bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Properties with Relevance for the Automotive Sector in Mechanically Recycled and Virgin Polypropylene
Authors: Ladhari Abdelhak , Kucukpinar Esra , Stoll Henning , Sängerlaub Sven
Published: December 2021

Polypropylene (PP) has a high recycling potential. However, the properties of mechanically recycled PP (R-PP) have not been fully compared to those of virgin PP (V-PP). Therefore, in this study, properties of R-PP and V-PP were compared using data from recyclers, virgin plastic suppliers, and the literature. The properties of recyclates could not be directly correlated either with the properties of the virgin polymers from which the recyclates were made or the recycling parameters. It was found that the MFR of R-PP was higher; MFR R-PP had a median value (m) of 11 g/10 min while MFR V-PP had a median value of 6.3 g/10 min (at 230 °C and with 2.16 kg). In terms of mechanical properties, in many cases R-PP exhibited stiffer and more brittle behavior, with a slightly higher Young’s modulus (ER-PP = 1400 and EV-PP = 1200 MPa), a reduced elongation at break (ɛbR-PP = 4 l.-% and ɛbV-PP = 83 l.-%), and notched charpy impact strength (NCISR-PP = 4.8 and NCISV-PP = 7.5 kJ/m2). However, the values for every property had a broad distribution. In addition to existing information from the literature, our research sheds fresh light on the variation of the characteristics of recycled polypropylenes presently on the market.
Recycling of Rubber Wastes as Fuel and Its Additives
Authors: Akbas Ahmed , Yuhana Nor Yuliana
Published: December 2021

Economic, social, and urban developments generally require improvements in the transportation sector, which includes automobiles such as trucks, buses, trailers, airplanes, and even bicycles. All these vehicles use rubber tires. After consumption, these tires become waste, leading to enlarged landfill areas for used tires and implying additional harm to the environment. This review summarizes the growth of rubber recycling application and the sustainability of using waste rubber in the construction field. Furthermore, we provide methods to convert rubber waste to fuel or fuel additives by using tire-derived fuel and concentrate to pyrolysis, which are environmentally friendly and efficient ways. The related parameters such as temperature, pressure, and feedstock composition were studied. Most research papers observed that 500 °C is the optimal temperature at atmospheric pressure in the presence of a specific type of catalyst to improve pyrolysis rate, oil yield, and quality.
Psychedelic Epistemology: William James and the “Noetic Quality” of Mystical Experience
Authors: Cole-Turner Ron
Published: December 2021

William James proposed in 1902 that states of mystical experience, central to his idea of religious experience, can be identified based on their ineffability and their noetic quality. The epistemological category of the noetic quality, modified by W. T. Stace in 1960, plays a central but somewhat confounding role in today’s biomedical research involving psychedelic drugs such as psilocybin and LSD. Using scales based on James, it can be shown that psychedelics “reliably occasion” intense subjective states of experience or mystical states. It is debated whether these states are necessary for the wide range of possible mental health therapeutic benefits that appear to follow. This paper reviews what James said about the noetic quality and its relationship to religious experience, epistemology, and states of mystical experience. It explores how the noetic quality is measured in today’s research, addressing a growing list of concerns that psychedelic science can be epistemologically biased, that it is hostile to atheistic or physicalist views, that it injects religion unduly into science, or that it needs to find ways to eliminate the mystical element, if not the entire intense subjective experience altogether.
From Mary’s Motherhood to the Immaculate Conception: An Iconographic Analysis of Marian Art in Spain during the Tenth to Nineteenth Centuries
Authors: Moreno-Almárcegui Antonio , Scalzo Germán
Published: December 2021

This article analyzes Marian art in Spain from the tenth to nineteenth centuries in order to show how popular piety represented Mary’s motherhood. Through art, including architecture, painting, sculpture, and oral preaching, a popular image of Mary emerged and, in turn, became key for understanding the history of the family in western Catholic countries. Studying the evolution of Marian iconography during this thousand-year period reveals a kind of grandeur, and then a certain crisis, surrounding Mary’s motherhood. This crisis specifically involves the meaning of the body as an effective sign of the personal gift-of-self. We argue that this process ran parallel to growing problems in theological culture related to reconciling the natural and supernatural realms, and we further sustain that it contains a true cultural revolution, a shift that is at the origin of many later transformations. This interpretation helps better understand the dilemmas surrounding the history of the family in the West, and specifically of motherhood, from the point of view of the Christian tradition.
Sound Biting Conspiracy: From India with “Love Jihad”
Authors: Frøystad Kathinka
Published: December 2021

