bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermally Resistant, Self-Extinguishing Thermoplastic Composites Enabled by Tannin-Based Carbonaceous Particulate
Authors: Missio André L. , Delucis Rafael A. , Otoni Caio Gomide , de Cademartori Pedro H. G. , Coldebella Rodrigo , Aramburu Arthur B. , Mattos Bruno D. , Rodrigues Marlon B. B. , Lunkes Nayara , Gatto Darci A. , Labidi Jalel
Published: September 2022

Flame-resistant materials are key components in buildings and several other engineering applications. In this study, flame retardancy and thermal stability were conferred to a highly flammable technical thermoplastic—polypropylene (PP)—upon compositing with a carbonaceous tannin-based particulate (CTP). Herein, we report on a straightforward, facile, and green approach to prepare self-extinguishing thermoplastic composites by thermoblending highly recalcitrant particulate. The thermal stability and mechanical properties of the composites are tethered to the CTP content. We demonstrate that the addition of up to 65 wt% of CTP improved the viscoelastic properties and hydrophobicity of the PP, whereas having marginal effects on bulk water interactions. Most importantly, compositing with CTP remarkably improved the thermal stability of the composites, especially over 300 °C, which is an important threshold associated with the combustion of volatiles. PP-CTP composites demonstrated great capacity to limit and stop fire propagation. Therefore, we offer an innovative route towards thermally resistant and self-extinguishing PP composites, which is enabled by sustainable tannin-based flame retardants capable of further broadening the technical range of commodity polyolefins to high temperature scenarios.
Novel Rubber Composites Based on Copper Particles, Multi-Wall Carbon Nanotubes and Their Hybrid for Stretchable Devices
Authors: Kumar Vineet , Azam Siraj , Alam Md. Najib , Hong Won-Beom , Park Sang-Shin
Published: September 2022

New technologies are constantly addressed in the scientific community for updating novel stretchable devices, such as flexible electronics, electronic packaging, and piezo-electric energy-harvesting devices. The device promoted in the present work was found to generate promising ~6V and durability of >0.4 million cycles. This stretchable device was based on rubber composites. These rubber composites were developed by solution mixing of room temperature silicone rubber (RTV-SR) and nanofiller, such as multi-wall carbon nanotube (MWCNT) and micron-sized copper particles and their hybrid. The hybrid composite consists of 50:50 of both fillers. The mechanical stretchability and compressive modulus of the composites were studied in detail. For example, the compressive modulus was 1.82 MPa (virgin) and increased at 3 per hundred parts of rubber (phr) to 3.75 MPa (MWCNT), 2.2 MPa (copper particles) and 2.75 MPa (hybrid). Similarly, the stretching ability for the composites used in fabricating devices was 148% (virgin) and changes at 3 phr to 144% (MWCNT), 230% (copper particles) and 199% (hybrid). Hence, the hybrid composite was found suitable with optimum stiffness and robust stretching ability to be useful for stretching electronic devices explored in this work. These improved properties were tested for a real-time stretchable device, such as a piezoelectric energy-harvesting device and their improved voltage output and durability were reported. In the end, a series of experiments conducted were summarized and a discussion on the best candidate with higher properties useful for prospective applications was reported.
A Highly Selective and Sensitive Nano-Silver sol Sensor for Hg2+ and Fe3+: Green Preparation and Mechanism
Authors: Yang Yining , Zhou Xiaodong , Dong Ruitao , Wang Yanwei , Li Zichao , Xue Yun , Li Qun
Published: September 2022

The development of highly selective and highly sensitive nanometer colorimetric chemical sensors is an urgent requirement in the immediate detection of heavy metal ions. In this work, silver-nanoparticle (Ag NPs)-based chemosensors were prepared by a simple and green method, in which the silver nitrate, carboxymethyl cellulose sodium (CMS) and Polyvinylpyrrolidone (PVP), and glucose are used as the silver source, double stabilizer and green reductant, respectively. The obtained colloidal CMS/PVP-Ag NPs showed a high dispersibility and stability, and creating a high selectivity and sensitivity to detect Hg2+ and Fe3+ with remarkable and rapid color variation. Low limits of detection (LOD) of 7.1 nM (0–20 μM) and 15.2 nM (20–100 μM) for Hg2+ and 3.6 nM for Fe3+ were achieved. More importantly, the CMS/PVP-Ag NPs has a high sensitivity even in a complex system with multiple heavy ions, the result of the practical ability to detect Hg2+ and Fe3+ in tap water and seawater reached a rational range of 98.33~104.2% (Hg2+) and 98.85~104.80% (Fe3+), indicating the great potential of the as-prepared nanocomposites colorimetric chemosensor for practical applications.
Synthesis and Characterization of Bipyridine-Based Polyaminal Network for CO2 Capture
Authors: Alkayal Nazeeha S. , Alotaibi Maha M. , Tashkandi Nada Y. , Alrayyani Maymounah A.
Published: September 2022

