Miracle-dz/UniFAR-FTUs huggingface dataset:
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data

ABSTRACT In recent years, the data science and remote sensing communities have started to align due to user-friendly programming tools, access to high-end consumer computing power, and the availability of free satellite data. In particular, publicly available data from the European Space Agency’s Sentinel missions have been used in various remote sensing applications. However, there is a lack of studies that utilize these data to assess the performance of machine learning algorithms in complex boreal landscapes. In this article, I compare the classification performance of four non-parametric algorithms: support vector machines (SVM), random forests (RF), extreme gradient boosting (Xgboost), and deep learning (DL). The study area chosen is a complex mixed-use landscape in south-central Sweden with eight land-cover and land-use (LCLU) classes. The satellite imagery used for the classification were multi-temporal scenes from Sentinel-2 covering spring, summer, autumn and winter conditions. Using stratified random sampling, each LCLU class was allocated 1477 samples, which were divided into training (70%) and evaluation (30%) subsets. Accuracy was assessed through metrics derived from an error matrix, but primarily overall accuracy was used in allocating algorithm hierarchy. A two-proportion Z-test was used to compare the proportions of correctly classified pixels of the algorithms and a McNemar’s chi-square test was used to compare class-wise predictions. The results show that the highest overall accuracy was produced by support vector machines (0.758 ± 0.017), closely followed by extreme gradient boosting (0.751 ± 0.017), random forests (0.739 ± 0.018), and finally deep learning (0.733 ± 0.0023). The Z-test comparison of classifiers showed that a third of algorithm pairings were statistically different. On a class-wise basis, McNemar’s test results showed that 62% of class-wise predictions were significant from one another at the 5% level or less. Variable importance metrics show that nearly half of the top twenty Sentinel-2 bands belonged to the red edge (25%) and shortwave infrared (23%) portions of the electromagnetic spectrum, and were dominated by scenes from spring (38%) and summer (40%). The results are discussed within the scope of recent studies involving machine learning and Sentinel-2 data and key knowledge gaps identified. The article concludes with recommendations for future research.
New parameterization of air-sea exchange coefficients and its impact on intensity prediction under major tropical cyclones

Understanding and quantifying air-sea exchanges of enthalpy and momentum fluxes are crucial for the advanced prediction of tropical cyclone (TC) intensity. Here, we present a new parameterization of air-sea fluxes at extreme wind speeds from 40 m s−1 to 75 m s−1, which covers the range of major TCs. Our approach assumes that the TC can reach its maximum potential intensity (MPI) if there are no influences of external forces such as vertical wind shear or other environmental constraints.This method can estimate the ratio of the enthalpy and momentum exchange coefficient (C k/C d) under the most intense TCs without direct flux measurements. The estimation showed that C k/C d increases with wind speed at extreme winds above 40 m s−1. Two types of surface layer schemes of the Hurricane Weather and Research Forecast (HWRF) were designed based on the wind speed dependency of the C k/C d found at high winds: (i) an increase of C k/C d based on decreasing C d (Cd_DC) and (ii) an increase of C k/C d based on increasing C k (Ck_IC). The modified surface layer schemes were compared to the original HWRF scheme (using nearly fixed C d and C k at extreme winds; CTRL) through idealized experiments and real-case predictions. The idealized experiments showed that Cd_DC reduced frictional dissipation in the air-sea interface as well as significantly reduced sea surface cooling, making the TC stronger than other schemes. As a result, Cd_DC reduced the mean absolute error and negative bias by 15.0% (21.0%) and 19.1% (32.0%), respectively, for all lead times of Hurricane Irma in 2017 (Typhoon Mangkhut in 2018) compared to CTRL. This result suggests that new parameterization of C k/C d with decreasing C d at high winds can help improve TC intensity prediction, which currently suffers from underestimating the intensity of the strongest TCs.
Density estimation for waste cooking oil biodiesel

