bluuebunny/crossref_metadata_2025 huggingface dataset:
The Relationship between Quality of Life and Physical Exercise with Depression and Perceived Stress during the Second COVID-19 Lockdown in Greece
Authors: Kolias Pavlos , Pliafa Ourania
Published: September 2022

Anxiety, depression, and psychological stress were the most common mental health issues that surfaced during and after the COVID-19 lockdowns. The aim of this paper is to investigate the psychological impact of the second COVID-19 lockdown on the Greek population. A cross-sectional anonymous study was designed, which measured perceived stress, depression symptoms, physical activity, and quality of life. The sample was collected during the period of the second lockdown and consisted of 330 adult individuals (219 females and 111 males) with a mean age of 34.3 years, who were located in Greece. Four scales were applied to measure the constructs, the Perceived Stress Scale (PSS-14), Beck’s Depression Inventory (BDI), Quality of Life-BREF (WHOQOL-BREF), and the International Physical Activity Questionnaire (IPAQ). The main findings suggest that depression and perceived stress were more prevalent during the second lockdown compared to previous time periods. The psychological impact was elevated for women and younger individuals. Unemployed people dealt with more stress compared to full-time working individuals. The two quality-of-life domains, physical and psychological health, were negatively correlated with perceived stress and depression. We suggest targeted interventions in order to support the most vulnerable groups and enhance their well-being.
“Who Is the FAIRest of Them All?” Authors, Entities, and Journals Regarding FAIR Data Principles
Authors: Corujo Luis
Published: September 2022

The perceived need to improve the infrastructure supporting the re-use of scholarly data since the second decade of the 21st century led to the design of a concise number of principles and metrics, named FAIR Data Principles. This paper, part of an extended study, intends to identify the main authors, entities, and scientific journals linked to research conducted within the FAIR Data Principles. The research was developed by means of a qualitative approach, using documentary research and a constant comparison method for codification and categorization of the sampled data. The sample studied showed that most authors were located in the Netherlands, with Europe accounting for more than 70% of the number of authors considered. Most of these are researchers and work in higher education institutions. These entities can be found in most of the territorial-administrative areas under consideration, with the USA being the country with more entities and Europe being the world region where they are more numerous. The journal with more texts in the used sample was Insights, with 2020 being the year when more texts were published. Two of the most prominent authors present in the sample texts were located in the Netherlands, while the other two were in France and Australia.
Mid-Late Quaternary Fluvial Archives near the Margin of the MIS 12 Glaciation in Southern East Anglia, UK: Amalgamation of Multi-Disciplinary and Citizen-Science Data Sources
Authors: Allen Peter , Bain David , Bridgland David , Buisson Paul , Buylaert Jan-Pieter , Bynoe Rachel , George William , Haggart B. , Horne David , Littlewood Ellen-May , Lord Alan , March Anna , Mercer Ian , Mercer Rosalind , Murray Andrew , Penkman Kirsty , Preece Richard , Ratford John , Schreve Danielle , Snelling Andrew , Sohar Kadri , Whittaker John , White Mark , White Tom
Published: September 2022

This paper presents an updated geological reconstruction of the Quaternary evolution of the River Thames at its downstream extremities, close to the North Sea coast, based on new data from multi-disciplinary and citizen-science sources. In this area, the interaction of the Thames with the MIS 12 (Anglian) glaciation is an important part of the Quaternary archive. The Anglian ice sheet, which reached parts of north and east London, was responsible for diverting the Thames southwards into its present course, although the footprint of the maximum ice sheet(s) does not reach the North Sea coast south of Hollesley, Suffolk. Further south, the coastal zone hosts pre-Anglian and early Anglian river-terrace deposits of the pre-diversion Thames system, superimposed upon which are products of later post-Anglian rivers, of both Middle and Late Pleistocene age. On the peninsula between the Stour and Blackwater–Colne estuaries, the lowest and most recent terrace of the pre-diversion Thames includes evidence directly pertaining to the glacial disruption event, for which geochronological data are reported here for the first time. The first post-diversion terrace of the Thames also reaches this peninsula, the river having essentially re-joined its original valley before crossing the alignment of the modern coastline. This terrace passes beneath Clacton-on-Sea, where it includes the type locality of the Clactonian Palaeolithic Industry. The area of interest to this paper, in NE Essex and southern Suffolk, includes a number of interglacial and Palaeolithic sites, the data from which assist in constraining the chronostratigraphy of the sequence. In some cases, there has been uncertainty as to whether these sites represent pre-Anglian environments and hominin occupations, part of the palaeo-Thames sequence, or whether they are the product of later post-Anglian streams, formed after the Thames had migrated southwards. This paper compiles evidence from a wide range of recent sources, including developer-funded archaeological appraisal and citizen-science activities, to explore and update the evidence from sites at Ipswich, Upper Dovercourt and Thorpe-le-Soken, as well as a number of localities associated with the Clacton Channel Deposits (host to the type-Clactonian), amongst others. The resulting new data are placed within the wider context of the Quaternary fluvial archives in southern Britain, with a discussion of how disparate sources of information, including the work of citizen scientists, have contributed.
Plastics Crash Course: A Website for Teaching Plastics Recycling and Microplastics Prevention through Infographics
Authors: Reed Madison R. , Chen Wan-Ting
Published: September 2022

