bluuebunny/crossref_metadata_2025 huggingface dataset:
The Current State, Challenges, and Opportunities of Recycling Plastics in Western Australia
Authors: Cáceres Ruiz Ana María , Zaman Atiq
Published: September 2022

In 2018–2019, 85% of discarded plastics were landfilled in Australia. In Western Australia (WA), only 5.6% of plastics were recovered for reprocessing. With several Asian Countries imposing import restrictions, which were the prime destination for recyclables from Australia, the whole scenario for the waste industry has changed. Australia has now adopted export bans for recyclables, including plastics. WA is at a fork in the road; WA needs to rethink its relationship with plastic materials. This study explores how to create local markets for recycled plastics underpinning circular principles. The study examines barriers and drivers to enable markets for recycled plastics in WA through questionnaires, surveys, and interviews with relevant stakeholders. Poor source separation, low and inconsistent plastic waste feedstock, and virgin plastic competition are some of the challenges, while new investments in recycling infrastructure, WA’s take-back scheme for beverage containers and circularity frameworks are drivers. This study concludes that a modulated fee-based product stewardship model focused on product design, along with strategies such as green procurement and landfill management modifications would promote a circular plastic waste economy in WA. This can create markets for secondary recycled plastics, minimize the over-reliance on fossil fuels and prevent plastics from leaking into ecosystems.
Living with the Enemies: Japanese Imperialism, Protestant Christianity, and Marxist Socialism in Colonial Korea, 1919–1945
Authors: Shin Seungyop
Published: September 2022

During the Korean War, conflicts between right-wing Protestants and radical socialists escalated and erupted into massacres, killing thousands of Korean civilians. Such extreme violence and tumultuous events afterwards—including Korea’s division into two separate states and the Cold War system—eclipsed the imbricated interactions between Protestant Christianity and socialism under Japanese colonial rule. While focusing on Korean Protestantism and socialism to probe their contest and compromise for survival, this article traces the tripartite relationship among the followers of Protestant Christianity, Marxist socialism, and Japanese imperialism as it evolved throughout colonial Korea between 1910 and 1945. These 35 years comprised a period of multiple possibilities for interaction among Korean Protestants, socialists, and Japanese authorities in the changing global environment. The international organizations with which they were associated influenced Korean Protestants and Marxist socialists while facing the common crisis of Japan’s assimilation. Namely, the Korean Protestant churches affiliated with Western missionaries’ denomination headquarters in their home countries and world Christian conferences, while the Korean socialists allied with Moscow’s Comintern and other radical political movements abroad. Within this broader context, these two religious and ideological forces competed for supremacy, cooperated in a joint struggle against the colonial regime, and antagonized each other over their divergent worldviews. By examining their complicated tripartite relationship, this essay comprehensively depicts the dynamic history of the Western-derived religious and political doctrines meeting a non-Western empire in a foreign land.
The Shifting Depictions of Xiàng in German Translations of the Dao De Jing: An Analysis from the Perspective of Conceptual Metaphor Field Theory
Authors: Zhu Yubo , Song Weihan
Published: September 2022

Like the Bible, the Dào Dé Jīng is one of the most translated classics with worldwide influence, and its translation sets a good example in cross-cultural communication. Among the Dào Dé Jīng’s translations, the number of German versions is second only to the English ones. Since its introduction to the German regions, the Dào Dé Jīng has been popular among German-speaking scholars and readers, casting profound and far-reaching influences in various fields. Based on the theory of the conceptual metaphor field, the article explores the relationship between Dào 道 (way or Dao) and Xiàng 象 (Symbolic Imagery, images) in the Dào Dé Jīng and builds the mapping from Xiàng to Dào. In the Dào Dé Jīng, Laozi uses images (Xiàng 象) as collective concepts to illustrate his Dào and make his idea better understood. Thus, this article focuses on the translation of different key images (Xiàng) in six representative German translations of the Dào Dé Jīng and summarizes three main translation techniques used in translating Xiàng: shifting, conversion, and concealment. After balancing the cultural differences and translation requirements, the German translators take these techniques to translate Xiàng and make relevant concepts more understandable and acceptable for German readers, which facilitates the spread of the Dào Dé Jīng in the German regions. Inspired by the German translation of Xiàng, contemporary translators shall balance the cultural differences between the source language and target language, choose the appropriate translation strategies and techniques in translating ancient Chinese classics and make their translation a bridge between different civilizations.
Model of the Factors Affecting the Eco-Innovation Activity of Bulgarian Industrial Enterprises
Authors: Nikolova-Alexieva Valentina , Alexieva Iordanka , Valeva Katina , Petrova Mariana
Published: September 2022

