bluuebunny/crossref_metadata_2025 huggingface dataset:
Religious Affiliation’s Association with Suicidality across Sexual Orientations and Gender Identities
Authors: Dyer W. Justin , Goodman Michael A.
Published: October 2022

The objective was to replicate and extend earlier findings examining the intersection of sexual orientation and religious affiliation predicting suicidality. Current analyses used updated data and extended prior work by examining how affiliation relates to suicidality for transgender individuals. Data were collected in 2021 from 46,562 adolescents and were representative of Utah adolescents in grades 8, 10, and 12. In regressions, affiliation predicted suicidality and subsequent models added demographics, family functioning, drug use, feeling socially integrated, and interaction terms between sexual orientation, gender identity, and affiliation. In baseline models, affiliation was related to fewer mental health difficulties. When including drug use and family functioning, most differences became non-significant. This did not differ for sexual minorities. Interactions between affiliation and gender identity were significant. Cisgender males had the fewest mental health difficulties. When other differences were significant, transgender individuals had the highest mental health difficulties. There were no differences for transgender individuals across affiliation except those affiliated with “Other” religions had less depression than those “not affiliated.” However, “Other affiliated” females were higher in suicide attempts than the “not affiliated” and Latter-day Saint males were lower in ideation than the “not affiliated.” Findings largely replicate prior work. In final models, religious affiliation was unrelated to mental health for sexual or gender minorities; though “Other affiliation” related to protection for transgender individuals. The proposition that religious affiliation is negative for sexual or gender minorities was not supported. Longitudinal research is required to determine how affiliation may impact mental health.
The Argument from Evil, the Argument from Hiddenness, and Supernaturalistic Alternatives to Theism
Authors: Lataster Raphael
Published: October 2022

In this brief article, I consider James Sterba’s logical argument from evil, finding it to be ultimately unsuccessful. Not for the various issues Sterba raises, which do seem to be problematic if God exists, but for the logical approach itself. I encourage Sterba to shift tack, to embrace the evidential argument from evil, which is not at all concessionary, as he seems to think, and is an extremely powerful argument against the probability of theism, especially when we open the debate to the supernaturalistic alternatives to theism. I also encourage Sterba to reconsider his dismissive attitude towards the argument from hiddenness, which, in its evidential form, is also a very powerful argument against God’s existence, either employed independently or incorporated into the argument from evil.
Cardiac-Device Implantation and Pneumothorax—A Symptom-Based Approach: Experience from a District General Hospital
Authors: George Grace , Aujayeb Avinash
Published: October 2022

In patients undergoing cardiac-device implantation, pneumothorax incidence occurs in 1–6%, and is more common in women over 80 years of age with chronic obstructive pulmonary disease (COPD). The aim of this study was to do a service review to identify ways to improve care delivery. Caldicott approval was gained. Those complicated by a pneumothorax were further analysed regarding basic demographics and pleural interventions and outcomes. Continuous variables are presented as mean (range) or median (with interquartile ranges) and categorical variables are presented as percentages where appropriate. A total of 2056 implantation episodes from January 2010 to December 2020 occurred with 70 pneumothoraxes (3.4%), which were all related to pacemaker insertion. The mean age was 68.1 years (17–97), 39 were female, and 31 were male. A total of 36 pneumothoraxes were small and were 34 large according to British Thoracic Society (BTS) criteria. We initially observed 56 patients with minimal or no symptoms (30 were large pneumothoraxes), with five requiring intercostal drainage (ICD). A total of 14 pneumothoraxes were treated with ICD as a first-line treatment: the mean age was 78 years (69–89) and eight patients had COPD. Five pneumothoraxes were large. All had significant symptoms. All pneumothoraxes resolved within six weeks with no associated mortality.
Apple Watch for Pulse Rate Assessment Detects Unidentified Paroxysmal Atrial Fibrillation
Authors: Hirota Kiichi , Hirai Maretoshi
Published: October 2022

Consumer rhythm-monitoring devices, such as the Apple Watch, are becoming more readily available. Irregular pulses can be detected using an optical sensor that is built into the wearable device. The Apple Watch (Apple Inc., Cupertino, CA, USA) is a class II medical device with pulse rate and electrocardiography (ECG) monitoring capabilities. Here, we report a case in which an arrhythmia that was conventionally perceived but undiagnosed was identified as an atrial fibrillation by the self-acquisition of the ECG data using an Apple Watch.
Separation of Magnesium and Lithium from Brine Water and Bittern Using Sodium Silicate Precipitation Agent
Authors: Sulistiyono Eko , Harjanto Sri , Lalasari Latifa Hanum
Published: October 2022

