bluuebunny/crossref_metadata_2025 huggingface dataset:
Coseismic Deformation Mechanisms of the 2021 Ms 6.4 Yangbi Earthquake, Yunnan Province, Using InSAR Observations
Authors: Zhang Bing , Xu Guochang , Lu Zhiping , He Yufang , Peng Mimi , Feng Xueshang
Published: October 2021

At 21:48 on 21 May 2021, an Ms 6.4 earthquake occurred in Yangbi County, Dali Prefecture, Yunnan Province. At present, uncertainty remains regarding the source parameters and deformation mechanism of the Yangbi earthquake. In this study, we determine fault geometry and slip distribution of the earthquake by InSAR analysis. Then, the Coulomb stress loading caused by the Yangbi earthquake is further analyzed. The results show that the moment magnitude of the Yangbi earthquake was Mw 6.14. The slip mainly occurred at depths of 3–13 km, with a maximum slip of approximately 61 cm at a depth of 6.98 km. The Yangbi earthquake was triggered by a blind fault in the NW-SE in the west parallel to the Weixi-Weishan Fault and its seismogenic fault exhibits strike-slip displacement. A large number of aftershocks were distributed along the fault rupture surface where the Coulomb stress increases. As the depth of the crust increases, the area where the Coulomb stress increases in the Yangbi earthquake, decreases. The occurrence of this earthquake also caused a significant increase in the Coulomb stress in the southeastern section of the Weixi-Weishan Faul. We should pay more attention to its seismic hazards.
Evaluating the Ability to Use Contextual Features Derived from Multi-Scale Satellite Imagery to Map Spatial Patterns of Urban Attributes and Population Distributions
Authors: Chao Steven , Engstrom Ryan , Mann Michael , Bedada Adane
Published: October 2021

With an increasing global population, accurate and timely population counts are essential for urban planning and disaster management. Previous research using contextual features, using mainly very-high-spatial-resolution imagery (<2 m spatial resolution) at subnational to city scales, has found strong correlations with population and poverty. Contextual features can be defined as the statistical quantification of edge patterns, pixel groups, gaps, textures, and the raw spectral signatures calculated over groups of pixels or neighborhoods. While they correlated with population and poverty, which components of the human-modified landscape were captured by the contextual features have not been investigated. Additionally, previous research has focused on more costly, less frequently acquired very-high-spatial-resolution imagery. Therefore, contextual features from both very-high-spatial-resolution imagery and lower-spatial-resolution Sentinel-2 (10 m pixels) imagery in Sri Lanka, Belize, and Accra, Ghana were calculated, and those outputs were correlated with OpenStreetMap building and road metrics. These relationships were compared to determine what components of the human-modified landscape the features capture, and how spatial resolution and location impact the predictive power of these relationships. The results suggest that contextual features can map urban attributes well, with out-of-sample R2 values up to 93%. Moreover, the degradation of spatial resolution did not significantly reduce the results, and for some urban attributes, the results actually improved. Based on these results, the ability of the lower resolution Sentinel-2 data to predict the population density of the smallest census units available was then assessed. The findings indicate that Sentinel-2 contextual features explained up to 84% of the out-of-sample variation for population density.
Extracting Frequent Sequential Patterns of Forest Landscape Dynamics in Fenhe River Basin, Northern China, from Landsat Time Series to Evaluate Landscape Stability
Authors: Zhang Yue , Liu Xiangnan , Yang Qin , Liu Zhaolun , Li Yu
Published: October 2021

The forest landscape pattern evolution can reveal the intensity and mode of action of human–land relationships at different times and in different spaces, providing scientific support for regional ecological security, human settlement health, and sustainable development. In this study, we proposed a novel method for analyzing the dynamics of landscape patterns. First, patch density (PD), largest patch index (LPI), landscape shape index (LSI), and contiguity index (CI) were used to identify the types of forest spatial patterns. The frequent sequential pattern mining method was used to detect the frequent subsequences from the time series of landscape pattern types from 1991 to 2020 and further evaluate the forest landscape stability of the Fenhe River Basin in China. The results show that different frequent sequence patterns have conspicuous spatial and temporal differences, which describe the evolution processes and stability changes during a certain period of forest evolution and play an important role in the analysis of forest dynamics. The proportion of the disturbed regions to the total forest area exhibited a downward trend. The long-term evolution pattern indicates that there are many evolution processes and trends in the forest at the same time, showing an aggregation distribution law. Compared with 2016, the forest landscape has become complete in 2020, and the overall stability of the Fenhe River Basin has improved. This study can provide scientific support to land managers and policy implementers and offer a new perspective for studying forest landscape pattern changes and evaluating landscape stability.
Vegetation Growth Analysis of UNESCO World Heritage Hyrcanian Forests Using Multi-Sensor Optical Remote Sensing Data
Authors: Khare Suyash , Latifi Hooman , Khare Siddhartha
Published: October 2021

Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, and also focused on 15 UNESCO World Heritage Sites. We extracted bi-weekly MODIS-NDVI between 2017 and 2020 in GEE, which was used to identify the range of NDVI between two temporal stages. Then, changes in phenology and growth were analyzed by Sentinel 2-derived Temporal Normalized Phenology Index. We modelled between seasonal phenology and growth by additionally considering elevation, surface temperature, and monthly precipitation. Results indicated considerable difference in onset of forests along the longitudinal gradient of the HF. Faster growth was observed in low- and uplands of the western zone, whereas it was lower in both the mid-elevations and the western outskirts. Longitudinal range was a major driver of vegetation growth, to which environmental factors also differently but significantly contributed (p < 0.0001) along the west-east gradient. Our study developed at GEE provides a benchmark to examine the effects of environmental parameters on the vegetation growth of HF, which cover mountainous areas with partly no or limited accessibility.
Using BDS MEO and IGSO Satellite SNR Observations to Measure Soil Moisture Fluctuations Based on the Satellite Repeat Period
Authors: Shen Fei , Sui Mingming , Zhu Yifan , Cao Xinyun , Ge Yulong , Wei Haohan
Published: October 2021

Soil moisture is an important geophysical parameter for studying terrestrial water and energy cycles. It has been proven that Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) can be applied to monitor soil moisture. Unlike the Global Positioning System (GPS) that has only medium earth orbit (MEO) satellites, the Beidou Navigation Satellite System (BDS) also has geosynchronous earth orbit (GEO) satellites and inclined geosynchronous satellite orbit (IGSO) satellites. Benefiting from the distribution of three different orbits, the BDS has better coverage in Asia than other satellite systems. Previous retrieval methods that have been confirmed on GPS cannot be directly applied to BDS MEO satellites due to different satellite orbits. The contribution of this study is a proposed multi-satellite soil moisture retrieval method for BDS MEO and IGSO satellites based on signal-to-noise ratio (SNR) observations. The method weakened the influence of environmental differences in different directions by considering satellite repeat period. A 30-day observation experiment was conducted in Fengqiu County, China and was used for verification. The satellite data collected were divided according to the satellite repeat period, and ensured the response data moved in the same direction. The experimental results showed that the BDS IGSO and MEO soil moisture estimation results had good correlations with the in situ soil moisture fluctuations. The BDS MEO B1I estimation results had the best performance; the estimation accuracy in terms of correlation coefficient was 0.9824, root mean square error (RMSE) was 0.0056 cm3cm−3, and mean absolute error (MAE) was 0.0040 cm3cm−3. The estimations of the BDS MEO B1I, MEO B2I, and IGSO B2I performed better than the GPS L1 and L2 estimations. For the BDS IGSO satellites, the B1I signal was more suitable for soil moisture retrieval than the B2I signal; the correlation coefficient was increased by 19.84%, RMSE was decreased by 42.64%, and MAE was decreased by 43.93%. In addition, the BDS MEO satellites could effectively capture sudden rainfall events.
Serial GANs: A Feature-Preserving Heterogeneous Remote Sensing Image Transformation Model
Authors: Tan Daning , Liu Yu , Li Gang , Yao Libo , Sun Shun , He You
Published: October 2021

In recent years, the interpretation of SAR images has been significantly improved with the development of deep learning technology, and using conditional generative adversarial nets (CGANs) for SAR-to-optical transformation, also known as image translation, has become popular. Most of the existing image translation methods based on conditional generative adversarial nets are modified based on CycleGAN and pix2pix, focusing on style transformation in practice. In addition, SAR images and optical images are characterized by heterogeneous features and large spectral differences, leading to problems such as incomplete image details and spectral distortion in the heterogeneous transformation of SAR images in urban or semiurban areas and with complex terrain. Aiming to solve the problems of SAR-to-optical transformation, Serial GANs, a feature-preserving heterogeneous remote sensing image transformation model, is proposed in this paper for the first time. This model uses the Serial Despeckling GAN and Colorization GAN to complete the SAR-to-optical transformation. Despeckling GAN transforms the SAR images into optical gray images, retaining the texture details and semantic information. Colorization GAN transforms the optical gray images obtained in the first step into optical color images and keeps the structural features unchanged. The model proposed in this paper provides a new idea for heterogeneous image transformation. Through decoupling network design, structural detail information and spectral information are relatively independent in the process of heterogeneous transformation, thereby enhancing the detail information of the generated optical images and reducing its spectral distortion. Using SEN-2 satellite images as the reference, this paper compares the degree of similarity between the images generated by different models and the reference, and the results revealed that the proposed model has obvious advantages in feature reconstruction and the economical volume of the parameters. It also showed that Serial GANs have great potential in decoupling image transformation.
Automatic Features Detection in a Fluvial Environment through Machine Learning Techniques Based on UAVs Multispectral Data
Authors: Pontoglio Emanuele , Dabove Paolo , Grasso Nives , Lingua Andrea Maria
Published: October 2021

