bluuebunny/crossref_metadata_2025 huggingface dataset:
Shelf Replenishment Based on Object Arrangement Detection and Collapse Prediction for Bimanual Manipulation
Authors: Motoda Tomohiro , Petit Damien , Nishi Takao , Nagata Kazuyuki , Wan Weiwei , Harada Kensuke
Published: September 2022

Object manipulation automation in logistic warehouses has recently been actively researched. However, shelf replenishment is a challenge that requires the precise and careful handling of densely piled objects. The irregular arrangement of objects on a shelf makes this task particularly difficult. This paper presents an approach for generating a safe replenishment process from a single depth image, which is provided as an input to two networks to identify arrangement patterns and predict the occurrence of collapsing objects. The proposed inference-based strategy provides an appropriate decision and course of action on whether to create an insertion space while considering the safety of the shelf content. In particular, we exploit the bimanual dexterous manipulation capabilities of the associated robot to resolve the task safely, without re-organizing the entire shelf. Experiments with a real bimanual robot were performed in three typical scenarios: shelved, stacked, and random. The objects were randomly placed in each scenario. The experimental results verify the performance of our proposed method in randomized situations on a shelf with a real bimanual robot.
Evaluating Long-Term Variability of the Arctic Stratospheric Polar Vortex Simulated by CMIP6 Models
Authors: Zhao Siyi , Zhang Jiankai , Zhang Chongyang , Xu Mian , Keeble James , Wang Zhe , Xia Xufan
Published: September 2022

The Arctic stratospheric polar vortex is a key component of the climate system, which has significant impacts on surface temperatures in the mid-latitudes and polar regions. Therefore, understanding polar vortex variability is helpful for extended-range weather forecasting. The present study evaluates long-term changes in the position and strength of the polar vortex in the Arctic lower stratosphere during the winters from 1980/81 to 2013/14. Simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models are compared with Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA2) reanalysis dataset. Overall, the CMIP6 models well capture the spatial characteristics of the polar vortex with spatial correlation coefficients between the potential vorticity (PV) in the lower stratosphere from simulations and MERRA2 products generally greater than 0.85 for all CMIP6 models during winter. There is a good agreement in the position and shape of the polar vortex between the CMIP6 multi-model mean and MERRA2, although there exist differences between simulations of individual CMIP6 models. However, most CMIP6 models underestimate the strength of polar vortex in the lower stratosphere, with the largest negative bias up to about −20%. The present study further reveals that there is an anticorrelation between the polar vortex strength bias and area bias simulated by CMIP6 models. In addition, there is a positive correlation between the trend of EP-flux divergence for wavenumber one accumulated in early winter and the trend in zonal mean zonal wind averaged in late winter. As for the long-term change in polar vortex position, CanESM5, IPSL-CM5A2-INCA, UKESM1-0-LL, and IPSL-CM6A-LR well capture the persistent shift of polar vortex towards the Eurasian continent and away from North America in February, which has been reported in observations. These models reproduce the positive trend of wavenumber-1 planetary waves since the 1980s seen in the MERRA2 dataset. This suggests that realistic wave activity processes in CMIP6 models play a key role not only in the simulation of the strength of the stratospheric polar vortex but also in the simulation of the polar vortex position shift.
Landsat Data Based Prediction of Loblolly Pine Plantation Attributes in Western Gulf Region, USA
Authors: Chen Chongzhi , Wang Ke , Fang Luming , Grogan Jason , Talmage Clinton , Weng Yuhui
Published: September 2022

The suitability of using Landsat sensor variables to predict key stand attributes, including stand average dominant/codominant tree height (HT), mean diameter at breast height (DBH), the number of trees per hectare (NT), basal area per hectare (BA), and stand density index (SDI), of intensively managed loblolly pine plantations in the Western Gulf Region at the plot/stand level was assessed. In total, thirty Landsat sensor variables including six original bands, three vegetation indices, three Tasseled Cap transformed indices, and eighteen texture measure variables were used as predictors. Field data of 125 permanent plots located across east Texas and western Louisiana were used as reference data. Individual trees of those plots were measured at plot establishment (referred to as the first cycle measurement; average about 4.5 years old) and remeasured in three-year intervals (the second cycle measurement at approximately seven years old and the third cycle measurement at approximately 10 years old). Thus, field reference data represent stand development from open- (first cycle) to closed-canopy (third cycle). Models to predict stand HT, DBH, NT, BA, and SDI were developed by cycle using multiple linear regression (MLR) and also random forests (RF) methods. Results indicated that the first cycle stands HT, DBH, BA, and SDI were well predicted using the Landsat sensor variables with R2 > 0.7 and low RMSEs. These relationships weakened with stand age, although still moderate with R2 being around 0.45 for the second cycle measurement and became practically useless (R2 < 0.30) for the third cycle measurement. For NT, no meaningful models were achieved regardless of the measurement cycle. The MLR and RF models were comparable in accuracy and had similar key predictors. Overall, the shortwave infrared bands, red band, and wetness index were the most important predictors, but their dominance declined with the cycle. Texture measure variables were relatively less important but a trend of increasing their importance with cycle was noted. Results show promise for operationally predicting stand variables for young pine plantations, an age class that typically presents significant challenges using conventional forest measurement methodologies. Potential methods to further improve model accuracy and how to use the results within the context of pine plantation management planning in the region were discussed.
Coastal Upwelling in the Western Bay of Bengal: Role of Local and Remote Windstress
Authors: Ray Sthitapragya , Swain Debadatta , Ali Meer M. , Bourassa Mark A.
Published: September 2022

