used in real-time scenarios of autonomous driving.
K-MEANS CLUSTERING BASED ON OMNIVARIANCE ATTRIBUTE FOR BUILDING DETECTION FROM AIRBORNE LIDAR DATA
Authors: dos Santos R. C. , Galo M. , Habib A. F.
Published: May 2022

Abstract. Building detection is an important process in urban applications. In the last decades, 3D point clouds derived from airborne LiDAR have been widely explored. In this paper, we propose a building detection method based on K-means clustering and the omnivariance attribute derived from eigenvalues. The main contributions lie on the automatic detection without the need for training and optimal neighborhood definition for local attribute estimation. Additionally, one refinement step based on mathematical morphology (MM) operators to minimize the classification errors (commission and omission errors) is proposed. The experiments were conducted in three study areas. In general, the results indicated the potential of proposed method, presenting an average Fscore around 97%.
SURFACE APPROXIMATION OF COASTAL REGIONS: LR B-SPLINE FOR DETECTION OF DEFORMATION PATTERN
Authors: Kermarrec G. , Skytt V. , Dokken T.
Published: May 2022

Abstract. Geospatial data acquisition of terrains produces huge, noisy and scattered point clouds. An efficient use of the acquired data requires structured and compact data representations. Working directly in a point cloud is often not appealing. To face this challenge, approximation with tensor product B-spline surfaces is attractive. It reduces the point cloud description to relatively few coefficients compared to the volume of the original point cloud. However, this representation lacks the ability to adapt the resolution of the shape to local variations in the point cloud. The result is frequently that noise is approximated and that surfaces have unwanted oscillations.Locally Refined (LR) B-spline surfaces were introduced to face this challenge and provide a tool for approximating Geographic Information System point clouds. In our LR B-spline based approximation algorithm, iterative least-squares approximation is combined with a Multilevel B-spline Approximation to reduce memory consumption. We apply the approach to data sets from coastal regions in Norway and the Netherlands, and compare the obtained approximation with a raster method. We further highlight the potential of LR B-spline volumes for spatio-temporal visualisation of deformation patterns.
SELF-SUPERVISED ADVERSARIAL SHAPE COMPLETION
Authors: Peters T. , Schindler K. , Brenner C.
Published: May 2022

Abstract. The goal of this paper is 3D shape completion: given an incomplete instance of a known category, hallucinate a complete version of it that is geometrically plausible. We develop an adversarial framework that makes it possible to learn shape completion in a self-supervised fashion, only from incomplete examples. This is enabled by a discriminator network that rejects incomplete shapes, via a loss function that separately assesses local sub-regions of the generated example and accepts only regions with sufficiently high point count. This inductive bias against empty regions forces the generator to output complete shapes. We demonstrate the effectiveness of this approach on synthetic data from ShapeNet and ModelNet, and on a real mobile mapping dataset with nearly 9’000 incomplete cars. Moreover, we apply it to the KITTI autonomous driving dataset without retraining, to highlight its ability to generalise to different data characteristics.
CONSTRAINED BUNDLE ADJUSTMENT FOR STRUCTURE FROM MOTION USING UNCALIBRATED MULTI-CAMERA SYSTEMS
Authors: Huang D. , Elhashash M. , Qin R.
Published: May 2022

Abstract. Structure from motion using uncalibrated multi-camera systems is a challenging task. This paper proposes a bundle adjustment solution that implements a baseline constraint respecting that these cameras are static to each other. We assume these cameras are mounted on a mobile platform, uncalibrated, and coarsely synchronized. To this end, we propose the baseline constraint that is formulated for the scenario in which the cameras have overlapping views. The constraint is incorporated in the bundle adjustment solution to keep the relative motion of different cameras static. Experiments were conducted using video frames of two collocated GoPro cameras mounted on a vehicle with no system calibration. These two cameras were placed capturing overlapping contents. We performed our bundle adjustment using the proposed constraint and then produced 3D dense point clouds. Evaluations were performed by comparing these dense point clouds against LiDAR reference data. We showed that, as compared to traditional bundle adjustment, our proposed method achieved an improvement of 29.38%.
IMPLICITY: CITY MODELING FROM SATELLITE IMAGES WITH DEEP IMPLICIT OCCUPANCY FIELDS
Authors: Stucker C. , Ke B. , Yue Y. , Huang S. , Armeni I. , Schindler K.
Published: May 2022