The present work aims to demonstrate how machine learning (ML) techniques can be used for automatic feature detection and extraction in fluvial environments. The use of photogrammetry and machine learning algorithms has improved the understanding of both environmental and anthropic issues. The developed methodology was applied considering the acquisition of multiple photogrammetric images thanks to unmanned aerial vehicles (UAV) carrying multispectral cameras. These surveys were carried out in the Salbertrand area, along the Dora Riparia River, situated in Piedmont (Italy). The authors developed an algorithm able to identify and detect the water table contour concerning the landed areas: the automatic classification in ML found a valid identification of different patterns (water, gravel bars, vegetation, and ground classes) in specific hydraulic and geomatics conditions. Indeed, the RE+NIR data gave us a sharp rise in terms of accuracy by about 11% and 13.5% of F1-score average values in the testing point clouds compared to RGB data. The obtained results about the automatic classification led us to define a new procedure with precise validity conditions.
Multilabel Image Classification with Deep Transfer Learning for Decision Support on Wildfire Response
Authors: Park Minsoo , Tran Dai Quoc , Lee Seungsoo , Park Seunghee
Published: October 2021

Given the explosive growth of information technology and the development of computer vision with convolutional neural networks, wildfire field data information systems are adopting automation and intelligence. However, some limitations remain in acquiring insights from data, such as the risk of overfitting caused by insufficient datasets. Moreover, most previous studies have only focused on detecting fires or smoke, whereas detecting persons and other objects of interest is equally crucial for wildfire response strategies. Therefore, this study developed a multilabel classification (MLC) model, which applies transfer learning and data augmentation and outputs multiple pieces of information on the same object or image. VGG-16, ResNet-50, and DenseNet-121 were used as pretrained models for transfer learning. The models were trained using the dataset constructed in this study and were compared based on various performance metrics. Moreover, the use of control variable methods revealed that transfer learning and data augmentation can perform better when used in the proposed MLC model. The resulting visualization is a heatmap processed from gradient-weighted class activation mapping that shows the reliability of predictions and the position of each class. The MLC model can address the limitations of existing forest fire identification algorithms, which mostly focuses on binary classification. This study can guide future research on implementing deep learning-based field image analysis and decision support systems in wildfire response work.
Increased Ecosystem Carbon Storage between 2001 and 2019 in the Northeastern Margin of the Qinghai-Tibet Plateau
Authors: Wei Peijie , Chen Shengyun , Wu Minghui , Jia Yinglan , Xu Haojie , Liu Deming
Published: October 2021

Global alpine ecosystems contain a large amount of carbon, which is sensitive to global change. Changes to alpine carbon sources and sinks have implications for carbon and climate feedback processes. To date, few studies have quantified the spatial-temporal variations in ecosystem carbon storage and its response to global change in the alpine regions of the Qinghai-Tibet Plateau (QTP). Ecosystem carbon storage in the northeastern QTP between 2001 and 2019 was simulated and systematically analyzed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Furthermore, the Hurst exponent was obtained and used as an input to perform an analysis of the future dynamic consistency of ecosystem carbon storage. Our study results demonstrated that: (1) regression between the normalized difference vegetation index (NDVI) and biomass (coefficient of determination (R2) = 0.974, p < 0.001), and between NDVI and soil organic carbon density (SOCD) (R2 = 0.810, p < 0.001) were valid; (2) the spatial distribution of ecosystem carbon storage decreased from the southeast to the northwest; (3) ecosystem carbon storage increased by 13.69% between 2001 and 2019, and the significant increases mainly occurred in the low-altitude regions; (4) climate and land use (LULC) changes caused increases in ecosystem carbon storage of 4.39 Tg C and 2.25 Tg C from 2001 to 2019, respectively; and (5) the future trend of ecosystem carbon storage in 92.73% of the study area shows high inconsistency but that in 7.27% was consistent. This study reveals that climate and LULC changes have positive effects on ecosystem carbon storage in the alpine regions of the QTP, which will provide valuable information for the formulation of eco-environmental policies and sustainable development.
Extensive Marine Heatwaves at the Sea Surface in the Northwestern Pacific Ocean in Summer 2021
Authors: Kuroda Hiroshi , Setou Takashi
Published: October 2021