This paper focuses on the biodiesel density predictive models and examines the accuracy of the HCC methodology which bases on Rackett equation modified by Spencer and Danner to estimate the density of waste cooking oil (WCO) biodiesel in a wide range of temperature based on its fatty acid methyl ester components. The calculated results are compared with experimental measurements for the density aiming to validate the chosen method. The analysis results obtained show that this method does not, in all cases, yield high accuracy even though its use is simple. Particularly, in case of the biodiesel derived from waste cooking oil (WCO), this method is not accurate enough to predict its density with the deviations between experimental data and calculated density getting relatively high, around 3.4%
From BIM to Scan Planning and Optimization for Construction Control

Scan planning of buildings under construction is a key issue for an efficient assessment of work progress. This work presents an automatic method aimed to determinate the optimal scan positions and the optimal route based on the use of Building Information Models (BIM) and considering data completeness as stopping criteria. The method is considered for a Terrestrial Laser Scanner mounted on a mobile robot following a stop & go procedure. The method starts by extracting floor plans from the BIM model according to the planned construction status, and including geometry and semantics of the building elements considered for construction control. The navigable space is defined from a binary map considering a security distance to building elements. After a grid-based and a triangulation-based distribution are implemented for generating scan position candidates, a visibility analysis is carried out to determine the optimal number and position of scans. The optimal route to visit all scan positions is addressed by using a probabilistic ant colony optimization algorithm. The method has been tested in simulated and real buildings under very dissimilar conditions and structural construction elements. The two approaches for generating scan position candidates are evaluated and results show the triangulation-based distribution as the more efficient approach in terms of processing and acquisition time, especially for large-scale buildings.
Remote Sensing for the Quantification of Land Surface Dynamics in Large River Delta Regions - A Review

River deltas and estuaries belong to the most significant coastal landforms on our planet and are usually very densely populated. Nearly 600 million people live in river deltas, benefiting from the large variety of locational advantages and rich resources. Deltas are highly dynamic and vulnerable environments that are exposed to a wide range of natural and manmade threats. Sustainable management of river deltas therefore requires a holistic assessment of historic and recent ongoing changes and the dynamics in settlement sprawl, land cover and land use change, ecosystem development, as well as river and coastline geomorphology, all of which is difficult to achieve solely with traditional land-based surveying techniques. This review paper presents the potential of Earth Observation for analyses and quantification of land surface dynamics in the large river deltas globally, emphasizing the different geo-information products that can be derived from medium resolution, high resolution and highest resolution optical, multispectral, thermal and SAR data. Over 200 journal papers on remote sensing related studies for large river deltas and estuaries have been analyzed and categorized into thematic fields such as river course morphology, coastline changes, erosion and accretion processes, flood and inundation dynamics, regional land cover and land use dynamics, as well as the monitoring of compliance with respect to anthropogenic activity such as industry expansion-related habitat destruction. Additionally, our own exemplary analyses are interwoven into the review to visualize related delta work.
Observation of Rapid Seabed Erosion Near Closure Depth During a Storm Period at Hujeong Beach, South Korea

We report extraordinary seabed erosion that occurred at a depth of ~8.3 m at Hujeong Beach, South Korea. The seabed was eroded for 0.7 m during a ~2.3‐day period under storm conditions. The maximum significant wave height was ~3.8 m, which was unexpected considering the traditional closure depth concept. Four storm events were recorded within 36 day of the experimental period, but the severe erosion only occurred during the first storm. The erosion started when the nearbed flow velocity rapidly increased to reach its maximum at 0.27 m/s, and it continued until this velocity remained at its maximum. Additional factors responsible for the unanticipated erosion process were the current and wave skewness. The erosion was facilitated when the current direction was actively changing and the magnitude of positive skewness increased during the storm period. These factors are likely to have intensified the nearbed flow turbulence and bed shear stress.
Seasonal Predictability of the Winter North Atlantic Oscillation From a Jet Stream Perspective