In recent years, modern society has faced a number of challenges related to the achievement of global goals for sustainable development. Industrial enterprises are challenged to generate, stimulate, and demand changes in networks and supply chains, but these challenges require flexibility and innovation activity in different directions. The data for Bulgaria show that the country is last among the countries of the European Union in terms of the creation and implementation of eco-innovations. Despite this result, the pace at which the country is developing shows that in the next few years, Bulgaria has the potential to move from a modest to a moderate eco-innovator, provided that it succeeds in filling the structural gaps in the system of ecological innovation. These gaps are related not only to the need for changes in the investment of resources but also to the need for changes in individual and related systems such as science and innovation, support for SMEs, the energy system, etc. Most of the research on sustainable innovation and eco-innovation has, however, focused on firm innovation models dominated by short-term profit-maximizing approaches. Therefore, there is a need to conduct research and propose adequate strategies for modern business environments and design models that facilitate the implementation of eco-innovations in industrial enterprises. The purpose of this report is to investigate the factors influencing the development of eco-innovation activities of Bulgarian industrial enterprises, examining how they can help to achieve success through eco-innovation and improve business results. A factorial model is proposed, through which the relationships between technological, financial, organizational, informational resources, research and development activities (R&D), and company cooperation are analyzed. The PLS structural equation modeling technique was used to validate the proposed theoretical model. The survey was conducted among 380 industrial enterprises from all over the sectors of the economy in Bulgaria with the help of a specially developed questionnaire within the period of April 2019 to December 2021. The obtained results show that human resources, financial resources, and cooperation positively influence research and development activities. In addition, the achievement of a positive effect on the management of eco-innovations affects the innovation activities of industrial enterprises, their ability to carry out research and development activities, as well as their ability to manage the technical and technological resources at their disposal effectively. Finally, the innovation activity aimed at carrying out scientific research and development activity, products and processes obtained as a result of the eco-innovation activity, and adequate information management directly affect the efficiency of business processes and financial results.
Social Robots Outdo the Not-So-Social Media for Self-Disclosure: Safe Machines Preferred to Unsafe Humans?
Authors: Luo Rowling L. , Zhang Thea X. Y. , Chen Derrick H.-C. , Hoorn Johan F. , Huang Ivy S.
Published: September 2022

COVID-19 may not be a ‘youth disease’ but it nevertheless impacts the life of young people dramatically, loneliness and a negative mood being an unexpected additional pandemic. Many young people rely on social media for their feeling of connectedness with others. However, social media is suggested to have many negative effects on people’s anxiety. Instead of self-disclosing to others, design may develop alternatives to employ social robots for self-disclosure. In a follow-up on earlier work, we report on a lab experiment of self-disclosing negative emotions to a social media group as compared to writing a conventional diary journal or to talking to an AI-driven social robot after negative mood induction (i.e., viewing shocking earthquake footage). Participants benefitted the most from talking to a robot rather than from writing a journal page or sharing their feelings on social media. Self-disclosure on social media or writing a journal page did not differ significantly. In the design of interventions for mental well-being, human helpers thus far took center stage. Based on our results, we propose design alternatives for an empathic smart home, featuring social robots and chatbots for alleviating stress and anxiety: a social-media interference chatbot, smart watch plus speaker, and a mirror for self-reflection.
Robust MIMO Waveform Design in the Presence of Unknown Mutipath Return
Authors: Fan Chongyi , Xie Zhuang , Wang Jian , Xu Zhou , Huang Xiaotao
Published: September 2022

Assuming uncertain multipath return, this paper considers the robust joint transmit waveform and the receiving filter bank design of a multiple-input–multiple-output (MIMO) radar for multipath exploitation. The actual multipath return is considered to belong to an uncertain set, and we focus on the worst-case optimization of the signal-to-interference-plus-noise ratio (SINR) in the output of the filter bank. The design is cast as a non-convex max–min problem, which is very hard to solve. To tackle it, an equivalent reformulation is utilized and a cyclic optimization paradigm is devised. At each iteration, the filter’s optimization problem is equal to a set of separate solvable problems, the closed-form solution to which can be given directly. Moreover, we have shown that the max–min problem for the waveform optimization belongs to the area of generalized fractional programming, and it can be globally solved by resorting to Dinkelbach’s algorithm. Through simulations, the superiority of the proposed algorithm is demonstrated via a number of examples.
Complex-Valued Sparse SAR-Image-Based Target Detection and Classification
Authors: Song Chen , Deng Jiarui , Liu Zehao , Wang Bingnan , Wu Yirong , Bi Hui
Published: September 2022

It is known that synthetic aperture radar (SAR) images obtained by typical matched filtering (MF)-based algorithms always suffer from serious noise, sidelobes and clutter. However, the improvement in image quality means that the complexity of SAR systems will increase, which affects the applications of SAR images. The introduction of sparse signal processing technologies into SAR imaging proposes a new way to solve this problem. Sparse SAR images obtained by sparse recovery algorithms show better image performance than typical complex SAR images with lower sidelobes and higher signal-to-noise ratios (SNR). As the most widely applied fields of SAR images, target detection and target classification rely on SAR images with high quality. Therefore, in this paper, a target detection framework based on sparse images recovered by complex approximate message passing (CAMP) algorithm and a novel classification network via sparse images reconstructed by the new iterative soft thresholding (BiIST) algorithm are proposed. Experimental results show that sparse SAR images have better performance whether for target classification or for target detection than the images recovered by MF-based algorithms, which validates the huge application potentials of sparse images.
Dictionary Learning-Cooperated Matrix Decomposition for Hyperspectral Target Detection
Authors: Yao Yuan , Wang Mengbi , Fan Ganghui , Liu Wendi , Ma Yong , Mei Xiaoguang
Published: September 2022