The response to the high demand for decreasing the amount of CO2 in the atmosphere, a new polyaminal-based polymer network was designed and successfully prepared through one-pot polycondensation reaction of melamine and [2,2′-Bipyridine]-5,5′-dicarbaldehyde. The formation of the polymer structure was confirmed by FT-IR, solid-state 13C NMR, and powder-X-ray diffraction. The porous properties of the polymeric structure were confirmed by field-emission scanning electron microscope and N2 adsorption–desorption methods at 77 K. The prepared polymer can take up 1.02 mmol/g and 0.71 mmol/g CO2 at 273 K and 298 K, respectively, despite its relatively modest Brunauer–Emmett–Teller (BET) surface area (160.7 m2/g), due to the existence of superabundant polar groups on the pore surfaces.
Comparative Structure–Property Relationship between Nanoclay and Cellulose Nanofiber Reinforced Natural Rubber Nanocomposites
Authors: Wongvasana Bunsita , Thongnuanchan Bencha , Masa Abdulhakim , Saito Hiromu , Sakai Tadamoto , Lopattananon Natinee
Published: September 2022

Natural rubber (NR) nanocomposites reinforced with five parts per hundred rubber (phr) of two different nano-fillers, i.e., nanoclay (abbrev. NC) and cellulose nanofiber (abbrev. CNF), were prepared by using latex mixing approach, followed by mill-compounding and molding. The morphology, stress–strain behavior, strain-induced crystallization, and bound rubber of the NR nanocomposites were systematically compared through TEM, tensile test, WAXS, DMA, and bound rubber measurement. The aggregated CNFs were observed in the NR matrix, while the dispersed nanosized clay tactoids were detected across the NR phase. The reinforcement effects of NC and CNF were clearly distinct in the NR nanocomposites. At the same nano-filler content, the addition of NC and CNF effectively accelerated strain-induced crystallization of NR. The high tensile strength obtained in the NC-filled NR nanocomposite was attributed to strain-induced crystallization of NR accelerated by well-dispersed NC. However, the larger tensile modulus and low strain for the CNF-filled NR were related to the formation of immobilized NR at the interface between CNF aggregate and NR. The immobilization effect of NR at the CNF surface offered by a mutual entanglement of CNF aggregate and NR chain led to local stress concentration and accelerated strain-induced crystallization of CNF/NR nanocomposite. From the present study, the NR nanocomposites combined with 5 phr CNF shows high-tensile modulus and acceptable breaking tensile stress and strain, suggesting the application of CNF/NR based nanocomposite in automotive and stretchable sensors for next-generation electronic devices.
Headspace Extraction of Chlorobenzenes from Water Using Electrospun Nanofibers Fabricated with Calix[4]arene-Doped Polyurethane–Polysulfone
Authors: Najarzadekan Hamid , Kamboh Muhammad Afzal , Sereshti Hassan , Ahmad Irfan , Sridewi Nanthini , Shahabuddin Syed , Rashidi Nodeh Hamid
Published: September 2022

Chlorobenzenes (CBs) are persistent and potentially have a carcinogenic effect on mammals. Thus, the determination of CBs is essential for human health. Hence, in this study, novel polyurethane–polysulfone/calix[4]arene (PU-PSU/calix[4]arene) nanofibers were synthesized using an electrospinning approach over in-situ coating on a stainless-steel wire. The nanosorbent was comprehensively characterized using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FT-IR) techniques. The SEM analysis depicted the nanofiber’s unique morphology and size distribution in the range of 50–200 nm. To determine the levels of 1,2,4-trichlorobenzene, 1,2,3-trichlorobenzene, and 1,2,3,4-tetrachlorobenzene in water samples, freshly prepared nanosorbent was employed using headspace-solid phase microextraction (HS-SPME) in combination with gas chromatography micro electron capture detector (GC-µECD). Other calixarenes, such as sulfonated calix[4]arene, p-tert-calixarene, and calix[6]arene were also examined, and among the fabricated sorbents, the PU–PSU/calix[4]arene showed the highest efficiency. The key variables of the procedure, including ionic strength, extraction temperature, extraction duration, and desorption conditions were examined. Under optimal conditions, the LOD (0.1–1.0 pg mL−1), the LDR (0.4–1000 pg mL−1), and the R2 > 0.990 were determined. Additionally, the repeatability from fiber to fiber and the intra-day and inter-day reproducibility were determined to be 1.4–6.0, 4.7–10.1, and 0.9–9.7%, respectively. The nanofiber adsorption capacity was found to be 670–720 pg/g for CBs at an initial concentration of 400 pg mL−1. A satisfactory recovery of 80–106% was attained when the suggested method’s application for detecting chlorobenzenes (CBs) in tap water, river water, sewage water, and industrial water was assessed.
Thermal Degradation Characteristics of Styrene-Butadiene-Styrene Copolymer Asphalt Binder Filled with an Inorganic Flame-Retarding Agent
Authors: Chen Xiaobing , Ma Zhenyu , Zhou Jianguang , Wang Juntian , Zhang Xiaorui , Zhao Ronglong , Tong Jinhu
Published: September 2022

