bluuebunny/crossref_metadata_2025 huggingface dataset:
Responses of the Remote Sensing Drought Index with Soil Information to Meteorological and Agricultural Droughts in Southeastern Tibet
Authors: Wang Ziyu , Wang Zegen , Xiong Junnan , He Wen , Yong Zhiwei , Wang Xin
Published: December 2022

The Temperature–Vegetation–Precipitation–Drought Index (TVPDI) has a good performance in drought monitoring in China. However, different regions have different responses to droughts due to terrain differences. In southeastern Tibet, the drought monitoring capacity of some drought indices without soil information has to be assessed on account of the poor sensitivity between temperature and soil humidity. Therefore, soil moisture was added to calculate a new drought index based on TVPDI in southeastern Tibet, named the Temperature–Vegetation–Soil-Moisture–Precipitation–Drought Index (TVMPDI). Then, the TVMPDI was validated by using the Standardized Precipitation Evapotranspiration Index (SPEI) and other remote sensing drought indices, including the Vegetation Health Index (VHI) and Scale Drought Conditions Index (SDCI), during the growing seasons of 2003–2018. The Standardized Precipitation Index (SPI) and SPEI were used to represent meteorological drought and Gross Primary Productivity (GPP) was used to represent agricultural drought. The relation between TVMPDI and these drought indices was compared. Finally, the time trends of TVMPDI were also analyzed. The relation coefficients of TVMPDI and SPEI were above 0.5. The correlations between TVMPDI and drought indices, including the Vegetation Health Index (VHI) and Scale Drought Conditions Index (SDCI), also had a good performance. The correlation between the meteorological drought indices (SPI and SPEI) and TVMPDI were not as good as for the TVPDI, but the temporal correlation between the TVMPDI and GPP was greater than that between the TVPDI and GPP. This indicates that the TVMPDI is more suitable for monitoring agricultural drought than the TVPDI. In addition, historical drought monitoring had values that were consistent with those of the actual situation. The trend of the TVMPDI showed that drought in the study area was alleviated from 2003 to 2018. Furthermore, GPP was negatively correlated with SPEI (r = −0.4) and positively correlated with Soil Moisture (SM) drought index (TVMPDI, SMCI) (r = 0.4) in the eastern part of the study area, which suggests that SM, rather than precipitation, could promote the growth of vegetation in the region. A correct understanding of the role of soil information in drought comprehensive indices may monitor meteorological drought and agricultural drought more accurately.
Assessment of the Steering Precision of a UAV along the Flight Profiles Using a GNSS RTK Receiver
Authors: Lewicka Oktawia , Specht Mariusz , Specht Cezary
Published: December 2022

Photogrammetric surveys are increasingly being carried out using Unmanned Aerial Vehicles (UAV). Steering drones along the flight profiles is one of the main factors that determines the quality of the compiled photogrammetric products. The aim of this article is to present a methodology for performing and processing measurements, which are used in order to determine the accuracy of steering any drone along flight profiles. The study used a drone equipped with a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) receiver. The measurements were performed on two routes which comprised parallel profiles distant from each other by 10 m and 20 m. The study was conducted under favourable meteorological conditions (windless and sunny weather) at three speeds (10 km/h, 20 km/h and 30 km/h). The cross track error (XTE), which is the distance between a UAV’s position and the flight profile, calculated transversely to the course, was adopted as the accuracy measure of steering a UAV along the flight profiles. Based on the results obtained, it must be concluded that the values of XTE measures for two representative routes are very similar and are not determined by the flight speed. The XTE68 measure (p = 0.68) ranged from 0.39 m to 1.00 m, while the XTE95 measure (p = 0.95) ranged from 0.60 m to 1.22 m. Moreover, analyses demonstrated that the statistical distribution of the XTE measure was most similar to the gamma and Weibull (3P) distributions.
Optimizing Landsat Next Shortwave Infrared Bands for Crop Residue Characterization
Authors: Lamb Brian T. , Dennison Philip E. , Hively W. Dean , Kokaly Raymond F. , Serbin Guy , Wu Zhuoting , Dabney Philip W. , Masek Jeffery G. , Campbell Michael , Daughtry Craig S. T.
Published: December 2022