Microplastic particles have been found virtually everywhere, including within our food and drinking water. While the implications of microplastics on human health are not fully known, early effects have been seen on marine life and the environment. Studies have shown that microplastics can cause changes in the reproductive habits of marine life by blocking digestive tracts, causing abrasions to the mouth and esophagi of small animals upon ingestion, and altering feeding behavior. While much of the blame for our plastics pollution problem should be shifted to irresponsible manufacturing, we as consumers must make choices to benefit the environment by reducing our use and learning how to effectively recycle plastic waste. The Plastics Crash Course combines visual learning with plastics recycling knowledge to educate the public about why we need plastics and why we should recycle them. Microplastics formation and general guides for plastic recycling were also included in the Plastics Crash Course. Out of 120 participants, 95% responded that they had learned new information. From the pre-survey, participants responded, saying they thought all plastic was the same and that it just varied in density to provide different properties, so they would recycle everything. After reading the infographics on the Plastics Crash Course website, most participants said they learned what plastics can be recycled and what their resin identifying codes mean, how microplastics form, and that there is more than one type of plastic.
The Anti-Nationalist Patriotism of Venerable Archbishop Fulton J. Sheen
Authors: Patterson James M.
Published: September 2022

Scholars today regard Venerable Archbishop Fulton J. Sheen as a supporting player in the American efforts to drum up support for the Cold War; however, this view limits Sheen’s influence to the years he spent on television hosting his program, Life Is Worth Living (1952–1957). Yet, by the time Sheen left his program, he had been part of public discussions of religion and American politics for almost thirty years. Before his 1930 debut as an authoritative Catholic voice in America, Sheen had become a decorated Catholic scholar, both in his home country and in Europe, earning him a papal audience and broad support in the American Catholic hierarchy. His early contributions to public discussion were sophisticated adaptations of Leonine Catholic social teaching to American circumstances. Critical to his teachings was his view of the American people as the source for political legitimacy. In this respect, he defied the more reactionary clergy of Europe; however, Sheen’s views were vital to his efforts to distinguish why America had a just war against the totalitarian governments of the Axis powers but also a duty to spare people who were as likely to be victims of the regime as they were supporters. Sheen carried this distinction into the Cold War, in which he called for Americans to support the Russian people by opposing totalitarian government there. Therefore, Sheen never advocated the “us vs. them” nationalism so common among Cold War propaganda, which is consistent with his initial opposition to the Vietnam War and his only partial reconsideration of that opposition later.
Multi-Variate Risk Measures under Wasserstein Barycenter
Authors: Arias-Serna M. Andrea , Loubes Jean Michel , Caro-Lopera Francisco J.
Published: September 2022

When the uni-variate risk measure analysis is generalized into the multi-variate setting, many complex theoretical and applied problems arise, and therefore the mathematical models used for risk quantification usually present model risk. As a result, regulators have started to require that the internal models used by financial institutions are more precise. For this task, we propose a novel multi-variate risk measure, based on the notion of the Wasserstein barycenter. The proposed approach robustly characterizes the company’s exposure, filtering the partial information available from individual sources into an aggregate risk measure, providing an easily computable estimation of the total risk incurred. The new approach allows effective computation of Wasserstein barycenter risk measures in any location–scatter family, including the Gaussian case. In such cases, the Wasserstein barycenter Value-at-Risk belongs to the same family, thus it is characterized just by its mean and deviation. It is important to highlight that the proposed risk measure is expressed in closed analytic forms which facilitate its use in day-to-day risk management. The performance of the new multi-variate risk measures is illustrated in United States market indices of high volatility during the global financial crisis (2008) and during the COVID-19 pandemic situation, showing that the proposed approach provides the best forecasts of risk measures not only for “normal periods”, but also for periods of high volatility.
Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)
Authors: Smith Douglas Shane , Sevil Hakki Erhan
Published: September 2022

The aim of this research effort was to develop a framework for a structure from motion (SfM)-based 3D reconstruction approach with a team of autonomous small unmanned aerial systems (sUASs) using a distributed behavior model. The framework is composed of two major goals to accomplish this: a distributed behavior model for a team of sUASs and a SfM-based 3D reconstruction using team of sUASs. The developed distributed behavior model is based on the entropy of the system, and when the entropy of the system is high, the sUASs get closer to reducing the overall entropy. This is called the grouping phase. If the entropy is less than the predefined threshold, then the sUASs switch to the 3D reconstruction phase. The novel part of the framework is that sUASs are only given the object of interest to reconstruct the 3D model, and they use the developed distributed behavior to coordinate their motion for that goal. A comprehensive parameter analysis was performed, and optimum sets of parameters were selected for each sub-system. Finally, optimum parameters for two sub-systems were combined in a simulation to demonstrate the framework’s operability and evaluate the completeness and speed of the reconstructed model. The simulation results show that the framework operates successfully and is capable of generating complete models as desired, autonomously.
Collision Avoidance for Redundant 7-DOF Robots Using a Critically Damped Dynamic Approach
Authors: Simas Henrique , Di Gregorio Raffaele
Published: September 2022