Asphalt binder is a complex mixture of dark brown polymers composed of hydrocarbons with generally poor fire resistance. To improve its flame retardancy when used in tunnel asphalt pavements, a new inorganic flame-retardant filler (FR) containing magnesium hydroxide, aluminum hydroxide, inorganic phosphate, and melamine salt was explored. Thereafter, limiting oxygen index (LOI) and smoke suppression tests for the flame-retarded asphalt binder (FRA) mastics mixed with FR and styrene-butadiene-styrene (SBS) copolymer asphalt binder were conducted. Thermogravimetric (TG) and differential scanning calorimetry (DSC) curves for the FRA were correspondingly generated. Based on the TG data, the reaction function g(α), apparent activation energy Ea, and pre-exponential factor A were quantitatively evaluated using kinetic analysis. In addition, a Fourier transform infrared spectrometry (FTIR) test was utilized to assess the effects of the presence of FR on the chemical composition of the asphalt binder. Dynamic shear rheometer (DSR) tests were also performed to evaluate the rheological behavior of FRA. Results show that the presence of the FR significantly reduced the LOI and improved the smoke suppression during combustion of the asphalt binder mastics. The presence of FR was found to increase the Ea and the complexity of the combustion reaction, thereby improving the flame retardancy of the asphalt binder. FTIR analysis indicated that the presence of FR did not induce any strong chemical reactions to significantly impact or alter the functional groups of the asphalt binder. Furthermore, it was also observed that the rutting parameter and critical failure temperature of FRA increased with the addition of FR due to the stiffening effect of the solid FR particles.
Fabrication of Graphene-Modified Styrene–Acrylic Emulsion by In Situ Aqueous Polymerization
Authors: Li Yalin , Luo Jieling , Huang Baoquan , Jin Hongjun , Sun Xiaoli , Cao Changlin , Chen Qinghua , Qian Qingrong
Published: September 2022

With the aim of developing green coatings, styrene–acrylic emulsion has been widely used in architectural coatings due to its excellent environmental protection and energy conservation. Nevertheless, the lack of water and oxygen resistance of water-based styrofoam coatings has promoted various nanomaterials being studied for modification. To improve the performance of waterborne styrofoam coating, we introduced the graphene nanopowder and expected to enable it with the function of electromagnetic interference (EMI) shielding to reduce the damage of electromagnetic radiation. In this paper, the problem of poor interface compatibility between graphene and polymer resin was successfully addressed by in situ polymerization. In the process of pre-polymerization of styrene–acrylic emulsion monomer, graphene-modified styrene–acrylic emulsion was obtained by introducing graphene aqueous dispersion. The results showed that the styrene–acrylic emulsion with 4 wt% aqueous graphene dispersions exhibited the best dispersion stability, improved water and oxygen resistance, and the conductivity reached 1.89 × 10−2 S/cm. Then, the graphene-modified coating for building was prepared by using graphene-modified styrofoam emulsion. All the performance indexes of the coating are in line with the industry standards, and it still showed benign EMI shielding effect even when the graphene content was low. It is demonstrated that in situ polymerization technology and the application of graphene in resin coatings modification will promote the development of green coatings.
Stretchable and Self-Healable Graphene–Polymer Conductive Composite for Wearable EMG Sensor
Authors: Song Jihyang , Kim Yewon , Kang Kyumin , Lee Sangkyu , Shin Mikyung , Son Donghee
Published: September 2022

In bioelectronics, stretchable and self-healable electrodes can reliably measure electrophysiological signals from the human body because they have good modulus matching with tissue and high durability. In particular, the polymer–graphene composite has advantages when it is used as an electrode for bioelectronic sensor devices. However, it has previously been reported that external stimuli such as heat or light are required for the self-healing process of polymer/graphene composites. In this study, we optimized a conducting composite by mixing a self-healing polymer (SHP) and graphene. The composite materials can not only self-heal without external stimulation but also have rapid electrical recovery from repeated mechanical damage such as scratches. In addition, they had stable electrical endurance even when the cyclic test was performed over 200 cycles at 50% strain, so they can be useful for a bioelectronic sensor device with high durability. Finally, we measured the electromyogram signals caused by the movement of arm muscles using our composite, and the measured data were transmitted to a microcontroller to successfully control the movement of the robot’s hand.
Birth and Death: Studying Ritual, Embodied Practices and Spirituality at the Start and End of Life
Authors: Wojtkowiak Joanna , Mathijssen Brenda
Published: September 2022

Birth and death are fundamental human experiences [...]
Surviving Persia: Esther’s Scroll, Anti-Black Racism and the Propaganda of Peace and Progress
Authors: De-Whyte Janice P.
Published: September 2022