Abstract. High-resolution optical satellite sensors, combined with dense stereo algorithms, have made it possible to reconstruct 3D city models from space. However, these models are, in practice, rather noisy and tend to miss small geometric features that are clearly visible in the images. We argue that one reason for the limited quality may be a too early, heuristic reduction of the triangulated 3D point cloud to an explicit height field or surface mesh. To make full use of the point cloud and the underlying images, we introduce IMPLICITY, a neural representation of the 3D scene as an implicit, continuous occupancy field, driven by learned embeddings of the point cloud and a stereo pair of ortho-photos. We show that this representation enables the extraction of high-quality DSMs: with image resolution 0.5 m, IMPLICITY reaches a median height error of ≈0.7m and outperforms competing methods, especially w.r.t. building reconstruction, featuring intricate roof details, smooth surfaces, and straight, regular outlines.
SEMANTIC SEGMENTATION OF HISTORICAL PHOTOGRAPHS OF THE ANTARCTICA PENINSULA
Authors: Dahle F. , Tanke J. , Wouters B. , Lindenbergh R.
Published: May 2022

Abstract. A huge archive of historical images of the Antarctica taken by the US Navy between 1940 and 2000 is publicly available. These images have not yet been used for large-scale computer-driven analysis as they were captured with analog cameras. They were only later digitized and contain no semantic information. Most modern deep-learning based semantic segmentation algorithms are trained on modern images and fail on these scanned historical images, due to varying image quality, lack of color information, and most crucially, due to artifacts in both imaging as well as scanning (e.g. Newton’s rings). The analysis of such historic data can give a view on Antarctica’s glaciers predating modern satellite imagery and provide a unique insight into the long-term impact of changing climate conditions with essential validation data for climate modelling. An important first step for analysis of such data is the extraction and localization of semantic information, e.g. where in the image is water, rocks, or snow. In this work we present the first deep-learning based method to perform semantic segmentation on historical imagery archives of the Antarctic Peninsula. Our results show that our method can handle very challenging images even after being trained with only a low number of training data and catch the general semantic meaning of a scene. For eight test images we achieve an accuracy of 74%, where the majority of errors can be explained by the classification of ice as snow.
ROAD CONDITION ASSESSMENT FROM AERIAL IMAGERY USING DEEP LEARNING
Authors: Merkle N. , Henry C. , Azimi S. M. , Kurz F.
Published: May 2022

Abstract. Terrestrial sensors are commonly used to inspect and document the condition of roads at regular intervals and according to defined rules. For example in Germany, extensive data and information is obtained, which is stored in the Federal Road Information System and made available in particular for deriving necessary decisions. Transverse and longitudinal evenness, for example, are recorded by vehicles using laser techniques. To detect damage to the road surface, images are captured and recorded using area or line scan cameras. All these methods provide very accurate information about the condition of the road, but are time-consuming and costly. Aerial imagery (e.g. multi- or hyperspectral, SAR) provide an additional possibility for the acquisition of the specific parameters describing the condition of roads, yet a direct transfer from objects extractable from aerial imagery to the required objects or parameters, which determine the condition of the road is difficult and in some cases impossible. In this work, we investigate the transferability of objects commonly used for the terrestrial-based assessment of road surfaces to an aerial image-based assessment. In addition, we generated a suitable dataset and developed a deep learning based image segmentation method capable of extracting two relevant road condition parameters from high-resolution multispectral aerial imagery, namely cracks and working seams. The obtained results show that our models are able to extraction these thin features from aerial images, indicating the possibility of using more automated approaches for road surface condition assessment in the future.
MULTI-DATE WIND TURBINE DETECTION ON OPTICAL SATELLITE IMAGES
Authors: Mandroux N. , Drouyer S. , Grompone von Gioi R.
Published: May 2022

Abstract. Satellite imaging is shifting from the photo-interpreter era to one of automatic monitoring. Indeed, the vast amount of data provided by the recent constellations of satellites, performing recurrent observation of every point on the globe, can only be handled by automatic methods; controlling false detections is thus crucial. The low costs of those satellites often imply lower resolution; the fusion of multi-date images can compensate to some extent the low resolution. Given their future role in the energetic transition and their spread over countries or continents, monitoring wind turbines is a natural candidate for such studies. This work details an algorithm for automatic, multi-date wind turbine detection on low resolution optical satellite images. The method is based on the a contrario statistical approach to provide a control of false detections and exploits the geometry of wind turbines’ shadows and hubs.
AUTOMATIC EXTRACTION OF SOLAR AND SENSOR IMAGING GEOMETRY FROM UAV-BORNE PUSH-BROOM HYPERSPECTRAL CAMERA
Authors: Bhadra S. , Sagan V. , Nguyen C. , Braud M. , Eveland A. L. , Mockler T. C.
Published: May 2022