This study focused on optimizing the placement of shortwave infrared (SWIR) bands for pixel-level estimation of fractional crop residue cover (fR) for the upcoming Landsat Next mission. We applied an iterative wavelength shift approach to a database of crop residue field spectra collected in Beltsville, Maryland, USA (n = 916) and computed generalized two- and three-band spectral indices for all wavelength combinations between 2000 and 2350 nm, then used these indices to model field-measured fR. A subset of the full dataset with a Normalized Difference Vegetation Index (NDVI) < 0.3 threshold (n = 643) was generated to evaluate green vegetation impacts on fR estimation. For the two-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized normalized difference using 2226 nm and 2263 nm bands produced the top fR estimation performance (R2 = 0.8222; RMSE = 0.1296). These findings were similar to the established two-band Shortwave Infrared Normalized Difference Residue Index (SINDRI) (R2 = 0.8145; RMSE = 0.1324). Performance of the two-band generalized normalized difference and SINDRI decreased for the full-NDVI dataset (R2 = 0.5865 and 0.4144, respectively). For the three-band wavelength shift analyses applied to the NDVI < 0.3 dataset, a generalized ratio-based index with a 2031–2085–2216 nm band combination, closely matching established Cellulose Absorption Index (CAI) bands, was top performing (R2 = 0.8397; RMSE = 0.1231). Three-band indices with CAI-type wavelengths maintained top fR estimation performance for the full-NDVI dataset with a 2036–2111–2217 nm band combination (R2 = 0.7581; RMSE = 0.1548). The 2036–2111–2217 nm band combination was also top performing in fR estimation (R2 = 0.8690; RMSE = 0.0970) for an additional analysis assessing combined green vegetation cover and surface moisture effects. Our results indicate that a three-band configuration with band centers and wavelength tolerances of 2036 nm (±5 nm), 2097 nm (±14 nm), and 2214 (±11 nm) would optimize Landsat Next SWIR bands for fR estimation.
Cloudformer: A Cloud-Removal Network Combining Self-Attention Mechanism and Convolution
Authors: Wu Peiyang , Pan Zongxu , Tang Hairong , Hu Yuxin
Published: December 2022

Optical remote-sensing images have a wide range of applications, but they are often obscured by clouds, which affects subsequent analysis. Therefore, cloud removal becomes a necessary preprocessing step. In this paper, a novel and superior transformer-based network is proposed, named Cloudformer. The proposed method novelly combines the advantages of convolution and a self-attention mechanism: it uses convolution layers to extract simple features over a small range in the shallow layer, and exerts the advantage of a self-attention mechanism in extracting correlation in a large range in the deep layer. This method also introduces Locally-enhanced Positional Encoding (LePE) to flexibly generate suitable positional encodings for different inputs and to utilize local information to enhance encoding capabilities. Exhaustive experiments on public datasets demonstrate the superior ability of the method to remove both thin and thick clouds, and the effectiveness of the proposed modules is validated by ablation studies.
Effect of Cloud Mask on the Consistency of Snow Cover Products from MODIS and VIIRS
Authors: Liu Anwei , Che Tao , Huang Xiaodong , Dai Liyun , Wang Jing , Deng Jie
Published: December 2022

Snow cover has significant impacts on the global water cycle, ecosystem, and climate change. At present, satellite remote sensing is regarded as the most efficient approach to detect long-term and multiscale observations of snow cover extent. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard Joint Polar Satellite System (JPSS) satellites will replace the Moderate-Resolution Imaging Spectroradiometer (MODIS) to prolong data recording in the future. Therefore, it is a fundamental task to analyze and evaluate the consistency of the snow cover products retrieved from these two sensors. In this study, we performed comparisons and a consistency evaluation between the MODIS and VIIRS snow cover products in three major snow distribution regions in China: Northeast China (NE), Northwest China (NW) and the Qinghai–Tibet Plateau (QT). The results demonstrated that (1) the normalized difference snow index (NDSI)-derived snow cover products showed suitable consistency between VIIRS and MODIS under clear sky conditions, with a mean difference value of less than 5%; (2) the VIIRS snow cover product presented much more snow and fewer clouds than that of MODIS in the snow season due to the differences in cloud-masking algorithms; (3) cloud mask strongly affects the potential of snow cover observation, and presents seasonal pattern in the test regions; and (4) VIIRS is able to distinguish clouds from snow with greater accuracy. The comparisons indicated that the greater the difference in cloud cover, the poorer the agreement in snow cover. This evaluation implies that perfecting the cloud-masking algorithm of VIIRS to update the MODIS would be the best solution to achieve better consistency for long-term and high-quality snow cover products.
An Effusive Lunar Dome Near Fracastorius Crater: Spectral and Morphometric Properties
Authors: Ahrens Caitlin , Lena Raffaello
Published: December 2022

We examine a dome within the boundary between Fracastorius crater and Mare Nectaris. The dome has a noticeable vent structure and appears to be perpendicular to wrinkle ridges in the southern Mare Nectaris basin. The spectral signature of this dome, derived from Clementine UVVIS and Chandrayaan-1 M3 reflectance data, revealed that Fracastorius has low TiO2 content and primarily basaltic material. Using altimeter data, we measured the dome diameter to be 28.6 km, with a dome height of 241.5 m, and a flank slope of 1°. Based on rheological modeling of the dome and a viscoelastic model of the presumed feeder dike, we obtained a magma viscosity of 3.1 × 105 Pa s, an effusion rate of 5.9 m3 s−1, a duration of multiple effusion processes of 4.15 years, and a magma rise speed of 2.1 × 10−4 m s−1. From these measurements, we estimate the feeder dike geometry to have a horizontal dike length of 234 km and a width of 11.8 m. A comparison of the Fracastorius dome with other noted lunar domes with similar morphometric properties reveal similar magma viscosities to domes found near craters Mee, Milichius and Petavius.
SegDetector: A Deep Learning Model for Detecting Small and Overlapping Damaged Buildings in Satellite Images
Authors: Yu Zhengbo , Chen Zhe , Sun Zhongchang , Guo Huadong , Leng Bo , He Ziqiong , Yang Jinpei , Xing Shuwen
Published: December 2022