Esther, Mordecai and the Jewish People’s survival in ancient Persia alert readers that anti-Semitism can exist even in professedly peaceful and progressive settings. Esther’s scroll is not only designed to be read, but it too reads contemporary circumstances of trauma and oppression. This Hebrew Bible narrative offers a critique and a challenge to present interpreters, especially those belonging to professed progressive and peaceful societies. Esther’s scroll exposes how the propaganda of peace and progress, foundational to various imperial and institutional contexts, can veneer structures and events of violence and trauma. Informed by the death-dealing realities of anti-Black racism, a reading of Esther’s key scenes and themes provides insight into the destructive and deadly ways that injustices such as anti-Black racism are supported and sustained by institutional policies and practices. Inherent within this Jewish survival account is an indictment of complicit and culpable individuals and institutions that enable, fund and sanction violence against marginalized members. Although there are numerous sites of oppression and violence throughout society, this essay highlights the institution of academia, which is often upheld as a paragon of progress and peace but which is frequently a prime site of racism and its attendant inequities. As a piece of trauma and survival literature, Esther’s scroll makes a valuable contribution to the repertoire of resilience and resistance curated by many Black individuals and communities to counter anti-Black racism.
Nurturing Intercultural Theological Education towards Social Justice Ideals in South Africa
Authors: Naidoo Marilyn
Published: September 2022

Post-apartheid South Africa, almost three decades into the democracy, is a society in crisis, facing burgeoning economic and social challenges. Religion is seen as a potential force in supporting social cohesion and nation building. Theological education in its handling of diversity and decoloniality reveals complicity and avoidance. A significant task is to embrace the ‘other’ and to affirm the equality and dignity of all people, bearing in mind there is sufficient theological impetus for this. A key question for this study is how theological education can engage in an intercultural ideal towards authentic participation in the development of society. This article reveals the resources and process of embodied formative education within a mediated learning environment to create a hospitable space for learning about differences. It also allows for antiracist pedagogies to be realised within this safer community. Attention is also given to on how epistemological justice and decolonisation is engaged, envisioning a less domineering approach to theological education that makes space for other voices. This contextual case study affirms African identity, revealing humanising education that can support political change at an interpersonal level, as well as at a geo-political level, in its decolonial agenda of creating an engagement of equals.
Max Scheler and the Objectivity of Goodness: Towards a Phenomenology of Value
Authors: Zhong Hanchuan
Published: September 2022

An analogy between goodness and color is often drawn since George Edward Moore to demonstrate the objective validity of goodness, and this way has elicited many responses. German philosopher Max Scheler also frequently analogizes goodness to color. However, his theory of the good distinguishes two approaches to claim for objective validity under the framework of fact and value, and is thus based on value theory: the goods are based on the qualities of values, while what is morally good is based on material value. From the perspective of phenomenology of value, Scheler traces the former approach back to the latter. This paper analytically exposes the reasons, progress, and problems of Scheler’s grounding of the objective validity of goodness in value theory, and endeavors to clarify some misunderstandings of Scheler’s conception of value in contemporary phenomenological research, as well as to highlight the intrinsic correlation of his phenomenology of value with the good life.
Conceptions of Power and Role of Religion in Community Organising
Authors: Ozzano Luca , Fenoglio Sara
Published: September 2022

Community organising, an approach aiming at building local leadership and empowering local communities, has become increasingly popular in the last few decades because of the crisis of more traditional practises of civil society building and political action. In this paper, the authors first describe the main tenets of this approach, formalised between the 1930s and the 1940s in Chicago by Saul Alinsky, and its history and evolution to the present day. The following paragraphs describe the role played by religious values and religious communities, often representing key institutions in rundown social and urban contexts, in this approach. In the last paragraph, the authors finally discuss the conception of power implied in the version of community organising proposed by the Industrial Areas Foundation (an organisation created by Alinsky) and its affiliates, and the role of religion in it. With this work, the authors argue that the relational and bottom-up idea of power proposed by the IAF and its affiliates, although often focused on the development of a local power base able to place political pressure on the authorities from below and even economic boycott campaigns, has increasingly also relied on soft power after Alinsky’s death, especially because of the development of the ‘relational’ side of community organising, a process where the involvement of religious congregations (with the weight of their moral authority) has played a major role.
A Revolution in Red Robes: Tibetan Nuns Obtaining the Doctoral Degree in Buddhist Studies (Geshema)
Authors: Schneider Nicola
Published: September 2022

In the past, Tibetan nuns had no access to formal monastic education and thus could not obtain the two main diplomas and titles that are common in Tibetan Buddhism: the khenpo (mkhan po) degree in the more practice-oriented Nyingmapa school and the geshe (dge bshes) degree in the scholastic curriculum of the Gelukpa school; this essay traces the introduction of the Gelukpa study program in different nunneries based in India and Nepal in recent times; it addresses the question of gender asymmetry by showing the different hurdles that had to be overcome and the solutions, which have been found to allow nuns to become geshemas—the female form of geshe. Finally, I propose the first glimpse into the impact that the opening of higher Buddhist education to nuns has had and what this means for the future of the position of women in the religious sphere, as well as for Tibetan monasticism more generally.
Estimating Rangeland Fine Fuel Biomass in Western Texas Using High-Resolution Aerial Imagery and Machine Learning
Authors: Li Zheng , Angerer Jay P. , Jaime Xavier , Yang Chenghai , Wu X. Ben
Published: September 2022