In July–August 2021, intense marine heatwaves (MHWs) occurred at the sea surface over extensive areas of the northwestern Pacific Ocean, including the entire Sea of Japan and part of the Sea of Okhotsk. In extent and intensity, these MHWs were the largest since 1982, when satellite measurements of global sea surface temperatures started. The MHWs in summer 2021 were observed at the sea surface and occurred concomitantly with a stable shallow oceanic surface boundary layer. The distribution of the MHWs was strongly related to heat fluxes at the sea surface, indicating that the MHWs were generated mainly by atmospheric forcing. The MHWs started to develop after around 10 July, concurrent with an extreme northward shift of the atmospheric westerly jet. The MHWs developed rapidly under an atmospheric high-pressure system near the sea surface, associated with a northwestward expansion of the North Pacific Subtropical High. The MHWs exhibited peaks around 30 July to 1 August. Subsequently, following the southward displacement of the westerly jet, the MHWs weakened and then shrank abruptly, synchronously with rapid deepening of the oceanic surface boundary layer. By 18 August, the MHWs had disappeared.
Integrated Preprocessing of Multitemporal Very-High-Resolution Satellite Images via Conjugate Points-Based Pseudo-Invariant Feature Extraction
Authors: Kim Taeheon , Han Youkyung
Published: October 2021

Multitemporal very-high-resolution (VHR) satellite images are used as core data in the field of remote sensing because they express the topography and features of the region of interest in detail. However, geometric misalignment and radiometric dissimilarity occur when acquiring multitemporal VHR satellite images owing to external environmental factors, and these errors cause various inaccuracies, thereby hindering the effective use of multitemporal VHR satellite images. Such errors can be minimized by applying preprocessing methods such as image registration and relative radiometric normalization (RRN). However, as the data used in image registration and RRN differ, data consistency and computational efficiency are impaired, particularly when processing large amounts of data, such as a large volume of multitemporal VHR satellite images. To resolve these issues, we proposed an integrated preprocessing method by extracting pseudo-invariant features (PIFs), used for RRN, based on the conjugate points (CPs) extracted for image registration. To this end, the image registration was performed using CPs extracted using the speeded-up robust feature algorithm. Then, PIFs were extracted based on the CPs by removing vegetation areas followed by application of the region growing algorithm. Experiments were conducted on two sites constructed under different acquisition conditions to confirm the robustness of the proposed method. Various analyses based on visual and quantitative evaluation of the experimental results were performed from geometric and radiometric perspectives. The results evidence the successful integration of the image registration and RRN preprocessing steps by achieving a reasonable and stable performance.
Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations
Authors: Peron-Danaher Raquel , Russell Blake , Cotrozzi Lorenzo , Mohammadi Mohsen , Couture John
Published: October 2021

Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R2 = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs.
Contribution of Biophysical Factors to Regional Variations of Evapotranspiration and Seasonal Cooling Effects in Paddy Rice in South Korea
Authors: Xue Wei , Jeong Seungtaek , Ko Jonghan , Yeom Jong-Min
Published: October 2021

Previous studies have observed seasonal cooling effects in paddy rice as compared to temperate forest through enhanced evapotranspiration (ET) in Northeast Asia, while rare studies have revealed biophysical factors responsible for spatial variations of ET and its cooling effects. In this study, we adopted a data fusion method that integrated MODIS 8-day surface reflectance products, gridded daily climate data of ground surface, and a remote sensing pixel-based Penman-Monteith ET model (i.e., the RS–PM model) to quantify ET patterns of paddy rice in South Korea from 2011 to 2014. Results indicated that the regional variations of the rice-growing season ET (RGS-ET, the sum of daily ET from the season onset of rapid canopy expansion (SoS) to the end of the rice-growing season (EGS)) were primarily influenced by phenological factors (i.e., the length of growing period-LGP), followed by growing season mean climatic factors (i.e., vapor pressure deficit-VPD, and air temperature). For regional variations of the paddy field ET (PF-ET, the sum of daily ET from the field flooding and transplanting date detected by satellite observations (FFTDsat) to SoS, and to EGS), the extents were substantially reduced, only accounting for 54% of the RGS-ET variations. The FFTDsat and SoS were considered critical for the reduced PF-ET variations. In comparison to the temperate forest, changes in monthly ground surface air temperature (Ts) in paddy fields showed the V-shaped seasonal pattern with significant cooling effects found in late spring and early summer, primarily due to a large decline in daytime Ts that exceeded the nighttime warming. Bringing FFTDsat towards late spring and early summer was identified as vital field management practices, causing significant declines in daytime Ts due to enhanced ET. Results highlighted climate-warming mitigation by paddy fields due to early flooding practices.
Satellite Image Time Series Clustering via Time Adaptive Optimal Transport
Authors: Zhang Zheng , Tang Ping , Zhang Weixiong , Tang Liang
Published: October 2021

Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised analysis methods need to be developed. Similarity measure is critical for clustering, however, currently established methods represented by Dynamic Time Warping (DTW) still exhibit several issues when coping with SITS, such as pathological alignment, sensitivity to spike noise, and limitation on capacity. In this paper, we introduce a new time series similarity measure method named time adaptive optimal transport (TAOT) to the application of SITS clustering. TAOT inherits several promising properties of optimal transport for the comparing of time series. Statistical and visual results on two real SITS datasets with two different settings demonstrate that TAOT can effectively alleviate the issues of DTW and further improve the clustering accuracy. Thus, TAOT can serve as a usable tool to explore the potential of precious SITS data.
Algorithm Research Using GNSS-TEC Data to Calibrate TEC Calculated by the IRI-2016 Model over China
Authors: Zhang Wen , Huo Xingliang , Yuan Yunbin , Li Zishen , Wang Ningbo
Published: October 2021

The International Reference Ionosphere (IRI) is an empirical model widely used to describe ionospheric characteristics. In the previous research, high-precision total ionospheric electron content (TEC) data derived from global navigation satellite system (GNSS) data were used to adjust the ionospheric global index IG12 used as a driving parameter in the standard IRI model; thus, the errors between IRI-TEC and GNSS-TEC were minimized, and IRI-TEC was calibrated by modifying IRI with the updated IG12 index (IG-up). This paper investigates various interpolation strategies for IG-up values calculated from GNSS reference stations and the calibrated TEC accuracy achieved using the modified IRI-2016 model with the interpolated IG-up values as driving parameters. Experimental results from 2015 and 2019 show that interpolating IG-up with a 2.5° × 5° spatial grid and a 1-h time resolution drives IRI-2016 to generate ionospheric TEC values consistent with GNSS-TEC. For 2015 and 2019, the mean absolute error (MAE) of the modified IRI-TEC is improved by 78.57% and 77.42%, respectively, and the root mean square error (RMSE) is improved by 78.79% and 77.14%, respectively. The corresponding correlations of the linear regression between GNSS-TEC and the modified IRI-TEC are 0.986 and 0.966, more than 0.2 higher than with the standard IRI-TEC.
Applying a Hand-Held Laser Scanner to Monitoring Gully Erosion: Workflow and Evaluation
Authors: Kinsey-Henderson Anne , Hawdon Aaron , Bartley Rebecca , Wilkinson Scott N. , Lowe Thomas
Published: October 2021

Detailed understanding of gully erosion processes is essential for monitoring gully remediation and requires fine-scale monitoring. Hand-held laser scanning systems (HLS) enable rapid ground-based data acquisition at centimeter precision and ranges of 10–100 m. This study quantified errors in measuring gully morphology and erosion over a four year period using two models of HLS. Reference datasets were provided by Real-Time-Kinematic (RTK) GPS and a RIEGL Terrestrial Laser Scanner (TLS). The study site was representative of linear gullies that occur extensively on hillslopes throughout Great Barrier Reef catchments, where gully erosion is the dominant source of fine sediment. The RMSE error against RTK survey points varied 0.058–0.097 m over five annual scans. HLS was found to measure annual gully headcut extension within 0.035 m of RTK. HLS was, on average, within 6% of TLS for morphological metrics of depth, area and volume. Volumetric change over a 60 m length of the gully and four years was estimated to within 23% of TLS. Errors could potentially be improved by scanning at times of year with lower ground vegetation cover. HLS provided similar levels of error and was relatively more rapid than TLS and RTK for monitoring gully morphology and change.
Gap-Filling of NDVI Satellite Data Using Tucker Decomposition: Exploiting Spatio-Temporal Patterns
Authors: Þórðarson Andri Freyr , Baum Andreas , García Mónica , Vicente-Serrano Sergio M. , Stockmarr Anders
Published: October 2021

Remote sensing satellite images in the optical domain often contain missing or misleading data due to overcast conditions or sensor malfunctioning, concealing potentially important information. In this paper, we apply expectation maximization (EM) Tucker to NDVI satellite data from the Iberian Peninsula in order to gap-fill missing information. EM Tucker belongs to a family of tensor decomposition methods that are known to offer a number of interesting properties, including the ability to directly analyze data stored in multidimensional arrays and to explicitly exploit their multiway structure, which is lost when traditional spatial-, temporal- and spectral-based methods are used. In order to evaluate the gap-filling accuracy of EM Tucker for NDVI images, we used three data sets based on advanced very-high resolution radiometer (AVHRR) imagery over the Iberian Peninsula with artificially added missing data as well as a data set originating from the Iberian Peninsula with natural missing data. The performance of EM Tucker was compared to a simple mean imputation, a spatio-temporal hybrid method, and an iterative method based on principal component analysis (PCA). In comparison, imputation of the missing data using EM Tucker consistently yielded the most accurate results across the three simulated data sets, with levels of missing data ranging from 10 to 90%.
Identifying the Spectral Signatures of Invasive and Native Plant Species in Two Protected Areas of Pakistan through Field Spectroscopy
Authors: Iqbal Iram M. , Balzter Heiko , Firdaus-e-Bareen  , Shabbir Asad
Published: October 2021