The presence of collaborative robots in industrial environments requires that their control strategies include collision avoidance in the generation of trajectories. In general, collision avoidance is performed via additional displacements of the kinematic chain that make the robot move far from the objects that are occasionally inserted into its safety workspace. The variability of the coordinates of the collision points inside the safety volume leads to abrupt movements for the robot. This paper presents a general method for smoothing abrupt movements in robots with one degree of redundancy for collision-avoidance trajectories, employing a second-order digital filter designed with adjustable critical damping. The method is illustrated by applying it to a redundant robot with a spherical–revolute–spherical type (SRS-type) kinematic chain, which is a benchmark used to test the algorithms ideated for solving this problem. This paper also presents an alternative algorithm for the inverse kinematics of the SRS-type robot and the computational experiments that show the collision avoidance proposal’s performance and its properties through graphical results.
Road Condition Detection and Emergency Rescue Recognition Using On-Board UAV in the Wildness
Authors: Liu Chang , Szirányi Tamás
Published: September 2022

Unmanned aerial vehicle (UAV) vision technology is becoming increasingly important, especially in wilderness rescue. For humans in the wilderness with poor network conditions and bad weather, this paper proposes a technique for road extraction and road condition detection from video captured by UAV multispectral cameras in real-time or pre-downloaded multispectral images from satellites, which in turn provides humans with optimal route planning. Additionally, depending on the flight altitude of the UAV, humans can interact with the UAV through dynamic gesture recognition to identify emergency situations and potential dangers for emergency rescue or re-routing. The purpose of this work is to detect the road condition and identify emergency situations in order to provide necessary and timely assistance to humans in the wild. By obtaining a normalized difference vegetation index (NDVI), the UAV can effectively distinguish between bare soil roads and gravel roads, refining the results of our previous route planning data. In the low-altitude human–machine interaction part, based on media-pipe hand landmarks, we combined machine learning methods to build a dataset of four basic hand gestures for sign for help dynamic gesture recognition. We tested the dataset on different classifiers, and the best results show that the model can achieve 99.99% accuracy on the testing set. In this proof-of-concept paper, the above experimental results confirm that our proposed scheme can achieve our expected tasks of UAV rescue and route planning.
A Novel Historical Landslide Detection Approach Based on LiDAR and Lightweight Attention U-Net
Authors: Fang Chengyong , Fan Xuanmei , Zhong Hao , Lombardo Luigi , Tanyas Hakan , Wang Xin
Published: September 2022

Rapid and accurate identification of landslides is an essential part of landslide hazard assessment, and in particular it is useful for land use planning, disaster prevention, and risk control. Recent alternatives to manual landslide mapping are moving in the direction of artificial intelligence—aided recognition of these surface processes. However, so far, the technological advancements have not produced robust automated mapping tools whose domain of validity holds in any area across the globe. For instance, capturing historical landslides in densely vegetated areas is still a challenge. This study proposed a deep learning method based on Light Detection and Ranging (LiDAR) data for automatic identification of historical landslides. Additionally, it tested this method in the Jiuzhaigou earthquake-hit region of Sichuan Province (China). Specifically, we generated a Red Relief Image Map (RRIM), which was obtained via high-precision airborne LiDAR data, and on the basis of this information we trained a Lightweight Attention U-Net (LAU-Net) to map a total of 1949 historical landslides. Overall, our model recognized the aforementioned landslides with high accuracy and relatively low computational costs. We compared multiple performance indexes across several deep learning routines and different data types. The results showed that the Multiple-Class based Semantic Image Segmentation (MIOU) and the F1_score of the LAU-Net and RRIM reached 82.29% and 87.45%, which represented the best performance among the methods we tested.
Multi-Platform LiDAR for Non-Destructive Individual Aboveground Biomass Estimation for Changbai Larch (Larix olgensis Henry) Using a Hierarchical Bayesian Approach
Authors: Wang Man , Im Jungho , Zhao Yinghui , Zhen Zhen
Published: September 2022