Rangeland fine fuel biomass is a key factor in determining fire spread and intensity, while the accuracy of biomass estimation is limited due to inherent heterogeneity in rangeland ecosystems. In this study, high spatial resolution (0.23 m) images were used to classify fuel types and predict rangeland fine fuel biomass in west Texas based on the random forest algorithm. Two biomass models, including one with the fuel type, original spectral bands, and vegetation indices as explanatory variables, and the other that contained a combination of the fuel type, original spectral bands, vegetation, and texture indices as explanatory variables, were assessed. Furthermore, the biomass models were also examined by upscaling the remote sensing images from high to medium (30 m) spatial resolution with the spectral curves derived from Landsat images. The fuel type map had an accuracy of more than 95%, and herbaceous fuel types were kept for estimating fine fuel biomass. The results showed that around 76% and 80% of biomass variances were explained by models without texture indices and with texture indices, respectively. The fuel type and the normalized difference vegetation index (NDVI) were two significant input variables influencing fine fuel biomass for both models and adding texture indices contributed to the improvement of model accuracy. An upscaling analysis for biomass estimation using medium spatial resolution imagery showed that approximately 60% of the variance in biomass was explained by the model. The addition of fractional vegetation cover improved the model performance by explaining an additional 5% of the variance in biomass estimation. These findings indicate that high spatial resolution images have the potential to effectively estimate rangeland fuel types and fine fuel biomass, which can be helpful for mapping the spatial distribution of fine fuels to aid in monitoring and fire management on rangelands.
A Forest Fire Susceptibility Modeling Approach Based on Light Gradient Boosting Machine Algorithm
Authors: Sun Yanyan , Zhang Fuquan , Lin Haifeng , Xu Shuwen
Published: September 2022

A forest fire susceptibility map generated with the fire susceptibility model is the basis of fire prevention resource allocation. A more reliable susceptibility map helps improve the effectiveness of resource allocation. Thus, further improving the prediction accuracy is always the goal of fire susceptibility modeling. This paper developed a forest fire susceptibility model based on an ensemble learning method, namely light gradient boosting machine (LightGBM), to produce an accurate fire susceptibility map. In the modeling, a subtropical national forest park in the Jiangsu province of China was used as the case study area. We collected and selected eight variables from the fire occurrence driving factors for modeling based on correlation analysis. These variables are from topographic factors, climatic factors, human activity factors, and vegetation factors. For comparative analysis, another two popular modeling methods, namely logistic regression (LR) and random forest (RF) were also applied to construct the fire susceptibility models. The results show that temperature was the main driving factor of fire in the area. In the produced fire susceptibility map, the extremely high and high susceptibility areas that were classified by LR, RF, and LightGBM were 5.82%, 18.61%, and 19%, respectively. The F1-score of the LightGBM model is higher than the LR and RF models. The accuracy of the model of LightGBM, RF, and LR is 88.8%, 84.8%, and 82.6%, respectively. The area under the curve (AUC) of them is 0.935, 0.918, and 0.868, respectively. The introduced ensemble learning method shows better ability on performance evaluation metrics.
Detection of Small Floating Target on Sea Surface Based on Gramian Angular Field and Improved EfficientNet
Authors: Xi Caiping , Liu Renqiao
Published: September 2022

In order to exploit the advantages of CNN models in the detection of small floating targets on the sea surface, this paper proposes a new framework for encoding radar echo Doppler spectral sequences into images and explores two different ways of encoding time series: Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF). To emphasize the importance of the location of texture information in the GAF-encoded map, this paper introduces the coordinate attention (CA) mechanism into the mobile inverted bottleneck convolution (MBConv) structure in EfficientNet and optimizes the model convergence by the adaptive AdamW optimization algorithm. Finally, the improved EfficientNet model is used to train and test on the constructed GADF and GASF datasets, respectively. The experimental results demonstrate the effectiveness of the proposed algorithm. The recognition accuracy of the improved EfficientNet model reaches 96.13% and 96.28% on the GADF and GASF datasets, respectively, which is 1.74% and 2.06% higher than that that of the pre-improved network model. The number of parameters of the improved EfficientNet model is 5.38 M, which is 0.09 M higher than that of the pre-improved network model. Compared with the classical image classification algorithm, the proposed algorithm achieves higher accuracy and maintains lighter computation.
Insights into the Landslides Triggered by the 2022 Lushan Ms 6.1 Earthquake: Spatial Distribution and Controls
Authors: Zhao Bo , Li Weile , Su Lijun , Wang Yunsheng , Wu Haochen
Published: September 2022

On 1 June 2022, a magnitude Ms 6.1 (Mw 5.8) earthquake, named the 2022 Lushan earthquake, struck the southern segment of the Longmenshan fault zone, with an epicenter at 30.395°N, 102.958°E and a focal depth of approximately 12.0 km. To gain insight into the landslides triggered by this event and the characteristics of coseismic landslides in the Longmenshan fault zone, we collected multitemporal satellite images and carried out field investigations. The results reveal that the 2022 Lushan event triggered at least 1288 landslides over an affected area of 1470 km2. The total landslide area is 5.33 km2, and the highest landslide concentration reaches 22.3 landslides/km2. The landslide distribution has a hanging wall effect, and the right bank area of the Qingyi River, featuring deep-cutting gorges, is part of an area with obvious concentrated landslides; this area consists mainly of intrusive rocks, including granite, gabbro and hornblende. The coseismic landslides in the Longmenshan fault zone have hanging wall effects, and the landslides triggered by the 2022 Lushan event are distributed in higher and steeper areas.
An Unsupervised Cascade Fusion Network for Radiometrically-Accurate Vis-NIR-SWIR Hyperspectral Sharpening
Authors: Huang Sihan , Messinger David
Published: September 2022