Globally, biological invasions are considered as one of the major contributing factors for the loss of indigenous biological diversity. Hyperspectral remote sensing plays an important role in the detection and mapping of invasive plant species. The main objective of this study was to discriminate invasive plant species from adjacent native species using a ground-based hyperspectral sensor in two protected areas, Lehri Reserve Forest and Jindi Reserve Forest in Punjab, Pakistan. Field spectral measurements were collected using an ASD FieldSpec handheld2TM spectroradiometer (325–1075 nm) and the discrimination between native and invasive plant species was evaluated statistically using hyperspectral indices as well as leaf wavelength spectra. Finally, spectral separability was calculated using Jeffries Matusita distance index, based on selected wavebands. The results reveal that there were statistically significant differences (p < 0.05) between the different spectral indices of most of the plant species in the forests. However, the red-edge parameters showed the highest potential (p < 0.001) to discriminate different plant species. With leaf spectral signatures, the mean reflectance between all plant species was significantly different (p < 0.05) at 562 (75%) wavelength bands. Among pairwise comparisons, invasive Leucaena leucocephala showed the best discriminating ability, with Dodonaea viscosa having 505 significant wavebands showing variations between them. Jeffries Matusita distance analysis revealed that band combinations of the red-edge region (725, 726 nm) showed the best spectral separability (85%) for all species. Our findings suggest that it is possible to identify and discriminate invasive species through field spectroscopy for their future monitoring and management. However, the upscaling of hyperspectral measurements to airborne and satellite sensors can provide a reliable estimation of invasion through mapping inside the protected areas and can help to conserve biodiversity and environmental ecosystems in the future.
Vegetation Productivity Losses Linked to Mediterranean Hot and Dry Events
Authors: Ermitão Tiago , Gouveia Célia M. , Bastos Ana , Russo Ana C.
Published: October 2021

Persistent hot and dry conditions play an important role in vegetation dynamics, being generally associated with reduced activity. In the Mediterranean region, ecosystems are adapted to such conditions. However, prolonged and intense heat and drought or the occurrence of compound hot and dry events may still have a negative impact on vegetation activity. This work aims to study how the productivity of Mediterranean vegetation is affected by hot and dry events, examining a set of severe episodes that occurred in three different regions (Iberian Peninsula, Eastern Mediterranean and Western Europe) between 2001 and 2019. The analysis relies on remote sensing products, namely Gross Primary Production from MODIS to detect and monitor vegetative stress and LST from MODIS and SM from ESA CCI to evaluate the influence of temperature and soil water availability on stressed vegetation. Of all events, the 2005 episode in the Iberian Peninsula was the most significant, affecting large sectors of low tree cover areas and crops and leading to reductions of annual plant productivity in affected vegetation of ~47 TgC/year. The obtained results highlight the influence of land-atmosphere coupling on vegetation productivity and clarified the role of warm springs on vegetation activity and soil moisture that may amplify summer temperatures. The functional recovery of affected vegetation productivity after these episodes varied across events, ranging from months to years. This work highlights the influence of hot and dry events on vegetation productivity in the Mediterranean basin and the usefulness of remote-sensing products to assess the response of different land covers to such episodes.
On-Demand Satellite Payload Execution Strategy for Natural Disasters Monitoring Using LoRa: Observation Requirements and Optimum Medium Access Layer Mechanisms
Authors: Fernandez Lara , Ruiz-de-Azua Joan Adria , Calveras Anna , Camps Adriano
Published: October 2021

Natural disasters and catastrophes are responsible for numerous casualties and important economic losses. They can be monitored either with in-situ or spaceborne instruments. However, these monitoring systems are not optimal for an early detection and constant monitoring. An optimisation of these systems could benefit from networks of Internet of Things (IoT) sensors on the Earth’s surface, capable of automatically triggering on-demand executions of the spaceborne instruments. However, having a vast amount of sensors communicating at once with one satellite in view also poses a challenge in terms of the medium access layer (MAC), since, due to packet collisions, packet losses can occur. As part of this study, the monitoring requirements for an ideal spatial nodes density and measurement update frequencies of those sensors are provided. In addition, a study is performed to compare different MAC protocols, and to assess the sensors density that can be achieved with each of these protocols, using the LoRa technology, and concluding the feasibility of the monitoring requirements identified.
Time Signature Detection: A Survey
Authors: Abimbola Jeremiah , Kostrzewa Daniel , Kasprowski Pawel
Published: October 2021