Individual-tree aboveground biomass (AGB) estimation is vital for precision forestry and still worth exploring using multi-platform LiDAR data for high accuracy and efficiency. Based on the unmanned aerial vehicle and terrestrial LiDAR data, this study explores the feasibility of the individual tree AGB estimation of Changbai larch (Larix olgensis Henry) of eight plots from three different regions in Maoershan Forest Farm of Heilongjiang, China, using nonlinear mixed effect model with hierarchical Bayesian approach. Results showed that the fused LiDAR data estimated the individual tree parameters (i.e., diameter at breast height (DBH), tree height (TH), and crown projection area (CPA)) with high accuracies (all R2 > 0.9 and relatively low RMSE and rRMSE) using region-based hierarchical cross-section analysis (RHCSA) algorithm. Considering regions as random variables, the nonlinear mixed-effects AGB model with three predictor variables (i.e., DBH, TH, and CPA) performed better than its corresponding nonlinear model. In addition, the hierarchical Bayesian method provided better model-fitting performances and more stable parameter estimates than the classical method (i.e., nonlinear mixed-effect model), especially for small sample sizes (e.g., <50). This methodology (i.e., multi-platform LiDAR data and the hierarchical Bayesian method) provides a potential solution for non-destructive individual-tree AGB modeling with small sample size and high accuracy in both forestry and remote sensing communities.
Local Matrix Feature-Based Kernel Joint Sparse Representation for Hyperspectral Image Classification
Authors: Chen Xiang , Chen Na , Peng Jiangtao , Sun Weiwei
Published: September 2022

Hyperspectral image (HSI) classification is one of the hot research topics in the field of remote sensing. The performance of HSI classification greatly depends on the effectiveness of feature learning or feature design. Traditional vector-based spectral–spatial features have shown good performance in HSI classification. However, when the number of labeled samples is limited, the performance of these vector-based features is degraded. To fully mine the discriminative features in small-sample case, a novel local matrix feature (LMF) was designed to reflect both the correlation between spectral pixels and the spectral bands in a local spatial neighborhood. In particular, the LMF is a linear combination of a local covariance matrix feature and a local correntropy matrix feature, where the former describes the correlation between spectral pixels and the latter measures the similarity between spectral bands. Based on the constructed LMFs, a simple Log-Euclidean distance-based linear kernel is introduced to measure the similarity between them, and an LMF-based kernel joint sparse representation (LMFKJSR) model is proposed for HSI classification. Due to the superior performance of region covariance and correntropy descriptors, the proposed LMFKJSR shows better results than existing vector-feature-based and matrix-feature-based support vector machine (SVM) and JSR methods on three well-known HSI data sets in the case of limited labeled samples.
Assessment of Real-Time GPS/BDS-2/BDS-3 Single-Frequency PPP and INS Tight Integration Using Different RTS Products
Authors: Lv Jie , Gao Zhouzheng , Xu Qiaozhuang , Lan Ruohua , Yang Cheng , Peng Junhuan
Published: September 2022

Due to the virtues of low-cost and high positioning accuracy, Single-Frequency Precise Point Positioning (SF-PPP) is becoming a prospective technique. However, SF-PPP is not as widely used as dual-frequency and triple-frequency PPP at present, owing to the effect of ionospheric delay residuals after model rectification. In recent years, with the evolution of multi-constellation Global Navigation Satellite Systems (multi-GNSS, i.e., GPS, BDS-2, and BDS-3), it has become possible to obtain credible and continuous positioning results using SF-PPP. However, such performance would be significantly degraded in challenging environments (i.e., boulevards, tunnels, and tall buildings). Under these circumstances, GNSS signals are obstructed, and it is difficult to provide sufficient observations for SF-PPP. Therefore, the Inertial Navigation System (INS) is employed to promote the positioning performance of SF-PPP. The PPP/INS integration is regarded as one of the most efficient approaches in GNSS-denied environments. To satisfy the request of supplying real-time positioning information, the Real-Time Services (RTS) of the International GNSS Service (IGS) provide real-time precise orbit and clock products for globally distributed users through the internet. In this paper, a real-time GPS/BDS-2/BDS-3 SF-PPP and INS tight integration model is proposed, and it is assessed using the data gathered by vehicle and real-time products afforded by CAS (Chinese Academy of Sciences), GFZ (Deutsche GeoForschungsZentrum), and WHU (Wuhan University). The outcomes illustrate the following: (1) GPS + BDS SF-PPP/INS can provide more accurate and continuous positioning solutions compared with those of GPS + BDS SF-PPP, with improvements of 52.8%, 31.1%, and 42.8% in the north, east, and vertical components, respectively. (2) In general, the orbit and clock products’ accuracies in terms of GPS afforded by the three analysis centers are consistent with each other. For BDS, the orbit product from WHU is more accurate compared to those of CAS and GFZ. However, the accuracy of the clock product afforded by WHU is lower compared with those provided by the other two centers, especially for BDS-2 satellites. (3) The positioning accuracy in terms of Root Mean Square (RMS) values based on GFZ products are much higher than the results based on CAS and WHU products in the three directions.
Surface Deformation Mechanism Analysis in Shanghai Areas Based on TS-InSAR Technology
Authors: Li Jiahao , Zhou Lv , Zhu Zilin , Qin Jie , Xian Lingxiao , Zhang Di , Huang Ling
Published: September 2022