Abstract. Calculating solar-sensor zenith and azimuth angles for hyperspectral images collected by UAVs are important in terms of conducting bi-directional reflectance function (BRDF) correction or radiative transfer modeling-based applications in remote sensing. These applications are even more necessary to perform high-throughput phenotyping and precision agriculture tasks. This study demonstrates an automated Python framework that can calculate the solar-sensor zenith and azimuth angles for a push-broom hyperspectral camera equipped in a UAV. First, the hyperspectral images were radiometrically and geometrically corrected. Second, the high-precision Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data for the flight path was extracted and corresponding UAV points for each pixel were identified. Finally, the angles were calculated using spherical trigonometry and linear algebra. The results show that the solar zenith angle (SZA) and solar azimuth angle (SAA) calculated by our method provided higher precision angular values compared to other available tools. The viewing zenith angle (VZA) was lower near the flight path and higher near the edge of the images. The viewing azimuth angle (VAA) pattern showed higher values to the left and lower values to the right side of the flight line. The methods described in this study is easily reproducible to other study areas and applications.
UPDATING STRATEGIES FOR DISTANCE BASED CLASSIFICATION MODEL WITH RECURSIVE LEAST SQUARES
Authors: Raita-Hakola A.-M. , Pölönen I.
Published: May 2022

Abstract. The idea is to create a self-learning Minimal Learning Machine (MLM) model that is computationally efficient, easy to implement and performs with high accuracy. The study has two hypotheses. Experiment A examines the possibilities of introducing new classes with Recursive Least Squares (RLS) updates for the pre-trained self learning-MLM model. The idea of experiment B is to simulate the push broom spectral imagers working principles, update and test the model based on a stream of pixel spectrum lines on a continuous scanning process. Experiment B aims to train the model with a significantly small amount of labelled reference points and update it continuously with (RLS) to reach maximum classification accuracy quickly.The results show that the new self-learning MLM method can classify new classes with RLS update but with a cost of decreasing accuracy. With a larger amount of reference points, one class can be introduced with reasonable accuracy. The results of experiment B indicate that self-learning MLM can be trained with a few reference points, and the self-learning model quickly reaches accuracy results comparable with nearest-neighbour NN-MLM. It seems that the self-learning MLM could be a comparable machine learning method for the application of hyperspectral imaging and remote sensing.
IMAGE-BASED BRDF MEASUREMENT
Authors: Weil-Zattelman S. , Kizel F.
Published: May 2022

Abstract. One of the most challenging effects of remote sensing is landcover materials' Bidirectional Reflectance Distribution Function (BRDF). A wide range of approaches and measuring methods address the BRDF in various studies. However, there is a requirement for an accurate measurement setup and costly special equipment. Furthermore, the measurements and calculations are applied to model the BRDF for a single point on the object's surface. Considering these limitations, we propose a new modular framework and methodology for measuring, modeling, and analyzing the BRDF without the need for unique instruments. Instead, we suggest acquiring multiple overlapping images in a simple and time-saving way, sampling the desired object's Region Of Interest (ROI) in one image and automatically tracking it in the other images. Experimental results using laboratory data acquired under controlled conditions clearly show the advantages of our framework in retrieving the camera positions, tracking ROIs in the different images, and accurately measuring the BRDF of various land-cover types. Moreover, we observed the variability of the obtained measurements before and after applying the kernel-driven approach to minimize the BRDF effect. The results show that the applied correction reduces this variability significantly, indicating the high accuracy of measuring the directional reflectance using the proposed approach.
A CNN-BASED CHANGE DETECTION METHOD FOR SQUATTER STRUCTURE RECOGNITION FROM AERIAL IMAGES AND DSM
Authors: Zhang M. , Li W. , Shi W.
Published: May 2022

