bluuebunny/crossref_metadata_2025 huggingface dataset:
Early Detection of Encroaching Woody Juniperus virginiana and Its Classification in Multi-Species Forest Using UAS Imagery and Semantic Segmentation Algorithms
Authors: Wang Lin , Zhou Yuzhen , Hu Qiao , Tang Zhenghong , Ge Yufeng , Smith Adam , Awada Tala , Shi Yeyin
Published: May 2021

Woody plant encroachment into grasslands ecosystems causes significantly ecological destruction and economic losses. Effective and efficient management largely benefits from accurate and timely detection of encroaching species at an early development stage. Recent advances in unmanned aircraft systems (UAS) enabled easier access to ultra-high spatial resolution images at a centimeter level, together with the latest machine learning based image segmentation algorithms, making it possible to detect small-sized individuals of target species at early development stage and identify them when mixed with other species. However, few studies have investigated the optimal practical spatial resolution of early encroaching species detection. Hence, we investigated the performance of four popular semantic segmentation algorithms (decision tree, DT; random forest, RF; AlexNet; and ResNet) on a multi-species forest classification case with UAS-collected RGB images in original and down-sampled coarser spatial resolutions. The objective of this study was to explore the optimal segmentation algorithm and spatial resolution for eastern redcedar (Juniperus virginiana, ERC) early detection and its classification within a multi-species forest context. To be specific, firstly, we implemented and compared the performance of the four semantic segmentation algorithms with images in the original spatial resolution (0.694 cm). The highest overall accuracy was 0.918 achieved by ResNet with a mean interaction over union at 85.0%. Secondly, we evaluated the performance of ResNet algorithm with images in down-sampled spatial resolutions (1 cm to 5 cm with 0.5 cm interval). When applied on the down-sampled images, ERC segmentation performance decreased with decreasing spatial resolution, especially for those images coarser than 3 cm spatial resolution. The UAS together with the state-of-the-art semantic segmentation algorithms provides a promising tool for early-stage detection and localization of ERC and the development of effective management strategies for mixed-species forest management.
Comparison of Accuracy of Surface Temperature Images from Unmanned Aerial Vehicle and Satellite for Precise Thermal Environment Monitoring of Urban Parks Using In Situ Data
Authors: Kim Dongwoo , Yu Jaejin , Yoon Jeongho , Jeon Seongwoo , Son Seungwoo
Published: May 2021

Rapid urbanization has led to several severe environmental problems, including so-called heat island effects, which can be mitigated by creating more urban green spaces. However, the temperature of various surfaces differs and precise measurement and analyses are required to determine the “coolest” of these. Therefore, we evaluated the accuracy of surface temperature data based on thermal infrared (TIR) cameras mounted on unmanned aerial vehicles (UAVs), which have recently been utilized for the spatial analysis of surface temperatures. Accordingly, we investigated land surface temperatures (LSTs) in green spaces, specifically those of different land cover types in an urban park in Korea. We compared and analyzed LST data generated by a thermal infrared (TIR) camera mounted on an unmanned aerial vehicle (UAV) and LST data from the Landsat 8 satellite for seven specific periods. For comparison and evaluation, we measured in situ LSTs using contact thermometers. The UAV TIR LST showed higher accuracy (R2 0.912, root mean square error (RMSE) 3.502 °C) than Landsat TIR LST accuracy (R2 value lower than 0.3 and RMSE of 7.246 °C) in all periods. The Landsat TIR LST did not show distinct LST characteristics by period and land cover type; however, grassland, the largest land cover type in the study area, showed the highest accuracy. With regard to the accuracy of the UAV TIR LST by season, the accuracy was higher in summer and spring (R2 0.868–0.915, RMSE 2.523–3.499 °C) than in autumn and winter (R2 0.766–0.79, RMSE 3.834–5.398 °C). Some land cover types (concrete bike path, wooden deck) were overestimated, showing relatively high total RMSEs of 4.439 °C and 3.897 °C, respectively, whereas grassland, which has lower LST, was underestimated—showing a total RMSE of 3.316 °C. Our results showed that the UAV TIR LST could be measured with sufficient reliability for each season and land cover type in an urban park with complex land cover types. Accordingly, our results could contribute to decision-making for urban spaces and environmental planning in consideration of the thermal environment.
Snowcover Survey over an Arctic Glacier Forefield: Contribution of Photogrammetry to Identify “Icing” Variability and Processes
Authors: Bernard Éric , Friedt Jean-Michel , Griselin Madeleine
Published: May 2021