Monsoon winds drive upwelling along the eastern coast of India. This study examined the role of coastally trapped Kelvin waves in modulating the seasonal variability of local alongshore windstress (AWS)-driven coastal upwelling along the western Bay of Bengal. The winds generated AWS resulting in a positive cross-shore Ekman transport (ET) from March to the end of September, which forced coastal upwelling along the eastern coast of India. However, coastally trapped Kelvin waves could also modulate this process by raising or lowering the thermocline. Remotely sensed windstress, sea surface temperature (SST), and sea surface height anomaly (SSHA) were used to compute the AWS (the wind-based proxy upwelling index) and an SST-based proxy upwelling index (UISST). A new parametric method of the estimation of coastal angles was developed to estimate the AWS and ET. Coastal upwelling and the Kelvin waves were identified based on the climatology of SSHA, AWS, and UISST, in addition to a complex principal component (CEOF) analysis of the SSHA. The UISST and AWS were found to be closely correlated along the southern section of the east coast of India (between Kavali and Point Calimere), where the coastal upwelling was largely local AWS-driven. However, along the northern section of the coast (between Kashinagara and Kakinada), coastal upwelling was triggered by the first upwelling Kelvin wave, sustained by the local AWS, and then terminated by the first downwelling Kelvin wave. This analysis illustrated that remote equatorial windstress caused coastal upwelling along the northern part of the Indian east coast, while it was primarily locally driven in the southern coast. The findings are helpful in better understanding the mechanisms modulating coastal upwelling along the western Bay of Bengal. These would provide useful insights into the primary productivity and the air–sea interactions in the region.
Cooling by Cyprus Lows of Surface and Epilimnion Water in Subtropical Lake Kinneret in Rainy Seasons
Authors: Kishcha Pavel , Lechinsky Yury , Starobinets Boris
Published: September 2022

Comparison between high-precipitation (HP) years and low-precipitation (LP) years led to our main findings which are as follows: Cyprus lows are instrumental in the cooling of surface and epilimnion water in subtropical Lake Kinneret and in the cooling of eastern Mediterranean surface water. Cyprus lows are responsible for cold weather, rainfall, and for an increase in cloudiness causing a decrease in solar radiation over the eastern Mediterranean and north Israel (including Lake Kinneret). In the daytime, comparison between HP and LP years of Kinneret surface water temperature (SWT) and epilimnion water temperature (WT) showed water cooling of up to 2 °C in HP years. This study was carried out using the 21-year period of satellite and in-situ data: (1) MODIS 1 km × 1 km resolution records of SWT, in (2) shipboard measurements of WT vertical profiles down to a depth of ~40 m (2000–2020). We found that a decrease in solar radiation caused by Cyprus lows (due to an increase in cloudiness) was the main factor contributing to Kinneret water cooling. In winter (December–January) when solar radiation (SR) was minimal, no water cooling was observed: the WT difference between HP and LP years was insignificant. However, in spring (March–April) when SR increased and became the main factor contributing to water heating, water cooling was observed: SWT and epilimnion WT, averaged over the HP years, was lower by ~2 °C and ~1.4 °C, respectively, than SWT and epilimnion WT, averaged over the LP years. Not only was water cooling observed in Lake Kinneret, but also in eastern Mediterranean surface water. Comparison of SWT over the eastern Mediterranean between the same HP and LP years in spring showed SWT cooling by ~1.2 °C. This is evidence of the regional character of the daytime water-cooling phenomenon caused by Cyprus lows.
Biomass Calculations of Individual Trees Based on Unmanned Aerial Vehicle Multispectral Imagery and Laser Scanning Combined with Terrestrial Laser Scanning in Complex Stands
Authors: Lian Xugang , Zhang Hailang , Xiao Wu , Lei Yunping , Ge Linlin , Qin Kai , He Yuanwen , Dong Quanyi , Li Longfei , Han Yu , Fan Haodi , Li Yu , Shi Lifan , Chang Jiang
Published: September 2022