Abstract. Squatter structures have been a serious threat to human safety and health for a long time. And monitoring their changes is important to facilitate government management of squatters. However, existing methods are still not automatic, accurate and fast enough to meet the actual needs of practical applications. In this paper, we propose a novel deep learning-based method to detect squatter structure changes from bi-temporal remotely sensed (RS) images and digital surface models (DSMs). The proposed convolutional neural network (CNN) takes the advantages of the spectral information from high resolution image and the height information from the DSM, so as to detect changes more accurately in type and height of squatter structures. Moreover, we create a data set for deep learning model training, covering a variety of squatter structures in Hong Kong. Compared with three existing representative methods, Our model performs the best, with Kappa of 0.6786 and 0.6458 in the detection results of the two test regions, respectively, which indicates that it has application potential.
STATE-WIDE WETLAND INVENTORY MAP OF MINNESOTA USING MULTI-SOURCE AND MULTI-TEMPORALREMOTE SENSING DATA
Authors: Igwe V. , Salehi B. , Mahdianpari M.
Published: May 2022

Abstract. Carbon sequestration coupled with flood mitigation and other functions of wetlands, such as water filtration, coastal protection, biodiversity, and providing recreational spots, make wetland mapping and monitoring important for different countries. Google Earth Engine (GEE) cloud computing platform is becoming a very important tool for lots of environmental studies as it provides a suite of tools and access to data that facilitate large-scale environmental monitoring projects through its powerful parallel processing capabilities. In this study, we use GEE to access multi-source remote sensing datasets and implement an object-based image analysis, and random forest algorithm for the classification of wetlands in the state of Minnesota. Emergent, forested, and scrub-shrub wetland classes, water, as well as urban, forest, and agriculture land cover types were classified using Sentinel-2, Sentinel-1, USGS 3D Elevation Program 10-meter DEM, and gridded soil data. NDVI, EVI, BSI, NDBI, and NDWI spectral indices were calculated from Sentinel-2 imagery, VV and VH polarization channels, and their ratio, as well as span parameters, were calculated from Sentinel-1 imagery, and slope and aspect features were extracted from DEM. Simple Non-Iterative Clustering (SNIC), Gray-Level Co-occurrence Matrix (GLCM), Principal Components Analysis (PCA), and random forest algorithms were implemented to classify wetlands from the GEE platform. Emergent wetlands, water, urban, and agriculture classes performed well with producer accuracies greater than 90%. Sentinel-1, DEM, and soil datasets improve the identification of wetland classes and highlight the importance of multi-source approaches for wetland mapping.
ANALYZING SPATIO-TEMPORAL PATTERN OF THE FOREST FIRE BURNT AREA IN UTTARAKHAND USING SENTINEL-2 DATA
Authors: Mamgain S. , Karnatak H. , Roy A. , Chauhan P.
Published: May 2022

Abstract. Forest fire burnt area estimation using Normalized Burn Ratio at regional level helps in understanding the pattern of the frequency and severity of forest fires. In this study, burnt area is estimated for all the thirteen districts of Indian state Uttarakhand for last six years from 2016 to 2021 using Sentinel 2A and 2B datasets. The spatial and temporal pattern of the burnt area was analyzed by incorporating different parameters such as meteorological parameters like land surface temperature, rainfall; edaphic parameter like surface soil moisture and vegetation parameters like Normalized Difference Vegetation Index & Enhanced Vegetation Index. The estimated burnt area was statistically analyzed with respect to the parameters stated and the relationship among them was quantified. It was found that burnt area is positively correlated with the land surface temperature, while it showed negative correlation with the pre-fire precipitation, pre-fire NDVI & EVI and the surface soil moisture for 11 out of 13 districts. The district-wise forest fire burnt area assessment and analysis of its spatio-temporal pattern can be used in the preparedness and mitigation planning to prevent drastic ecological impacts of forest fires on the landscape.
ON THE QUALITY INSPECTION SOFTWARE SYSTEM OF SURVEYING AND MAPPING GEOGRAPHIC INFORMATION ACHIEVEMENTS UNDER MULTI-DIMENSION SYNERGY
Authors: Chen H. , Wang X. , Li H. , Zha Z. , Shen J. , Zhang L.
Published: May 2022