Since 2013, India has seen a remarkable growth of a conspiracy theory known as “love jihad”, which holds that Muslim men conspire to lure Hindu women for marriage to alter India’s religious demography as part of a political takeover strategy. While earlier scholarship on “love jihad” emphasizes the Hindu nationalist propagation of this conspiracy theory, this article pays equal attention to its appeal among conservative Hindus. Making its point of departure in the generative effects of speech, it argues that the “love jihad” neologism performs two logical operations simultaneously. Firstly, it fuses the long-standing Hindu anxiety about daughters marrying against their parents’ will, with the equally long-standing anxiety about unfavorable religious demographic trends. Secondly, it attributes a sinister political takeover intent to every Muslim man who casts his eyes on a young Hindu woman. To bring out these points, this article pays equal empirical attention to marriage and kinship practices as to the genealogy of, and forerunners to, the “love jihad” neologism, and develops the concept of “sound biting” to bring out its meaning-making effect.
Dataset Modelling of the Financial Risk Management of Social Entrepreneurship in Emerging Economies
Authors: Popkova Elena G. , Sergi Bruno S.
Published: December 2021

The relevance of this study lies in the fact that financial risk is a serious obstacle to the development of social entrepreneurship, preventing the implementation of potential support for sustainable development goals in business. The purpose of this article is to clarify specific aspects of financing factors and financial risk related to social entrepreneurship in developing countries (in comparison with the standard financial risk related to commercial entrepreneurship) in order to analyze the influence of the financing factors of social entrepreneurship on sustainable development, as well as to determine the potential for the development of social entrepreneurship through financial risk management. To achieve this goal, this article uses the methodology of econometrics—dataset modelling of financial risk management in social entrepreneurship to achieve sustainable development in emerging economies. On the basis of the results of this study, firstly, it is substantiated that the financial risks entailed by social entrepreneurship differ from the standard financial risk present in commercial entrepreneurship. Specific factors of the financing of sustainable development in emerging economies are determined and, on the basis of this, financial risks specific to social entrepreneurship in emerging economies are identified as follows: (1) reduced stimulus to use financial resources in long-term investments, which disrupts the stability and decreases inclusion; (2) joint public–private investments and decreased investment in R&D; and (3) expanded investment in the skills required for jobs and “markets of tomorrow”. Secondly, a contradictory influence of financing factors on sustainable development is demonstrated. Thirdly, a large potential for the development of social entrepreneurship by means of financial risk management (maximum reduction) was identified. With the minimization of financial risk, social entrepreneurship would demonstrate substantial progress, with an increase of 99.61% (more than 50%) from 45.18 points to 90.18 points. A novel contribution of this paper to the extant literature consists of the specification of the essence and specifics of social entrepreneurship in emerging economies through the identification of financial risks and the provision of recommendations for their management.
Decomposition of Natural Catastrophe Risks: Insurability Using Parametric CAT Bonds
Authors: Tavanaie Marvi Morteza , Linders Daniël
Published: December 2021

Nat Cat risks are not insurable by traditional insurance mainly because of producing highly correlated losses. The source of such correlation among buildings of a region subject to a natural hazard is discussed. A decomposition method is proposed to split Nat Cat risk into idiosyncratic (and hence insurable) risk and systematic risk (carrying the correlated part). It is explained that the systematic risk can be transferred to capital markets using a set of parametric CAT bonds. Premium calculation is presented for insuring the decomposed risk. Portfolio risk-return trade-off measures for investing on the parametric CAT bond are derived. Multi-regional and multi-hazard parametric CAT bonds are introduced to reduce the risk of the investment. The methodology is applied on a region with about 3000 residential buildings subject to flood hazards.
A Laser Vision System for Relative 3-D Posture Estimation of an Underwater Vehicle with Hemispherical Optics
Authors: Constantinou Christos C. , Georgiades George P. , Loizou Savvas G.
Published: December 2021