The global climate shift currently underway has significant impacts on both the quality and quantity of snow precipitation. This directly influences the spatial variability of the snowpack as well as cumulative snow height. Contemporary glacier retreat reorganizes periglacial morphology: while the glacier area decreases, the moraine area increases. The latter is becoming a new water storage potential that is almost as important as the glacier itself, but with considerably more complex topography. Hence, this work fills one of the missing variables of the hydrological budget equation of an arctic glacier basin by providing an estimate of the snow water equivalent (SWE) of the moraine contribution. Such a result is achieved by investigating Structure from Motion (SfM) image processing that is applied to pictures collected from an Unmanned Aerial Vehicle (UAV) as a method for producing snow depth maps over the proglacial moraine area. Several UAV campaigns were carried out on a small glacial basin in Spitsbergen (Arctic): the measurements were made at the maximum snow accumulation season (late April), while the reference topography maps were acquired at the end of the hydrological year (late September) when the moraine is mostly free of snow. The snow depth is determined from Digital Surface Model (DSM) subtraction. Utilizing dedicated and natural ground control points for relative positioning of the DSMs, the relative DSM georeferencing with sub-meter accuracy removes the main source of uncertainty when assessing snow depth. For areas where snow is deposited on bare rock surfaces, the correlation between avalanche probe in-situ snow depth measurements and DSM differences is excellent. Differences in ice covered areas between the two measurement techniques are attributed to the different quantities measured: while the former only measures snow accumulation, the latter includes all of the ice accumulation during winter through which the probe cannot penetrate, in addition to the snow cover. When such inconsistencies are observed, icing thicknesses are the source of the discrepancy that is observed between avalanche probe snow cover depth measurements and differences of DSMs.
Satellite-Based Mapping of High-Resolution Ground-Level PM2.5 with VIIRS IP AOD in China through Spatially Neural Network Weighted Regression
Authors: Chen Yijun , Wu Sensen , Wang Yuanyuan , Zhang Feng , Liu Renyi , Du Zhenhong
Published: May 2021

Satellite-retrieved aerosol optical depth (AOD) data are extensively integrated with ground-level measurements to achieve spatially continuous fine particulate matters (PM2.5). Current satellite-based methods however face challenges in obtaining highly accurate and reasonable PM2.5 distributions due to the inability to handle both spatial non-stationarity and complex non-linearity in the PM2.5–AOD relationship. High-resolution (<1 km) PM2.5 products over the whole of China for fine exposure assessment and health research are also lacking. This study aimed to predict 750 m resolution ground-level PM2.5 in China with the high-resolution Visible Infrared Imaging Radiometer Suite (VIIRS) intermediate product (IP) AOD data using a newly developed geographically neural network weighted regression (GNNWR) model. The performance evaluations demonstrated that GNNWR achieved higher prediction accuracy than the widely used methods with cross-validation and predictive R2 of 0.86 and 0.85. Satellite-derived monthly 750 m resolution PM2.5 data in China were generated with robust prediction accuracy and almost complete coverage. The PM2.5 pollution was found to be greatly improved in 2018 in China with annual mean concentration of 31.07 ± 17.52 µg/m3. Nonetheless, fine-scale PM2.5 exposures at multiple administrative levels suggested that PM2.5 pollution in most urban areas needed further control, especially in southern Hebei Province. This work is the first to evaluate the potential of VIIRS IP AOD in modeling high-resolution PM2.5 over large-scale. The newly satellite-derived PM2.5 data with high spatial resolution and high prediction accuracy at the national scale are valuable to advance environmental and health researches in China.
Preliminary Validation of Surface Reflections from Fengyun-3C Radio Occultation Data
Authors: Chen Weiwei , Xiong Yongliang , Li Xinzhong , Zhao Ban , Zhang Rui , Xu Shaoguang
Published: May 2021

Fengyun-3C (FY-3C) is a Global Navigation Satellite Systems (GNSS) Radio Occultation (RO) mission founded which was by China on 23 September 2013. In this study, under a specific temporal and spatial domain, we systematically compare FY-3C refractivity profiles with Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) refractivity profiles for the year 2015. The COSMIC profiles used in this study contain reflections, as identified in the Radio Occultation Meteorology Satellite Application Facility (ROM SAF) flag database. From 0 to 25 km altitude, the mean biases and relative standard deviations of the comparisons between FY-3C and COSMIC are less than 1% and 2% when COSMIC profiles present reflected signals. Radio holographic analysis is used to visualize and identify the spectra of FY-3C-reflected signals in the time-frequency domain. It is confirmed that the reflected signals in the lower troposphere and near the surface can be tracked by an FY-3C receiver. Further, most of the FY-3C events that matched with COSMIC reflected events show reflection patterns at a lower height, especially above the ocean’s surface. Under Bouguer’s rule and spherical symmetry assumptions, we reconstructed the reflected bending angle models by Abel transformation, which are valuable for reducing N-bias in the ducting layer. Three examples of FY-3C events show that the reflected bending branch is near the surface. Overall, the reflected signal of FY-3C could be used as a supplementary data portion for FY-3C atmospheric products.
On-Site Global Calibration of Mobile Vision Measurement System Based on Virtual Omnidirectional Camera Model
Authors: Chai Binhu , Wei Zhenzhong
Published: May 2021