Hyperspectral target detection is one of the most challenging tasks in remote sensing due to limited spectral information. Many algorithms based on matrix decomposition (MD) are proposed to promote the separation of the background and targets, but they suffer from two problems: (1) Targets are detected with the criterion of reconstruction residuals, and the imbalanced number of background and target atoms in union dictionary may lead to misclassification of targets. (2) The detection results are susceptible to the quality of the apriori target spectra, thus obtaining inferior performance because of the inevitable spectral variability. In this paper, we propose a matrix decomposition-based detector named dictionary learning-cooperated matrix decomposition (DLcMD) for hyperspectral target detection. The procedure of DLcMD is two-fold. First, the low rank and sparse matrix decomposition (LRaSMD) is exploited to separate targets from the background due to its insensitivity to the imbalanced number of background and target atoms, which can reduce the misclassification of targets. Inspired by dictionary learning, the target atoms are updated during LRaSMD to alleviate the impact of spectral variability. After that, a binary hypothesis model specifically designed for LRaSMD is proposed, and a generalized likelihood ratio test (GLRT) is performed to obtain the final detection result. Experimental results on five datasets have shown the reliability of the proposed method. Especially in the Los Angeles-II dataset, the area under the curve (AUC) value is nearly 16% higher than the average value of the other seven detectors, which reveals the superiority of DLcMD in hyperspectral target detection.
Data-Driven Random Forest Models for Detecting Volcanic Hot Spots in Sentinel-2 MSI Images
Authors: Corradino Claudia , Amato Eleonora , Torrisi Federica , Del Negro Ciro
Published: September 2022

Volcanic thermal anomalies are monitored with an increased application of optical satellite sensors to improve the ability to identify renewed volcanic activity. Hotspot detection algorithms adopting a fixed threshold are widely used to detect thermal anomalies with a minimal occurrence of false alerts. However, when used on a global scale, these algorithms miss some subtle thermal anomalies that occur. Analyzing satellite data sources with machine learning (ML) algorithms has been shown to be efficient in extracting volcanic thermal features. Here, a data-driven algorithm is developed in Google Earth Engine (GEE) to map thermal anomalies associated with lava flows that erupted recently at different volcanoes around the world (e.g., Etna, Cumbre Vieja, Geldingadalir, Pacaya, and Stromboli). We used high spatial resolution images acquired by a Sentinel-2 MultiSpectral Instrument (MSI) and a random forest model, which avoids the setting of fixed a priori thresholds. The results indicate that the model achieves better performance than traditional approaches with good generalization capabilities and high sensitivity to less intense volcanic thermal anomalies. We found that this model is sufficiently robust to be successfully used with new eruptive scenes never seen before on a global scale.
Editorial for the Special Issue ″Climate Modelling and Monitoring Using GNSS″
Authors: Van Malderen Roeland , Santos Marcelo , Zhang Kefei
Published: September 2022

Reliably modelling and monitoring the climate requires robust data that can be used to feed meteorological models, and, most importantly, to independently validate those models [...]
Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach
Authors: Wang Xiucheng , Fu Lianhao , Cheng Nan , Sun Ruijin , Luan Tom , Quan Wei , Aldubaikhy Khalid
Published: September 2022

Unmanned aerial vehicles (UAVs) are widely used in Internet-of-Things (IoT) networks, especially in remote areas where communication infrastructure is unavailable, due to flexibility and low cost. However, the joint optimization of locations of UAVs and relay path selection can be very challenging, especially when the numbers of IoT devices and UAVs are very large. In this paper, we formulate the joint optimization of UAV locations and relay paths in UAV-relayed IoT networks as a graph problem, and propose a graph neural network (GNN)-based approach to solve it in an efficient and scalable way. In the training procedure, we design a reinforcement learning-based relay GNN (RGNN) to select the best relay path for each user. The theoretical analysis shows that the time complexity of RGNN is two orders lower than the conventional optimization method. Then, we jointly exploit location GNN (LGNN) and RGNN trained to optimize the locations of all UAVs. Both GNNs can be trained without relying on the training data, which is usually unavailable in the context of wireless networks. In inference procedure, LGNN is first used to optimize the location of UAVs, and then RGNN is used to select the best relay path based on the output of LGNN. Simulation results show that the proposed approach can achieve comparable performance to brute-force search with much lower time complexity when the network is relatively small. Remarkably, the proposed approach is highly scalable to large-scale networks and adaptable to dynamics in the environment, which can hardly be achieved using conventional methods.
Full-Coupled Convolutional Transformer for Surface-Based Duct Refractivity Inversion
Authors: Wu Jiajing , Wei Zhiqiang , Zhang Jinpeng , Zhang Yushi , Jia Dongning , Yin Bo , Yu Yunchao
Published: September 2022