The winter North Atlantic Oscillation (NAO) has varied on interannual and decadal timescales over the last century, associated with variations in the speed and latitude of the eddy‐driven jet stream. This paper uses hindcasts from two operational seasonal forecast systems (the European Centre for Medium‐range Weather Forecasts's seasonal forecast system, and the U.K. Met Office global seasonal forecast system) and a century‐long atmosphere‐only experiment (using the European Centre for Medium‐range Weather Forecasts's Integrated Forecasting System model) to relate seasonal prediction skill in the NAO to these aspects of jet variability. This shows that the NAO skill realized so far arises from interannual variations in the jet, largely associated with its latitude rather than speed. There likely remains further potential for predictability on longer, decadal timescales. In the small sample of models analyzed here, improved representation of the structure of jet variability does not translate to enhanced seasonal forecast skill.
EFFECT OF MAGNETIZED IRRIGATION WATER LEVELS AND COMPOST ON VEGETATIVE GROWTH, LEAF MINERAL CONTENT AND WATER USE EFFICINECY OF WASHINGTON NAVEL ORANGE TREES

This investigation was carried out during two successive seasons of 2014 and 2015 on 6 years old, Washington Navel orange trees (Citrus sinensis L.) budded on sour orange rootstock. Tree spaced at 5 x 5 meter and grown on sandy soil and irrigated with saline water (EC was 4 ds/m) under drip irrigation system in private orchard located at El-Behera Governorate, Egypt. The objection to study the impact of different levels of magnetized irrigation water and compost on growth, leaf mineral content and water use efficiency (WUE) of Washington Navel orange tree growing on sandy soil under drip irrigation saline water. Three levels of magnetized irrigation water 3658.5, 2743.8 and 1829 m3/fed. with and without compost at 4kg/tree along with control (non-magnetic water). Results indicated that, the highest values of vegetative growth parameters i.e., shoot length, shoot thickness, number of leaves/shoot, leaf area/leaf, as well as chemical constituents of leaves (N, P, K, Ca, Mg) obtained by trees irrigated with magnetized water levels combined with or without compost. Leaf Na content were increased under control treatment(nonmagnetic water). The best results of both vegetative growth parameters and chemical constituents of leaves were recorded when trees irrigated with magnetized water at level 1(3658.5m3/fed/season) + 4kg compost followed by level 2(2743.8 m3/fed/season) + 4kg compost, while the highest yield efficiency (YE) and WUE obtained from irrigation treatment at level3(1829 m3/fed/season) + 4kg compost in both seasons . Therefore, magnetizing water at level 1+ 4kg compost or level 2+ 4kg compost treatment could be recommended for improving citrus trees performance under saline water conditions.
Relationship between psychological capital and depression in Chinese physicians: The mediating role of organizational commitment and coping style

Background It is well known that the medical profession is a high-risk practice, with intense work and complex situations. The physicians are prone to suffer from depression due to occupational stress under high workloads for long periods of time. Depression not only impairs physicians’ mental health, but also affects the quality of health services, so it is important to explore the factors and mechanisms affecting depression among physicians. Methods In this cross-sectional study, 1,139 physicians from Heilongjiang Province were surveyed by cluster sampling procedures. The questionnaires included Psychological Capital Questionnaire, Chinese Employee Organizational Commitment Questionnaire, Trait Coping Style Questionnaire and Self-rating Depression Scale. Pearson correlation analysis and Bootstrap were used for statistical analysis. Results This study found that 41.6% of physicians suffering from depression. Among them, 17.0% of physicians showed moderate depression, and 2.6% of physicians exhibited severe depression. The serial-multiple mediation of organizational commitment and positive coping styles in the relationship between psychological capital and depression was significant. Conclusion The results showed that the psychological capital was sequentially associated with increased organizational commitment, and then increased positive coping styles, which resulted in reduced depression among physicians.
Application of savonius turbine behind the propeller as energy source of fishing vessel in indonesia