To address the problem that surface deformation causes in urban areas by affecting urban security and threatening human life and property, this study first measured the surface deformation in Shanghai from 2016 to 2020 using the time series InSAR method. Then, the spatial–temporal distribution and evolution characteristics of deformation were investigated in detail. The deformation mechanism is explained by factors including groundwater and rainfall. By introducing the seasonal changes of tides and sediment accumulation, the reason for the uplift in the Shanghai area is further explained. Finally, the surface deformation of the reclamation area is detected further. Meanwhile, the spatial–temporal variation characteristics of the surface in the reclamation area are explored. Through time series InSAR technology, the results of surface deformation in Shanghai demonstrate the following: (1) The deformation in the study area is uneven in time, and the subsidence is especially apparent during the 2016–2017 period. The maximum cumulative subsidence amounts to −131.1 mm, and the PS points with subsidence rates greater than −5 mm/y occupy 41.36% of all the subsidence points. In addition, PS points with uplift rates greater than 5 mm/y account for 39.55% of all the uplift points. The overall spatial distribution in the Shanghai area is characterized by the uplift in the north and subsidence in the south, whereas the cumulative subsidence in the time series presents a slowing trend; (2) Surface subsidence and groundwater, rainfall, and urban development in the Shanghai area are correlated. Seasonal changes in tides contribute to surface uplift in coastal areas. Coastal sediment accumulation and soil changes also make direct contributions to the occurrence of surface uplift; (3) The deformation of the reclamation area and the completion time are correlated, and the subsidence points of the reclamation area are mainly concentrated on the surrounding dikes from 2016 to 2020. The cumulative subsidence of the two years from 2016 to 2017 is up to −102.2 mm. The results of this study systematically explore the spatial–-temporal evolution and causes of surface deformation in Shanghai, providing scientific data which can support the development of Shanghai.
Cloud Detection of Gaofen-2 Multi-Spectral Imagery Based on the Modified Radiation Transmittance Map
Authors: Lin Yi , He Lin , Zhang Yi , Wu Zhaocong
Published: September 2022

For optical remote sensing images with high spatial resolution and low spectral number, the complexity of ground objects poses great challenges to cloud detection algorithms, such as the differentiation of clouds from objects with similar features as clouds and the identification of thin clouds. In this paper, a novel cloud detection method is proposed for Gaofen-2 remote sensing imagery. The radiation transmittance is estimated based on the dark channel prior, and the overestimated radiation transmittance is corrected using spectral features. A three-step post-processing strategy is adopted to eliminate misidentification introduced by the highlighted surfaces based on object geometric, textural, and boundary features. In the experiments, Gaofen-2 multispectral images with different cloud categories and cloud thicknesses are involved to evaluate the performance of the proposed method. The results show that the proposed method can obtain an average cloud detection accuracy of 0.9573 on six different clouds. The proposed algorithm can also effectively detect both thick and thin clouds with an average accuracy of more than 0.9517. The advantages of the method for thin cloud detection are further demonstrated by comparison with existing algorithms.
Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
Authors: Farahani Ali , Moradikhaneghahi Mahsa , Ghayoomi Majid , Jacobs Jennifer M.
Published: September 2022

The proven relationship between soil moisture and seismic ground response highlights the need for a tool to track the Earth’s surface soil moisture before and after seismic events. This paper introduces the application of Soil Moisture Active Passive (SMAP) satellite data for global soil moisture measurement during earthquakes and consequent events. An approach is presented to study areas that experienced high level of increase in soil moisture during eleven earthquakes. Two ancillary datasets, Global Precipitation Measurement (GPM) and Global Land Data Assimilation (GLDAS), were used to isolate areas that had an earthquake-induced increase in soil moisture from those that were due to hydrological processes. SMAP-based soil moisture changes were synthesized with seismic records developed by the United States Geological Survey (USGS), mapped ground failures in reconnaissance reports, and surface changes marked by Synthetic Aperture Radar (SAR)-based damage proxy maps. In the majority of the target earthquakes, including Croatia 2020, Greece 2020, Indonesia 2018, Taiwan 2016, Ecuador 2016, and Nepal 2015, a relationship between the SMAP soil moisture estimates and seismic events was evident. For these events, the earthquake-induced soil moisture response occurred in liquefaction-prone seismic zones. The New Zealand 2016 event was the only study region for which there was a clear inconsistency between ΔSMSMAP and the seismic records. The promising relationship between soil moisture changes and ground deformations indicates that SMAP would be a useful data resource for geotechnical earthquake engineering applications and reconnaissance efforts.
Hyperspectral Band Selection via Band Grouping and Adaptive Multi-Graph Constraint
Authors: You Mengbo , Meng Xiancheng , Wang Yishu , Jin Hongyuan , Zhai Chunting , Yuan Aihong
Published: September 2022