Biomass is important in monitoring global carbon storage and the carbon cycle, which quickly and accurately estimates forest biomass. Precision forestry and forest modeling place high requirements on obtaining the individual parameters of various tree species in complex stands, and studies have included both the overall stand and individual trees. Most of the existing literature focuses on calculating the individual tree species’ biomass in a single stand, and there is little research on calculating the individual tree biomass in complex stands. This paper calculates the individual tree biomass of various tree species in complex stands by combining multispectral and light detection and ranging (LIDAR) data. The main research steps are as follows. First, tree species are classified through multispectral data combined with field investigations. Second, multispectral classification data are combined with LIDAR point cloud data to classify point cloud tree species. Finally, the divided point cloud tree species are used to compare the diameter at breast height (DBH) and height of each tree species to calculate the individual tree biomass and classify the overall stand and individual measurements. The results show that under suitable conditions, it is feasible to identify tree species through multispectral classification and calculate the individual tree biomass of each species in conjunction with point-cloud data. The overall accuracy of identifying tree species in multispectral classification is 52%. Comparing the DBH of the classified tree species after terrestrial laser scanning (TLS) and unmanned aerial vehicle laser scanning (UAV-LS) to give UAV-LS+TLS, the concordance correlation coefficient (CCC) is 0.87 and the root-mean-square error (RMSE) is 10.45. The CCC and RMSE are 0.92 and 1.41 compared with the tree height after UAV-LS and UAV-LS+TLS.
An Observation Density Based Method for Independent Baseline Searching in GNSS Network Solution
Authors: Liu Tong , Du Yujun , Nie Wenfeng , Liu Jian , Ma Yongchao , Xu Guochang
Published: September 2022

With applications such as precise geodetic product generation and reference frame maintenance, the global GNSS network solution is a fundamental problem that has constantly been a focus of concern. Independent baseline search is a prerequisite step of the double-differenced (DD) GNSS network. In this process, only empirical methods are usually used, i.e., the observation-max (OBS-MAX), which allows for obtaining more redundant DD observations, and the shortest-path (SHORTEST), which helps to better eliminate tropospheric and ionospheric errors between stations. Given the possible limitations that neither of the methods can always guarantee baselines of the highest accuracy to be selected, a strategy based on the ‘density’ of common satellites (OBS-DEN) is proposed. It takes the number of co-viewing satellites per unit distance between stations as the criterion. This method ensures that the independent baseline network has both sufficient observations and short baselines. With single-day solutions and annual statistics computed with parallel processing, the method demonstrates that it has the ability to obtain comparable or even higher positioning accuracy than the conventional methods. With a clearer meaning, OBS-DEN can be an option alongside the previous methods in the independent baseline search.
Enhanced Understanding of Groundwater Storage Changes under the Influence of River Basin Governance Using GRACE Data and Downscaling Model
Authors: Sun Jianchong , Hu Litang , Liu Xin , Sun Kangning
Published: September 2022

The low spatial resolution of Gravity Recovery and Climate Experiment (GRACE) data limits their application in practical groundwater resource management. To overcome this limitation, this study developed a dynamic downscaling method based on a model using groundwater storage anomaly (GWSA) data to study groundwater storage changes in an inland arid region. The groundwater storage model was calibrated using publicly accessible data at a spatial resolution of 1°. The constructed model had a satisfactory fitting effect in both the calibration and validation periods, with correlation coefficients over 0.60, in general, and a root mean square error of less than 1.00 cm equivalent water height (EWH). It was found that the hydraulic gradient coefficient was the most sensitive parameter, whereas the boundary condition had an obvious influence on the simulated GWSA compared to the different forcing data. The model was then refined at a higher resolution (0.05°) using driving data to obtain downscaled GWSA data. The downscaled results had a similar pattern to the GRACE-derived GWSA and reflected the spatial heterogeneity across the basin scale and subregion scales. The downscaled GWSA shows that the groundwater storage had an overall downward trend during the period from 2003 to 2019 and the annual decline rates ranged from 0.22 to 0.32 cm/year in four subregions. A four-month time lag between the field-observed and downscaled GWSA was observed downstream of the study area. This study provides an applicable method for assessing groundwater storage changes for groundwater management at the local scale.
On-Orbit Radiometric Calibration of Hyperspectral Sensors on Board Micro-Nano Satellite Constellation Based on RadCalNet Data
Authors: Zhang Qiang , Zhao Yongguang , Zhang Lei , Wu Jiaqi , Li Wan , Yan Jun , Jiang Xiaohua , Yan Zhiyu , Zhao Jing
Published: September 2022

The stability and accuracy of the on-orbit radiometric calibration of hyperspectral sensors are prerequisites for the quantitative application of satellite hyperspectral data. The Zhuhai-1 micro-nano satellite constellation is composed of eight hyperspectral satellite missions. The Orbita Hyperspectral Sensor (OHS) on board each satellite has a gradient filter spectroscopic design. When observing the Earth, eight integration stages can be set for each band according to different lighting conditions. Due to high manufacturing costs, OHSs are not equipped with on-board calibration devices. Therefore, it is very difficult to accurately calibrate OHSs for all of the integration stages. On the other hand, it is extremely important to ensure radiometric consistency between different OHSs within the Zhuhai-1 micro-nano satellite constellation. To carry out the rapid radiometric calibration of the Zhuhai-1 constellation, an on-orbit radiometric calibration model considering all of the integration stages related to hyperspectral sensors was built based on the BOA reflectance and atmosphere parameters published by the Committee on Earth Observation Satellites (CEOS) radiometric calibration network (RadCalNet). The RadCalNet product was used to derive the TOA radiance base in the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer (RT) model. In this paper, we analyzed the radiometric stability of the same sensor and the consistency of different calibration results regarding four RadCalNet sites, and the on-orbit radiometric performance evaluation of OHSs was also carried out. The data retrieved from OHSs regarding hyperspectral surface reflectance were preliminarily validated using site-synchronous surface reflectance measurements.
Combining Deep Semantic Edge and Object Segmentation for Large-Scale Roof-Part Polygon Extraction from Ultrahigh-Resolution Aerial Imagery
Authors: Van den Broeck Wouter A. J. , Goedemé Toon
Published: September 2022