Hyperspectral sharpening has been considered an important topic in many earth observation applications. Many studies have been performed to solve the Visible-Near-Infrared (Vis-NIR) hyperpectral sharpening problem, but there is little research related to hyperspectral sharpening including short-wave infrared (SWIR) bands despite many hyperspectral imaging systems capturing this wavelength range. In this paper, we introduce a novel method to achieve full-spectrum hyperspectral sharpening by fusing the high-resolution (HR) Vis-NIR multispectral image (MSI) and the Vis-NIR-SWIR low-resolution (LR) hyperspectral image (HSI). The novelty of the proposed approach lies in three points. Firstly, our model is designed for sharpening the full-spectrum HSI with high radiometric accuracy. Secondly, unlike most of the big-dataset-driven deep learning models, we only need one LR-HSI and HR-MSI pair for training. Lastly, per-pixel classification is implemented to test the spectral accuracy of the results.
Knowledge Graph Representation Learning-Based Forest Fire Prediction
Authors: Chen Jiahui , Yang Yi , Peng Ling , Chen Luanjie , Ge Xingtong
Published: September 2022

Forest fires destroy the ecological environment and cause large property loss. There is much research in the field of geographic information that revolves around forest fires. The traditional forest fire prediction methods hardly consider multi-source data fusion. Therefore, the forest fire predictions ignore the complex dependencies and correlations of the spatiotemporal kind that usually bring valuable information for the predictions. Although the knowledge graph methods have been used to model the forest fires data, they mainly rely on artificially defined inference rules to make predictions. There is currently a lack of a representation and reasoning methods for forest fire knowledge graphs. We propose a knowledge-graph- and representation-learning-based forest fire prediction method in this paper for addressing the issues. First, we designed a schema for the forest fire knowledge graph to fuse multi-source data, including time, space, and influencing factors. Then, we propose a method, RotateS2F, to learn vector-based knowledge graph representations of the forest fires. We finally leverage a link prediction algorithm to predict the forest fire burning area. We performed an experiment on the Montesinho Natural Park forest fire dataset, which contains 517 fires. The results show that our method reduces mean absolute deviation by 28.61% and root-mean-square error by 53.62% compared with the previous methods.
Near-Field Multiple Target Localization in Frequency Diverse Array Based on Tensor Decomposition
Authors: Xie Ningbo , Ouyang Shan , Liao Kefei , Wang Haitao , Jiang Junzheng
Published: September 2022

Target localization is a fundamental problem in array signal processing. The problem of locating near-field targets with multiple-input multiple-output (MIMO) radar has been studied extensively; however, most of the conventional matrix-based approaches suffer from limitations in terms of the representation and exploitation of the multidimensional nature of MIMO radar signals. In this paper, we addressed the problem of localizing multiple targets in the near-field region, aiming at pursuing a solution applicable for multidimensional signal that is able to achieve sufficient accuracy. A tensor-based signal model impinging on a monostatic frequency diverse array multiple-input multiple-output (FDA-MIMO) radar was formulated, and a corresponding tensor decomposition-based localization algorithm (TenDLA) that showcases the connection between the tensor-based analysis and the localization problem was developed. Additionally, a correction procedure to mitigate the estimation deviations on the range and angle was presented, yielding significant improvements in estimation accuracy. Numerical examples demonstrated the validity and effectiveness of the proposed approach, and it was shown that this approach is superior to conventional methods due to its high-resolution estimation accuracy as well as its relatively low computational costs.
Remote Sensing Precursors Analysis for Giant Landslides
Authors: Lan Hengxing , Liu Xiao , Li Langping , Li Quanwen , Tian Naiman , Peng Jianbing
Published: September 2022

