Miracle-dz/UniFAR-FTUs huggingface dataset:
Seasonal Trends in Movement Patterns of Birds and Insects Aloft Simultaneously Recorded by Radar

Airspace is a key but not well-understood habitat for many animal species. Enormous amounts of insects and birds use the airspace to forage, disperse, and migrate. Despite numerous studies on migration, the year-round flight activities of both birds and insects are still poorly studied. We used a 2 year dataset from a vertical-looking radar in Central Europe and developed an iterative hypothesis-testing algorithm to investigate the general temporal pattern of migratory and local movements. We estimated at least 3 million bird and 20 million insect passages over a 1 km transect annually. Most surprisingly, peak non-directional bird movement intensities during summer were of the same magnitude as seasonal directional movement peaks. Birds showed clear peaks in seasonally directional movements during day and night, coinciding well with the main migration period documented in this region. Directional insect movements occurred throughout the year, paralleling non-directional movements. In spring and summer, insect movements were non-directional; in autumn, their movements concentrated toward the southwest, similar to birds. Notably, the nocturnal movements of insects did not appear until April, while directional movements mainly occurred in autumn. This simple monitoring reveals how little we still know about the movement of biomass through airspace.
Generation Z' Young Adults and M-Commerce Use in Romania

Generation Z spends much time on mobile Internet and less time accessing Internet services using desktop computers. These traits make them an important niche that can be targeted by retailers. The objective of the present study is to analyze the impact of online advertising, social influence, and usage motivation on the behavioral intention related to m-commerce use among Romanian young adults. The research methodology is based on applying partial least square equation modelling (PLS-SEM), using data collected through a questionnaire. The proposed structural model includes four constructs that are first order reflective. The results reveal that among the influencing factors that determine Generation Z to use m-commerce, the usage motivation factor is the most important one, followed by social influence and online advertising. The research is useful for marketing professionals and retailers in establishing marketing strategies in accordance with the factors that influence the buying decision of young adults, representing Generation Z.
Reconstruction of the Daily MODIS Land Surface Temperature Product Using the Two-Step Improved Similar Pixels Method

The MODIS land surface temperature (LST) product is one of the most widely used data sources to study the climate and energy-water cycle at a global scale. However, the large number of invalid values caused by cloud cover limits the wide application of the MODIS LST. In this study, a two-step improved similar pixels (TISP) method was proposed for cloudy sky LST reconstruction. The TISP method was validated using a temperature-based method over various land cover types. The ground measurements were collected at fifteen stations from 2013 to 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) field campaign in China. The estimated theoretical clear-sky temperature indicates that clouds cool the land surface during the daytime and warm it at nighttime. For bare land, the surface temperature shows a clear seasonal trend and very similar across stations, with a cooling amplitude of 4.14 K in the daytime and a warming amplitude of 3.99 K at nighttime, as a yearly average. The validation result showed that the reconstructed LST is highly consistent with in situ measurements and comparable with MODIS LST validation accuracy, with a mean bias of 0.15 K at night (−0.43 K in the day), mean RMSE of 2.91 K at night (4.41 K in the day), and mean R2 of 0.93 at night (0.90 in the day). The developed method maximizes the potential of obtaining quality MODIS LST retrievals, ancillary data, and in situ observations, and the results show high accuracy for most land cover types.
Astrape: A System for Mapping Severe Abiotic Forest Disturbances Using High Spatial Resolution Satellite Imagery and Unsupervised Classification