The roofscape plays a vital role in the support of sustainable urban planning and development. However, availability of detailed and up-to-date information on the level of individual roof-part topology remains a bottleneck for reliable assessment of its present status and future potential. Motivated by the need for automation, the current state-of-the-art focuses on applying deep learning techniques for roof-plane segmentation from light-detection-and-ranging (LiDAR) point clouds, but fails to deliver on criteria such as scalability, spatial predictive continuity, and vectorization for use in geographic information systems (GISs). Therefore, this paper proposes a fully automated end-to-end workflow capable of extracting large-scale continuous polygon maps of roof-part instances from ultra-high-resolution (UHR) aerial imagery. In summary, the workflow consists of three main steps: (1) use a multitask fully convolutional network (FCN) to infer semantic roof-part edges and objects, (2) extract distinct closed shapes given the edges and objects, and (3) vectorize to obtain roof-part polygons. The methodology is trained and tested on a challenging dataset comprising of UHR aerial RGB orthoimagery (0.03 m GSD) and LiDAR-derived digital elevation models (DEMs) (0.25 m GSD) of three Belgian urban areas (including the famous touristic city of Bruges). We argue that UHR optical imagery may provide a competing alternative for this task over classically used LiDAR data, and investigate the added value of combining these two data sources. Further, we conduct an ablation study to optimize various components of the workflow, reaching a final panoptic quality of 54.8% (segmentation quality = 87.7%, recognition quality = 62.6%). In combination with human validation, our methodology can provide automated support for the efficient and detailed mapping of roofscapes.
The Spatial Relationship and Evolution of World Cultural Heritage Sites and Neighbouring Towns
Authors: Xie Yihan , Yang Ruixia , Liang Yongqi , Li Wei , Chen Fulong
Published: September 2022

The past few decades have witnessed unprecedented global urbanisation, with direct or indirect impacts on global cultural heritage sites. Research on the spatial relationship between cultural heritage sites and urban areas has provided a new perspective for understanding the impact processes between them, which have previously been discussed at the regional scale. In this article, we analyse the spatial relationship between world cultural heritage sites and neighbouring towns through systematic observations at the global scale and attempt to model change processes and identify impact mechanisms. We adopt spatial analysis and spatial statistics to analyse the changing characteristics of the spatial relationship between world cultural heritage sites and neighbouring towns from 1990 to 2018 and to analyse the impact processes at different spatial and temporal scales by combining indicators, such as income levels and urbanisation rates, at the national scale. The results show that 8.52% of world cultural heritage sites have been incorporated into urban areas over the aforementioned 28 years, with a certain aggregation in the spatial distribution of these sites, and that the growth rate can be divided into three phases, including two periods of rapid growth. The spatial relationship between towns and the 523 world cultural heritage sites that were previously located outside towns has not yet changed substantially, but the distances between most of the towns and these sites have been decreasing, with 81% of the world cultural heritage sites having a variation in distance from the corresponding town of 7.60 km or less. We also analysed the variation in distance between cultural heritage sites and neighbouring towns and found a relationship with indicators, such as the income level and urbanisation rate of the country to which a site belongs. Among the indicators, variation in national urbanisation rates most greatly affected the distance between heritage sites and towns. This study shows that world cultural heritage sites are affected by urbanisation and that particular attention should be given to the relationship between cultural heritage sites and neighbouring towns, especially in countries undergoing rapid urbanisation, so that the authenticity and integrity of cultural heritage are not compromised. This article provides a basis for development plans and policies in urban design, especially those that are sensitive to cultural heritage, and may also provide ideas and references for heritage conservation against the background of urbanisation.
Landscape Ecological Risk Assessment and Impact Factor Analysis of the Qinghai–Tibetan Plateau
Authors: Wang Sishi , Tan Xin , Fan Fenglei
Published: September 2022