Potential natural resources of lithium in Indonesia from brine water and bittern generally have low lithium and high magnesium levels, which need to be separated before further extraction. This research investigates the separation process of magnesium from brine water and bittern using a sodium silicate solution. The experimental results showed that the magnesium precipitation efficiency using sodium silicate was better in brine water than in bittern. A separation selectivity ratio of magnesium to lithium (Mg/Li) below 1 was obtained in brine water of 0.59 and bittern of 0.11 with the addition of a 1.25 mole fraction of sodium silicate solution to magnesium ions. After the precipitation at optimum addition of sodium silicate and water leaching process using distilled water, lithium’s recovery in the brine water and bittern filtrate was 84% and 35%, respectively. In brine water, water leaching increased lithium and magnesium ions in the filtrate. However, in bittern, the water leaching increased lithium recovery without dissolving magnesium ions into the filtrate. The precipitation products from the bittern were identified as complex lithium compounds in the forms of Li2MgO4SiLi2(MgSiO4) and LiMg4Na3O30Si12 phases, while the precipitation products in brine water mostly had a phase of CaO·MgO·Si2O5 (Diopside) and LiCl.
Inhomogeneous Financial Markets in a Low Interest Rate Environment—A Cluster Analysis of Eurozone Economies
Authors: Tatay Tibor , Orlovits Zsanett , Novák Zsuzsanna
Published: October 2022

In the present paper, we investigate the financial homogeneity of the euro area economies by contrasting eurozone countries’ responses to monetary policy steps to the theoretical assumptions of the liquidity trap phenomenon. Our assumption is that the euro area economies are not completely homogeneous. Hence, in a zero-interest rate environment, the asset holding decisions of economic agents exhibit detectable differences across countries. We verify our assumptions using Eurostat data. We use the financial asset stocks of the euro area countries to cluster the countries concerned. Previous literature has not examined changes in the ratio of financial assets to GDP, nor differences in structural changes in the total stock of financial assets under the zero lower bound. The paper uses k-centers cluster analysis based on Euclidean distance for detecting changes in the portfolio holdings of eurozone economic actors owing to economic crises and monetary policy responses. The results confirm that euro area financial markets are fragmented. There are significant differences across asset markets of different Eurozone countries, both during and after the crisis. Despite some similarities in the portfolio rearrangement across countries, the ECB’s monetary policy does not have a uniform impact on euro area financial markets, and notable differences prevail in the financial asset structures of the economies concerned.
When Robots Fail—A VR Investigation on Caregivers’ Tolerance towards Communication and Processing Failures
Authors: Klüber Kim , Onnasch Linda
Published: October 2022

Robots are increasingly used in healthcare to support caregivers in their daily work routines. To ensure an effortless and easy interaction between caregivers and robots, communication via natural language is expected from robots. However, robotic speech bears a large potential for technical failures, which includes processing and communication failures. It is therefore necessary to investigate how caregivers perceive and respond to robots with erroneous communication. We recruited thirty caregivers, who interacted in a virtual reality setting with a robot. It was investigated whether different kinds of failures are more likely to be forgiven with technical or human-like justifications. Furthermore, we determined how tolerant caregivers are with a robot constantly returning a process failure and whether this depends on the robot’s response pattern (constant vs. variable). Participants showed the same forgiveness towards the two justifications. However, females liked the human-like justification more and males liked the technical justification more. Providing justifications with any reasonable content seems sufficient to achieve positive effects. Robots with a constant response pattern were liked more, although both patterns achieved the same tolerance threshold from caregivers, which was around seven failed requests. Due to the experimental setup, the tolerance for communication failures was probably increased and should be adjusted in real-life situations.
Deep Learning Classification by ResNet-18 Based on the Real Spectral Dataset from Multispectral Remote Sensing Images
Authors: Zhao Yi , Zhang Xinchang , Feng Weiming , Xu Jianhui
Published: October 2022

Owing to the limitation of spatial resolution and spectral resolution, deep learning methods are rarely used for the classification of multispectral remote sensing images based on the real spectral dataset from multispectral remote sensing images. This study explores the application of a deep learning model to the spectral classification of multispectral remote sensing images. To address the problem of the large workload with respect to selecting training samples during classification by deep learning, first, linear spectral mixture analysis and the spectral index method were applied to extract the pixels of impervious surfaces, soil, vegetation, and water. Second, through the Euclidean distance threshold method, a spectral dataset of multispectral image pixels was established. Third, a deep learning classification model, ResNet-18, was constructed to classify Landsat 8 OLI images based on pixels’ real spectral information. According to the accuracy assessment, the results show that the overall accuracy of the classification results can reach 0.9436, and the kappa coefficient can reach 0.8808. This study proposes a method that allows for the more optimized establishment of the actual spectral dataset of ground objects, addresses the limitations of difficult sample selection in deep learning classification and of spectral similarity in traditional classification methods, and applies the deep learning method to the classification of multispectral remote sensing images based on a real spectral dataset.
Variability of Chl a Concentration of Priority Marine Regions of the Northwest of Mexico
Authors: Robles-Tamayo Carlos Manuel , García-Morales Ricardo , Romo-León José Raúl , Figueroa-Preciado Gudelia , Peñalba-Garmendia María Cristina , Enríquez-Ocaña Luis Fernando
Published: October 2022