The mobile vision measurement system (MVMS) is widely used for location and attitude measurement in aircraft takeoff and landing, and its on-site global calibration is crucial to obtaining high-accuracy measurement aimed at obtaining the transformation relationship between the MVMS coordinate system and the local-tangent-plane coordinate system. In this paper, several new ideas are proposed to realize the global calibration of the MVMS effectively. First, the MVMS is regarded as azimuth and pitch measurement equipment with a virtual single image plane at focal length 1. Second, a new virtual omnidirectional camera model constructed by three mutual orthogonal image planes is put forward, which effectively resolves the problem of global calibration error magnification when the angle between the virtual single image plane and view axis of the system becomes small. Meanwhile, an expanded factorial linear method is proposed to solve the global calibration equations, which effectively restrains the influence of calibration data error. Experimental results with synthetic data verify the validity of the proposed method.
DTM-Based Morphometric Analysis of Scoria Cones of the Chaîne des Puys (France)—The Classic and a New Approach
Authors: Vörös Fanni , van Wyk de Vries Benjamin , Karátson Dávid , Székely Balázs
Published: May 2021

Scoria cones are favorite targets of morphometric research. However, in-depth, DTM-based studies have appeared only recently, and new methods are being developed. This study provides a classic evaluation of the cones of Chaîne des Puys (Auvergne, France) as well as introduces a more detailed and statistics-based set of properties. Beside the classic parameters, a sectorial approach is applied to the slope distributions calculated from high resolution DTMs for 25 cones of different lithologies, in order to study the various (a)symmetries of the cones. DTM-based morphometric characteristics have been found to be different from classic descriptors, whereas the sectorial approach describes correctly the more and the less regular shapes. The distribution of interquartile ranges of the sectorial slope distributions is skewed. Sectorization discriminates various types of symmetries: there are almost circular cones, but the majority are elongated and have some asymmetry. The relationship between size parameters reflects the lithology, rather than the age of the cone. The attempt to relate morphometric parameters to age data is only partially successful: although there is a certain trend, within the same lithological group, subtle but possibly systematic trends can be detected for decreasing morphometric values (e.g., slope) with the age. The regression models indicate various outcomes. Further work is needed to understand all the diverse parameters, especially the lithology–shape relationship, and how symmetry is connected to different factors.
A Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data
Authors: Nussbaumer Raphaël , Schmid Baptiste , Bauer Silke , Liechti Felix
Published: May 2021

Recent and archived data from weather radar networks are extensively used for the quantification of continent-wide bird migration patterns. While the process of discriminating birds from weather signals is well established, insect contamination is still a problem. We present a simple method combining two Doppler radar products within a Gaussian mixture model to estimate the proportions of birds and insects within a single measurement volume, as well as the density and speed of birds and insects. This method can be applied to any existing archives of vertical bird profiles, such as the European Network for the Radar surveillance of Animal Movement repository, with no need to recalculate the huge amount of original polar volume data, which often are not available.
The 2008–2010 Subsidence of Dallol Volcano on the Spreading Erta Ale Ridge: InSAR Observations and Source Models
Authors: Battaglia Maurizio , Pagli Carolina , Meuti Stefano
Published: May 2021

In this work, we study the subsidence of Dallol, an explosive crater and hydrothermal area along the spreading Erta Ale ridge of Afar (Ethiopia). No volcanic products exist at the surface. However, a diking episode in 2004, accompanied by dike-induced faulting, indicates that Dallol is an active volcanic area. The 2004 diking episode was followed by quiescence until subsidence started in 2008. We use InSAR to measure the deformation, and inverse, thermoelastic and poroelastic modelling to understand the possible causes of the subsidence. Analysis of InSAR data from 2004–2010 shows that subsidence, centered at Dallol, initiated in October 2008, and continued at least until February 2010 at an approximately regular rate of up to 10 cm/year. The inversion of InSAR average velocities finds that the source causing the subsidence is shallow (depth between 0.5 and 1.5 km), located under Dallol and with a volume decrease between −0.63 and −0.26 × 106 km3/year. The most likely explanation for the subsidence of Dallol volcano is a combination of outgassing (depressurization), cooling and contraction of the roof of a shallow crustal magma chamber or of the hydrothermal system.
Assessment of the EUMETSAT Multi Decadal Land Surface Albedo Data Record from Meteosat Observations
Authors: Lattanzio Alessio , Grant Michael , Doutriaux-Boucher Marie , Roebeling Rob , Schulz Jörg
Published: May 2021