Severe forest disturbance events are becoming more common due to climate change and many forest managers rely heavily upon airborne surveys to map damage. However, when the damage is extensive, airborne assets are in high demand and it can take managers several weeks to account for the damage, delaying important management actions. While some satellite-based systems exist to help with this process, their spatial resolution or latency can be too large for the needs of managers, as evidenced by the continued use of airborne imaging. Here, we present a new, operational-focused system capable of leveraging high spatial and temporal resolution Sentinel-2 and Planet Dove imagery to support the mapping process. This system, which we have named Astrape (“ah-STRAH-pee”), uses recently developed techniques in image segmentation and machine learning to produce maps of damage in different forest types and regions without requiring ground data, greatly reducing the need for potentially dangerous airborne surveys and ground sampling needed to accurately quantify severe damage. Although some limited field work is required to verify results, similar to current operational systems, Astrape-produced maps achieved 78–86% accuracy with respect to damage severity when evaluated against reference data. We present the Astrape framework and demonstrate its flexibility and potential with four case studies depicting four different disturbance types—fire, hurricane, derecho and tornado—in three disparate regions of the United States. Astrape is capable of leveraging various sources of satellite imagery and offers an efficient, flexible and economical option for mapping severe damage in forests.
Determinants of the Intention to Use Cross-Border Mobile Payments in Korea among Chinese Tourists: An Integrated Perspective of UTAUT2 with TTF and ITM

With the acceleration of global economic integration and digital economy, cross-border mobile payment has gained growing attention. On the other hand, as COVID-19 keeps on spreading, the popularization of cross-border mobile payment can lower the risk of infection caused by cash payment of tourists. However, though previous researchers already examined mobile payment users’ behavioral intention from different perspectives, these research findings tend to concentrate on the non-cross-border section of mobile payment. Therefore, the purpose of this research is to examine factors influencing cross-border mobile payment use intention from the user perspective, and combining three theoretical models, chiefly UTAUT2 (extended unified theory of acceptance and use of technology), ITM (initial trust model), and TTF (task technology fit). In this research, 786 Chinese with the experience of using cross-border mobile payment while traveling to South Korea are adopted as respondents, and the structural equation model is used to empirically analyze the data of these research samples. The study found that initial trust, performance expectancy, effort expectancy, facilitating conditions, price value, task technology fit, and initial trust have significant effects on use intention. Research findings of this paper can deepen people’s understanding of users’ intention to use cross-border mobile payment, and provide theoretical support for the development of cross-border mobile payment. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Mapping Particle Size and Soil Organic Matter in Tropical Soil Based on Hyperspectral Imaging and Non-Imaging Sensors

We evaluated the use of airborne hyperspectral imaging and non-imaging sensors in the Vis—NIR—SWIR spectral region to assess particle size and soil organic matter in the surface layer of tropical soils (Oxisols, Ultisols, Entisols). The study area is near Piracicaba municipality, São Paulo state, Brazil, in a sugarcane cultivation area of 135 hectares. The study area, with bare soil, was imaged in April 2016 by the AisaFENIX aerotransported hyperspectral sensor, with spectral resolution of 3.5 nm between 380 and 970 nm, and 12 nm between 970 and 2500 nm. We collected 66 surface soil samples. The samples were analyzed for particle size and soil organic matter content. Laboratory spectral measurements were performed using a non-imaging spectroradiometer (ASD FieldSpec 3 Jr). Partial Least Square Regression (PLSR) was used to predict clay, silt, sand and soil organic matter (SOM). The PLSR functions developed were applied to the hyperspectral image of the study area, allowing development of a prediction map of clay, sand, and SOM. The developed PLSR models demonstrated the relationship between the predictor variables at the cross-validation step, both for the non-imaging and imaging sensors, when the highest r and R2 values were obtained for clay, sand, and SOM, with R2 over 0.67. We did not obtain a satisfactory model for silt content. For the non-imaging sensor at the prediction step, R2 values for clay and SOM were over 0.7 and sand was lower than 0.54. The imaging sensor yielded models for clay, sand, and SOM with R2 values of 0.62, 0.66, and 0.67, respectively. Pearson correlation between sensors was greater than 0.849 for the prediction of clay, sand, and SOM. Our study successfully generated, from the imaging sensor, a large-scale and detailed predicted soil maps for particle size and SOM, which are important in the management of tropical soils.
Drone-Based Community Assessment, Planning, and Disaster Risk Management for Sustainable Development