Unsupervised band selection has gained increasing attention recently since massive unlabeled high-dimensional data often need to be processed in the domains of machine learning and data mining. This paper presents a novel unsupervised HSI band selection method via band grouping and adaptive multi-graph constraint. A band grouping strategy that assigns each group different weights to construct a global similarity matrix is applied to address the problem of overlooking strong correlations among adjacent bands. Different from previous studies that are limited to fixed graph constraints, we adjust the weight of the local similarity matrix dynamically to construct a global similarity matrix. By partitioning the HSI cube into several groups, the model is built with a combination of significance ranking and band selection. After establishing the model, we addressed the optimization problem by an iterative algorithm, which updates the global similarity matrix, its corresponding reconstruction weights matrix, the projection, and the pseudo-label matrix to ameliorate each of them synergistically. Extensive experimental results indicate our method outperforms the other five state-of-the-art band selection methods in the publicly available datasets.
Assessment of Ecological Cumulative Effect due to Mining Disturbance Using Google Earth Engine
Authors: Yang Wenfu , Mu Yao , Zhang Wenkai , Wang Wenwen , Liu Jin , Peng Junhuan , Liu Xiaosong , He Tingting
Published: September 2022

Open-pit mining and reclamation damage the land, resulting in unknown and significant changes to the regional ecology and ecosystem services. Surface mining restoration procedures necessitate a significant amount of money, typically at an unclear cost. Due to temporal and regional variability, few studies have focused on the cumulative impacts of mining activities. To investigate the ecological cumulative effects (ECE) of past mining and reclamation activities, this study continuously tracked land cover changes spatially and temporally based on phenological indices and focuses on the spatial and temporal evolution of past mining and reclamation areas using the LandTrendr algorithm. The cumulative trends of ecosystem services in the Pingshuo mining area from 1986 to 2021 were revealed using a uniform standard value equivalent coefficient. Meanwhile, the cumulative ecological effects due to essential ecosystem service functions were analyzed, including soil formation and protection, water containment, biodiversity maintenance, climate regulation, and food production. The synergistic effects and trade-offs among the functions were also explored using Spearman’s correlation coefficient. The results showed that (1) open-pit mining resulted in 93.51 km2 of natural land, 39.60 km2 of disturbed land, and 44.58 km2 of reclaimed land in the Pingshuo mine; (2) open-pit mining in the mine mainly resulted in the loss of 122.18 km2 (80.91%) of native grassland, but, through reclamation into grassland (31.30 km2), cropland (72.95 km2), and forest land (10.62 km2), the damaged area caused by mining only slightly increased; (3) the cumulative ecological value of the mining area declined by 128.78 million RMB; however, the real cumulative value per unit area was lower in the disturbance area (1483.47 million RMB) and the reclamation area (1297.00 million RMB) than in the natural area (2120.98 million RMB); (4) the cumulative value of the food production function in the study area increased, although the values of all individual functions in the study area decreased. Most of the cumulative values of services had a strong synergistic relationship. However, in the natural area, food production (FP) showed a trade-off relationship with the cumulative value of biodiversity maintenance (BM), soil formation and protection (SP), and water conservation (WC) service functions, respectively. This study constructed a methodology for analyzing mining-impacted ecosystem services using time-series processes, reproducing historically complete information for policymakers and environmental regulators.
Using Sentinel-2 Imagery and Machine Learning Algorithms to Assess the Inundation Status of Nebraska Conservation Easements during 2018–2021
Authors: Zhang Ligang , Hu Qiao , Tang Zhenghong
Published: September 2022

Conservation easements (CEs) play an important role in the provision of ecological services. This paper aims to use the open-access Sentinel-2 satellites to advance existing conservation management capacity to a new level of near-real-time monitoring and assessment for the conservation easements in Nebraska. This research uses machine learning and Google Earth Engine to classify inundation status using Sentinel-2 imagery during 2018–2021 for all CE sites in Nebraska, USA. The proposed machine learning approach helps monitor the CE sites at the landscape scale in an efficient and low-cost manner. The results confirmed effective inundation performance in these floodplain or wetland-related CE sites. The CE sites under the Emergency Watershed Protection-Floodplain Easement (EWPP-FPE) had the highest inundated area rate of 18.72%, indicating active hydrological inundation in the floodplain areas. The CE sites under the Wetlands Reserve Program (WRP) reached a mean annual surface water cover rate area of 8.07%, indicating the core wetland areas were inundated periodically or regularly. Other types of CEs serving upland conservation purposes had a lower level of inundation while these uplands conservation provided critical needs in soil erosion control. The mean annual surface water cover rate is 0.96% for the CE sites under the Grassland Reserve Program (GRP). The conservation of the CEs on uplands is an important component to reduce soil erosion and improve downstream wetland hydrological inundation performance. The findings support that the sites with higher inundation frequencies can be considered for future wetland-related conservation practices. The four typical wetland-based CE sites suggested that conservation performance can be improved by implementing hydrological restoration and soil erosion reduction at the watershed scale. The findings provided robust evidence to discover the surface water inundation information on conservation assessment to achieve the long-term goals of conservation easements.
SS R-CNN: Self-Supervised Learning Improving Mask R-CNN for Ship Detection in Remote Sensing Images
Authors: Jian Ling , Pu Zhiqi , Zhu Lili , Yao Tiancan , Liang Xijun
Published: September 2022