A surface-based duct (SBD) is an abnormal atmospheric structure with a low probability of occurrence buta strong ability to trap electromagnetic waves. However, the existing research is based on the assumption that the range direction of the surface duct is homogeneous, which will lead to low productivity and large errors when applied in a real-marine environment. To alleviate these issues, we propose a framework for the inversion of inhomogeneous SBD M-profile based on a full-coupled convolutional Transformer (FCCT) deep learning network. We first designed a one-dimensional residual dilated causal convolution autoencoder to extract the feature representations from a high-dimension range direction inhomogeneous M-profile. Second, to improve efficiency and precision, we proposed a full-coupled convolutional Transformer (FCCT) that incorporated dilated causal convolutional layers to gain exponentially receptive field growth of the M-profile and help Transformer-like models improve the receptive field of each range direction inhomogeneous SBD M-profile information. We tested our proposed method performance on two sets of simulated sea clutter power data where the inversion of the simulated data reached 96.99% and 97.69%, which outperformed the existing baseline methods.
Tracking Spatiotemporal Patterns of Rwanda’s Electrification Using Multi-Temporal VIIRS Nighttime Light Imagery
Authors: Ru Yuanxi , Li Xi , Belay Wubetu Anley
Published: September 2022

After recovering from the Rwanda Genocide in the last century, Rwanda is experiencing rapid economic growth and urban expansion. With increasing demand for electricity and a strong desire to achieve the Sustainable Development Goals (SDGs), it is significant to further investigate the electrification progress in Rwanda. This study analyzes the characteristics of electrification in Rwanda from 2012 to 2020 using VIIRS nighttime light imagery. Firstly, by analysis of the nighttime light change patterns on a national scale, we find that the electrification in Rwanda is seriously unbalanced, as electrification progress in Kigali is much faster than that in the rest of the country. Secondly, there is a common phenomenon where power grid expansion in Rwanda fails to keep pace with rapid urbanization, especially in areas with an inadequate electricity infrastructure foundation. Quantitatively, original electricity infrastructure level shows a positive impact on the grid access of new settlements, with an R2 value of 0.695 in the linear regression. In addition, new settlements inside the urban boundary tend to achieve more extensive grid access compared to those outside the boundary. Finally, the grid access rates are calculated on multi-spatial scales. By comparing the calculated results with the official electricity access rate data, we analyze the development of off-grid access in Rwanda. The results imply that, since 2016, off-grid access has rapidly developed in Rwanda, especially in the rural areas, playing an important role in achieving the SDGs.
Effect of Image-Processing Routines on Geographic Object-Based Image Analysis for Mapping Glacier Surface Facies from Svalbard and the Himalayas
Authors: Jawak Shridhar D. , Wankhede Sagar F. , Luis Alvarinho J. , Balakrishna Keshava
Published: September 2022

Advancements in remote sensing have led to the development of Geographic Object-Based Image Analysis (GEOBIA). This method of information extraction focuses on segregating correlated pixels into groups for easier classification. This is of excellent use in analyzing very-high-resolution (VHR) data. The application of GEOBIA for glacier surface mapping, however, necessitates multiple scales of segmentation and input of supportive ancillary data. The mapping of glacier surface facies presents a unique problem to GEOBIA on account of its separable but closely matching spectral characteristics and often disheveled surface. Debris cover can induce challenges and requires additions of slope, temperature, and short-wave infrared data as supplements to enable efficient mapping. Moreover, as the influence of atmospheric corrections and image sharpening can derive variations in the apparent surface reflectance, a robust analysis of the effects of these processing routines in a GEOBIA environment is lacking. The current study aims to investigate the impact of three atmospheric corrections, Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH), and two pansharpening methods, viz., Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on the classification of surface facies using GEOBIA. This analysis is performed on VHR WorldView-2 imagery of selected glaciers in Ny-Ålesund, Svalbard, and Chandra–Bhaga basin, Himalaya. The image subsets are segmented using multiresolution segmentation with constant parameters. Three rule sets are defined: rule set 1 utilizes only spectral information, rule set 2 contains only spatial and contextual features, and rule set 3 combines both spatial and spectral attributes. Rule set 3 performs the best across all processing schemes with the highest overall accuracy, followed by rule set 1 and lastly rule set 2. This trend is observed for every image subset. Among the atmospheric corrections, DOS displays consistent performance and is the most reliable, followed by QUAC and FLAASH. Pansharpening improved overall accuracy and GS performed better than HCS. The study reports robust segmentation parameters that may be transferable to other VHR-based glacier surface facies mapping applications. The rule sets are adjusted across the processing schemes to adjust to the change in spectral characteristics introduced by the varying routines. The results indicate that GEOBIA for glacier surface facies mapping may be less prone to the differences in spectral signatures introduced by different atmospheric corrections but may respond well to increasing spatial resolution. The study highlighted the role of spatial attributes for mapping fine features, and in combination with appropriate spectral features may enhance thematic classification.
Real-Time Multi-GNSS Precise Orbit Determination Based on the Hourly Updated Ultra-Rapid Orbit Prediction Method
Authors: Tan Bingfeng , Yuan Yunbin , Ai Qingsong , Zha Jiuping
Published: September 2022