This paper describes the development and experimental validation of algorithms for a novel laser vision system (LVS), suitable for measuring the relative posture from both solid and mesh-like targets in underwater environments. The system was developed in the framework of the AQUABOT project, a research project dedicated to the development of an underwater robotic system for inspection of offshore aquaculture installations. In particular, an analytical model for three-medium refraction that takes into account the nonlinear hemispherical optics for image rectification has been developed. The analytical nature of the model allows the online estimation of the refractive index of the external medium. The proposed LVS consists of three line-lasers within the field of view of the underwater robot camera. The algorithms that have been developed in this work provide appropriately filtered point-cloud datasets from each laser, as well as high-level information such as distance and relative orientation of the target with respect to the ROV. In addition, an automatic calibration procedure, along with the accompanying hardware for the underwater laser vision system has been developed to reduce the calibration overhead required by regular maintenance operations for underwater robots operating in seawater. Furthermore, a spatial image filter was developed for discriminating between mesh and non-mesh-like targets in the LVS measurements. Finally, a set of experiments was carried out in a controlled laboratory environment, as well as in real conditions at offshore aquaculture installations demonstrating the performance of the system.
Quick Detection of Field-Scale Soil Comprehensive Attributes via the Integration of UAV and Sentinel-2B Remote Sensing Data
Authors: Zhu Wanxue , Rezaei Ehsan Eyshi , Nouri Hamideh , Yang Ting , Li Binbin , Gong Huarui , Lyu Yun , Peng Jinbang , Sun Zhigang
Published: December 2021

Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0–10 cm and 10–20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes.
Seamless Mosaicking of UAV-Based Push-Broom Hyperspectral Images for Environment Monitoring
Authors: Yi Lina , Chen Jing M. , Zhang Guifeng , Xu Xiao , Ming Xing , Guo Wenji
Published: December 2021

This paper proposes a systematic image mosaicking methodology to produce hyperspectral image for environment monitoring using an emerging UAV-based push-broom hyperspectral imager. The suitability of alternative methods in each step is assessed by experiments of an urban scape, a river course and a forest study area. First, the hyperspectral image strips were acquired by sequentially stitching the UAV images acquired by push-broom scanning along each flight line. Next, direct geo-referencing was applied to each image strip to get initial geo-rectified result. Then, with ground control points, the curved surface spline function was used to transform the initial geo-rectified image strips to improve their geometrical accuracy. To further remove the displacement between pairs of image strips, an improved phase correlation (IPC) and a SIFT and RANSAC-based method (SR) were used in image registration. Finally, the weighted average and the best stitching image fusion method were used to remove the spectral differences between image strips and get the seamless mosaic. Experiment results showed that as the GCPs‘ number increases, the mosaicked image‘s geometrical accuracy increases. In image registration, there exists obvious edge information that can be accurately extracted from the urban scape and river course area; comparative results can be achieved by the IPC method with less time cost. However, for the ground objects with complex texture like forest, the edges extracted from the image is prone to be inaccurate and result in the failure of the IPC method, and only the SR method can get a good result. In image fusion, the best stitching fusion method can get seamless results for all three study areas. Whereas, the weighted average fusion method was only useful in eliminating the stitching line for the river course and forest areas but failed for the urban scape area due to the spectral heterogeneity of different ground objects. For different environment monitoring applications, the proposed methodology provides a practical solution to seamlessly mosaic UAV-based push-broom hyperspectral images with high geometrical accuracy and spectral fidelity.
Transforming Access to and Use of Climate Information Products Derived from Remote Sensing and In Situ Observations
Authors: Nsengiyumva Gloriose , Dinku Tufa , Cousin Remi , Khomyakov Igor , Vadillo Audrey , Faniriantsoa Rija , Grossi Amanda
Published: December 2021

Making climate-sensitive economic sectors resilient to climate trends and shocks, through adaptation to climate change and managing uncertainties associated with climate extremes, will require effective use of climate information to help practitioners make climate-informed decisions. The provision of weather and climate information will depend on the availability of climate data and its presentation in formats that are useful for decision making at different levels. However, in many places around the world, including most African countries, the collection of climate data has been seriously inadequate, and even when available, poorly accessible. On the other hand, the availability of climate data by itself may not lead to the uptake and use of such data. These data must be presented in user-friendly formats addressing specific climate information needs in order to be used for decision-making by governments, as well as the public and private sectors. The generated information should also be easily accessible. The Enhancing National Climate Services (ENACTS) initiative, led by Columbia University’s International Research Institute for Climate and Society (IRI), has been making efforts to overcome these challenges by supporting countries to improve the available climate data, as well as access to and use of climate information products at relevant spatial and temporal scales. Challenges to the availability of climate data are alleviated by combining data from the national weather observation network with remote sensing and other global proxies to generate spatially and temporally complete climate datasets. Access to climate information products is enhanced by developing an online mapping service that provides a user-friendly interface for analyzing and visualizing climate information products such as maps and graphs.
The Automation of Hyperspectral Training Library Construction: A Case Study for Wheat and Potato Crops
Authors: Appeltans Simon , Apolo-Apolo Orly Enrique , Rodríguez-Vázquez Jaime Nolasco , Pérez-Ruiz Manuel , Pieters Jan , Mouazen Abdul M.
Published: December 2021