Accessible, low-cost technologies and tools are needed in the developing world to support community planning, disaster risk assessment, and land tenure. Enterprise-scale geographic information system (GIS) software and high-resolution aerial or satellite imagery are tools which are typically not available to or affordable for resource-limited communities. In this paper, we present a concept of aerial data collection, 3D cadastre modeling, and disaster risk assessment using low-cost drones and adapted open-source software. Computer vision/machine learning methods are used to create a classified 3D cadastre that contextualizes and quantifies potential natural disaster risk to existing or planned infrastructure. Building type and integrity are determined from aerial imagery. Potential flood damage risk to a building is evaluated as a function of three mechanisms: undermining (erosion) of the foundation, hydraulic pressure damage, and building collapse due to water load. Use of Soil and Water Assessment Tool (SWAT) provides water runoff estimates that are improved using classified land features (urban ecology, erosion marks) to improve flow direction estimates. A convolutional neural network (CNN) is trained to find these flood-induced erosion marks from high-resolution drone imagery. A flood damage potential metric scaled by property value estimates results in individual and community property risk assessments.
Diurnal Variation of the Diffuse Attenuation Coefficient for Downwelling Irradiance at 490 nm in Coastal East China Sea

The diurnal variation of the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd(490)) has complex characteristics in the coastal regions. However, owing to the scarcity of in situ data, our knowledge on the diurnal variation is inadequate. In this study, an optical-buoy dataset was used to investigate the diurnal variation of Kd(490) in the coastal East China Sea, and to evaluate the Kd(490) L2 products of geostationary ocean color imager (GOCI), as well as the performance of six empirical algorithms for Kd(490) estimation in the Case-2 water. The results of validation show that there was high uncertainty in GOCI L2 Kd(490), with mean absolute percentage errors (MAPEs) of 69.57% and 68.86% and root mean square errors (RMSEs) of 0.70 and 0.71 m−1 compared to buoy-measured Kd12(490) and Kd13(490), respectively. Meanwhile, with the coefficient of determination (R2) of 0.71, as well as the lowest MAPE of 27.31% and RMSE of 0.29 m−1, the new dual ratio algorithm (NDRA) performed the best in estimating Kd(490) in the target area, among the six algorithms. Further, four main types of Kd(490) diurnal variation were found from buoy data, showing different variabilities compared to the area closer to the shore. One typical diurnal variation pattern showed that Kd(490) decreased at flood tide and increased at ebb tide, which was confirmed by GOCI images through the use of NDRA. Hydrometeorological factors influencing the diurnal variations of Kd(490) were also studied. In addition to verifying the predominant impact of tide, we found that the dominant effect of tide and wind on the water column is intensifying sediment resuspension, and the change of sediment transport produced by them are secondary to it.
Spatiotemporal Patterns of Hillslope Erosion Investigated Based on Field Scouring Experiments and Terrestrial Laser Scanning