Priority Marine Regions (PMR) are important areas for biodiversity conservation in the Northwest Pacific Ocean in Mexico. The oceanographic dynamics of these regions are very important to understand their variability, generate analyses, and predict climate change trends by generating an adequate management of marine resources and their ecological characterization. Chlorophyll a (Chl a) is important to quantify phytoplankton biomass, consider the main basis of the trophic web in marine ecosystems, and determine the primary productivity levels and trends of change. The objective of this research is to analyze the oceanographic variability of 24 PMR through monthly 1-km satellite image resolution Chl a data from September 1997 to October 2018. A cluster analysis of Chl a data yielded 18 regions with clear seasonal variability in the Chl a concentration in the South-Californian Pacific (maximum values in spring-summer and minimum ones in autumn-winter) and Gulf of California (maximum values in winter-spring and minimum ones in summer-autumn). Significant differences (p < 0.05) were observed in Chl a concentration analyses for each one of the regions when climate patterns—El Niño/La Niña Southern Oscillation (ENSO) and normal events—were compared for all the seasons of the year (spring, summer, autumn, and winter).
A Novel Algorithm Modelling for UWB Localization Accuracy in Remote Sensing
Authors: Yu Zhengyu , Chaczko Zenon , Shi Jiajia
Published: October 2022

At present, the ultra-wideband (UWB) technology plays a vital role in the environment of indoor localization. As a new technology of wireless communications, UWB has many advantages, such as high accuracy, strong anti-multipath ability, and high transmission rate. However, in real-time operation, the accuracy of UWB is reduced by multi-sensor interference, antenna variations and system operation noise. We have developed a novel error modelling based on the curve fitted Kalman filter (CFKF) algorithm to solve these issues. This paper involves investigating and developing the error modelling algorithm that can calibrate the signal sensors, reduce the errors, and mitigate noise levels and interference signals. As part of the research investigation, a range of experiments was executed to validate the CFKF error modelling approach’s accuracy, reliability and viability. The experimental results indicate that this novel approach significantly improves the accuracy and precision of beacon-based localization. Validation tests also show that the CFKF error modelling method can improve the localization accuracy of UWB-based solutions.
Improving Estimates and Change Detection of Forest Above-Ground Biomass Using Statistical Methods
Authors: Turton Amber E. , Augustin Nicole H. , Mitchard Edward T. A.
Published: October 2022

Forests store approximately as much carbon as is in the atmosphere, with potential to take in or release carbon rapidly based on growth, climate change and human disturbance. Above-ground biomass (AGB) is the largest carbon pool in most forest systems, and the quickest to change following disturbance. Quantifying AGB on a global scale and being able to reliably map how it is changing, is therefore required for tackling climate change by targeting and monitoring policies. AGB can be mapped using remote sensing and machine learning methods, but such maps have high uncertainties, and simply subtracting one from another does not give a reliable indication of changes. To improve the quantification of AGB changes it is necessary to add advanced statistical methodology to existing machine learning and remote sensing methods. This review discusses the areas in which techniques used in statistical research could positively impact AGB quantification. Nine global or continental AGB maps, and a further eight local AGB maps, were investigated in detail to understand the limitations of techniques currently used. It was found that both modelling and validation of maps lacked spatial consideration. Spatial cross validation or other sampling methods, which specifically account for the spatial nature of this data, are important to introduce into AGB map validation. Modelling techniques which capture the spatial nature should also be used. For example, spatial random effects can be included in various forms of hierarchical statistical models. These can be estimated using frequentist or Bayesian inference. Strategies including hierarchical modelling, Bayesian inference, and simulation methods can also be applied to improve uncertainty estimation. Additionally, if these uncertainties are visualised using pixelation or contour maps this could improve interpretation. Improved uncertainty, which is commonly between 30% and 40%, is in addition needed to produce accurate change maps which will benefit policy decisions, policy implementation, and our understanding of the carbon cycle.
Assessment of Iran’s Mangrove Forest Dynamics (1990–2020) Using Landsat Time Series
Authors: Erfanifard Yousef , Lotfi Nasirabad Mohsen , Stereńczak Krzysztof
Published: October 2022

Mangrove forests distributed along the coast of southern Iran are an important resource and a vital habitat for species communities and the local people. In this study, accurate mapping and spatiotemporal change detection were conducted on Iran’s mangroves for three decades, using the Landsat imagery available for the years 1990, 2000, 2010, and 2020. Four general vegetation indices and eight mangrove-specific indices were employed for mangrove mapping in three study sites. Additionally, six important landscape metrics were implemented to quantify the spatiotemporal alteration of the mangrove forests during the study period. Our results showed the robustness of the submerged mangrove recognition index (SMRI), validated as the most effective index (F1-score ≥ 0.89), which was used for mangrove identification within all nine sites. The mangrove area of southern Iran was estimated at approximately 13,000 ha in 2020, with an overall increase of 2313 ha over the whole period. A similar trend could be observed for both the landscape connectivity and complexity. Our results revealed that a stronger connectivity and higher complexity could be detected in most sites, while there was increased fragmentation and a weaker connection in some locations. This study provides an accurate map of Iran’s mangrove forests over time and space.
Hyperspectral Reconnaissance: Joint Characterization of the Spectral Mixture Residual Delineates Geologic Unit Boundaries in the White Mountains, CA
Authors: Sousa Francis J. , Sousa Daniel J.
Published: October 2022