Monitoring and early warning systems for landslides are urgently needed worldwide to effectively reduce the losses of life and property caused by these natural disasters. Detecting the precursors of giant landslides constitutes the premise of landslide monitoring and early warning, and remote sensing is a powerful means to achieve this goal. In this work, we aim to summarize the basic types and evolutionary principles of giant landslide precursors, describe the remote sensing methods capable of identifying those precursors, and present typical cases of related sliding. Based on a review of the literature and an analysis of remote sensing imagery, the three main types of remote sensing techniques for capturing the geomorphological, geotechnical, and geoenvironmental precursors of giant landslides are optical, synthetic aperture radar (SAR), and thermal infrared methods, respectively. Time-series optical remote sensing data from medium-resolution satellites can be used to obtain abundant information on geomorphological changes, such as the extension of cracks and erosion ditches, and band algebraic analysis, image enhancement, and segmentation techniques are valuable for focusing on the locations of geomorphological landslide precursors. SAR sensors have the ability to monitor the slight slope deformation caused by unfavorable geological structures and can provide precursor information on imminent failure several days before a landslide; furthermore, persistent scatterer interferometric SAR has significant advantages in large-scale surface displacement monitoring. Thermal infrared imagery can identify landslide precursors by monitoring geoenvironmental information, especially in permafrost regions where glaciers are widely distributed; the reason may be that freeze–thaw cycles and snowmelt caused by increased temperatures affect the stability of the surface. Optical, SAR, and thermal remote sensing all exhibit unique advantages and play an essential role in the identification of giant landslide precursors. The combined application of these three remote sensing technologies to obtain the synthetic geomorphological, geotechnical, and geoenvironmental precursors of giant landslides would greatly promote the development of landslide early warning systems.
A New Framework for Reconstructing Time Series DMSP-OLS Nighttime Light Data Using the Improved Stepwise Calibration (ISC) Method
Authors: Wang Mingyue , Feng Chunhui , Hu Bifeng , Wang Nan , Xu Jintao , Ma Ziqiang , Peng Jie , Shi Zhou
Published: September 2022

Calibration and reconstruction of time series DMSP-OLS nighttime light images are critical for understanding urbanization processes and the evolution of urban spatial patterns from a unique perspective. In this study, we developed an improved stepwise calibration (ISC) method based on numerical constancy to correct and reconstruct the time series of China’s regional nighttime light data, thus eliminating the drawbacks of the invariant target region method. We evaluated the different calibration methods and quantitatively validated the calibrated nighttime light data using gross domestic product (GDP) and electricity consumption (EC) at municipal, provincial, and national scales. The results indicated that the ISC method demonstrated its advantage in screening stable lit pixels and maintaining the temporal variability of multi-year nighttime light variation. The variation curve of reconstructed multi-year nighttime light obtained by the ISC method based on numerical constancy was more consistent with the actual urban development. The ISC method retained the original data’s most abundant and complete information than other calibration methods. Moreover, the significant advantages of this method in the low-light high-variation regions and high-light low-variation regions offered new possibilities for understanding the development of small- and medium-sized nighttime light centers such as towns and villages from a nighttime light perspective. This is an advantage that other calibration methods do not offer. The correlation between the multi-year nighttime light dataset obtained by the ISC method and the socio-economic data was significantly improved. The correlation coefficients with GDP and EC are 0.9695 and 0.9923, respectively. Last but not least, the ISC method is more straightforward to implement. The new framework developed in this study produces a more accurate and reliable long time series nighttime light dataset and provides quality assurance for subsequent research in socio-economic development, urban development, natural disasters, and other fields.
Examining the Role of UAV Lidar Data in Improving Tree Volume Calculation Accuracy
Authors: Liao Kuo , Li Yunhe , Zou Bingzhang , Li Dengqiu , Lu Dengsheng
Published: September 2022

Traditional forest inventories are based on field surveys of established sample plots, which involve field measurements of individual trees within a sample plot and the selection of proper allometric equations for tree volume calculation. Thus, accurate field measurements and properly selected allometric equations are two crucial factors for providing high-quality tree volumes. One key problem is the difficulty in accurately acquiring tree height data, resulting in high uncertainty in tree volume calculation when the diameter at breast height (DBH) alone is used. This study examined the uncertainty of tree height measurements using different means and the impact of allometric models on tree volume estimation accuracy. Masson pine and eucalyptus plantations in Fujian Province, China, were selected as examples; their tree heights were measured three ways: using an 18-m telescopic pole, UAV Lidar (unmanned aerial vehicle, light detection and ranging) data, and direct measurement of felled trees, with the latest one as a reference. The DBH-based and DBH–height-based allometric equations corresponding to specific tree species were used for the calculations of tree volumes. The results show that (1) tree volumes calculated from the DBH-based models were lower than those from the DBH–height-based models. On average, tree volumes were underestimated by 0.018 m3 and 0.117 m3 for Masson pine and eucalyptus, respectively, while the relative root-mean-squared errors (RMSEr) were 24.04% and 33.90%, respectively, when using the DBH-based model; (2) the tree height extracted from UAV Lidar data was more accurate than that measured using a telescopic pole, because the pole measurement method generally underestimated the tree height, especially when the trees were taller than the length of the pole (18 m in our study); (3) the tree heights measured using different methods greatly impacted the accuracies of tree volumes calculated using the DBH–height model. The telescopic-pole-measured tree heights resulted in a relative error of 9.1–11.8% in tree volume calculations. This research implies that incorporation of UAV Lidar data with DBH field measurements can effectively improve tree volume estimation and could be a new direction for sample plot data collection in the future.
Creation of Wildfire Susceptibility Maps in Plumas National Forest Using InSAR Coherence, Deep Learning, and Metaheuristic Optimization Approaches
Authors: Nur Arip Syaripudin , Kim Yong Je , Lee Chang-Wook
Published: September 2022