The energy wasted by the propeller of vessel can be gained as benefit by converting it using turbine as the source of electrical energy. The electrical energy can be used as the main energy source of fisherman vessel. The method in this research is redesigning the fisherman vessel combined with savonius turbine to obtain the correct dimension. Vessel dimension is built within Loa = 30.71 m; B = 7.67 m; H = 3.5 m; T = 2.2 m. From those dimensions can be obtained the coefficient between the propeller into the pitch 0.8 m, with the turbine diameter 0.5 m and the position of the turbine 0.1 m from the propeller. Savonius turbine captures electrical energy that produced by the low fluid flow that comes out from the propeller rotation, and will be converted by the generator inside the turbine to become energy. The power produced by the rotation of 1.31 rpm of the turbine is 25.689 kilowatt. It is enough to supply the engine generator with the power of 20.1 kilowatt from the power that produced by the turbine as big as 25.689 kilowatt to lighting the fisherman vessel. This technology can be applied massively to the fishermen vessel as renewable energy.
The life cycle of small- to medium-sized icebergs in the Amundsen Sea Embayment

An object-based method for automatic iceberg detection has been applied to Advanced Synthetic Aperture Radar images in the Amundsen Sea Embayment (ASE), Antarctica. The images were acquired between 1 January 2006 and 8 April 2012 under varying meteorological, oceanographic and sea-ice conditions. During this time period, the icebergs were counted (average 1370 ± 50) and their surface area was estimated (average 1537.5 km2). The average surface area was about 2.5 times larger than the annual calved area (620 km2), indicating that the average iceberg age in the ASE is about 2.5 years, which was confirmed by observed residence times based on drift tracks. Most of the ASE icebergs were less than 1500 m long, and almost 90% of them were smaller than 2 km2. The proportion of small- and medium-sized icebergs (84.4%) was significantly higher than in the open ocean, where large icebergs (>10 km2) account for nearly the whole iceberg surface area. The opposite was true for the freshly calved icebergs in the ASE. The data indicate that the creation of icebergs in the ASE is dominated by steady small- to medium-scale calving from ice shelves fringing the embayment. In addition, rare calving events of giant icebergs occur on a decadal timescale. There is also some import of icebergs from the Bellingshausen Sea further east along the coast, in particular after large calving events there.
Integration of super-pixel segmentation and deep-learning methods for evaluating earthquake-damaged buildings using single-phase remote sensing imagery

ABSTRACT Rapid identification of post-earthquake collapsed buildings can be used to conduct immediate damage assessments (scope and extent), which could potentially be conducive to the formulation of emergency response strategies. Up to the present, the assessments of earthquake damage are mainly achieved through artificial field investigations, which are time-consuming and cannot meet the urgent requirements of quick-response emergency relief allocation. In this research study, an intelligent assessment method based on deep-learning, super-pixel segmentation, and mathematical morphology was proposed to evaluate the damage degrees of earthquake-damaged buildings. This method firstly utilized the Deeplab v2 neural network to obtain the initial damaged building areas. Then, the simple linear iterative cluster (SLIC) method was employed to segment the test images so as to accurately extract the area boundaries of the earthquake-damaged buildings. Next, the images subdivided by SLIC can be merged according to the initial damaged building areas identified by Deeplab v2 neural network. Finally, a mathematical morphological method was introduced to eliminate the background noise. Experimental results demonstrated that the proposed algorithm was superior to others in both convergent speed and accuracy. Besides, its parameter selection was flexible and easily realized which was of great significance to earthquake damage assessments and provided valuable guidance for the formulation of future emergency response plans after earthquake events.
Comparing daily temperature averaging methods: the role of synoptic climatology in determining spatial and seasonal variability