Offering real-time precise point positioning (PPP) services for global and large areas based on global navigation satellite systems (GNSS) has drawn more and more attention from institutions and companies. A precise and reliable satellite orbit is a core premise for multi-GNSS real-time services, especially for the GPS and GLONASS, which are undergoing modernization, whereas the Galileo, BDS and QZSS have just fulfilled the construction stage. In this contribution, a real-time precise orbit determination (POD) strategy for the five operational constellations based on the hourly updated ultrarapid orbit prediction method is presented. After combination of 72 h arc through three adjacent 24 h arc normal equations, the predicted orbits are finally generated (hourly updated). The POD results indicate that the mean one-dimensional (1-D) root mean square (RMS) values compared with the Deutsches GeoForschungsZentrum (GFZ) final multi-GNSS orbits are approximately 3.7 cm, 10.2 cm, 5.8 cm, 5.7 cm, 4.1 cm and 25.1 cm for GPS, BDS IGSOs, BDS MEOs, GLONASS, Galileo and QZSS NONE GEOs, respectively. The mean 1-D RMS values of the hourly updated ultrarapid orbit boundary overlapping comparison are approximately 1.6 cm, 6.9 cm, 3.2 cm, 2.7 cm, 1.8 cm and 22.2 cm for GPS, BDS IGSOs, BDS MEOs, GLONASS, Galileo and QZSS NONE GEOs, respectively. The satellite laser ranging (SLR) validation illuminates that the mean RMS values are approximately 4.53 cm and 4.73 cm for the four MEOs of BDS-3 and four BDS-2 satellites, respectively.
Interannual and Decadal Variability of Sea Surface Temperature and Sea Ice Concentration in the Barents Sea
Authors: Mohamed Bayoumy , Nilsen Frank , Skogseth Ragnheid
Published: September 2022

Sea ice loss and accelerated warming in the Barents Sea have recently been one of the main concerns of climate research. In this study, we investigated the trends and possible relationships between sea surface temperature (SST), sea ice concentration (SIC), and local and large-scale atmospheric parameters over the last 39 years (1982 to 2020). We examined the interannual and long-term spatiotemporal variability of SST and SIC by performing an empirical orthogonal function (EOF) analysis. The SST warming rate from 1982 through 2020 was 0.35 ± 0.04 °C/decade and 0.40 ± 0.04 °C/decade in the ice-covered and ice-free regions, respectively. This climate warming had a significant impact on sea-ice conditions in the Barents Sea, such as a strong decline in the SIC (−6.52 ± 0.78%/decade) and a shortening of the sea-ice season by about −26.1 ± 7.5 days/decade, resulting in a 3.4-month longer summer ice-free period over the last 39 years. On the interannual and longer-term scales, the Barents Sea has shown strong coherent spatiotemporal variability in both SST and SIC. The temporal evolution of SST and SIC are strongly correlated, whereas the Atlantic Multidecadal Oscillation (AMO) influences the spatiotemporal variability of SST and SIC. The highest spatial variability (i.e., the center of action of the first EOF mode) of SST was observed over the region bounded by the northern and southern polar fronts, which are influenced by both warm Atlantic and cold Arctic waters. The largest SIC variability was found over the northeastern Barents Sea and over the Storbanken and Olga Basin. The second EOF mode revealed a dipole structure with out-of-phase variability between the ice-covered and ice-free regions for the SST and between the Svalbard and Novaya Zemlya regions for SIC. In order to investigate the processes that generate these patterns, a correlation analysis was applied to a set of oceanic (SST) and atmospheric parameters (air temperature, zonal, and meridional wind components) and climate indices. This analysis showed that SST and SIC are highly correlated with air temperature and meridional winds and with two climate indices (AMO and East Atlantic Pattern (EAP)) on an interannual time scale. The North Atlantic Oscillation (NAO) only correlated with the second EOF mode of SST on a decadal time scale.
Climatology of Dust Aerosols over the Jianghan Plain Revealed with Space-Borne Instruments and MERRA-2 Reanalysis Data during 2006–2021
Authors: Liu Chuan , Yin Zhenping , He Yun , Wang Longlong
Published: September 2022

In recent years, climate change and the intervention of anthropogenic activities have altered the seasonal features of Asian dust storms. This may also cause seasonal variations (including dust occurrence frequency and optical/microphysical properties) in dust aerosols transported to downstream regions. The Jianghan Plain is dramatically influenced by multiple dust sources due to its geographical location in central China. In this study, we focused on the climatology of dust aerosols over the Jianghan Plain based on the 15-year (2006–2021) continuous space-borne observations of the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) as well as Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalysis data. A typical dust event that intrudes the Jianghan Plain was studied in detail. According to the statistical results, dust aerosols frequently intrude into the Jianghan Plain in spring and winter, with occurrence frequencies (under cloud free condition hereafter) exceeding 0.70 and higher altitudes of 4–6 km. The dust occurrence frequency declined to approximately 0.40 in autumn and nearly zero in summer, while the dust plumes were generally located at lower altitudes of 1–3 km. The dust plumes observed in the Jianghan Plain were simultaneously linked to the Taklimakan Desert and Gobi Desert in spring and mainly originated from the Taklimakan Desert in winter and autumn. The dust particles were mainly distributed below 4-km altitude, with the largest dust extinction coefficients and dust mass concentrations in spring. In all seasons, the particle depolarization ratios are 0.1–0.2 below 4-km altitude, suggesting a possible mix with local anthropogenic aerosols. The mean dust column mass concentrations in spring showed an evident declining trend from 210 µg m−2 in 2006 to 100 µg m−2 in 2021 in the Jianghan Plain, attributed to the reduced dust activity in the source regions of Asian dust.
Application and Analysis of XCO2 Data from OCO Satellite Using a Synthetic DINEOF–BME Spatiotemporal Interpolation Framework
Authors: Jiang Yutong , Gao Zekun , He Junyu , Wu Jiaping , Christakos George
Published: September 2022