Hillslope erosion is an essential source of catchment sediment yield. However, the current understanding of the spatiotemporal patterns of field hillslope erosion processes is limited. In this study, fourteen runoff scouring experiments were undertaken on two plots (A and B) established on one field slope of the hilly and gully loess plateau in China. Terrestrial laser scanning (TLS) was employed to investigate soil erosion processes across the hillslopes of the plots. The results demonstrated that the TLS-derived cumulative sediment yields of the hillslopes were more accurate than the TLS-derived consecutive sediment yields (i.e., the sediment yields for individual experiments). The magnitudes of the mean absolute/relative errors for the TLS-derived cumulative sediment yield for slopes A and B were 0.87 kg/25.02% and 1.26 kg/56.82%, respectively, with the linear relation R2 between the calculated and measured values over 0.60 (p < 0.001). The sediment yields from the hillslopes fluctuated considerably even when the runoff production became stable, leading to a weak relationship between the sediment yield and runoff discharge (the R2 values for slopes A and B were 0.57 (p = 0.002) and 0.08 (p = 0.321) for inter-experiments, and 0.37 (p < 0.001) and 0.06 (p = 0.035) for intra-experiments, respectively). The development of hillslope erosion was found to experience three major stages, which included a rapid increase and widespread distribution, a sharp decrease, and a stable distribution of the area with erosion/deposition. The rill development impacted the cumulative erosion and sediment yield rather than the cumulative deposition, with the impacts of rill depth and rill width development being stronger than those of rill length. The peak sediment yield corresponded well with the evolution of rills, partly accounting for the weak relationship between runoff and sediment yield. Our results provide a useful reference for the development of process-based soil erosion models and the establishment of spatially targeted control of soil erosion.
Modeling and Classification of Alluvial Fans with DEMs and Machine Learning Methods: A Case Study of Slovenian Torrential Fans

Alluvial (torrential) fans, especially those created from debris-flow activity, often endanger built environments and human life. It is well known that these kinds of territories where human activities are favored are characterized by increasing instability and related hydrological risk; therefore, treating the problem of its assessment and management is becoming strongly relevant. The aim of this study was to analyze and model the geomorphological aspects and the physical processes of alluvial fans in relation to the environmental characteristics of the territory for classification and prediction purposes. The main geomorphometric parameters capable of describing complex properties, such as relative fan position depending on the neighborhood, which can affect their formation or shape, or properties delineating specific parts of fans, were identified and evaluated through digital elevation model (DEM) data. Five machine learning (ML) methods, including a hybrid Euler graph ML method, were compared to analyze the geomorphometric parameters and physical characteristics of alluvial fans. The results obtained in 14 case studies of Slovenian torrential fans, validated with data of the empirical model proposed by Bertrand et al. (2013), confirm the validity of the developed method and the possibility to identify alluvial fans that can be considered as debris-flow prone.
Detection and Quantification of Irrigation Water Amounts at 500 m Using Sentinel-1 Surface Soil Moisture

Detailed information about irrigation timing and water use at a high spatial resolution is critical for monitoring and improving agricultural water use efficiency. However, neither statistical surveys nor remote sensing-based approaches can currently accommodate this need. To address this gap, we propose a novel approach based on the TU Wien Sentinel-1 Surface Soil Moisture product, characterized by a spatial sampling of 500 m and a revisit time of 1.5–4 days over Europe. Spatiotemporal patterns of soil moisture are used to identify individual irrigation events and estimate irrigation water amounts. To retrieve the latter, we include formulations of evapotranspiration and drainage losses to account for vertical fluxes, which may significantly influence sub-daily soil moisture variations. The proposed approach was evaluated against field-scale irrigation data reported by farmers at three sites in Germany with heterogeneous field sizes, crop patterns, irrigation systems and management. Our results show that most field-scale irrigation events can be detected using soil moisture information (mean F-score = 0.77). Irrigation estimates, in terms of temporal dynamics as well as spatial patterns, were in agreement with reference data (mean Pearson correlation = 0.64) regardless of field-specific characteristics (e.g., crop type). Hence, the proposed approach has the potential to be applied over large regions with varying cropping systems.
Detection of Flooding Attack on OBS Network Using ant Colony Optimization and Machine Learning

Optical burst switching (OBS) has become one of the best and widely used optical networking techniques. It offers more efficient bandwidth usage than optical packet switching (OPS) and optical circuit switching (OCS). However, it undergoes more attacks than other techniques and the Classical security approach cannot solve its security problem. Therefore, a new security approach based on machine learning and cloud computing is proposed in this article. We used the Google Colab platform to apply Support Vector Machine (SVM) and Extreme Learning Machine (ELM)to Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network Data Set.
Predicting Height to Crown Base of Larix olgensis in Northeast China Using UAV-LiDAR Data and Nonlinear Mixed Effects Models