The Qinghai–Tibetan Plateau (QTP) is one of the most vulnerable ecosystems worldwide. Over the last few decades, the QTP has been subjected to increasing external pressures, such as climate change, human activity, and natural hazards. Therefore, ecological risk assessment is vital for the environmental protection and sustainable development of the QTP. A landscape ecological risk (LER) assessment based on landscape disturbance and vulnerability was performed to explore the spatiotemporal characteristics associated with LER in the QTP from 1990 to 2020. Furthermore, the impact of LER was quantitatively evaluated with a boosted regression tree model. Results showed that more than 70% of the locations in the QTP exhibited below-medium LER. The LER for the QTP demonstrated downward trends from 1990 to 2020. The LER presented downward trends during the periods from 1990 to 2001 and from 2012 to 2020 and no significant trend during the period from 2002 to 2011. Additionally, high-LER areas were concentrated in the northwestern QTP, whereas low-LER areas were mainly in the southeastern QTP. The LER displayed clustering characteristics across the QTP. Changes in climate, topographic distribution, and human activity influenced the ecological stability of the study area. Precipitation and temperature had the strongest effects on the LER, followed by elevation and grazing intensity. Lower precipitation and temperatures were associated with higher LER. Our results provide precise and specific support for the environmental protection and ecological management of the QTP and other ecologically fragile areas.
Complex Mountain Road Extraction in High-Resolution Remote Sensing Images via a Light Roadformer and a New Benchmark
Authors: Zhang Xinyu , Jiang Yu , Wang Lizhe , Han Wei , Feng Ruyi , Fan Runyu , Wang Sheng
Published: September 2022

Mountain roads are of great significance to traffic navigation and military road planning. Extracting mountain roads based on high-resolution remote sensing images (HRSIs) is a hot spot in current road extraction research. However, massive terrain objects, blurred road edges, and sand coverage in complex environments make it challenging to extract mountain roads from HRSIs. Complex environments result in weak research results on targeted extraction models and a lack of corresponding datasets. To solve the above problems, first, we propose a new dataset: Road Datasets in Complex Mountain Environments (RDCME). RDCME comes from the QuickBird satellite, which is at an elevation between 1264 m and 1502 m with a resolution of 0.61 m; it contains 775 image samples, including red, green, and blue channels. Then, we propose the Light Roadformer model, which uses a transformer module and self-attention module to focus on extracting more accurate road edge information. A post-process module is further used to remove incorrectly predicted road segments. Compared with previous related models, the Light Roadformer proposed in this study has higher accuracy. Light Roadformer achieved the highest IoU of 89.5% for roads on the validation set and 88.8% for roads on the test set. The test on RDCME using Light Roadformer shows that the results of this study have broad application prospects in the extraction of mountain roads with similar backgrounds.
A Characterisation of Benthic Currents from Seabed Bathymetry: An Object-Based Image Analysis of Cold-Water Coral Mounds
Authors: Summers Gerard , Lim Aaron , Wheeler Andrew J.
Published: September 2022

Seabed sedimentary bedforms (SSBs) are strong indicators of current flow (direction and velocity) and can be mapped in high resolution using multibeam echosounders. Many approaches have been designed to automate the classification of such SSBs imaged in multibeam echosounder data. However, these classification systems only apply a geomorphological contextualisation to the data without making direct assertions on the velocities of benthic currents that form these SSBs. Here, we apply an object-based image analysis (OBIA) workflow to derive a geomorphological classification of SSBs in the Moira Mounds area of the Belgica Mound Province, NE Atlantic through k-means clustering. Cold-water coral reefs as sessile filter-feeders benefit from strong currents are often found in close association with sediment wave fields. This OBIA provided the framework to derive SSB wavelength and wave height, these SSB attributes were used as predictor variables for a multiple linear regression to estimate current velocities. Results show a bimodal distribution of current flow directions and current speed. Furthermore, a 5 k-means classification of the SSB geomorphology exhibited an imprinting of current flow consistency which altered throughout the study site due to the interaction of regional, local, and micro scale topographic steering forces. This study is proof-of-concept for an assessment tool applied to vulnerable marine ecosystems but has wider applications for applied seabed appraisals and can inform management and monitoring practice across a variety of spatial and temporal scales. Deriving spatial patterns of hydrodynamic processes from widely available multibeam echosounder maps is pertinent to many avenues of research including scour predictions for offshore structures such as wind turbines, sediment transport modelling, benthic fisheries, e.g., scallops, cable route and pipeline risk assessment and habitat mapping.
Methodology for Lidar Monitoring of Biomass Burning Smoke in Connection with the Land Cover
Authors: Adam Mariana , Fragkos Konstantinos , Solomos Stavros , Belegante Livio , Andrei Simona , Talianu Camelia , Mărmureanu Luminița , Antonescu Bogdan , Ene Dragos , Nicolae Victor , Amiridis Vassilis
Published: September 2022

Lidar measurements of 11 smoke layers recorded at Măgurele, Romania, in 2014, 2016, and 2017 are analyzed in conjunction with the vegetation type of the burned biomass area. For the identified aerosol pollution layers, the mean optical properties and the intensive parameters in the layers are computed. The origination of the smoke is estimated by the means of the HYSPLIT dispersion model, taking into account the location of the fires and the injection height for each fire. Consequently, for each fire location, the associated land cover type is acquired by satellite-derived land cover products. We explore the relationship between the measured intensive parameters of the smoke layers and the respective land cover of the burned area. The vegetation type for the cases we analyzed was either broadleaf crops or grasses/cereals. Overall, the intensive parameters are similar for the two types, which can be associated with the fact that both types belong to the broader group of agricultural crops. For the cases analyzed, the smoke travel time corresponding to the effective predominant vegetation type is up to 2.4 days.
Increasing Negative Impacts of Climatic Change and Anthropogenic Activities on Vegetation Variation on the Qinghai–Tibet Plateau during 1982–2019
Authors: Zhu Bin , Zhang Zengxin , Tian Jiaxi , Kong Rui , Chen Xi
Published: September 2022