The potential of hyperspectral measurements for early disease detection has been investigated by many experts over the last 5 years. One of the difficulties is obtaining enough data for training and building a hyperspectral training library. When the goal is to detect disease at a previsible stage, before the pathogen has manifested either its first symptoms or in the area surrounding the existing symptoms, it is impossible to objectively delineate the regions of interest containing the previsible pathogen growth from the areas without the pathogen growth. To overcome this, we propose an image labelling and segmentation algorithm that is able to (a) more objectively label the visible symptoms for the construction of a training library and (b) extend this labelling to the pre-visible symptoms. This algorithm is used to create hyperspectral training libraries for late blight disease (Phytophthora infestans) in potatoes and two types of leaf rust (Puccinia triticina and Puccinia striiformis) in wheat. The model training accuracies were compared between the automatic labelling algorithm and the classic visual delineation of regions of interest using a logistic regression machine learning approach. The modelling accuracies of the automatically labelled datasets were higher than those of the manually labelled ones for both potatoes and wheat, at 98.80% for P. infestans in potato, 97.69% for P. striiformis in soft wheat, and 96.66% for P. triticina in durum wheat.
Freeze-Thaw Deformation Cycles and Temporal-Spatial Distribution of Permafrost along the Qinghai-Tibet Railway Using Multitrack InSAR Processing
Authors: Wang Jing , Wang Chao , Zhang Hong , Tang Yixian , Duan Wei , Dong Longkai
Published: December 2021

The Qinghai-Tibet Railway (QTR) is the railway with the highest elevation and longest distance in the world, spanning more than 1142 km from Golmud to Lhasa across the continuous permafrost region. Due to climate change and anthropogenic activities, geological disasters such as subsidence and thermal melt collapse have occurred in the QTR embankment. To conduct the large-scale permafrost monitoring and geohazard investigation along the QTR, we collected 585 Sentinel-1A images based on the composite index model using the multitrack time-series interferometry synthetic aperture radar (MTS-InSAR) method to retrieve the surface deformation over a 3.15 × 105 km2 area along the QTR. Meanwhile, a new method for permafrost distribution mapping based on InSAR time series deformation was proposed. Finally, the seasonal deformation map and a new map of permafrost distribution along the QTR from Golmud to Lhasa were obtained. The results showed that the estimated seasonal deformation range of the 10 km buffer zone along the QTR was −50–10 mm, and the LOS deformation rate ranged from −30 to 15 mm/yr. In addition, the deformation results were validated by leveling measurements, and the range of absolute error was between 0.1 and 4.62 mm. Most of the QTR was relatively stable. Some geohazard-prone sections were detected and analyzed along the QTR. The permafrost distribution results were mostly consistent with the simulated results of Zou’s method, based on the temperature at the top of permafrost (TTOP) model. This study reveals recent deformation characteristics of the QTR, and has significant scientific implications and applicational value for ensuring the safe operation of the QTR. Moreover, our method, based on InSAR results, provides new insights for permafrost classification on the Qinghai-Tibet Plateau (QTP).
Evaluating Total Electron Content (TEC) Detrending Techniques in Determining Ionospheric Disturbances during Lightning Events in A Low Latitude Region
Authors: Osei-Poku Louis , Tang Long , Chen Wu , Mingli Chen
Published: December 2021

Total Electron Content (TEC) from Global Navigation Satellite Systems (GNSS) is used to ascertain the impact of space weather events on navigation and communication systems. TEC is detrended by several methods to show this impact. Information from the detrended TEC may or may not necessarily represent a geophysical parameter. In this study, two commonly used detrending methods, Savitzky–Golay filter and polynomial fitting, are evaluated during thunderstorm events in Hong Kong. A two-step approach of detection and distinguishing is introduced alongside linear correlation in order to determine the best detrending model. Savitzky–Golay filter on order six and with a time window length of 120 min performed the best in detecting lightning events, and had the highest moderate positive correlation of 0.4. That the best time frame was 120 min suggests that the observed disturbances could be travelling ionospheric disturbance (TID), with lightning as the potential source.
Prediction of Erosion-Prone Areas in the Catchments of Big Lowland Rivers: Implementation of Maximum Entropy Modelling—Using the Example of the Lower Vistula River (Poland)
Authors: Brzezińska Marta , Szatten Dawid , Babiński Zygmunt
Published: December 2021