Surface albedo, defined as the ratio of the surface-reflected irradiance to the incident irradiance, is one of the parameters driving the Earth energy budget and it is for this reason an essential variable in climate studies. Instruments on geostationary satellites provide suitable observations allowing long-term monitoring of surface albedo from space. In 2012, EUMETSAT published Release 1 of the Meteosat Surface Albedo (MSA) data record. The main limitation effecting the quality of this release was non-removed clouds by the incorporated cloud screening procedure that caused too high albedo values, in particular for regions with permanent cloud coverage. For the generation of Release 2, the MSA algorithm has been replaced with the Geostationary Surface Albedo (GSA) one, able to process imagery from any geostationary imager. The GSA algorithm exploits a new, improved, cloud mask allowing better cloud screening, and thus fixing the major limitation of Release 1. Furthermore, the data record has an extended temporal and spatial coverage compared to the previous release. Both Black-Sky Albedo (BSA) and White-Sky Albedo (WSA) are estimated, together with their associated uncertainties. A direct comparison between Release 1 and Release 2 clearly shows that the quality of the retrieval improved significantly with the new cloud mask. For Release 2 the decadal trend is less than 1% over stable desert sites. The validation against Moderate Resolution Imaging Spectroradiometer (MODIS) and the Southern African Regional Science Initiative (SAFARI) surface albedo shows a good agreement for bright desert sites and a slightly worse agreement for urban and rain forest locations. In conclusion, compared with MSA Release 1, GSA Release 2 provides the users with a significantly more longer time range, reliable and robust surface albedo data record.
GRACE Satellites Enable Long-Lead Forecasts of Mountain Contributions to Streamflow in the Low-Flow Season
Authors: Liu Xingcai , Tang Qiuhong , Hosseini-Moghari Seyed-Mohammad , Shi Xiaogang , Lo Min-Hui , Scanlon Bridget
Published: May 2021

Terrestrial water storage (TWS) in high mountain areas contributes large runoff volumes to nearby lowlands during the low-flow season when streamflow is critical to downstream water supplies. The potential for TWS from GRACE (Gravity Recovery and Climate Experiment) satellites to provide long-lead streamflow forecasting in adjacent lowlands during the low-flow season was assessed using the upper Yellow River as a case study. Two linear models were trained for forecasting monthly streamflow with and without TWS anomaly (TWSA) from 2002 to 2016. Results show that the model based on streamflow and TWSA is superior to the model based on streamflow alone at up to a five-month lead-time. The inclusion of TWSA reduced errors in streamflow forecasts by 25% to 50%, with 3–5-month lead-times, which represents the role of terrestrial hydrologic memory in streamflow changes during the low-flow season. This study underscores the high potential of streamflow forecasting using GRACE data with long lead-times that should improve water management in mountainous water towers and downstream areas.
Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach
Authors: Minghelli Audrey , Vadakke-Chanat Sayoob , Chami Malik , Guillaume Mireille , Migne Emmanuelle , Grillas Patrick , Boutron Olivier
Published: May 2021

The relevant benefits of hyperspectral sensors for water column determination and seabed features mapping compared to multispectral data, especially in coastal areas, have been demonstrated in recent studies. In this study, we used hyperspectral satellite data in the accurate mapping of the bathymetry and the composition of water habitats for inland water. Particularly, the identification of the bottom diversity for a shallow lagoon (less than 2 m in depth) was examined. Hyperspectral satellite data were simulated based on aerial hyperspectral imagery acquired above a lagoon, namely the Vaccarès lagoon (France), considering the spatial and spectral resolutions, and the signal-to-noise ratio of a satellite sensor, BIODIVERSITY, that is under study by the French space agency (CNES). Various sources of uncertainties such as inter-band calibration errors and atmospheric correction were considered to make the dataset realistic. The results were compared with a recently launched hyperspectral sensor, namely the DESIS sensor (DLR, Germany). The analysis of BIODIVERSITY-like sensor simulated data demonstrated the feasibility to satisfactorily estimate the bathymetry with a root-mean-square error of 0.28 m and a relative error of 14% between 0 and 2 m. In comparison to open coastal waters, the retrieval of bathymetry is a more challenging task for inland waters because the latter usually shows a high abundance of hydrosols (phytoplankton, SPM, and CDOM). The retrieval performance of seabed abundance was estimated through a comparison of the bottom composition with in situ data that were acquired by a recently developed imaging camera (SILIOS Technologies SA., France). Regression coefficients for the retrieval of the fractional species abundances from the theoretical inversion and measurements were obtained to be 0.77 (underwater imaging camera) and 0.80 (in situ macrophytes data), revealing the potential of the sensor characteristics. By contrast, the comparison of the in situ bathymetry and macrophyte data with the DESIS inverted data showed that depth was estimated with an RSME of 0.38 m and a relative error of 17%, and the fractional species abundance was estimated to have a regression coefficient of 0.68.
Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments
Authors: Tu Zhiyong , Lou Yidong , Guo Wenfei , Song Weiwei , Wang Yusheng
Published: May 2021