Climate change, combined with ever-increasing anthropogenic activities, has had significant impacts on the vegetation of the Qinghai–Tibet Plateau (QTP). This study quantitatively analyzed the impacts of climate change and human activities on vegetation variation on the QTP from 1982 to 2019 based on AVHRR NDVI data and the residual trend method. The main results were as follows: (1) From 1982 to 2000, the vegetation of the QTP had an obvious restoration process, whereby 67.8% of vegetation coverage areas experienced an increasing trend, while it had a large range of degradation during 2001–2019, especially in the central QTP. (2) The positive effect of climate change on the vegetation of the QTP decreased, and the negative impact increased. The area of positive impact decreased from 68.54% in 1982–2000 to 47.13% in 2001–2019, while the negative-impact area increased from 31.46% to 52.87%. (3) The area negatively affected by human activities increased from 57.68% in 1982–2000 to 79.46% in 2001–2019 and was mainly concentrated in the grassland of the central QTP. The findings of this study provide a scientific basis for vegetation restoration and management in the QTP region.
Comparison of Lake Ice Extraction Methods Based on MODIS Images
Authors: Zhang Hongfang , Yao Xiaojun , Wei Qixin , Duan Hongyu , Zhang Yuan
Published: September 2022

As an important part of the cryosphere, lake ice is a sensitive indicator of climate change. Remote sensing technology can quickly and accurately monitor the process of its formation and decay, among which Moderate Resolution Imaging Spectroradiometer (MODIS) images are the most widely used data in the remote sensing monitoring of lake ice. The reasonable selection of monitoring methods is of great significance to grasp the dynamic process and response to climate change of lake ice. In this study, five commonly used remote sensing monitoring methods of lake ice based on MODIS MOD09GA data, including the single band threshold method (SBT), reflectance difference threshold method (RDT), normalized difference snow index method (NDSI), modified normalized difference snow index method (MNDSI) and lake ice index method (LII), were selected to compare their accuracies in extracting lake ice extent by combining them with four evaluation metrics of accuracy, precision, recall and mean intersection over union (MIoU). In addition, the ability of the high-precision LII method for extracting long time series lake ice phenology and its applicability to multiple types of lakes were verified. The results showed that compared with the NDSI method, the other four methods more easily distinguished between lake ice and lake water by setting thresholds. The SBT method and the RDT method had better extraction effects in the freezing process and the melting process, respectively. Compared with the NDSI and MNDSI methods, the LII method showed a significant improvement in lake ice extraction over the entire freeze–thaw cycle, with the smallest mean monitoring error of 1.53% for the percentage of lake ice area in different periods. Meanwhile, the LII method can be used to determine long term lake ice phenology dates and had good performance in extracting lake ice for different types of lakes on the Qinghai–Tibet Plateau with the optimal threshold interval of 0.05~0.07, which can be used for lake ice monitoring and long-term phenological studies in this region.
Representing Cattle Farming around the World: A Conceptual and Holistic Framework for Environmental and Economic Impact Assessment
Authors: Perin Lucie , Dumas Patrice , Vigne Mathieu
Published: September 2022

Around the world, cattle farming systems are diverse and lead to diverse environmental and socio-economic consequences. To assess these consequences, the diversity of cattle farming needs to be represented. A conceptual framework based on three inter-linked concepts (management types, animal profiles and lineage groups) is proposed resulting in two typologies, and tested on cattle systems in Kenya. The management type typology provides an understanding of the cattle farming practices across the world. Animal profiles, defined by the animal’s age and sex, and used together with management types, serve as a convenient unit for the analysis of feed use, environmental impacts, animal functions, and costs. Lineage groups bring together cattle and their progeny, making it possible to account for movements across management types and for all co-productions in impact assessments. The illustration on Kenya showed the completeness of the framework, the availability of management-type characteristics, and also the lack of precise data on shares of lineage groups and management types. The conceptual framework developed here should render it possible to capture and compare the multiple characteristics and functions of cattle farming around the world, including their environmental impact, which currently is a major issue for the global livestock sector.
Review of Advances in the Measurement of Skin Hydration Based on Sensing of Optical and Electrical Tissue Properties
Authors: Gidado Iman M. , Qassem Meha , Triantis Iasonas F. , Kyriacou Panicos A.
Published: September 2022

The presence of water in the skin is crucial for maintaining the properties and functions of the skin, in particular its outermost layer, known as the stratum corneum, which consists of a lipid barrier. External exposures can affect the skin’s hydration levels and in turn, alter its mechanical and physical properties. Monitoring these alterations in the skin’s water content can be applicable in clinical, cosmetic, athletic and personal settings. Many techniques measuring this parameter have been investigated, with electrical-based methods currently being widely used in commercial devices. Furthermore, the exploration of optical techniques to measure hydration is growing due to the outcomes observed through the penetration of light at differing levels. This paper comprehensively reviews such measurement techniques, focusing on recent experimental studies and state-of-the-art devices.
Machine Learning-Based Prediction of Specific Energy Consumption for Cut-Off Grinding
Authors: Awan Muhammad Rizwan , González Rojas Hernán A. , Hameed Saqib , Riaz Fahid , Hamid Shahzaib , Hussain Abrar
Published: September 2022