It is common knowledge that erosion depends on environmental factors modified by human activity. Erosion within a catchment area can be defined by local lithological, morphometric, hydrological features, etc., and land cover, with spatial distribution described by means of remote sensing tools. The study relied on spatial data for the catchment of the Lower Vistula—the biggest river in Poland. GIS (SAGA, QGIS) tools were used to designate the spatial distribution of independent environmental variables that determined the process of erosion according to land cover types within the Lower Vistula catchment (Corine Land Cover). In addition, soil loss in the catchment area was calculated using the USLE model (Universal Soil Loss Equation). The spatial data was used to determine the predictive power of variables for the process of erosion by applying the maximum entropy model (MaxEnt) commonly used in fields of science unrelated to fluvial hydrology. The results of the study pointed directly to environmental features strongly connected with the process of erosion, identifying areas susceptible to intensified erosion, and in addition positively verified by USLE. This testifies to the correct selection of the proposed method, which is a strong point of the presented study. The proposed interdisciplinary approach to predict erosion within the catchment area (MaxEnt), widely supported by GIS tools, will allow the identification of environmental pressures to support the decision-making process in erosion-prone areas.
Combinational Fusion and Global Attention of the Single-Shot Method for Synthetic Aperture Radar Ship Detection
Authors: Xu Libo , Pang Chaoyi , Guo Yan , Shu Zhenyu
Published: December 2021

Synthetic Aperture Radar (SAR), an active remote sensing imaging radar technology, has certain surface penetration ability and can work all day and in all weather conditions. It is widely applied in ship detection to quickly collect ship information on the ocean surface from SAR images. However, the ship SAR images are often blurred, have large noise interference, and contain more small targets, which pose challenges to popular one-stage detectors, such as the single-shot multi-box detector (SSD). We designed a novel network structure, a combinational fusion SSD (CF-SSD), based on the framework of the original SSD, to solve these problems. It mainly includes three blocks, namely a combinational fusion (CF) block, a global attention module (GAM), and a mixed loss function block, to significantly improve the detection accuracy of SAR images and remote sensing images and maintain a fast inference speed. The CF block equips every feature map with the ability to detect objects of all sizes at different levels and forms a consistent and powerful detection structure to learn more useful information for SAR features. The GAM block produces attention weights and considers the channel attention information of various scale feature information or cross-layer maps so that it can obtain better feature representations from the global perspective. The mixed loss function block can better learn the positions of the truth anchor boxes by considering corner and center coordinates simultaneously. CF-SSD can effectively extract and fuse the features, avoid the loss of small or blurred object information, and precisely locate the object position from SAR images. We conducted experiments on the SAR ship dataset SSDD, and achieved a 90.3% mAP and fast inference speed close to that of the original SSD. We also tested our model on the remote sensing dataset NWPU VHR-10 and the common dataset VOC2007. The experimental results indicate that our proposed model simultaneously achieves excellent detection performance and high efficiency.
Application of Supervised Machine Learning Technique on LiDAR Data for Monitoring Coastal Land Evolution
Authors: Barbarella Maurizio , Di Benedetto Alessandro , Fiani Margherita
Published: December 2021

Machine Learning (ML) techniques are now being used very successfully in predicting and supporting decisions in multiple areas such as environmental issues and land management. These techniques have also provided promising results in the field of natural hazard assessment and risk mapping. The aim of this work is to apply the Supervised ML technique to train a model able to classify a particular gravity-driven coastal hillslope geomorphic model (slope-over-wall) involving most of the soft rocks of Cilento (southern Italy). To train the model, only geometric data have been used, namely morphometric feature maps computed on a Digital Terrain Model (DTM) derived from Light Detection and Ranging (LiDAR) data. Morphometric maps were computed using third-order polynomials, so as to obtain products that best describe landforms. Not all morphometric parameters from literature were used to train the model, the most significant ones were chosen by applying the Neighborhood Component Analysis (NCA) method. Different models were trained and the main indicators derived from the confusion matrices were compared. The best results were obtained using the Weighted k-NN model (accuracy score = 75%). Analysis of the Receiver Operating Characteristic (ROC) curves also shows that the discriminating capacity of the test reached percentages higher than 95%. The model, resulting more accurate in the training area, will be extended to similar areas along the Tyrrhenian coastal land.
Intercalibration of Backscatter Measurements among Ku-Band Scatterometers Onboard the Chinese HY-2 Satellite Constellation
Authors: Wang Zhixiong , Zou Juhong , Zhang Youguang , Stoffelen Ad , Lin Wenming , He Yijun , Feng Qian , Zhang Yi , Mu Bo , Lin Mingsen
Published: December 2021