We use a classic locale for geology education in the White Mountains, CA, to demonstrate a novel approach for using imaging spectroscopy (hyperspectral imaging) to generate base maps for the purpose of geologic mapping. The base maps produced in this fashion are complementary to, but distinct from, maps of mineral abundance. The approach synthesizes two concepts in imaging spectroscopy data analysis: the spectral mixture residual and joint characterization. First, the mixture residual uses a linear, generalizable, and physically based continuum removal model to mitigate the confounding effects of terrain and vegetation. Then, joint characterization distinguishes spectrally distinct geologic units by isolating residual, absorption-driven spectral features as nonlinear manifolds. Compared to most traditional classifiers, important strengths of this approach include physical basis, transparency, and near-uniqueness of result. Field validation confirms that this approach can identify regions of interest that contribute significant complementary information to PCA alone when attempting to accurately map spatial boundaries between lithologic units. For a geologist, this new type of base map can complement existing algorithms in exploiting the coming availability of global hyperspectral data for pre-field reconnaissance and geologic unit delineation.
An Improved RANSAC Outlier Rejection Method for UAV-Derived Point Cloud
Authors: Salehi Bahram , Jarahizadeh Sina , Sarafraz Amin
Published: October 2022

A common problem with matching algorithms, in photogrammetry and computer vision, is the imperfection of finding all correct corresponding points, so-called inliers, and, thus, resulting in incorrect or mismatched points, so-called outliers. Many algorithms, including the well-known randomized random sample consensus (RANSAC)-based matching, have been developed focusing on the reduction of outliers. RANSAC-based methods, however, have limitations such as increased false positive rates of outliers, and, consequently resulting in fewer inliers, an unnecessary high number of iterations, and high computational time. Such deficiencies possibly result from the random sampling process, the presence of noise, and incorrect assumptions of the initial values. This paper proposes a modified version of RANSAC-based methods, called Empowered Locally Iterative SAmple Consensus (ELISAC). ELISAC improves RANSAC by utilizing three basic modifications individually or in combination. These three modifications are (a) to increase the stability and number of inliers using two Locally Iterative Least Squares (LILS) loops (Basic LILS and Aggregated-LILS), based on the new inliers in each loop, (b) to improve the convergence rate and consequently reduce the number of iterations using a similarity termination criterion, and (c) to remove any possible outliers at the end of the processing loop and increase the reliability of results using a post-processing procedure. In order to validate our proposed method, a comprehensive experimental analysis has been done on two datasets. The first dataset contains the commonly-used computer vision image pairs on which the state-of-the-art RANSAC-based methods have been evaluated. The second dataset image pairs were captured by a drone over a forested area with various rotations, scales, and baselines (from short to wide). The results show that ELISAC finds more inliers with a faster speed (lower computational time) and lower error (outlier) rates compared to M-estimator SAmple Consensus (MSAC). This makes ELISAC an effective approach for image matching and, consequently, for 3D information extraction of very high and super high-resolution imagery acquired by space-borne, airborne, or UAV sensors. In particular, for applications such as forest 3D modeling and tree height estimations where standard matching algorithms are problematic due to spectral and textural similarity of objects (e.g., trees) on image pairs, ELISAC can significantly outperform the standard matching algorithms.
Classifying Sparse Vegetation in a Proglacial Valley Using UAV Imagery and Random Forest Algorithm
Authors: Zangerl Ulrich , Haselberger Stefan , Kraushaar Sabine
Published: October 2022

Extreme hydro-meteorological events become an increasing risk in high mountain environments, resulting in erosion events that endanger human infrastructure and life. Vegetation is known to be an important stabilizing factor; however, little is known about the spatial patterns of species composition in glacial forelands. This investigation aims to differentiate sparse vegetation in a steep alpine environment in the Austrian part of the Central Eastern Alps using low-cost multispectral cameras on an unmanned aerial vehicle (UAV). Highly resolved imagery from a consumer-grade UAV proved an appropriate basis for the SfM-based modeling of the research area as well as for vegetation mapping. Consideration must be paid to changing light conditions during data acquisition, especially with multispectral sensors. Different approaches were tested, and the best results were obtained using the Random Forest (RF) algorithm with the target class discrimination based on the RGB orthomosaic and the DEM as supplementary dataset. Our work contributes to the field of biogeomorphic research in proglacial areas as well as to the field of small-scale remote sensing and vegetation measuring. Our findings show that the occurrence of vegetation patches differs in terms of density and diversity within this relatively recent deglaciated environment.
Multi-Modal Feature Fusion Network with Adaptive Center Point Detector for Building Instance Extraction
Authors: Yuan Qinglie , Mohd Shafri Helmi Zulhaidi
Published: October 2022