This paper designs a cascading vector tracking loop based on the Unscented Kalman Filter (UKF) for high dynamic environment. Constant improvement in dynamic performance is an enormous challenge to the traditional receiver. Due to the doppler effect, the satellite signals received by these vehicles contain fast changing doppler frequency shifts and the first and second derivatives of doppler frequency, which will directly cause a negative impact on the receiver’s stable tracking of the signals. In order to guarantee the dynamic performance and the tracking accuracy, this paper designs a vector carrier structure to estimate the doppler component of a signal. Firstly, after the coherence integral, the IQ values are reorganized into new observations. Secondly, the phase error and frequency of the carrier are estimated through the pre-filter. Then, the pseudorange and carrier frequency are used as the observations of the main filter to estimate the motion state of the aircraft. Finally, the current state is fed back to the carrier Numerical Controlled Oscillator (NCO) as a complete closed loop. In the whole structure, the cascading vector loop replaces the original carrier tracking loop, and the stable signal tracking of code loop is guaranteed by carrier assisted pseudo-code method. In this paper, with the high dynamic signals generated by the GNSS signal simulator, this designed algorithm is validated by a software receiver. The results show that this loop has a wider dynamic tracking range and lower tracking error than the second-order frequency locked loop assisted third-order phase locked loop in high dynamic circumstances. When the acceleration of carrier is 100 g, the convergence time of vector structure is about 100 ms, and the carrier phase error is lower than 0.6 mm.
A Compilation of Snow Cover Datasets for Svalbard: A Multi-Sensor, Multi-Model Study
Authors: Vickers Hannah , Malnes Eirik , van Pelt Ward J. J. , Pohjola Veijo A. , Killie Mari Anne , Saloranta Tuomo , Karlsen Stein Rune
Published: May 2021

Reliable and accurate mapping of snow cover are essential in applications such as water resource management, hazard forecasting, calibration and validation of hydrological models and climate impact assessments. Optical remote sensing has been utilized as a tool for snow cover monitoring over the last several decades. However, consistent long-term monitoring of snow cover can be challenging due to differences in spatial resolution and retrieval algorithms of the different generations of satellite-based sensors. Snow models represent a complementary tool to remote sensing for snow cover monitoring, being able to fill in temporal and spatial data gaps where a lack of observations exist. This study utilized three optical remote sensing datasets and two snow models with overlapping periods of data coverage to investigate the similarities and discrepancies in snow cover estimates over Nordenskiöld Land in central Svalbard. High-resolution Sentinel-2 observations were utilized to calibrate a 20-year MODIS snow cover dataset that was subsequently used to correct snow cover fraction estimates made by the lower resolution AVHRR instrument and snow model datasets. A consistent overestimation of snow cover fraction by the lower resolution datasets was found, as well as estimates of the first snow-free day (FSFD) that were, on average, 10–15 days later when compared with the baseline MODIS estimates. Correction of the AVHRR time series produced a significantly slower decadal change in the land-averaged FSFD, indicating that caution should be exercised when interpreting climate-related trends from earlier lower resolution observations. Substantial differences in the dynamic characteristics of snow cover in early autumn were also present between the remote sensing and snow model datasets, which need to be investigated separately. This work demonstrates that the consistency of earlier low spatial resolution snow cover datasets can be improved by using current-day higher resolution datasets.
The Hidden Cairns—A Case Study of Drone-Based ALS as an Archaeological Site Survey Method
Authors: Roiha Johanna , Heinaro Einari , Holopainen Markus
Published: May 2021

Conducting archaeological site surveys is time consuming, and large sites may have many small features or structures that are difficult to locate and interpret. Vegetation cover and dense forest hide small structures, like cairns, while at the same time forest cover can cause problems for LiDAR tools. In this case study, drone-based ALS (airborne laser scanning) was tested as an archaeological site survey tool. The research site was complex and located partially in a forested area, which made it possible to evaluate how forest cover affects data. The survey methods used were rather simple: visual analysis, point density calculations in the forest area, and, for site interpretation purposes, digitizing observations and viewshed analysis. Using straightforward methods allowed us to evaluate the minimum time and skills needed for this type of survey. Drone-based ALS provided good results and increased knowledge of the site and its structures. Estimates of the number of cairns interpreted as graves more than doubled as a result of the high-accuracy ALS data. Based on the results of this study, drone-based ALS could be a suitable high-accuracy survey method for large archaeological sites. However, forest cover affects the accuracy, and more research is needed.
Improvement of GPR-Based Rebar Diameter Estimation Using YOLO-v3
Authors: Park Sehwan , Kim Jinpyung , Jeon Kyoyoung , Kim Junkyeong , Park Seunghee
Published: May 2021