The Chinese HY-2D satellite was launched on 19 May 2021, carrying a Ku-band scatterometer. Together with the operating scatterometers onboard the HY-2B and HY-2C satellites, the HY-2 series scatterometer constellation was built, constituting different satellite orbits and hence opportunity for mutual intercomparison and intercalibration. To achieve intercalibration of backscatter measurements for these scatterometers, this study presents and performs three methods including: (1) direct comparison using collocated measurements, in which the nonlinear calibrations can also be derived; (2) intercalibration over the Amazon rainforest; (3) and the double-difference technique based on backscatter simulations over the global oceans, in which a geophysical model function and numerical weather prediction (NWP) model winds are needed. The results obtained using the three methods are comparable, i.e., the differences among them are within 0.1 dB. The intercalibration results are validated by comparing the HY-2 series scatterometer wind speeds with NWP model wind speeds. The curves of wind speed bias for the HY-2 series scatterometers are quite similar, particularly in wind speeds ranging from 4 to 20 m/s. Based on the well-intercalibrated backscatter measurements, consistent sea surface wind products from HY-2 series scatterometers can be produced, and greatly benefit data applications.
Towards a Deep-Learning-Based Framework of Sentinel-2 Imagery for Automated Active Fire Detection
Authors: Zhang Qi , Ge Linlin , Zhang Ruiheng , Metternicht Graciela Isabel , Liu Chang , Du Zheyuan
Published: December 2021

This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019–2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km2 (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans.
Easily Implemented Methods of Radiometric Corrections for Hyperspectral–UAV—Application to Guianese Equatorial Mudbanks Colonized by Pioneer Mangroves
Authors: Jaud Marion , Sicot Guillaume , Brunier Guillaume , Michaud Emma , Le Dantec Nicolas , Ammann Jérôme , Grandjean Philippe , Launeau Patrick , Thouzeau Gérard , Fleury Jules , Delacourt Christophe
Published: December 2021

Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations) is a custom, mini-UAV (unmanned aerial vehicle) platform (<20 kg), equipped with a light push broom hyperspectral sensor combined with a navigation module measuring position and orientation. Because of the particularities of UAV surveys (low flight altitude, small spatial scale, and high resolution), dedicated pre-processing methods have to be developed when reconstructing hyperspectral imagery. This article presents light, easy-implementation, in situ methods, using only two Spectralon® and a field spectrometer, allowing performance of an initial calibration of the sensor in order to correct “vignetting effects” and a field standardization to convert digital numbers (DN) collected by the hyperspectral camera to reflectance, taking into account the time-varying illumination conditions. Radiometric corrections are applied to a subset of a dataset collected above mudflats colonized by pioneer mangroves in French Guiana. The efficiency of the radiometric corrections is assessed by comparing spectra from Hyper-DRELIO imagery to in situ spectrometer measurements above the intertidal benthic biofilm and mangroves. The shapes of the spectra were consistent, and the spectral angle mapper (SAM) distance was 0.039 above the benthic biofilm and 0.159 above the mangroves. These preliminary results provide new perspectives for quantifying and mapping the benthic biofilm and mangroves at the scale of the Guianese intertidal mudbanks system, given their importance in the coastal food webs, biogeochemical cycles, and the sediment stabilization.
Land Cover Dynamics on the Lower Ganges–Brahmaputra Delta: Agriculture–Aquaculture Transitions, 1972–2017
Authors: Sousa Daniel , Small Christopher
Published: December 2021

Aquaculture in tropical and subtropical developing countries has expanded in recent years. This practice is controversial due to its potential for serious economic, food security, and environmental impacts—especially for intensive operations in and near mangrove ecosystems, where many shrimp species spawn. While considerable effort has been directed toward understanding aquaculture impacts, maps of spatial extent and multi-decade spatiotemporal dynamics remain sparse. This is in part because aquaculture ponds (ghers) can be challenging to distinguish from other shallow water targets on the basis of water-leaving radiance alone. Here, we focus on the Lower Ganges–Brahmaputra Delta (GBD), one of the most expansive areas of recent aquaculture growth on Earth and adjacent to the Sundarbans mangrove forest, a biodiversity hotspot. We use a combination of MODIS 16-day EVI composites and 45 years (1972–2017) of Landsat observations to characterize dominant spatiotemporal patterns in the vegetation phenology of the area, identify consistent seasonal optical differences between flooded ghers and other land uses, and quantify the multi-decade expansion of standing water bodies. Considerable non-uniqueness exists in the spectral signature of ghers on the GBD, propagating into uncertainty in estimates of spatial extent. We implement three progressive decision boundaries to explicitly quantify this uncertainty and provide liberal, moderate, and conservative estimates of flooded gher extent on three different spatial scales. Using multiple extents and multiple thresholds, we quantify the size distribution of contiguous regions of flooded gher extent at ten-year intervals. The moderate threshold shows standing water area within Bangladeshi polders to have expanded from less than 300 km2 in 1990 to over 1400 km2 in 2015. At all three scales investigated, the size distribution of standing water bodies is increasingly dominated by larger, more interconnected networks of flooded areas associated with aquaculture. Much of this expansion has occurred in immediate proximity to the Bangladeshi Sundarbans.
Climate Change and Livestock Management Drove Extensive Vegetation Recovery in the Qinghai-Tibet Plateau
Authors: Liu Enqin , Xiao Xiangming , Shao Huaiyong , Yang Xin , Zhang Yali , Yang Yang
Published: December 2021