Cut-off operation is widely used in the manufacturing industry and is highly energy-intensive. Prediction of specific energy consumption (SEC) using data-driven models is a promising means to understand, analyze and reduce energy consumption for cut-off grinding. The present article aims to put forth a novel methodology to predict and validate the specific energy consumption for cut-off grinding of oxygen-free copper (OFC–C10100) using supervised machine learning techniques. State-of-the-art experimental setup was designed to perform the abrasive cutting of the material at various cutting conditions. First, energy consumption values were predicted on the bases of input process parameters of feed rate, cutting thickness, and cutting tool type using the three supervised learning techniques of Gaussian process regression, regression trees, and artificial neural network (ANN). Among the three algorithms, Gaussian process regression performance was found to be superior, with minimum errors during validation and testing. The predicted values of energy consumption were then exploited to evaluate the specific energy consumption (SEC), which turned out to be highly accurate, with a correlation coefficient of 0.98. The relationship of the predicted specific energy consumption (SEC) with material removal rate agrees well with the relationship depicted in physical models, which further validates the accuracy of the prediction models.
Heart Rate Variability Control Using a Biofeedback and Wearable System
Authors: Viera Eduardo , Kaschel Hector , Valencia Claudio
Published: September 2022

Heart rate variability is an important physiological parameter in medicine. This parameter is used as an indicator of physiological and psychological well-being and even of certain pathologies. Research on biofeedback integrates the fields of biological application (physiological behavior), system modeling, and automated control. This study proposes a new method for modeling and controlling heart rate variability as heart rate acceleration, a model expressed in the frequency domain. The model is obtained from excitation and response signals from heart rate variability, which through the instrumental variables method and the minimization of a cost function delivers a transfer function that represents the physiological phenomenon. This study also proposes the design of an adaptive controller using the reference model. The controller controls heart rate variability based on the light actuators designed here, generating a conditioned reflex that allows individuals to self-regulate their state through biofeedback, synchronizing this action to homeostasis. Modeling is conducted in a target population of middle-aged men who work as firefighters and forest firefighters. This study validates the proposed model, as well as the design of the controllers and actuators, through a simple experiment based on indoor cycling. This experiment has different segments, namely leaving inertia, non-controlled segment, and actively controlled segment.
Video Compressive Sensing Reconstruction Using Unfolded LSTM
Authors: Xia Kaiguo , Pan Zhisong , Mao Pengqiang
Published: September 2022

Video compression sensing can use a few measurements to obtain the original video by reconstruction algorithms. There is a natural correlation between video frames, and how to exploit this feature becomes the key to improving the reconstruction quality. More and more deep learning-based video compression sensing (VCS) methods are proposed. Some methods overlook interframe information, so they fail to achieve satisfactory reconstruction quality. Some use complex network structures to exploit the interframe information, but it increases the parameters and makes the training process more complicated. To overcome the limitations of existing VCS methods, we propose an efficient end-to-end VCS network, which integrates the measurement and reconstruction into one whole framework. In the measurement part, we train a measurement matrix rather than a pre-prepared random matrix, which fits the video reconstruction task better. An unfolded LSTM network is utilized in the reconstruction part, deeply fusing the intra- and interframe spatial–temporal information. The proposed method has higher reconstruction accuracy than existing video compression sensing networks and even performs well at measurement ratios as low as 0.01.
The Optimal Sizing of Substation Capacity in a Distribution Network, Considering a Dynamic Time-of-Use Pricing Mechanism
Authors: Xu Dajun , Fu Qiang , Ye Lun , Lin Wenyu , Qian Yuzhou , Zhang Kebo , Yao Jiangang
Published: September 2022

Application of sensors in the smart grid has promoted the development of demand side management (DSM). However, the incentives of DSM such as peak–valley time-of-use (TOU) price will change the load pattern in the future; the substation capacity sizing will be further influenced accordingly. This paper proposes a substation capacity sizing method in distribution network considering DSM and establishes a peak-valley TOU pricing method based on the cost–benefit analysis of each participant in the TOU price. Compared with the conventional fixed peak–valley ratio, a dynamic division method is proposed to calculate the optimal pull-off ratio for the TOU pricing. By considering the proposed TOU pricing method, a substation capacity sizing model for the distribution network is further proposed. Finally, the economic benefits of the two substation capacity sizing schemes are compared and evaluated according to the selected economic indicators. The results of the case study demonstrate that under the premise of reasonable pricing, considering the impact of TOU on substation capacity sizing, the construction investment and the user cost of power supply companies can be saved while meeting the power demand. The economy and rationality of the planning scheme have been significantly improved.
Smart Sensing Multifunctionalities Based on Barium Strontium Titanate Thin Films
Authors: Wang Linghua , Zhu Minmin , Shao Yong , Zhao Yida , Wei Can , Gao Langfeng , Bao Yiping
Published: September 2022