As the frequency of earthquakes has increased in Korea in recent years, designing earthquake-resistant facilities has been increasingly emphasized. Structures constructed with rebars are vulnerable to shaking, which reduces their seismic performance and may result in damage to human life and property. Because the construction of facilities requires the maintenance of sub-constructions, such as by cutting rebars or compensating for missing rebars, information on rebar diameter is required. In this study, the YOLO-v3 algorithm, which has the fastest object recognition performance, was applied to the structural correction data, and a basic experiment was conducted in the air to predict the diameter of rebars in a facility, in real time based on ground-penetrating radar data. The reason for using the YOLO-v3 algorithm is that in the case of GPR data that change slightly according to the diameter of the reinforcing bar, it is difficult to discriminate with the naked eye, and the result may change depending on the inspector. The model achieved a higher accuracy than conventional rebar detection and diameter prediction methods. In addition, the possibility of real-time rebar diameter prediction during construction, using the proposed method, was verified.
Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow
Authors: Yu Yue , Pan Jinmei , Shi Jiancheng
Published: May 2021

Natural snow, one of the most important components of the cryosphere, is fundamentally a layered medium. In forward simulation and retrieval, a single-layer effective microstructure parameter is widely used to represent the emission of multiple-layer snowpacks. However, in most cases, this parameter is fitted instead of calculated based on a physical theory. The uncertainty under different frequencies, polarizations, and snow conditions is uncertain. In this study, we explored different methods to reduce the layered snow properties to a set of single-layer values that can reproduce the same brightness temperature (TB) signal. A validated microwave emission model of layered snowpack (MEMLS) was used as the modelling tool. Multiple-layer snow TB from the snow’s surface was compared with the bulk TB of single-layer snow. The methods were tested using snow profile samples from the locally validated and global snow process model simulations, which follow the natural snow’s characteristics. The results showed that there are two factors that play critical roles in the stability of the bulk TB error, the single-layer effective microstructure parameter, and the reflectivity at the air–snow and snow–soil boundaries. It is important to use the same boundary reflectivity as the multiple-layer snow case calculated using the snow density at the topmost and bottommost layers instead of the average density. Afterwards, a mass-weighted average snow microstructure parameter can be used to calculate the volume scattering coefficient at 10.65 to 23.8 GHz. At 36.5 and 89 GHz, the effective microstructure parameter needs to be retrieved based on the product of the snow layer transmissivity. For thick snow, a cut-off threshold of 1/e is suggested to be used to include only the surface layers within the microwave penetration depth. The optimal method provides a root mean squared error of bulk TB of less than 5 K at 10.65 to 36.5 GHz and less than 10 K at 89 GHz for snow depths up to 130 cm.
Analysing the Impact of Climate Change on Hydrological Ecosystem Services in Laguna del Sauce (Uruguay) Using the SWAT Model and Remote Sensing Data
Authors: Aznarez Celina , Jimeno-Sáez Patricia , López-Ballesteros Adrián , Pacheco Juan Pablo , Senent-Aparicio Javier
Published: May 2021

Assessing how climate change will affect hydrological ecosystem services (HES) provision is necessary for long-term planning and requires local comprehensive climate information. In this study, we used SWAT to evaluate the impacts on four HES, natural hazard protection, erosion control regulation and water supply and flow regulation for the Laguna del Sauce catchment in Uruguay. We used downscaled CMIP-5 global climate models for Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 projections. We calibrated and validated our SWAT model for the periods 2005–2009 and 2010–2013 based on remote sensed ET data. Monthly NSE and R2 values for calibration and validation were 0.74, 0.64 and 0.79, 0.84, respectively. Our results suggest that climate change will likely negatively affect the water resources of the Laguna del Sauce catchment, especially in the RCP 8.5 scenario. In all RCP scenarios, the catchment is likely to experience a wetting trend, higher temperatures, seasonality shifts and an increase in extreme precipitation events, particularly in frequency and magnitude. This will likely affect water quality provision through runoff and sediment yield inputs, reducing the erosion control HES and likely aggravating eutrophication. Although the amount of water will increase, changes to the hydrological cycle might jeopardize the stability of freshwater supplies and HES on which many people in the south-eastern region of Uruguay depend. Despite streamflow monitoring capacities need to be enhanced to reduce the uncertainty of model results, our findings provide valuable insights for water resources planning in the study area. Hence, water management and monitoring capacities need to be enhanced to reduce the potential negative climate change impacts on HES. The methodological approach presented here, based on satellite ET data can be replicated and adapted to any other place in the world since we employed open-access software and remote sensing data for all the phases of hydrological modelling and HES provision assessment.
A Robust Framework for Simultaneous Localization and Mapping with Multiple Non-Repetitive Scanning Lidars
Authors: Wang Yusheng , Lou Yidong , Zhang Yi , Song Weiwei , Huang Fei , Tu Zhiyong
Published: May 2021