The vegetation of the Qinghai-Tibet Plateau (QTP), China, is diverse and sensitive to climate change. Because of extensive grassland degradation in the QTP, several ecological restoration projects, which affect the livestock population, have been implemented in the QTP. Although many studies have reported the impacts of climate change on vegetation in the QTP, our knowledge on the impacts of both climate change and livestock on vegetation remains very limited. Here, we investigated the impacts of climate change and livestock population on vegetation growth by using the annual maximum normalized difference vegetation index (NDVImax) and growing-season climate data from 1981 to 2019. We analyzed the relationship between NDVImax and climate and livestock population using the modified Mann-Kendall trend Test and Pearson correlation analysis. For the entire QTP, NDVImax had a two-phase trend, with a slow rise during 1981–2000 and a rapid rise during 2000–2019. Overall, NDVImax in the QTP increased and decreased in 63.7% and 6.7% of the area in 2000–2019. In areas with significant changes in NDVImax, it was strongly correlated with relative humidity and vapor pressure. The small positive trend in NDVImax during 1981–2000 was influenced by warmer and wetter climate, and the overgrazing by a large population of livestock slowed down the rate of increase in NDVImax. Livestock population for Qinghai and Tibet in recent years has been lower than in the 1980s.The warmer and wetter climate and substantial drops in the livestock population contributed to large recovery in vegetation during 2001–2019. Vegetation degradation in Qinghai during 1981–2000 and central-northern Tibet during 2000–2019 was driven mainly by drier and hotter climatic. Although 63.7% of the area in the QTP became greener, the vegetation degradation in central-northern Tibet should not be ignored and more measures should be taken to alleviate the impact of warming and drying climate. Our findings provide a better understanding of the factors that drove changes in vegetation in the QTP.
Sub-Bottom Sediment Classification Using Reliable Instantaneous Frequency Calculation and Relaxation Time Estimation
Authors: Li Shaobo , Zhao Jianhu , Zhang Hongmei , Qu Siheng
Published: December 2021

The shift in IF (instantaneous frequency) series and the corresponding relaxation time have the potential to characterize sediment properties. However, these attributes derived from SBP (sub-bottom profiler) data are seldom used for offshore site investigations because of the unsoundness in attribute calculation. To overcome this problem, a new reliable method combining VMD (variational mode decomposition) and WVD (Wigner–Ville distribution), as well as relaxation time, is presented. Since the number of modes in classical VMD should be provided in advance, a modified VMD algorithm, MVMD (modified variational mode decomposition), is proposed here, where the distribution of the frequency domain of modes is taken into account to automatically determine the number of modes. Through the relaxation time model, the IF data of a series of pings calculated through MVMD-WVD are transformed into a relaxation time map. A robust estimation algorithm is applied to the relaxation time map to reduce the effects of interferences and obtain robust relaxation times. The final relaxation time data are used to determine the sediment types. Real data from SBP experiments, as well as borehole sampling and geotechnical analysis results, verified the good performance of the proposed method.
A New Method for UAV Lidar Precision Testing Used for the Evaluation of an Affordable DJI ZENMUSE L1 Scanner
Authors: Štroner Martin , Urban Rudolf , Línková Lenka
Published: December 2021