Due to the cost of acquiring and labeling remote sensing images, only a limited number of images with the target objects are obtained and labeled in some practical applications, which severely limits the generalization capability of typical deep learning networks. Self-supervised learning can learn the inherent feature representations of unlabeled instances and is a promising technique for marine ship detection. In this work, we design a more-way CutPaste self-supervised task to train a feature representation network using clean marine surface images with no ships, based on which a two-stage object detection model using Mask R-CNN is improved to detect marine ships. Experimental results show that with a limited number of labeled remote sensing images, the designed model achieves better detection performance than supervised baseline methods in terms of mAP. Particularly, the detection accuracy for small-sized marine ships is evidently improved.
Evaluating Statewide NAIP Photogrammetric Point Clouds for Operational Improvement of National Forest Inventory Estimates in Mixed Hardwood Forests of the Southeastern U.S.
Authors: Schroeder Todd A. , Obata Shingo , Papeş Monica , Branoff Benjamin
Published: September 2022

The U.S. Forest Service, Forest Inventory and Analysis (FIA) program is tasked with making and reporting estimates of various forest attributes using a design-based network of permanent sampling plots. To make its estimates more precise, FIA uses a technique known as post-stratification to group plots into more homogenous classes, which helps lower variance when deriving population means. Currently FIA uses a nationally available map of tree canopy cover for post-stratification, which tends to work well for forest area estimates but less so for structural attributes like volume. Here we explore the use of new statewide digital aerial photogrammetric (DAP) point clouds developed from stereo imagery collected by the National Agricultural Imagery Program (NAIP) to improve these estimates in the southeastern mixed hardwood forests of Tennessee and Virginia, United States (U.S.). Our objectives are to 1. evaluate the relative quality of NAIP DAP point clouds using airborne LiDAR and FIA tree height measurements, and 2. assess the ability of NAIP digital height models (DHMs) to improve operational forest inventory estimates above the gains already achieved from FIA’s current post-stratification approach. Our results show the NAIP point clouds were moderately to strongly correlated with FIA field measured maximum tree heights (average Pearson’s r = 0.74) with a slight negative bias (−1.56 m) and an RMSE error of ~4.0 m. The NAIP point cloud heights were also more accurate for softwoods (R2s = 0.60–0.79) than hardwoods (R2s = 0.33–0.50) with an error structure that was consistent across multiple years of FIA measurements. Several factors served to degrade the relationship between the NAIP point clouds and FIA data, including a lack of 3D points in areas of advanced hardwood senescence, spurious height values in deep shadows and imprecision of FIA plot locations (which were estimated to be off the true locations by +/− 8 m). Using NAIP strata maps for post-stratification yielded forest volume estimates that were 31% more precise on average than estimates stratified with tree canopy cover data. Combining NAIP DHMs with forest type information from national map products helped improve stratification performance, especially for softwoods. The monetary value of using NAIP height maps to post-stratify FIA survey unit total volume estimates was USD 1.8 million vs. the costs of installing more field plots to achieve similar precision gains. Overall, our results show the benefit and growing feasibility of using NAIP point clouds to improve FIA’s operational forest inventory estimates.
Extraction of Submarine Gas Plume Based on Multibeam Water Column Point Cloud Model
Authors: Ren Xin , Ding Dong , Qin Haosen , Ma Le , Li Guangxue
Published: September 2022

The gas plume is a direct manifestation of sea cold seep and one of the most significant symbol indicators of the presence of gas hydrate reservoirs. The multibeam water column (MWC) data can be used to extract and identify the gas plume efficiently and accurately. The current research methods mostly start from the perspective of image theory, which cannot identify the three-dimensional (3D) spatial structure features of gas plumes, reducing the efficiency and accuracy of detection. Therefore, this paper proposes a method for identifying and extracting the gas plume based on an MWC point cloud model, which calculates the spatially resolved homing of MWC data and constructs a 3D point cloud model of MWC containing acoustic reflection intensity information. It first performs noise suppression of the 3D point cloud of the MWC based on the symmetric subtraction and Otsu algorithm by leveraging the noise distribution of the MWC and the reflection intensity characteristics of the gas plume. Then, it extracts the point cloud clusters containing the gas plume based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) according to the density difference between the gas plume point cloud and the background MWC point cloud and next identifies the point cloud clusters by feature matching based on fast point feature histograms (FPFHs). Finally, it extracts the gas plume point cloud set in the MWC. As evidenced by the MWC data collected from gas hydrate enrichment zones in the Gulf of Mexico, the location of gas plume extracted by this method is highly consistent with that of gas leakage points measured during the cruise. Using this method, we obtained the point cloud data set of gas plume for the first time and accurately characterized the 3D spatial morphology of the subsea gas plume, providing technical support for gas hydrate exploration, subsea gas seepage area delineation, and subsea seepage gas flux estimation.
Optomechanical Accelerometers for Geodesy
Authors: Hines Adam , Nelson Andrea , Zhang Yanqi , Valdes Guillermo , Sanjuan Jose , Stoddart Jeremiah , Guzmán Felipe
Published: September 2022