Carbon dioxide (CO2) is one of the main greenhouse gases leading to global warming, and the ocean is the largest carbon reservoir on the earth that plays an important role in regulating CO2 concentration on a global scale. The column-averaged dry-air mole fraction of atmospheric CO2 (XCO2) is a key parameter in describing ocean carbon content. In this paper, the Data Interpolation Empirical Orthogonal Function (DINEOF) and the Bayesian Maximum Entropy (BME) methods are combined to interpolate XCO2 data of Orbiting Carbon Observatory 2 (OCO-2) and Orbiting Carbon Observatory 3 (OCO-3) from January to December 2020 occurring within the geographical range of 15–45°N and 120–150°E. At the first stage of our proposed analysis, spatiotemporal information was used by the DINEOF method to perform XCO2 interpolation that improved data coverage; at the second stage, the DINEOF-generated interpolation results were regarded as soft data and were subsequently assimilated using the BME method to obtain improved XCO2 interpolation values. The performance of the synthetic DINEOF–BME interpolation method was evaluated by means of a five-fold cross-validation method. The results showed that the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Bias of the DINEOF-based OCO-2 and OCO-3 interpolations were 2.106 ppm, 3.046 ppm, and 1.035 ppm, respectively. On the other hand, the MAE, RMSE, and Bias of the cross-validation results obtained by the DINEOF–BME were 1.285 ppm, 2.422 ppm, and −0.085 ppm, respectively, i.e., smaller than the results obtained by DINEOF. In addition, based on the in situ measured XCO2 data provided by the Total Carbon Column Observing Network (TCCON), the original OCO-2 and OCO-3 data were combined and compared with the interpolated products of the synthetic DINEOF–BME framework. The accuracy of the original OCO-2 and OCO-3 products is lower than the DINEOF–BME-generated XCO2 products in terms of MAE (1.751 ppm vs. 2.616 ppm), RMSE (2.877 ppm vs. 3.566 ppm) and Bias (1.379 ppm vs 1.622 ppm), the spatiotemporal coverage of XCO2 product also improved dramatically from 16% to 100%. Lastly, this study demonstrated the feasibility of the synthetic DINEOF–BME approach for XCO2 interpolation purposes and the ability of the BME method to be successfully combined with other techniques.
Crop Monitoring Using Sentinel-2 and UAV Multispectral Imagery: A Comparison Case Study in Northeastern Germany
Authors: Li Minhui , Shamshiri Redmond R. , Weltzien Cornelia , Schirrmann Michael
Published: September 2022

Monitoring within-field crop variability at fine spatial and temporal resolution can assist farmers in making reliable decisions during their agricultural management; however, it traditionally involves a labor-intensive and time-consuming pointwise manual process. To the best of our knowledge, few studies conducted a comparison of Sentinel-2 with UAV data for crop monitoring in the context of precision agriculture. Therefore, prospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated from a more practical viewpoint closer to agricultural routines. Multispectral UAV and Sentinel-2 imagery was collected over three dates in the season and compared with reference data collected at 20 sample points for plant leaf nitrogen (N), maximum plant height, mean plant height, leaf area index (LAI), and fresh biomass. Higher correlations of UAV data to the agronomic parameters were found on average than with Sentinel-2 data with a percentage increase of 6.3% for wheat and 22.2% for barley. In this regard, VIs calculated from spectral bands in the visible part performed worse for Sentinel-2 than for the UAV data. In addition, large-scale patterns, formed by the influence of an old riverbed on plant growth, were recognizable even in the Sentinel-2 imagery despite its much lower spatial resolution. Interestingly, also smaller features, such as the tramlines from controlled traffic farming (CTF), had an influence on the Sentinel-2 data and showed a systematic pattern that affected even semivariogram calculation. In conclusion, Sentinel-2 imagery is able to capture the same large-scale pattern as can be derived from the higher detailed UAV imagery; however, it is at the same time influenced by management-driven features such as tramlines, which cannot be accurately georeferenced. In consequence, agronomic parameters were better correlated with UAV than with Sentinel-2 data. Crop growers as well as data providers from remote sensing services may take advantage of this knowledge and we recommend the use of UAV data as it gives additional information about management-driven features. For future perspective, we would advise fusing UAV with Sentinel-2 imagery taken early in the season as it can integrate the effect of agricultural management in the subsequent absence of high spatial resolution data to help improve crop monitoring for the farmer and to reduce costs.
Correction of Terrain Effects on Forest Canopy Height Estimation Using ICESat-2 and High Spatial Resolution Images
Authors: Li Bin , Zhao Tianzhong , Su Xiaohui , Fan Guangpeng , Zhang Wenjie , Deng Zhuo , Yu Yonghui
Published: September 2022