This paper presents a thorough review of methods used in various research articles published in the field of time signature estimation and detection from 2003 to the present. The purpose of this review is to investigate the effectiveness of these methods and how they perform on different types of input signals (audio and MIDI). The results of the research have been divided into two categories: classical and deep learning techniques, and are summarized in order to make suggestions for future study. More than 110 publications from top journals and conferences written in English were reviewed, and each of the research selected was fully examined to demonstrate the feasibility of the approach used, the dataset, and accuracy obtained. Results of the studies analyzed show that, in general, the process of time signature estimation is a difficult one. However, the success of this research area could be an added advantage in a broader area of music genre classification using deep learning techniques. Suggestions for improved estimates and future research projects are also discussed.
LQG Control for Dynamic Positioning of Floating Caissons Based on the Kalman Filter
Authors: Sainz Jose Joaquin , Revestido Herrero Elías , Llata Jose Ramon , Gonzalez-Sarabia Esther , Velasco Francisco J. , Rodriguez-Luis Alvaro , Fernandez-Ruano Sergio , Guanche Raul
Published: October 2021

This paper presents the application of an linear quadratic gaussian (LQG) control strategy for concrete caisson deployment for marine structures. Currently these maneuvers are carried out manually with the risk that this entails. Control systems for these operations with classical regulators have begun to be implemented. They try to reduce risks, but they still need to be optimized due to the complexity of the dynamics involved during the sinking process and the contact with the sea bed. A linear approximation of the dynamic model of the caisson is obtained and an LQG control strategy is implemented based on the Kalman filter (KF). The results of the proposed LQG control strategy are compared to the ones given by a classic controller. It is noted that the proposed system is positioned with greater precision and accuracy, as shown in the different simulations and in the Monte Carlo study. Furthermore, the control efforts are less than with classical regulators. For all the reasons cited above, it is concluded that there is a clear improvement in performance with the control system proposed.
Mapping Relation between Contour Error Components of Crankshaft Pin Journal and Axis Position Control Error of Oscillating Grinding Machine
Authors: Fang Xiaoyan , Sheng Xiaowei , Sun Yize , Xu Yang
Published: October 2021

Automatic crankshaft production lines require high reliability and accuracy stability for the oscillating grinding machine. Crankshaft contour error represent the most intuitive data in production field selective inspection. If the mapping relation between the contour error components of the crankshaft pin journal and the axis position control error of the oscillating grinding machine can be found, it would be great significance for the reliability maintenance of the oscillating grinding machine. Firstly, a contour error decomposition method based on ensemble empirical mode decomposition (EEMD) is proposed. Secondly, according to the contour generating principle of the pin journal by oscillating grinding, a calculation method to obtain the effect of the axis position control error of the oscillating grinder on the contour error of the pin journal is proposed. Finally, through the grinding experiments, the error data are acquired and measured to calculate and decompose the contour error by using the proposed methods for obtaining the mapping relation between the crankshaft pin journal contour error and the axis position control error. The conclusions show that the proposed calculation and decomposition methods can obtain the mapping relation between the contour error components of the crankshaft pin journal and the axis position control error of the oscillating grinding machine, which can be used to predict the key functional component performance of the machine tool from the oscillating grinding workpiece contour error.
Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Unmanned-Aerial-Vehicle Assisted Edge Computing
Authors: Li Shuyang , Hu Xiaohui , Du Yongwen
Published: October 2021

Computation offloading technology extends cloud computing to the edge of the access network close to users, bringing many benefits to terminal devices with limited battery and computational resources. Nevertheless, the existing computation offloading approaches are challenging to apply to specific scenarios, such as the dense distribution of end-users and the sparse distribution of network infrastructure. The technological revolution in the unmanned aerial vehicle (UAV) and chip industry has granted UAVs more computing resources and promoted the emergence of UAV-assisted mobile edge computing (MEC) technology, which could be applied to those scenarios. However, in the MEC system with multiple users and multiple servers, making reasonable offloading decisions and allocating system resources is still a severe challenge. This paper studies the offloading decision and resource allocation problem in the UAV-assisted MEC environment with multiple users and servers. To ensure the quality of service for end-users, we set the weighted total cost of delay, energy consumption, and the size of discarded tasks as our optimization objective. We further formulate the joint optimization problem as a Markov decision process and apply the soft actor–critic (SAC) deep reinforcement learning algorithm to optimize the offloading policy. Numerical simulation results show that the offloading policy optimized by our proposed SAC-based dynamic computing offloading (SACDCO) algorithm effectively reduces the delay, energy consumption, and size of discarded tasks for the UAV-assisted MEC system. Compared with the fixed local-UAV scheme in the specific simulation setting, our proposed approach reduces system delay and energy consumption by approximately 50% and 200%, respectively.
Calibration of Planar Reflectors Reshaping LiDAR’s Field of View
Authors: Pełka Michał , Będkowski Janusz
Published: October 2021