With the ability to provide long range, highly accurate 3D surrounding measurements, while lowering the device cost, non-repetitive scanning Livox lidars have attracted considerable interest in the last few years. They have seen a huge growth in use in the fields of robotics and autonomous vehicles. In virtue of their restricted FoV, they are prone to degeneration in feature-poor scenes and have difficulty detecting the loop. In this paper, we present a robust multi-lidar fusion framework for self-localization and mapping problems, allowing different numbers of Livox lidars and suitable for various platforms. First, an automatic calibration procedure is introduced for multiple lidars. Based on the assumption of rigidity of geometric structure, the transformation between two lidars can be configured through map alignment. Second, the raw data from different lidars are time-synchronized and sent to respective feature extraction processes. Instead of sending all the feature candidates for estimating lidar odometry, only the most informative features are selected to perform scan registration. The dynamic objects are removed in the meantime, and a novel place descriptor is integrated for enhanced loop detection. The results show that our proposed system achieved better results than single Livox lidar methods. In addition, our method outperformed novel mechanical lidar methods in challenging scenarios. Moreover, the performance in feature-less and large motion scenarios has also been verified, both with approvable accuracy.
Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
Authors: Zhu Xiufang , Guo Rui , Liu Tingting , Xu Kun
Published: May 2021

Timely and reliable estimations of crop yield are essential for crop management and successful food trade. In previous studies, remote sensing data or climate data are often used alone in statistical yield estimation models. In this study, we synthetically used agrometeorological indicators and remote sensing vegetation parameters to estimate maize yield in Jilin and Liaoning Provinces of China. We applied two methods to select input variables, used the random forest method to establish yield estimation models, and verified the accuracy of the models in three disaster years (1997, 2000, and 2001). The results show that the R2 values of the eight yield estimation models established in the two provinces were all above 0.7, Lin’s concordance correlation coefficients were all above 0.84, and the mean absolute relative errors were all below 0.14. The mean absolute relative error of the yield estimations in the three disaster years was 0.12 in Jilin Province and 0.13 in Liaoning Province. A model built using variables selected by a two-stage importance evaluation method can obtain a better accuracy with fewer variables. The final yield estimation model of Jilin province adopts eight independent variables, and the final yield estimation model of Liaoning Province adopts nine independent variables. Among the 11 adopted variables in two provinces, ATT (accumulated temperature above 10 °C) variables accounted for the highest proportion (54.54%). In addition, the GPP (gross primary production) anomaly in August, NDVI (Normalized Difference Vegetation Index) anomaly in August, and standardized precipitation index with a two-month scale in July were selected as important modeling variables by all methods in the two provinces. This study provides a reference method for the selection of modeling variables, and the results are helpful for understanding the impact of climate on potential yield.
New Constraints on Slip Behavior of the Jianshui Strike-Slip Fault from Faulted Stream Channel Risers and Airborne Lidar Data, SE Tibetan Plateau, China
Authors: Guo Peng , Han Zhujun , Dong Shaopeng , Gao Fan , Li Jiani
Published: May 2021

The temporal slip behavior of a fault from displaced landforms when there are no chronological data remains poorly understood. The southern segment of the Xiaojiang fault zone (XJFZ) plays an important role in accommodating the lateral extrusion of the SE Tibetan plateau. However, there are few reports on the evolution of the offset landforms and slip behavior of the fault due to the dense vegetation in the region. Here, offset landforms along the Jianshui fault (JSF) in the southern segment of the XJFZ are systematically interpreted and measured using high-resolution satellite imagery, field investigations, and airborne lidar. The risers on the right banks of three stream channels feature similar left-lateral offset characteristics near the town of Dongshanzhai. The left-lateral offsets consist of multiple inflections produced by seismic events, and the offset of each event is similar. These inflections are distributed downstream in a stair-stepped manner. The newly formed inflections are located close to the fault, and the earlier formed ones are eroded by flowing water and migrate downstream. The difference between the amount of downstream erosion of two adjacent inflections varies. Assuming the stream’s long-term erosion rate remains steady, the estimated time intervals between seismic events are different. Combined with the cumulative offset probability density calculation for 92 offsets, the JSF is considered to show a nonperiodic characteristic earthquake recurrence pattern. We also propose a multistage offset evolution model of the stream channel riser. This provides a new way to analyze the seismic recurrence pattern of the fault over a relative time scale.
Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set
Authors: Wan Rongru , Huang Yanqi , Wu Xiaomei
Published: May 2021

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.
Radar-Based, Simultaneous Human Presence Detection and Breathing Rate Estimation
Authors: Regev Nir , Wulich Dov
Published: May 2021