The Ice, Cloud, and Land Elevation Satellite–2 (ICESat–2) carries the Advanced Topographic Laser Altimeter System (ATLAS), enabling global canopy height measurements from forest canopy height models (CHMs). Topographic slope is a crucial factor affecting the accuracy of canopy height estimates from ICESat–2 CHMs, but it has not been sufficiently studied. This paper aims to eliminate the influence of slope on canopy height estimates from ICESat–2 data and establishes a method for correcting forest canopy heights based on high spatial resolution digital orthophoto maps (DOM). The cross-track photons are corrected horizontally to eliminate the estimation error. Multi-resolution segmentation is used to segment tree crowns in the DOM, and the distance and relative position between the top of canopy (TOC) photons and the center point of the crown are calculated. TOC photon correction rules are established for different terrains, and the vertical error of the TOC photons is corrected. The results indicate that the vertical error increases exponentially with the slope. The cross-track photon correction and the TOC photon correction method eliminate the effect of slope on canopy height estimates. The cross-track photon correction method reduces the mean absolute error (MAE) and root mean square error (RMSE) of the canopy height estimates by 35.71% and 35.98%, respectively. The TOC photon correction approach further reduces the MAE and RMSE by 23% and 19.23%, respectively. The proposed method has significantly higher accuracy for forest canopy height estimation using ICESat–2 data than the traditional method.
Evaluating the Applicability of PERSIANN-CDR Products in Drought Monitoring: A Case Study of Long-Term Droughts over Huaihe River Basin, China
Authors: Yang Na , Yu Hang , Lu Ying , Zhang Yehui , Zheng Yunchuan
Published: September 2022

In this study, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) was evaluated for the assessment of long-term drought monitoring in Huaihe River Basin using daily gauge observation data for the period from 1983 to 2017. The evaluation results show that the PERSIANN-CDR algorithm has a good detection ability for small precipitation events over the whole basin, but a poor ability for extreme precipitation events (>50 mm/day). Daily PERSIANN-CDR estimates perform relatively better in areas with abundant precipitation, while the monthly and yearly PERSIANN-CDR estimates are highly consistent with gauge observations both in magnitude and space. The Standardized Precipitation Index (SPI) at various time scales (3, 6, and 12 months) was calculated based on PERSIANN-CDR and gauge observation, respectively. Grid-based values of statistics derived from those SPI values demonstrate that PERSIANN-CDR has a good ability to capture drought events of each time scale across the basin. However, caution should be applied when using PERSIANN-CDR estimates for basin-scale drought trend analysis. Furthermore, three drought events with long duration and large extent were selected to test the applicability of PERSIANN-CDR in drought monitoring. The results show that it has a good ability to capture when and where droughts occur and how far they spread. Due to the overestimation of small precipitation events, PERSIANN-CDR tends to overestimate the number of extreme droughts and their extents. This needs to be considered in future algorithm improvement.
Confidence and Error Analyses of the Radiosonde and Ka-Wavelength Cloud Radar for Detecting the Cloud Vertical Structure
Authors: Yuan Yun , Di Huige , Liu Yuanyuan , Cheng Danmin , Chen Ning , Yan Qing , Hua Dengxin
Published: September 2022

A macro-vertical structure is closely related to weather evolution and the energy budget balance of the atmospheric system of the Earth. In this study, radiosonde data were used to identify a cloud vertical structure (CVS) using the adjusted relative humidity threshold method. To evaluate the reliability and stability of this method, the results obtained based on the spatiotemporal matching criteria established in this study were compared with Ka-band millimetre-wave cloud radar (MMCR) observation data. This comparison showed that both devices exhibit high consistency in low-level cloud detection. With the increase in the cloud height, the frequency of the cloud appearance detection by the radiosonde became higher than that by the MMCR. In spring, the results of the CVS detection by the two devices were in good agreement. Specifically, the determination coefficients of the modified degrees of freedom (adjusted R-square) of the cloud base height (CBH) and cloud top height (CTH) detected by the two devices were 0.934 and 0.879, respectively. The horizontal drift of the radiosonde was the smallest in summer, and the adj. R-square values of the CBH and CTH were 0.814 and 0.852, respectively. The CVS observation results by the radiosonde and the MMCR were significantly different in autumn (the adj. R-Square values of the CBH and CTH were 0.715 and 0.629, respectively). In winter, the adj. R-Square values of the CBH and CTH observed by the radiosonde and the MMCR were 0.958 and 0.710, respectively. The statistics and analysis of the results of the distribution characteristics of the CVSs using radiosonde data from 2019 to 2021 from Xi’an showed that the average CTH and CBH were at 7–10 km and 3–5 km, respectively. The frequencies of the cloud absence, rainfall, and two- and three-layer clouds were the highest in the winter (34.36%), autumn (12.99%), and summer, respectively.
Horticultural Image Feature Matching Algorithm Based on Improved ORB and LK Optical Flow
Authors: Chen Qinhan , Yao Lijian , Xu Lijun , Yang Yankun , Xu Taotao , Yang Yuncong , Liu Yu
Published: September 2022