Lately, affordable unmanned aerial vehicle (UAV)-lidar systems have started to appear on the market, highlighting the need for methods facilitating proper verification of their accuracy. However, the dense point cloud produced by such systems makes the identification of individual points that could be used as reference points difficult. In this paper, we propose such a method utilizing accurately georeferenced targets covered with high-reflectivity foil, which can be easily extracted from the cloud; their centers can be determined and used for the calculation of the systematic shift of the lidar point cloud. Subsequently, the lidar point cloud is cleaned of such systematic shift and compared with a dense SfM point cloud, thus yielding the residual accuracy. We successfully applied this method to the evaluation of an affordable DJI ZENMUSE L1 scanner mounted on the UAV DJI Matrice 300 and found that the accuracies of this system (3.5 cm in all directions after removal of the global georeferencing error) are better than manufacturer-declared values (10/5 cm horizontal/vertical). However, evaluation of the color information revealed a relatively high (approx. 0.2 m) systematic shift.
Integrating Sentinel-1/2 Data and Machine Learning to Map Cotton Fields in Northern Xinjiang, China
Authors: Hu Tao , Hu Yina , Dong Jianquan , Qiu Sijing , Peng Jian
Published: December 2021

Timely and accurate information of cotton planting areas is essential for monitoring and managing cotton fields. However, there is no large-scale and high-resolution method suitable for mapping cotton fields, and the problems associated with low resolution and poor timeliness need to be solved. Here, we proposed a new framework for mapping cotton fields based on Sentinel-1/2 data for different phenological periods, random forest classifiers, and the multi-scale image segmentation method. A cotton field map for 2019 at a spatial resolution of 10 m was generated for northern Xinjiang, a dominant cotton planting region in China. The overall accuracy and kappa coefficient of the map were 0.932 and 0.813, respectively. The results showed that the boll opening stage was the best phenological phase for mapping cotton fields and the cotton fields was identified most accurately at the early boll opening stage, about 40 days before harvest. Additionally, Sentinel-1 and the red edge bands in Sentinel-2 are important for cotton field mapping, and there is great potential for the fusion of optical images and microwave images in crop mapping. This study provides an effective approach for high-resolution and high-accuracy cotton field mapping, which is vital for sustainable monitoring and management of cotton planting.
Application of Deep Learning Architectures for Satellite Image Time Series Prediction: A Review
Authors: Moskolaï Waytehad Rose , Abdou Wahabou , Dipanda Albert , Kolyang
Published: December 2021

Satellite image time series (SITS) is a sequence of satellite images that record a given area at several consecutive times. The aim of such sequences is to use not only spatial information but also the temporal dimension of the data, which is used for multiple real-world applications, such as classification, segmentation, anomaly detection, and prediction. Several traditional machine learning algorithms have been developed and successfully applied to time series for predictions. However, these methods have limitations in some situations, thus deep learning (DL) techniques have been introduced to achieve the best performance. Reviews of machine learning and DL methods for time series prediction problems have been conducted in previous studies. However, to the best of our knowledge, none of these surveys have addressed the specific case of works using DL techniques and satellite images as datasets for predictions. Therefore, this paper concentrates on the DL applications for SITS prediction, giving an overview of the main elements used to design and evaluate the predictive models, namely the architectures, data, optimization functions, and evaluation metrics. The reviewed DL-based models are divided into three categories, namely recurrent neural network-based models, hybrid models, and feed-forward-based models (convolutional neural networks and multi-layer perceptron). The main characteristics of satellite images and the major existing applications in the field of SITS prediction are also presented in this article. These applications include weather forecasting, precipitation nowcasting, spatio-temporal analysis, and missing data reconstruction. Finally, current limitations and proposed workable solutions related to the use of DL for SITS prediction are also highlighted.
Diversity of Remote Sensing-Based Variable Inputs Improves the Estimation of Seasonal Maximum Freezing Depth
Authors: Wang Bingquan , Ran Youhua
Published: December 2021

The maximum soil freezing depth (MSFD) is an important indicator of the thermal state of seasonally frozen ground. Its variation has important implications for the water cycle, ecological processes, climate and engineering stability. This study tested three aspects of data-driven predictions of MSFD in the Qinghai-Tibet Plateau (QTP), including comparison of three popular statistical/machine learning techniques, differences between remote sensing variables and reanalysis data as input conditions, and transportability of the model built by reanalysis data. The results show that support vector regression (SVR) performs better than random forest (RF), k-nearest neighbor (KNN) and the ensemble mean of the three models. Compared with the climate predictors, the remote sensing predictors are helpful for improving the simulation accuracy of the MSFD at both decadal and annual scales (at the annual and decadal scales, the root mean square error (RMSE) is reduced by 2.84 and 1.99 cm, respectively). The SVR model with climate predictor calibration using the in situ MSFD at the baseline period (2001–2010) can be used to simulate the MSFD over historical periods (1981–1990 and 1991–2000). This result indicates the good transferability of the well-trained machine learning model and its availability to simulate the MSFD of the past and the future when remote sensing predictors are not available.