We present a novel optomechanical inertial sensor for low-frequency applications and corresponding acceleration measurements. This sensor has a resonant frequency of 4.715 (1) Hz, a mechanical quality factor of 4.76(3) × 105, a test mass of 2.6 g, and a projected noise floor of approximately 5 × 10−11 ms−2/Hz at 1 Hz. Such performance, together with its small size, low weight, reduced power consumption, and low susceptibility to environmental variables such as magnetic field or drag conditions makes it an attractive technology for future space geodesy missions. In this paper, we present an experimental demonstration of low-frequency ground seismic noise detection by direct comparison with a commercial seismometer, and data analysis algorithms for the identification, characterization, and correction of several noise sources.
Reconstruction of Sentinel-2 Image Time Series Using Google Earth Engine
Authors: Yang Kaixiang , Luo Youming , Li Mengyao , Zhong Shouyi , Liu Qiang , Li Xiuhong
Published: September 2022

Sentinel-2 NDVI and surface reflectance time series have been widely used in various geoscience research, but the data is deteriorated or missing due to the cloud contamination, so it is necessary to reconstruct the Sentinel-2 NDVI and surface reflectance time series. At present, there are few studies on reconstructing the Sentinel-2 NDVI or surface reflectance time series, and these existing reconstruction methods have some shortcomings. We proposed a new method to reconstruct the Sentinel-2 NDVI and surface reflectance time series using the penalized least-square regression based on discrete cosine transform (DCT-PLS) method. This method iteratively identifies cloud-contaminated NDVI over NDVI time series from the Sentinel-2 surface reflectance data by adjusting the weights. The NDVI and surface reflectance time series are then reconstructed from cloud-free NDVI and surface reflectance using the adjusted weights as constraints. We have made some improvements to the DCT-PLS method. First, the traditional discrete cosine transformation (DCT) in the DCT-PLS method is matrix generated from discrete and equally spaced data, we reconfigured the DCT formulas to adapt for irregular interval time series, and optimized the control parameters N and s according to the typical vegetation samples in China. Second, the DCT-PLS method was deployed in the Google Earth Engine (GEE) platform for the efficiency and convenience of data users. We used the DCT-PLS method to reconstruct the Sentinel-2 NDVI time series and surface reflectance time series in the blue, green, red, and near infrared (NIR) bands in typical vegetation samples and the Zhangjiakou and Hangzhou study area. We found that this method performed better than the SG filter method in reconstructing the NDVI time series, and can identify and reconstruct the contaminated NDVI as well as surface reflectance with low root mean square error (RMSE) and high coefficient of determination (R2). However, in cases of a long range of cloud contamination, or above water surface, it may be necessary to increase the control parameter s for a more stable performance. The GEE code is freely available online and the link is in the conclusions of this article, researchers are welcome to use this method to generate cloudless Sentinel-2 NDVI and surface reflectance time series with 10 m spatial resolution, which is convenient for landcover classification and many other types of research.
Exploring the Individualized Effect of Climatic Drivers on MODIS Net Primary Productivity through an Explainable Machine Learning Framework
Authors: Li Luyi , Zeng Zhenzhong , Zhang Guo , Duan Kai , Liu Bingjun , Cai Xitian
Published: September 2022

Along with the development of remote sensing technology, the spatial–temporal variability of vegetation productivity has been well observed. However, the drivers controlling the variation in vegetation under various climate gradients remain poorly understood. Identifying and quantifying the independent effects of driving factors on a natural process is challenging. In this study, we adopted a potent machine learning (ML) model and an ML interpretation technique with high fidelity to disentangle the effects of climatic variables on the long-term averaged net primary productivity (NPP) across the Amazon rainforests. Specifically, the eXtreme Gradient Boosting (XGBoost) model was employed to model the Moderate-resolution Imaging Spectroradiometer (MODIS) NPP data, and the Shapley addictive explanation (SHAP) method was introduced to account for nonlinear relationships between variables identified by the model. Results showed that the dominant driver of NPP across the Amazon forests varied in different regions, with temperature dominating the most considerable portion of the ecoregion with a high importance score. In addition, light augmentation, increased CO2 concentration, and decreased precipitation positively contributed to Amazonia NPP. The wind speed for most vegetated areas was under the optimum, which benefits NPP, while sustained high wind speed would bring substantial NPP loss. We also found a non-monotonic response of Amazonia NPP to VPD and attributed this relationship to the moisture load in Amazon forests. Our application of the explainable machine learning framework to identify the underlying physical mechanism behind NPP could be a reference for identifying relationships between components in natural processes.