Building information extraction utilizing remote sensing technology has vital applications in many domains, such as urban planning, cadastral mapping, geographic information censuses, and land-cover change analysis. In recent years, deep learning algorithms with strong feature construction ability have been widely used in automatic building extraction. However, most methods using semantic segmentation networks cannot obtain object-level building information. Some instance segmentation networks rely on predefined detectors and have weak detection ability for buildings with complex shapes and multiple scales. In addition, the advantages of multi-modal remote sensing data have not been effectively exploited to improve model performance with limited training samples. To address the above problems, we proposed a CNN framework with an adaptive center point detector for the object-level extraction of buildings. The proposed framework combines object detection and semantic segmentation with multi-modal data, including high-resolution aerial images and LiDAR data, as inputs. Meanwhile, we developed novel modules to optimize and fuse multi-modal features. Specifically, the local spatial–spectral perceptron can mutually compensate for semantic information and spatial features. The cross-level global context module can enhance long-range feature dependence. The adaptive center point detector explicitly models deformable convolution to improve detection accuracy, especially for buildings with complex shapes. Furthermore, we constructed a building instance segmentation dataset using multi-modal data for model training and evaluation. Quantitative analysis and visualized results verified that the proposed network can improve the accuracy and efficiency of building instance segmentation.
Water Quality and Water Hyacinth Monitoring with the Sentinel-2A/B Satellites in Lake Tana (Ethiopia)
Authors: Mucheye Tadesse , Haro Sara , Papaspyrou Sokratis , Caballero Isabel
Published: October 2022

Human activities coupled with climate change impacts are becoming the main factors in decreasing inland surface water quantity and quality, leading to the disturbance of the aquatic ecological balance. Under such conditions, the introduction and proliferation of aquatic invasive alien species are more likely to occur. Hence, frequent surface water quality monitoring is required for aquatic ecosystem sustainability. The main objectives of the present study are to analyze the seasonal variation in the invasive plant species water hyacinth (Pontederia crassipes) and biogeochemical water quality parameters, i.e., chlorophyll-a (Chl-a) and total suspended matter (TSM), and to examine their relationship in Lake Tana (Ethiopia) during a one-year study period (2020). Sentinel-2A/B satellite images are used to monitor water hyacinth expansion and Chl-a and TSM concentrations in the water. The Case 2 Regional Coast Colour processor (C2RCC) is used for atmospheric and sunglint correction over inland waters, while the Sen2Cor atmospheric processor is used to calculate the normalized difference vegetation index (NDVI) for water hyacinth mapping. The water hyacinth cover and biomass are determined by NDVI values ranging from 0.60 to 0.95. A peak in cover and biomass is observed in October 2020, just a month after the peak of Chl-a (25.2 mg m−3) and TSM (62.5 g m−3) concentrations observed in September 2020 (end of the main rainy season). The influx of sediment and nutrient load from the upper catchment area during the rainy season could be most likely responsible for both Chl-a and TSM increased concentrations. This, in turn, created a fertile situation for water hyacinth proliferation in Lake Tana. Overall, the freely available Sentinel-2 satellite imagery and appropriate atmospheric correction processors are an emerging potent tool for inland water monitoring and management in large-scale regions under a global change scenario.
PerDet: Machine-Learning-Based UAV GPS Spoofing Detection Using Perception Data
Authors: Wei Xiaomin , Wang Yao , Sun Cong
Published: October 2022

To ensure that unmanned aerial vehicle (UAV) positioning is not affected by GPS spoofing signals, we propose PerDet, a perception-data-based UAV GPS spoofing detection approach utilizing machine learning algorithms. Based on the principle of the position estimation process and attitude estimation process, we choose the data gathered by the accelerometer, gyroscope, magnetometer, GPS and barometer as features. Although these sensors have different shortcomings, their variety makes sure that the selected perception data can compensate for each other. We collect the experimental data through real flights, which make PerDet more practical. Furthermore, we run various machine learning algorithms on our dataset and select the most effective classifier as the detector. Through the performance evaluation and comparison, we demonstrate that PerDet is better than existing methods and is an effective method with a detecting rate of 99.69%. For a fair comparison, we reproduce the existing method and run it on our dataset to compare the performance between this method and our PerDet approach.
Factors Controlling a Synthetic Aperture Radar (SAR) Derived Root-Zone Soil Moisture Product over The Seward Peninsula of Alaska
Authors: Dann Julian , Bennett Katrina E. , Bolton W. Robert , Wilson Cathy J.
Published: October 2022