As a core content of forest management, the height to crown base (HCB) model can provide a theoretical basis for the study of forest growth and yield. In this study, 8364 trees of Larix olgensis within 118 sample plots from 11 sites were measured to establish a two-level nonlinear mixed effect (NLME) HCB model. All predictors were derived from an unmanned aerial vehicle light detection and ranging (UAV-LiDAR) laser scanning system, which is reliable for extensive forest measurement. The effects of the different individual trees, stand factors, and their combinations on the HCB were analyzed, and the leave-one-site-out cross-validation was utilized for model validation. The results showed that the NLME model significantly improved the prediction accuracy compared to the base model, with a mean absolute error and relative mean absolute error of 0.89% and 9.71%, respectively. In addition, both site-level and plot-level sampling strategies were simulated for NLME model calibration. According to different prediction scale and accuracy requirements, selecting 15 trees randomly per site or selecting the three largest trees and three medium-size trees per plot was considered the most favorable option, especially when both investigations cost and the model’s accuracy are primarily considered. The newly established HCB model will provide valuable tools to effectively utilize the UAV-LiDAR data for facilitating decision making in larch plantations management.
Ambient Seismic Noise and Microseismicity Monitoring of a Prone-To-Fall Quartzite Tower (Ormea, NW Italy)

Remote sensing techniques are leading methodologies for landslide characterization and monitoring. However, they may be limited in highly vegetated areas and do not allow for continuously tracking the evolution to failure in an early warning perspective. Alternative or complementary methods should be designed for potentially unstable sites in these environments. The results of a six-month passive seismic monitoring experiment on a prone-to-fall quartzite tower are here presented. Ambient seismic noise and microseismicity analyses were carried out on the continuously recorded seismic traces to characterize site stability and monitor its possible irreversible and reversible modifications driven by meteorological factors, in comparison with displacement measured on site. No irreversible modifications in the measured seismic parameters (i.e., natural resonance frequencies of the tower, seismic velocity changes, rupture-related microseismic signals) were detected in the monitored period, and no permanent displacement was observed at the tower top. Results highlighted, however, a strong temperature control on these parameters and unusual preferential vibration directions with respect to the literature case studies on nearly 2D rock columns, likely due the tower geometric constraints, as confirmed by 3D numerical modeling. A clear correlation with the tower displacement rate was found in the results, supporting the suitability of passive seismic monitoring systems for site characterization and early waning purposes.
Impact of E-Commerce Adoption on Farmers' Participation in the Digital Financial Market: Evidence from Rural China

Although the increasing adoption of digital finance in recent years has exerted a wide-ranging influence on farmers’ consumption and production activities, many farmers in China still seriously suffer from digital financial exclusion. Few studies have documented the different impacts of e-commerce adoption characterized by online purchases and sales on farmers’ participation in the digital financial market measured by their engagement in digital payments, digital wealth management, and digital credit in rural China. Using survey data from 832 entrepreneurial households in rural China, we contribute to the literature by confirming that both online purchases and sales have a robust significant and positive impact on farmers’ participation in the digital financial market and that this impact on digital wealth management is successively larger than that on digital payments and digital credit, with the propensity score matching (PSM) method and instrument variable (IV) approach employed. We further discover that the impact of online purchases and sales on farmers’ participation in the digital financial market is significantly mediated by digital financial literacy. Moreover, the impact of online purchases and sales on farmers’ participation in the digital financial market is larger for those with high education levels, pursuing skills training, running new agricultural operation entities (i.e., family farms, professional cooperatives), and engaging in agricultural entrepreneurship. Our findings suggest that more effective measures to enhance adoption rates of online purchases and sales, innovation in rural market-oriented digital financial products and services, systematic training for farmers in e-commerce skills as well as digital financial literacy, and differentiated support measures for different groups of farmers to reduce the gap are urgently needed in China.