To solve the low accuracy of image feature matching in horticultural robot visual navigation, an innovative and effective image feature matching algorithm was proposed combining the improved Oriented FAST and Rotated BRIEF (ORB) and Lucas–Kanade (LK) optical flow algorithm. First, image feature points were extracted according to the adaptive threshold calculated using the Michelson contrast. Then, the extracted feature points were uniformed by the quadtree structure, which can reduce the calculated amount of feature matching, and the uniform ORB feature points were roughly matched to estimate the position of the feature points in the matched image using the improved LK optical flow. Finally, the Hamming distance between rough matching points was calculated for precise matching. Feature extraction and matching experiments were performed in four typical scenes: normal light, low light, high texture, and low texture. Compared with the traditional algorithm, the uniformity and accuracy of the feature points extracted by the proposed algorithm were enhanced by 0.22 and 50.47%, respectively. Meanwhile, the results revealed that the matching accuracy of the proposed algorithm increased by 14.59%, whereas the matching time and total time decreased by 39.18% and 44.79%, respectively. The proposed algorithm shows great potential for application in the visual simultaneous localization and mapping (V-SLAM) of horticultural robots to achieve higher accuracy of real-time positioning and map construction.
LiDAR as a Tool for Assessing Timber Assortments: A Systematic Literature Review
Authors: Alvites Cesar , Marchetti Marco , Lasserre Bruno , Santopuoli Giovanni
Published: September 2022

Forest ecosystems strongly contribute to the mitigation of climate change impacts through the carbon stored in forests and through harvested wood products, such as sawed wood and furniture, which are obtained from many types of timber assortments. Timber assortments are defined as log sections of specific dimensions (log length and maximum/minimum end diameters), gathered from felled trunks, that have both specific commercial timber utilisation and economic value. However, it is challenging to discriminate and assess timber assortment types, especially within a forest stand before the forest has been harvested. Accurate estimations of timber assortments are a fundamental prerequisite in supporting forest holdings and assisting practitioners in the optimisation of harvesting activities and promoting forest wood chains, in addition to forest policy and planning. Based on the georeferenced points cloud tool, light detection and ranging (LiDAR) is a powerful technology for rapidly and accurately depicting forest structure, even if the use of LiDAR for timber assortments estimation is lacking and poorly explored. This systematic literature review aimed to highlight the state-of-the-art applications of the LiDAR systems (spaceborne; airborne, including unmanned aerial UASs; and terrestrial) to quantify and classify different timber assortment types. A total of 304 peer-reviewed papers were examined. The results highlight a constant increment of published articles using LiDAR systems for forest-related aspects in the period between 2000 and 2021. The most recurring investigation topics in LiDAR studies were forest inventory and forest productivity. No studies were found that used spaceborne LiDAR systems for timber assortment assessments, as these were conditioned by the time and sample size (sample size = ~12 m/~25 m of laser footprint and 0.7 m/60 m of space along the track for ICESat-2, GEDI and time = since 2018). Terrestrial LiDAR systems demonstrated a higher performance in successfully characterising the trees belonging to an understory layer. Combining airborne/UAS systems with terrestrial LiDAR systems is a promising approach to obtain detailed data concerning the timber assortments of large forest covers. Overall, our results reveal that the interest of scientists in using machine and deep learning algorithms for LiDAR processes is steadily increasing.
Fusing Hyperspectral and Multispectral Images via Low-Rank Hankel Tensor Representation
Authors: Guo Siyu , Chen Xi’ai , Jia Huidi , Han Zhi , Duan Zhigang , Tang Yandong
Published: September 2022

Hyperspectral images (HSIs) have high spectral resolution and low spatial resolution. HSI super-resolution (SR) can enhance the spatial information of the scene. Current SR methods have generally focused on the direct utilization of image structure priors, which are often modeled in global or local lower-order image space. The spatial and spectral hidden priors, which are accessible from higher-order space, cannot be taken advantage of when using these methods. To solve this problem, we propose a higher-order Hankel space-based hyperspectral image-multispectral image (HSI-MSI) fusion method in this paper. In this method, the higher-order tensor represented in the Hankel space increases the HSI data redundancy, and the hidden relationships are revealed by the nonconvex penalized Kronecker-basis-representation-based tensor sparsity measure (KBR). Weighted 3D total variation (W3DTV) is further applied to maintain the local smoothness in the image structure, and an efficient algorithm is derived under the alternating direction method of multipliers (ADMM) framework. Extensive experiments on three commonly used public HSI datasets validate the superiority of the proposed method compared with current state-of-the-art SR approaches in image detail reconstruction and spectral information restoration.