Root-zone soil moisture exerts a fundamental control on vegetation, energy balance, and the carbon cycle in Arctic ecosystems, but it is still not well understood in vast, remote, and understudied regions of discontinuous permafrost. The root-zone soil moisture product (30 m resolution) used in this analysis was retrieved from a time-series P-Band (420–440 MHz) synthetic aperture radar (SAR) backscatter observations (August 2017 & October 2017). While similar approaches have been taken to retrieve surface (0 cm to 5 cm) soil moisture from L-Band (1.2 GHz) SAR backscatter, this is one of the first known attempts at reaching the root-zone in permafrost regions. Here, we analyze secondary factors (excluding primary factors, such as precipitation) controlling summer (August) soil moisture at depths of 6 cm, 12 cm, and 20 cm over a 4500 km2 area on the Seward Peninsula of Alaska. Using a random forest model, we quantify the impact of topography, vegetation, and meteorological factors on soil moisture distributions. In developing the random forest model, we explore a variety of feature scales (30 m, 60 m, 90 m, 120 m, 180 m, and 240 m), tune hyperparameters (the structure of individual decision trees making up the ensemble including the number and depth of trees), and perform the final feature selection using cross-validated recursive feature elimination. Results suggest that root-zone soil moisture on the Seward Peninsula is primarily controlled by vegetation at 6 cm, but deeper in the soil column topography and meteorological factors, such as predominant winter wind direction and summer insolation, play a larger role. The random forest model accounts for 40% to 60% of the variation observed (R2 = 0.44 at 6 cm, R2 = 0.52 at 12 cm, R2 = 0.58 at 20 cm). These results indicate that vegetation is the dominant control on soil moisture shallow in the soil column, but the impact of vegetation does not extend to deeper layers retrieved from P-Band SAR backscatter.
Characterizing Spatiotemporal Patterns of Winter Wheat Phenology from 1981 to 2016 in North China by Improving Phenology Estimation
Authors: Wang Shuai , Chen Jin , Shen Miaogen , Shi Tingting , Liu Licong , Zhang Luyun , Dong Qi , Wang Cong
Published: October 2022

Phenology provides important information for wheat growth management and the estimation of wheat yield and quality. The relative threshold method has been widely used to retrieve phenological metrics from remotely sensed data owing to its simplicity. However, the thresholds vary substantially among phenological metrics and locations, hampering us from effectively detecting spatial and temporal variations in winter wheat phenology. In this study, we developed a calibrated relative threshold method based on ground phenological observations. Compared with the traditional relative threshold method, our method can minimize the bias and uncertainty caused by unreasonable thresholds in determining phenological dates. On this basis, seven key phenological dates and three growth periods of winter wheat were estimated from long-term series (1981–2016) of the remotely sensed Normalized Difference Vegetation Index for North China (106°18′–122°41′E, 28°59′–39°57′N). Results show that the pre-wintering phenological dates of winter wheat (i.e., emergence and tillering) occurred in December in the south and in mid- to late- October in the north, while the post-wintering phenological dates (i.e., green-up onset, jointing, heading, milky stage, and maturity) exhibited the opposite pattern, that is, January to May in the south and February to June in the north. Consequently, the vegetative growth period increased from 49 days in the south to 77 in the north, and the reproductive growth period decreased from 51 days to 29 days. At the regional scale, all winter wheat phenological dates predominantly advanced, with the most pronounced advancement being for green-up onset (–0.10 days/year, p > 0.1), emergence (–0.09 days/year, p > 0.1), and jointing (–0.08 days/year, p > 0.1). The vegetative growth period and reproductive growth period at the regional scale predominantly extended by 0.03 (p > 0.1) and 0.09 (p < 0.001) days/year, respectively. In general, the later phenological events (i.e., heading, milky stage, and maturity) tended to advance with higher temperature, while the earlier phenological events (i.e., emergence, tillering, green-up onset, and jointing) showed a weak correlation with temperature, suggesting that the earlier events might be mainly affected by management while later ones were more responsive to warming. These findings provide a critical reference for improving winter wheat management under the ongoing climate warming.
Use of Remote Sensing Techniques to Estimate Plant Diversity within Ecological Networks: A Worked Example
Authors: Liccari Francesco , Sigura Maurizia , Bacaro Giovanni
Published: October 2022

As there is an urgent need to protect rapidly declining global diversity, it is important to identify methods to quickly estimate the diversity and heterogeneity of a region and effectively implement monitoring and conservation plans. The combination of remotely sensed and field-collected data, under the paradigm of the Spectral Variation Hypothesis (SVH), represents one of the most promising approaches to boost large-scale and reliable biodiversity monitoring practices. Here, the potential of SVH to capture information on plant diversity at a fine scale in an ecological network (EN) embedded in a complex landscape has been tested using two new and promising methodological approaches: the first estimates α and β spectral diversity and the latter ecosystem spectral heterogeneity expressed as Rao’s Quadratic heterogeneity measure (Rao’s Q). Both approaches are available thanks to two brand-new R packages: “biodivMapR” and “rasterdiv”. Our aims were to investigate if spectral diversity and heterogeneity provide reliable information to assess and monitor over time floristic diversity maintained in an EN selected as an example and located in northeast Italy. We analyzed and compared spectral and taxonomic α and β diversities and spectral and landscape heterogeneity, based on field-based plant data collection and remotely sensed data from Sentinel-2A, using different statistical approaches. We observed a positive relationship between taxonomic and spectral diversity and also between spectral heterogeneity, landscape heterogeneity, and the amount of alien species in relation to the native ones, reaching a value of R2 = 0.36 and R2 = 0.43, respectively. Our results confirmed the effectiveness of estimating and mapping α and β spectral diversity and ecosystem spectral heterogeneity using remotely sensed images. Moreover, we highlighted that spectral diversity values become more effective to identify biodiversity-rich areas, representing the most important diversity hotspots to be preserved. Finally, the spectral heterogeneity index in anthropogenic landscapes could be a powerful method to identify those areas most at risk of biological invasion.
Crop Water Productivity Mapping and Benchmarking Using Remote Sensing and Google Earth Engine Cloud Computing
Authors: Ghorbanpour Ali Karbalaye , Kisekka Isaya , Afshar Abbas , Hessels Tim , Taraghi Mahdi , Hessari Behzad , Tourian Mohammad J. , Duan Zheng
Published: October 2022