This paper describes the calibration method for calculating parameters (position and orientation) of planar reflectors reshaping LiDAR’s (light detection and ranging) field of view. The calibration method is based on the reflection equation used in the ICP (Iterative Closest Point) optimization. A novel calibration process as the multi-view data registration scheme is proposed; therefore, the poses of the measurement instrument and parameters of planar reflectors are calculated simultaneously. The final metric measurement is more accurate compared with parameters retrieved from the mechanical design. Therefore, it is evident that the calibration process is required for affordable solutions where the mechanical design can differ from the inaccurate assembly. It is shown that the accuracy is less than 20 cm for almost all measurements preserving long-range capabilities. The experiment is performed based on Livox Mid-40 LiDAR augmented with six planar reflectors. The ground-truth data were collected using Z + F IMAGER 5010 3D Terrestrial Laser Scanner. The calibration method is independent of mechanical design and does not require any fiducial markers on the mirrors. This work fulfils the gap between rotating and Solid-State LiDARs since the field of view can be reshaped by planar reflectors, and the proposed method can preserve the metric accuracy. Thus, such discussion concludes the findings. We prepared an open-source project and provided all the necessary data for reproducing the experiments. That includes: Complete open-source code, the mechanical design of reflector assembly and the dataset which was used in this paper.
Multifunctional Partially Reflective Surface for Smart Blocks
Authors: Kim Jae Hee , Lee Dong-Jin , An Tae-Ki , Hwang Jong-Gyu , Ahn Chi-Hyung
Published: October 2021

In general, a partially reflective surface (PRS) is mainly used to increase the gain of an antenna; some metallic objects placed on the PRS degrades the antenna performance because the objects change the periodic structure of the PRS. Herein, we propose a multifunctional PRS for smart block application. When a passenger passes over a smart block, the fare can be simultaneously collected and presented through the LED display. This requires high gain antenna with LED structure. The high gain characteristic helps the antenna identify passengers only when they pass over the block. The multifunctional PRS has a structure in which an LED can be placed in the horizontal direction while increasing the antenna gain. We used the antenna’s polarization characteristics to prevent performance deterioration when LED lines are placed in the PRS. We built the proposed antenna and measured its performance: At 2.41 GHz, the efficiency was 81.4%, and the antenna gain was 18.3 dBi. Furthermore, the half-power beamwidth was 18°, confirming a directional radiation pattern.
A Transformer-Based Neural Machine Translation Model for Arabic Dialects That Utilizes Subword Units
Authors: Baniata Laith H. , Ampomah Isaac. K. E. , Park Seyoung
Published: October 2021

Languages that allow free word order, such as Arabic dialects, are of significant difficulty for neural machine translation (NMT) because of many scarce words and the inefficiency of NMT systems to translate these words. Unknown Word (UNK) tokens represent the out-of-vocabulary words for the reason that NMT systems run with vocabulary that has fixed size. Scarce words are encoded completely as sequences of subword pieces employing the Word-Piece Model. This research paper introduces the first Transformer-based neural machine translation model for Arabic vernaculars that employs subword units. The proposed solution is based on the Transformer model that has been presented lately. The use of subword units and shared vocabulary within the Arabic dialect (the source language) and modern standard Arabic (the target language) enhances the behavior of the multi-head attention sublayers for the encoder by obtaining the overall dependencies between words of input sentence for Arabic vernacular. Experiments are carried out from Levantine Arabic vernacular (LEV) to modern standard Arabic (MSA) and Maghrebi Arabic vernacular (MAG) to MSA, Gulf–MSA, Nile–MSA, Iraqi Arabic (IRQ) to MSA translation tasks. Extensive experiments confirm that the suggested model adequately addresses the unknown word issue and boosts the quality of translation from Arabic vernaculars to Modern standard Arabic (MSA).
An Improvement of the Fire Detection and Classification Method Using YOLOv3 for Surveillance Systems
Authors: Abdusalomov Akmalbek , Baratov Nodirbek , Kutlimuratov Alpamis , Whangbo Taeg Keun
Published: October 2021

Currently, sensor-based systems for fire detection are widely used worldwide. Further research has shown that camera-based fire detection systems achieve much better results than sensor-based methods. In this study, we present a method for real-time high-speed fire detection using deep learning. A new special convolutional neural network was developed to detect fire regions using the existing YOLOv3 algorithm. Due to the fact that our real-time fire detector cameras were built on a Banana Pi M3 board, we adapted the YOLOv3 network to the board level. Firstly, we tested the latest versions of YOLO algorithms to select the appropriate algorithm and used it in our study for fire detection. The default versions of the YOLO approach have very low accuracy after training and testing in fire detection cases. We selected the YOLOv3 network to improve and use it for the successful detection and warning of fire disasters. By modifying the algorithm, we recorded the results of a rapid and high-precision detection of fire, during both day and night, irrespective of the shape and size. Another advantage is that the algorithm is capable of detecting fires that are 1 m long and 0.3 m wide at a distance of 50 m. Experimental results showed that the proposed method successfully detected fire candidate areas and achieved a seamless classification performance compared to other conventional fire detection frameworks.