Abstract. Quality inspection software is an important means of quality inspection and an important factor affecting quality inspection work. Facing the current situation and problems of quality inspection software for surveying and mapping geographic information achievements, and combining existing means and new technology, this paper puts forward a software system for quality inspection of surveying and mapping geographic information achievements under "Multi-Dimensional Synergy", which is composed of achievement dimension, project dimension, big data dimension, intelligence dimension and history dimension, expounds the work contents of surveying and mapping geographic information results quality inspection in various dimensions, introduces the relationship of business process and data flow between the multi dimensions. It provides the idea for the future research content and development direction of Surveying and mapping geographic information results quality inspection software.
AN EXPLORATORY STUDY TO EXAMINE ABUNDANCE OF PM2.5 AND ASSOCIATED DISEASE BURDEN IN BANGLADESH
Authors: Dewan A. , Shahid S. , Asaduzzaman M. , Faruque F.
Published: May 2022

Abstract. This study examined selected disease burdens in Bangladesh associated with particulate matter exposure using gridded population and PM2.5 data between 2001 and 2019. The Global Exposure Mortality Model (GEMM) was used to determine hazard ratio (HR) and disease specific mortality. Besides, trend of PM2.5 and selected diseases were evaluated. Results revealed that strong seasonality existed in PM2.5 with winter exhibited maximum concentration. The trend assessment showed PM2.5 was increasing over time. Among five diseases assessed, LRI was most sensitive to an increase of PM2.5, followed by IHD, LC, CEV and COPD. Excess mortality was found to be elevating because of PM2.5, particularly in major cities. This study could be useful in advancing research in the disease burden attributable to ambient air pollution in Bangladesh.
FINE-TUNING CNNS FOR DECREASED SENSITIVITY TO NON-VOLCANIC DEFORMATION VELOCITY SIGNALS
Authors: Beker T. , Ansari H. , Montazeri S. , Song Q. , Zhu X. X.
Published: May 2022

Abstract. Monitoring volcanic deformations allows us to track dynamic states of a volcano and to know where an eruptions could happen. Spaceborne Synthetic Aperture Radar (SAR) and SAR interferometry (InSAR) techniques created an opportunity to track volcanoes globally, even in inaccessible regions without ground measuring stations.This paper proposes a convolutional neural network (CNN) for detection of volcanic deformations in InSAR velocity maps. We had only a small amount of velocity maps over the region of central South American Andes, therefore the synthetic data are used to train the model from scratch. In the region of interest, the velocity maps contain the patterns of salt lakes and slope induced signal which confuse CNN models trained on synthetic data.In order to bridge the gap between the synthetic and real data, the hybrid synthetic-real data set is used for fine-tuning the model. The hybrid set consists of the real background signal data and synthetic volcanic data. Four fine-tuning sets which were created by different combinations of the original hybrid data, the filtered hybrid data, and simulated data have been used and compared with each other. Besides, we compared four fine-tuning approaches to determine where and how to fine-tune the model. Results show significant improvement in performance by majority of the approaches, and training the last or last two layers have given the best results. In addition, using the FT1 (containing only hybrid set), and FT4 (containing all sets) improved the area under the curve receiver operating characteristic (AUC ROC) from 55% to 86% and 88% respectively.
INVESTIGATION ON VISITING PATTERN CHANGE IN COMMERCIAL AREAS DURING COVID-19: A CASE STUDY OF 21 CITIES IN JAPAN
Authors: Eom S. , Nishihori Y.
Published: May 2022

Abstract. During the COVID-19 pandemic, the patterns of visiting commercial areas have changed due to numerous factors, including the risk of infection and the government’s state of emergency. This study investigated human mobility changes in commercial areas of 21 cities in Japan by applying time-series clustering of mobile big data during the COVID-19 pandemic. First, the analysis revealed that the human mobility changes were found to be area-specific and were classified into five patterns according to population change captured by mobile data: decreased cluster, slightly decreased cluster, no change cluster, slightly increased cluster, and increased cluster. There were some commercial areas, which were visited by more people, compared with the pre-COVID-19 period. Second, the increased clusters revealed a high proportion of commercial facilities that provide essential services. This finding suggests that the local-scale commercial areas were essential for supporting everyday life during the COVID-19 pandemic. Third, human mobility in commercial areas was temporarily altered, but ultimately returned to the pre-COVID-19 level. Overall, the proposed method and results provide basic information for resilient urban structures in Japan.
Landslides caught on seismic networks and satellite radars
Authors: Manconi Andrea , Mondini Alessandro C. ,   the AlpArray working group
Published: May 2022