ABSTRACT The two primary methods for averaging daily near-surface air temperature are twice-daily averaging (i.e., the sum of the maximum and minimum divided by two) and hourly averaging (i.e., using the 24 hourly values of a day). To quantify the influence of synoptic (air mass) climatology on the numerical difference between the two types of daily temperature averaging, a synoptic climatology for 215 weather stations across the Contiguous United States (CONUS) was developed for the period 1981–2010 and then correlated with those difference values. A significant correlation is found between air mass frequency and the temperature difference, especially during the non-transition seasons (i.e., summer and winter). This pattern is due to the influence of air mass type on the shape of the daily temperature curve. The correlation results also show spatial variability, as air masses that are more frequent in the northern CONUS (e.g., Dry Polar) influence the diurnal temperature curve differently than air masses more common in the southern CONUS (e.g., Moist Tropical). Future work can capitalize on these results by investigating different geographic regions and utilizing this correlation-based approach to analyze how anticipated future changes in the synoptic climatology of air masses influence the diurnal temperature curve.
AN OPEN DROUGHT MONITORING SYSTEM FOR THE DEDURU OYA BASIN IN SRI LANKA IN THE CONTEXT OF THE 4ONSE PROJECT

Abstract. During the last decade, many climatic parameters are more and more deviating from the mean values calculated over historical climatic time-series. The World Meteorological Organization (WMO) stated that years 2015, 2016 and 2017 were the warmest since 1850. According to the preliminary data analysis of the last year, this trend is going to continue in 2018 as well. These climatic changes have accelerated the occurrence of droughts in many parts of the world including Europe and Asia. The North Western region is one such region in Sri Lanka which always affected by droughts due to its inherent dry weather condition. Although drought is considered as a normal part of the climate, its increasing negative impacts on human activities and the environment urges the application of novel technologies in drought monitoring. The 4onse project (analysis of Open, Non-conventional, Sustainable and Effective monitoring systems), funded by the Swiss National Science Foundation (SNSF), is a project began in this context to address the aforementioned mentioned issues by deploying 27 open technologies based Environmental Monitoring Systems (EMS) in Deduru Oya basin of Sri Lanka. These systems measure weather parameters such as temperature, humidity, pressure, rainfall, wind speed and wind direction. Thanks to the collected data, it was possible to calculate some drought indexes to evaluate the intensity of the dry periods. The Standard Precipitation Index (SPI) has been selected as the principal indicator to evaluate droughts by integrating the 4onse data together with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset.
Is There a Pattern for Occurrence of Macrophytes in Polish Ponds?

Although ponds are common elements in the environment, dependences occurring in these ecosystems have not been fully investigated. Our study focuses on the correlation between environmental factors and changes occurring in ponds—mostly the distribution of macrophytes in space and time. The aim of our analyses was to indicate which physicochemical variables were characteristic of ponds in specific habitats (forest, agriculture field, and village) and whether they could associate the distribution of vegetation in these ponds. Thirteen ponds differing in morphometric parameters and location in landscape (ponds located in agriculture fields, forests, and villages) were analyzed. Our research was based on data covering a period of 10 years (2008–2018). The following parameters were analyzed: the water content of NH4+, NO3−, NO2−, TP, PO43−, Na, K, Mg, Ca, and Fe, pH, temperature and morphometric parameters. Macrophytes were observed during the growing season in July 2008, 2010, 2012, 2014, 2016 and 2018. Three homogeneous species groups were distinguished in statistical analyses and the dominance of selective environmental factors was assigned to them. The first group consisted of indicator species for forest ponds. Their waters were characterized by a low content of Ca, Mg, and Na as well as an increased content of NH4+ and Fe. The second group was composed of indicator species for field ponds, where the highest NO3− concentrations were found. The third group was formed by indicator species for village ponds. Water of these ponds was characterized by higher concentration levels of K, Na, and total P.
Lipoprotein (a) concentration and apolipoprotein (a) phenotype in subjects with type 2 diabetes mellitus

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