Scarce water resources present a major hindrance to ensuring food security. Crop water productivity (WP), embraced as one of the Sustainable Development Goals (SDGs), is playing an integral role in the performance-based evaluation of agricultural systems and securing sustainable food production. This study aims at developing a cloud-based model within the Google Earth Engine (GEE) based on Landsat -7 and -8 satellite imagery to facilitate WP mapping at regional scales (30-m resolution) and analyzing the state of the water use efficiency and productivity of the agricultural sector as a means of benchmarking its WP and defining local gaps and targets at spatiotemporal scales. The model was tested in three major agricultural districts in the Lake Urmia Basin (LUB) with respect to five crop types, including irrigated wheat, rainfed wheat, apples, grapes, alfalfa, and sugar beets as the major grown crops. The actual evapotranspiration (ET) was estimated using geeSEBAL based on the Surface Energy Balance Algorithm for Land (SEBAL) methodology, while for crop yield estimations Monteith’s Light Use Efficiency model (LUE) was employed. The results indicate that the WP in the LUB is below its optimum targets, revealing that there is a significant degree of work necessary to ameliorate the WP in the LUB. The WP varies between 0.49–0.55 (kg/m3) for irrigated wheat, 0.27–0.34 for rainfed wheat, 1.7–2.2 for apples, 1.2–1.7 for grapes, 5.5–6.2 for sugar beets, and 0.67–1.08 for alfalfa, which could be potentially increased up to 80%, 150%, 76%, 83%, 55%, and 48%, respectively. The spatial variation of the WP and crop yield makes it feasible to detect the areas with the best and poorest on-farm practices, thereby facilitating the better targeting of resources to bridge the WP gap through water management practices. This study provides important insights into the status and potential of WP with possible worldwide applications at both farm and government levels for policymakers, practitioners, and growers to adopt effective policy guidelines and improve on-farm practices.
Clouds in the Vicinity of the Stratopause Observed with Lidars at Midlatitudes (40.5–41°N) in China
Authors: Gong Shaohua , Wang Yuru , Guo Jianchun , Chen Weipeng , Zhang Yuhao , Li Faquan , Xun Yuchang , Xu Jiyao , Cheng Xuewu , Yang Guotao
Published: October 2022

Based on long-term lidar (light detection and ranging) observations at Yanqing (40.5°N, 116°E) and Pingquan (41°N, 118.7°E), cloud events occurred in the vicinity of the stratopause above Beijing were reported for the first time. These events occurred with tenuous and sparse layers within the altitude range of 33–65 km, and the maximum VBSC value ranged from 1×10−10m−1sr−1 to 5.5×10−9m−1sr−1. Considering temperature and water vapor measurements from SABER/TIMED, the occurrence mechanism of these lidar-observed cloud events was examined. It was found that some cloud layers resulted from the nucleation of water vapor due to the local meteorological changes in the middle atmosphere, while other lidar-observed clouds could comprise floating clusters of cosmic dust, hydrate droplets, volcanic ash, space traffic exhaust, etc. These cloud events are rare cloud-like phenomena in the middle atmosphere observed by lidars at midlatitudes in China; they differ from NLCs and PSCs in terms of altitude distribution and seasonal variation, and the relevant microphysics processes behind their occurrence are likely meaningful to meteorology at midlatitudes.
Aerosols on the Tropical Island of La Réunion (21°S, 55°E): Assessment of Climatology, Origin of Variability and Trend
Authors: Duflot Valentin , Bègue Nelson , Pouliquen Marie-Léa , Goloub Philippe , Metzger Jean-Marc
Published: October 2022

Aerosols are essential climate variables that need to be observed at a global scale to monitor the evolution of the atmospheric composition and potential climate impacts. We used the measurements performed over the May 2007–December 2019 period by a ground-based sun photometer installed at the island of La Réunion (21°S, 55°E), together with a linear regression fitting model, to assess the climatology and types of aerosols reaching this observation site located in a sparsely documented pristine area, and the forcings responsible for the variability of the observed aerosol optical depth (AOD) and related trend. The climatology of the aerosol optical depth (AOD) at 440 nm (AOD440) and Ångström exponent between 500 and 870 nm (α) revealed that sea salts could be considered as the La Réunion AOD440 and α baselines (0.06 ± 0.03 and 0.61 ± 0.40, respectively, from December to August), which were mainly modulated by biomass burning (BB) plumes passing over La Réunion (causing a doubling of AOD440 and α up to 0.13 ± 0.07 and 1.06 ± 0.34, respectively, in October). This was confirmed by the retrieved aerosol volume size distributions showing that the coarse-mode (fine-mode) dominated the total volume concentration for AOD440 lower (higher) than 0.2 with a mean radius equal to 3 μm (0.15 μm). The main contribution to the AOD440 variability over La Réunion was evaluated to be the BB activity (67.4 ± 28.1%), followed by marine aerosols (16.3 ± 4.2%) and large-scale atmospheric structures (5.5 ± 1.7%). The calculated trend for AOD440 equaled 0.02 ± 0.01 per decade (2.6 ± 1.3% per year). These results provide a scientific reference base for upcoming studies dedicated to the quantification of the impact of wildfire emissions on the southwestern Indian Ocean’s atmospheric composition and radiative balance.
Technical Challenges for Multi-Temporal and Multi-Sensor Image Processing Surveyed by UAV for Mapping and Monitoring in Precision Agriculture
Authors: Lambertini Alessandro , Mandanici Emanuele , Tini Maria Alessandra , Vittuari Luca
Published: October 2022