Plumas National Forest, located in the Butte and Plumas counties, has experienced devastating wildfires in recent years, resulting in substantial economic losses and threatening the safety of people. Mapping damaged areas and assessing wildfire susceptibility are necessary to prevent, mitigate, and manage wildfires. In this study, a wildfire susceptibility map was generated using a CNN and metaheuristic optimization algorithms (GWO and ICA) based on images of areas damaged by wildfires. The locations of damaged areas were identified using the damage proxy map (DPM) technique from Sentinel-1 synthetic aperture radar (SAR) data collected from 2016 to 2020. The DPMs’ depicting areas damaged by wildfires were similar to fire perimeters obtained from the California Department of Forestry and Fire Protection (CAL FIRE). Data regarding damaged areas were divided into a training set (50%) for modeling and a testing set (50%) for assessing the accuracy of the models. Sixteen conditioning factors, categorized as topographical, meteorological, environmental, and anthropological factors, were selected to construct the models. The wildfire susceptibility models were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and root mean square error (RMSE) analysis. The evaluation results revealed that the hybrid-based CNN-GWO model (AUC = 0.974, RMSE = 0.334) exhibited better performance than the CNN (AUC = 0.934, RMSE = 0.780) and CNN-ICA (AUC = 0.950, RMSE = 0.350) models. Therefore, we conclude that optimizing a CNN with metaheuristics considerably increased the accuracy and reliability of wildfire susceptibility mapping in the study area.
Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images
Authors: Tian Yuping , Wu Zechuan , Li Mingze , Wang Bin , Zhang Xiaodi
Published: September 2022

With the increasingly severe damage wreaked by forest fires, their scientific and effective prevention and control has attracted the attention of countries worldwide. The breakthrough of remote sensing technologies implemented in the monitoring of fire spread and early warning has become the development direction for their prevention and control. However, a single remote sensing data collection point cannot simultaneously meet the temporal and spatial resolution requirements of fire spread monitoring. This can significantly affect the efficiency and timeliness of fire spread monitoring. This article focuses on the mountain fires that occurred in Muli County, on 28 March 2020, and in Jingjiu Township on 30 March 2020, in Liangshan Prefecture, Sichuan Province, as its research objects. Multi-source satellite remote sensing image data from Planet, Sentinel-2, MODIS, GF-1, GF-4, and Landsat-8 were used for fire monitoring. The spread of the fire time series was effectively and quickly obtained using the remote sensing data at various times. Fireline information and fire severity were extracted based on the calculated differenced normalized burn ratio (dNBR). This study collected the meteorological, terrain, combustibles, and human factors related to the fire. The random forest algorithm analyzed the collected data and identified the main factors, with their order of importance, that affected the spread of the two selected forest fires in Sichuan Province. Finally, the vegetation coverage before and after the fire was calculated, and the relationship between the vegetation coverage and the fire severity was analyzed. The results showed that the multi-source satellite remote sensing images can be utilized and implemented for time-evolving forest fires, enabling forest managers and firefighting agencies to plan improved firefighting actions in a timely manner and increase the effectiveness of firefighting strategies. For the forest fires in Sichuan Province studied here, the meteorological factors had the most significant impact on their spread compared with other forest fire factors. Among all variables, relative humidity was the most crucial factor affecting the spread of forest fires. The linear regression results showed that the vegetation coverage and dNBR were significantly correlated before and after the fire. The vegetation coverage recovery effects were different in the fire burned areas depending on fire severity. High vegetation recovery was associated with low-intensity burned areas. By combining the remote sensing data obtained by multi-source remote sensing satellites, accurate and macro dynamic monitoring and quantitative analysis of wildfires can be carried out. The study’s results provide effective information on the fires in Sichuan Province and can be used as a technical reference for fire spread monitoring and analysis through remote sensing, enabling accelerated emergency responses.
Few-Shot Object Detection in Remote Sensing Image Interpretation: Opportunities and Challenges
Authors: Liu Sixu , You Yanan , Su Haozheng , Meng Gang , Yang Wei , Liu Fang
Published: September 2022

Recent years have witnessed rapid development and remarkable achievements on deep learning object detection in remote sensing (RS) images. The growing improvement of the accuracy is inseparable from the increasingly complex deep convolutional neural network and the huge amount of sample data. However, the under-fitting neural network will damage the detection performance facing the difficulty of sample acquisition. Thus, it evolves into few-shot object detection (FSOD). In this article, we first briefly introduce the object detection task and its algorithms, to better understand the basic detection frameworks followed by FSOD. Then, FSOD design methods in RS images for three important aspects, such as sample, model, and learning strategy, are respectively discussed. In addition, some valuable research results of FSOD in computer vision field are also included. We advocate a wide research technique route, and some advice about feature enhancement and multi-modal fusion, semantics extraction and cross-domain mapping, fine-tune and meta-learning strategies, and so on, are provided. Based on our stated research route, a novel few-shot detector that focuses on contextual information is proposed. At the end of the paper, we summarize accuracy performance on experimental datasets to illustrate the achievements and shortcomings of the stated algorithms, and highlight the future opportunities and challenges of FSOD in RS image interpretation, in the hope of providing insights into future research.