Human presence detection is an application that has a growing need in many industries. Hotel room occupancy is critical for electricity and energy conservation. Industrial factories and plants have the same need to know the occupancy status to regulate electricity, lighting, and energy expenditures. In home security there is an obvious necessity to detect human presence inside the residence. For elderly care and healthcare, the system would like to know if the person is sleeping in the room, sitting on a sofa or conversely, is not present. This paper focuses on the problem of detecting presence using only the minute movements of breathing while at the same time estimating the breathing rate, which is the secondary aim of the paper. We extract the suspected breathing signal, and construct its Fourier series (FS) equivalent. Then we employ a generalized likelihood ratio test (GLRT) on the FS signal to determine if it is a breathing pattern or noise. We will show that calculating the GLRT also yields the maximum likelihood (ML) estimator for the breathing rate. We tested this algorithm on sleeping babies as well as conducted experiments on humans aged 12 to 44 sitting on a chair in front of the radar. The results are reported in the sequel.
Comparison of Novelty Detection Methods for Detection of Various Rotary Machinery Faults
Authors: Górski Jakub , Jabłoński Adam , Heesch Mateusz , Dziendzikowski Michał , Dworakowski Ziemowit
Published: May 2021

Condition monitoring is an indispensable element related to the operation of rotating machinery. In this article, the monitoring system for the parallel gearbox was proposed. The novelty detection approach is used to develop the condition assessment support system, which requires data collection for a healthy structure. The measured signals were processed to extract quantitative indicators sensitive to the type of damage occurring in this type of structure. The indicator’s values were used for the development of four different novelty detection algorithms. Presented novelty detection models operate on three principles: feature space distance, probability distribution, and input reconstruction. One of the distance-based models is adaptive, adjusting to new data flowing in the form of a stream. The authors test the developed algorithms on experimental and simulation data with a similar distribution, using the training set consisting mainly of samples generated by the simulator. Presented in the article results demonstrate the effectiveness of the trained models on both data sets.
Recent Advances in Transducers for Intravascular Ultrasound (IVUS) Imaging
Authors: Peng Chang , Wu Huaiyu , Kim Seungsoo , Dai Xuming , Jiang Xiaoning
Published: May 2021

As a well-known medical imaging methodology, intravascular ultrasound (IVUS) imaging plays a critical role in diagnosis, treatment guidance and post-treatment assessment of coronary artery diseases. By cannulating a miniature ultrasound transducer mounted catheter into an artery, the vessel lumen opening, vessel wall morphology and other associated blood and vessel properties can be precisely assessed in IVUS imaging. Ultrasound transducer, as the key component of an IVUS system, is critical in determining the IVUS imaging performance. In recent years, a wide range of achievements in ultrasound transducers have been reported for IVUS imaging applications. Herein, a comprehensive review is given on recent advances in ultrasound transducers for IVUS imaging. Firstly, a fundamental understanding of IVUS imaging principle, evaluation parameters and IVUS catheter are summarized. Secondly, three different types of ultrasound transducers (piezoelectric ultrasound transducer, piezoelectric micromachined ultrasound transducer and capacitive micromachined ultrasound transducer) for IVUS imaging are presented. Particularly, the recent advances in piezoelectric ultrasound transducer for IVUS imaging are extensively examined according to their different working mechanisms, configurations and materials adopted. Thirdly, IVUS-based multimodality intravascular imaging of atherosclerotic plaque is discussed. Finally, summary and perspectives on the future studies are highlighted for IVUS imaging applications.
Automatic Detection of Atrial Fibrillation in ECG Using Co-Occurrence Patterns of Dynamic Symbol Assignment and Machine Learning
Authors: Ganapathy Nagarajan , Baumgärtel Diana , Deserno Thomas
Published: May 2021

Early detection of atrial fibrillation from electrocardiography (ECG) plays a vital role in the timely prevention and diagnosis of cardiovascular diseases. Various algorithms have been proposed; however, they are lacking in considering varied-length signals, morphological transitions, and abnormalities over long-term recordings. We propose dynamic symbolic assignment (DSA) to differentiate a normal sinus rhythm (SR) from paroxysmal atrial fibrillation (PAF). We use ECG signals and their interbeat (RR) intervals from two public databases namely, AF Prediction Challenge Database (AFPDB) and AF Termination Challenge Database (AFTDB). We transform RR intervals into a symbolic representation and compute co-occurrence matrices. The DSA feature is extracted using varied symbol-length V, word-size W, and applied to five machine learning algorithms for classification. We test five hypotheses: (i) DSA captures the dynamics of the series, (ii) DSA is a reliable technique for various databases, (iii) optimal parameters improve DSA’s performance, (iv) DSA is consistent for variable signal lengths, and (v) DSA supports cross-data analysis. Our method captures the transition patterns of the RR intervals. The DSA feature exhibit a statistically significant difference in SR and PAF conditions (p < 0.005). The DSA feature with W=3 and V=3 yield maximum performance. In terms of F-measure (F), rotation forest and ensemble learning classifier are the most accurate for AFPDB (F = 94.6%) and AFTDB (F = 99.8%). Our method is effective for short-length signals and supports cross-data analysis. The DSA is capable of capturing the dynamics of varied-lengths ECG signals. Particularly, the optimal parameters-based DSA feature and ensemble learning could help to detect PAF in long-term ECG signals. Our method maps time series into a symbolic representation and identifies abnormalities in noisy, varied-length, and pathological ECG signals.