Precision Agriculture (PA) is an approach to maximizing crop productivity in a sustainable manner. PA requires up-to-date, accurate and georeferenced information on crops, which can be collected from different sensors from ground, aerial or satellite platforms. The use of optical and thermal sensors from Unmanned Aerial Vehicle (UAV) platform is an emerging solution for mapping and monitoring in PA, yet many technological challenges are still open. This technical note discusses the choice of UAV type and its scientific payload for surveying a sample area of 5 hectares, as well as the procedures for replicating the study on a larger scale. This case study is an ideal opportunity to test the best practices to combine the requirements of PA surveys with the limitations imposed by local UAV regulations. In the field area, to follow crop development at various stages, nine flights over a period of four months were planned and executed. The usage of ground control points for optimal georeferencing and accurate alignment of maps created by multi-temporal processing is analyzed. Output maps are produced in both visible and thermal bands, after appropriate strip alignment, mosaicking, sensor calibration, and processing with Structure from Motion techniques. The discussion of strategies, checklists, workflow, and processing is backed by data from more than 5000 optical and radiometric thermal images taken during five hours of flight time in nine flights throughout the crop season. The geomatics challenges of a georeferenced survey for PA using UAVs are the key focus of this technical note. Accurate maps derived from these multi-temporal and multi-sensor surveys feed Geographic Information Systems (GIS) and Decision Support Systems (DSS) to benefit PA in a multidisciplinary approach.
Improving the Performance of Automated Rooftop Extraction through Geospatial Stratified and Optimized Sampling
Authors: Sun Zhuo , Zhang Zhixin , Chen Min , Qian Zhen , Cao Min , Wen Yongning
Published: October 2022

Accurate and timely access to building rooftop information is very important for urban management. The era of big data brings new opportunities for rooftop extraction based on deep learning and high-resolution satellite imagery. However, collecting representative datasets from such big data to train deep learning models efficiently is an essential problem that still needs to be explored. In this study, geospatial stratified and optimized sampling (GSOS) based on geographical priori information and optimization of sample spatial location distribution is proposed to acquire representative samples. Specifically, the study area is stratified based on land cover to divide the rooftop-dense stratum and the rooftop-sparse stratum. Within each stratum, an equal amount of samples is collected and their spatial locations are optimized. To evaluate the effectiveness of the proposed strategy, several qualitive and quantitative experiments are conducted. As a result, compared with other common sampling approaches (e.g., random sampling, stratified random sampling, and optimized sampling), GSOS is superior in terms of the abundance and types of collected samples. Furthermore, two quantitative metrics, the F1-score and Intersection over Union (IoU), are reported for rooftop extraction based on deep learning methods and different sampling methods, in which the results based on GSOS are on average 9.88% and 13.20% higher than those based on the other sampling methods, respectively. Moreover, the proposed sampling strategy is able to obtain representative training samples for the task of building rooftop extractions and may serve as a viable method to alleviate the labour-intensive problem in the construction of rooftop benchmark datasets.
Reconstruction of Rainfall Field Using Earth–Space Links Network: A Compressed Sensing Approach
Authors: Zhao Yingcheng , Liu Xichuan , Liu Lei , Pu Kang , Song Kun
Published: October 2022

High-precision rainfall information is of great importance for the improvement of the accuracy of numerical weather prediction and the monitoring of floods and mudslides that affect human life. With the rapid development of satellite constellation networks, there is great potential for reconstructing high-precision rainfall fields in large areas by using widely distributed Earth–space link (ESL) networks. In this paper, we have carried out research on reconstructing high-precision rainfall fields using an ESL network with the compressed sensing (CS) method in the case of a sparse distribution of the ESLs. Firstly, ESL networks with different densities are designed using the K-means clustering algorithm. The real rainfall fields are then reconstructed using the designed ESL networks with CS, and the reconstructed results are compared with that of the inverse distance weighting (IDW) algorithm. The results show that the root mean square error (RMSE) and correlation coefficient (CC) of the reconstructed rainfall fields using the ESL network with CS are lower than 0.15 mm/h and higher than 0.999, respectively, when the density is 0.05 links per square kilometer, indicating that the ESL network with CS is capable of reconstructing the high-precision rainfall fields under sparse sampling. Additionally, the performance of reconstructing the rainfall fields using the ESL networks with CS is superior compared to the reconstructed results of the IDW algorithm.