Sensors that have low power consumption, high scalability and the ability of rapidly detecting multitudinous external stimulus are of great value in cyber-physical interactive applications. Herein, we reported the fabrication of ferroelectric barium strontium titanate ((Ba70Sr30)TiO3, BST) thin films on silicon substrates by magnetron sputtering. The as-grown BST films have a pure perovskite structure and exhibit excellent ferroelectric characteristics, such as a remnant polarization of 2.4 μC/cm2, a ferro-to-paraelectric (tetragonal-to-cubic) phase transition temperature of 31.2 °C, and a broad optical bandgap of 3.58 eV. Capacitor-based sensors made from the BST films have shown an outstanding average sensitivity of 0.10 mV·Pa−1 in the 10–80 kPa regime and work extremely steadily over 1000 cycles. More importantly, utilizing the Pockels effect, optical manipulation in BST can be also realized by a smaller bias and its electro-optic coefficient reff is estimated to be 83.5 pmV−1, which is 2.6 times larger than in the current standard material (LiNbO3) for electro-optical devices. Our work established BST thin film as a powerful design paradigm toward on-chip integrations with diverse electronics into sensors via CMOS-comparable technique.
Accuracy of Inertial Measurement Units When Applied to the Countermovement Jump of Track and Field Athletes
Authors: Miranda-Oliveira Paulo , Branco Marco , Fernandes Orlando
Published: September 2022

In this study, we aimed to assess the countermovement jump (CMJ) using a developed instrument encompassing an off-the-shelf Inertial Measurement Unit (IMU) in order to analyze performance during the contraction phase, as well as to determine the jump height and the modified reactive strength index (RSImod), using force plate (FP) data as reference. Eight athletes (six males and two females) performed CMJs with the IMU placed on their fifth lumbar vertebra. Accuracy was measured through mean error (standard deviation), correlation, and comparison tests. The results indicated high accuracy, high correlation (r), and no statistical differences between the IMU and the FP for contraction time (r = 0.902; ρ < 0.001), negative impulse phase time (r = 0.773; ρ < 0.001), flight time (r = 0.737; ρ < 0.001), jump time (r = 0.708; ρ < 0.001), RSImod (r = 0.725; ρ < 0.001), nor minimum force (r = 0.758; ρ < 0.001). However, the values related to the positive impulse phase did not have the expected accuracy, as we used different devices and positions. Our results demonstrated that our developed instrument could be utilized to identify the contraction phase, jump height, RSImod, and minimum force in the negative impulse phase with high accuracy, obtaining a signal similar to that of an FP. This information can help coaches and athletes with training monitoring and control, as the device has simpler applicability making it more systematic.
Super-Resolution Procedure for Target Responses in KOMPSAT-5 Images
Authors: Lee Seung-Jae , Lee Sun-Gu
Published: September 2022

Recently, target analysis using satellite SAR images has received much attention in the area of satellite SAR remote sensing. Because the spatial resolution of the target response in the satellite SAR image is a main factor that has a large effect on target analysis performances, the improvement of the spatial resolution of target response is required to enhance the target analysis capability. However, the spatial resolution is already determined in the satellite SAR system design process. To solve the above problem, the super-resolution techniques that have been applied to radar images can be utilized. However, the application of the super-resolution techniques to the target response in the satellite SAR image is not simple due to the following reasons. First, the target’s motion induces severe blurring of the target response, which impedes the successful improvement of spatial resolution. Next, the zero-region in the frequency spectrum of the target image containing the target response also hinders the generation of the super-resolved image. To successfully improve spatial resolution of the satellite SAR image, the super-resolution techniques should be combined with proper preprocessing steps that can cope with the above two issues. In this paper, the whole super-resolution procedure for target responses in KOMPSAT-5 images is described. To the best of the authors’ knowledge, the description of the whole super-resolution procedure for target responses is the first ever attempt in the area of satellite SAR. First, a target image containing the target response is extracted from a large-scale KOMPSAT-5 image. Subsequently, the target image is transformed to be appropriate for the utilization of super-resolution techniques by proper preprocessing steps, considering the direction of super resolution and the motion of the target. Then, some super-resolution techniques are utilized to improve the spatial resolutions and qualities of the target images. The super-resolution performances of the proposed scheme are validated using various target images for point static, extended static, and extended moving targets. The novelties of this paper can be summarized as follows: (1) the practical design of whole super-resolution processing for real satellite SAR images; (2) the performance evaluation of super-resolution techniques on real satellite SAR images. The results show that the proposed scheme can led to noticeable improvements of spatial resolution of the target images for various types of targets with reliable computation times. In addition, the proposed scheme also enhanced PSLR, ISLR, and IC, leading to clearer scattering information of the principal scatterers. Consequently, the proposed method can assist in extracting more precise and meaningful information for targets represented in KOMPSAT-5 images, which means great potential for target recognition.