Abstract. We present a procedure to detect landslide events by analyzing in-sequence data acquired from regional broadband seismic networks and spaceborne radar imagery. The combined used of these techniques is meant to exploit their complementary elements and mitigate their limitations when used singularly. To test the method, we consider a series of six slope failures associated to the Piz Cengalo rock avalanche that recently occurred in the Swiss Alps, a region where we can benefit from high spatial density and quality of seismic data, as well as from the high spatial and temporal resolution of the European Space Agency (ESA) Copernicus Sentinel-1 radar satellites. The operational implementation of the proposed approach, in combination with the future increase in availability of seismic and satellite data, can offer a new and efficient solution to build and/or expand landslide catalogues based on quantitative measurements and, thus, help in hazard assessments and the definition of early warning systems at regional scales.
The Impact of Take-home Open-book Examinations due to COVID-19 among Business Students. Do Gender, Age, and Academic Skills Matter?
Authors: Opstad Leiv , Pettersen Ivar
Published: May 2022

Due to COVID-19, numerous universities and colleges have been forced to arrange home-based exams in many countries. We know relatively little about what consequences this might have for the ranking of students based on qualifications in the various subjects. This is an important issue for administrators, educators, and others involved in planning the design of higher education. The intention of this article is to get more insight into this issue. By analysing administrative data from a Norwegian Business School, we examined the impact of moving from traditional school exams to home-based exams in 2020 due to COVID-19. The chosen methodology is the comparison of means by using t-test and standard linear regression models. The results indicate a weaker link between high school performance and achievements in business administration courses. Furthermore, home-based exams might disadvantage older students. This is useful knowledge in the judgement as to whether or not to introduce home-based exams as a permanent arrangement.
Analisis Implementasi Promosi Kesehatan tentang Covid-19 pada Rumah Sakit di Daerah Jakarta
Authors: Harnum Rizky Maulidiyah , Nurrizka Rahmah Hida , Agustina Agustina , Permatasari Putri
Published: May 2022

Abstrak

Latar Belakang: Pada masa pandemi Covid-19, kegiatan promosi kesehatan sangat wajib dilakukan oleh setiap rumah sakit dalam upaya mencegah Covid-19. Akan tetapi, dalam pelaksanaannya ditemukan banyak kendala. Tujuan penelitian menganalisis implementasi promosi kesehatan serta faktor-faktor dalam melaksanakannya di salah satu rumah sakit di daerah Jakarta.

Metode: Penelitian ini merupakan penelitian metode kualitatif dengan jumlah informan sebanyak 10 orang.  Penelitian ini menggunakan pertanyaan untuk wawancara mendalam, pedoman observasi, dan juga pedoman telaah dokumen terhadap variabel-variabel yang akan diteliti sebagai alat untuk membantu penelitian. Triangulasi sumber, metode, dan teori merupakan teknik yang digunakan dalam menganalisis penelitian ini.

Hasil: Hasil yang didapat dalam penelitian adalah kegiatan promosi kesehatan di RS tersebut belum terlaksana dengan baik. Berikut beberapa hal yang menghambat pelaksanaan promosi kesehatan yaitu kepentingan kelompok sasaran belum optimal dan SDM yang kurang.

Kesimpulan: Diharapkan rumah sakit memaksimalkan factor-faktor yang masih menghambat pelaksanaan kegiatan promosi kesehatan.

Kata Kunci: Implementasi, Kebijakan, Promosi Kesehatan, Rumah Sakit

Abstract

Background: During the Covid-19 pandemi, health promotions are very mandatory for every hospital in an effort to prevent Covid-19. However, in its implementation, many obstacles were found. The purpose of the study was to analyze the implementation of health promotion and the factors in implementing it.

Methods: This research is a qualitative research method with 10 informants. This study uses questions for in-depth interviews, observation guidelines, and also document review guidelines for the variables to be studied as tools. Triangulation of sources, methods, and theories is a technique used in analyzing this research.

Result: The results obtained in the study are health promotion in the hospital have not been carried out properly. The following are some of the things that hinder the implementation of health promotion, namely the interests of the target group are not optimal and human resources are lacking.

Conclusion: It is hoped that the hospital will maximize the factors that still hinder the implementation of health promotion activities.

Keywords: Health Promotion, Hospital, Implementation, Policy
Hubungan Faktor Karakteristik Ibu terhadap Pemberian ASI Eksklusif pada Bayi Usia 6-12 Bulan di Puskesmas Tanah Tinggi Tangerang
Authors: Puspita Mareta , Handayani Rini , Azteria Veza , Vionalita Gisely
Published: May 2022

Abstrak

Latar Belakang: Pemberian ASI yang mengandung kolostrum tanpa tambahan makanan apapun dan diberikan kepada bayi dimulai ketika lahir hingga berusia 6 bulan. Prevalensi pemberian ASI eksklusif nasional tahun 2019 sebesar 67,74%. Studi ini ditujukan untuk mengetahui faktor karakteristik ibu yang berhubungan dengan ASI eksklusif pada bayi usia 6-12 bulan di Puskesmas Tanah Tinggi Tangerang.

Metode: Studi cross-sectional dilakukan pada bulan Januari-Agustus 2021 dengan sampel 128 bayi usia 6-12 bulan. Variabel dependen penelitian adalah pemberian ASI eksklusif sedangkan variabel independen adalah usia, pekerjaan, pendidikan dan paritas. Analisis yang dilakukan secara analisis univariat dan bivariat dengan uji Chi-square.

Hasil: Ditemukan proporsi tertinggi pada kelompok yang memberikan ASI eksklusif (50,8%), ibu usia 20 tahun sampai 35 tahun (70,3%), ibu yang bekerja (68%), ibu pendidikan lanjut (75%) dan ibu primipara (57%). Hasil uji Chi-square ditemukan adanya hubungan antara ASI eksklusif dengan pekerjaan (P-value=0,031, PR=1,649, 95%CI 1,037-2,622). Namun, usia (P-value=0,063, PR=1,457, 95%CI 1,039-2,045), pendidikan (P-value=0,759, PR=1,109, 95%CI 0,753-1,632) dan paritas (P-value=0,575, PR=1,145, 95%CI 0,796-1,648) ditemukan tidak signifikan terhadap pemberian ASI ekslusif.

Kesimpulan: Hanya variabel pekerjaan yang berhubungan terhadap pemberian ASI eksklusif.

Kata Kunci: ASI eksklusif, Paritas, Pekerjaan, Pendidikan, Usia

 Abstract

Background: Breastfeeding containing colostrum without any additional food and given to babies starting at birth until the age of 6 months. The national prevalence of exclusive breastfeeding in 2019 was 67.74%. This study was aimed to determine the characteristics of mothers related to exclusive breastfeeding in infants aged 6-12 months at the Tanah Tinggi Tangerang Health Center.

Methods: The cross-sectional study was conducted in January-August 2021 with a sample of 128 infants aged 6-12 months. The dependent variable of the study was exclusive breastfeeding while the independent variables were age, occupation, education and parity. The analysis was carried out by univariate and bivariate analysis with Chi-square test.

Results: The highest proportion was found in the group who gave exclusive breastfeeding (50.8%), mothers aged 20 to 35 years (70.3%), working mothers (68%), advanced education mothers (75%) and primiparous mothers (57). %). The results of the Chi-square test found a relationship between exclusive breastfeeding and work (P-value=0.031, PR=1.649, 95%CI 1.037-2.622). However, age (P-value=0.063, PR=1.457, 95%CI 1.039-2.045), education (P-value=0.759, PR=1.109, 95%CI 0.753-1.632) and parity (P-value=0.575, PR=1.145, 95%CI 0.796-1.648) was found not significant to exclusive breastfeeding.

Conclusion: Only occupation variables related to exclusive breastfeeding.

Keywords: Age, Education, Exclusive breastfeeding, Parity, Profession
CULTURAL ASPECTS OF CONTEMPORARY POLITICAL TOLERANCE IN UNITED STATES, FRANCE AND ARMENIA
Authors: DAVTYAN ELYA
Published: May 2022

The purpose of the article is to show that the culture of a society is expressed in language in general and in political communication in particular. Therefore, it is possible to study the political discourse and identify the cultural symbols, archetypes and values that dominate in a given society. Using the examples of American, French and Armenian political discourse, we intend to show that in all three cases we are dealing with different cultural meanings and values. During the study, a comparative method and a content analysis method were used. The main conclusions are consistent with the assumptions. In American culture, political discourse expresses such a cultural phenomenon as the American dream, while in French political discourse the concepts of country, history and citizenship are central, and in traditional Armenian society, political discourse is focused on traditional values - homeland, family, morality.