Buildings bear much of the damage from natural disasters, and determining the extent of this damage is of great importance to post-disaster emergency relief. The application of deep learning to satellite remote sensing imagery has become more and more mature in monitoring natural disasters, but there are problems such as the small pixel scale of targets and overlapping targets that hinder the effectiveness of the model. Based on the SegFormer semantic segmentation model, this study proposes the SegDetector model for difficult detection of small-scale targets and overlapping targets in target detection tasks. By changing the calculation method of the loss function, the detection of overlapping samples is improved and the time-consuming non-maximum-suppression (NMS) algorithm is discarded, and the horizontal and rotational detection of buildings can be easily and conveniently implemented. In order to verify the effectiveness of the SegDetector model, the xBD dataset, which is a dataset for assessing building damage from satellite imagery, was transformed and tested. The experiment results show that the SegDetector model outperforms the state-of-the-art (SOTA) models such as you-only-look-once (YOLOv3, v4, v5) in the xBD dataset with F1: 0.71, Precision: 0.63, and Recall: 0.81. At the same time, the SegDetector model has a small number of parameters and fast detection capability, making it more practical for deployment.
Image Inpainting with Bilateral Convolution
Authors: Huang Wenli , Deng Ye , Hui Siqi , Wang Jinjun
Published: December 2022

Due to sensor malfunctions and poor atmospheric conditions, remote sensing images often miss important information/pixels, which affects downstream tasks, therefore requiring reconstruction. Current image reconstruction methods use deep convolutional neural networks to improve inpainting performances as they have a powerful modeling capability. However, deep convolutional networks learn different features with the same group of convolutional kernels, which restricts their ability to handle diverse image corruptions and often results in color discrepancy and blurriness in the recovered images. To mitigate this problem, in this paper, we propose an operator called Bilateral Convolution (BC) to adaptively preserve and propagate information from known regions to missing data regions. On the basis of vanilla convolution, the BC dynamically propagates more confident features, which weights the input features of a patch according to their spatial location and feature value. Furthermore, to capture different range dependencies, we designed a Multi-range Window Attention (MWA) module, in which the input feature is divided into multiple sizes of non-overlapped patches for several heads, and then these feature patches are processed by the window self-attention. With BC and MWA, we designed a bilateral convolution network for image inpainting. We conducted experiments on remote sensing datasets and several typical image inpainting datasets to verify the effectiveness and generalization of our network. The results show that our network adaptively captures features between known and unknown regions, generates appropriate content for various corrupted images, and has a competitive performance compared with state-of-the-art methods.
Optimal Rain Gauge Network Design Aided by Multi-Source Satellite Precipitation Observation
Authors: Wang Helong , Chen Wenlong , Hu Zukang , Xu Yueping , Shen Dingtao
Published: December 2022

Optimized rain gauge networks minimize their input and maintenance costs. Satellite precipitation observations are particularly susceptible to the effects of terrain elevation, vegetation, and other topographical factors, resulting in large deviations between satellite and ground-based precipitation data. Satellite precipitation observations are more inaccurate where the deviations change more drastically, indicating that rain gauge stations should be utilized at these locations. This study utilized satellite precipitation observation data to facilitate rain gauge network optimization. The deviations between ground-based precipitation data and three types of satellite precipitation observation data were used for entropy estimation. The rain gauge network in the Oujiang River Basin of China was optimally designed according to the principle of maximum joint entropy. Two optimization schemes of culling and supplementing 40 existing sites and 35 virtual sites were explored. First, the optimization and ranking of the rain gauge station network showed good stability and consistency. In addition, the joint entropy of deviation was larger than that of ground-based precipitation data alone, leading to a higher degree of discrimination between rain gauge stations and enabling the use of deviation data instead of ground-based precipitation data to assist network optimization, with more reasonable and interpretable results.
Shallow and Deep Electric Structures in the Tolhuaca Geothermal System (S. Chile) Investigated by Magnetotellurics
Authors: Pavez Maximiliano , Diaz Daniel , Brasse Heinrich , Kapinos Gerhard , Budach Ingmar , Goldberg Valentin , Morata Diego , Schill Eva
Published: December 2022

The geoelectric properties of the geothermal system associated with the Tolhuaca volcano were investigated by three-dimensional (3D) inversion of magnetotelluric (MT) data. This study presents the first resistivity model of the Tolhuaca volcano derived from 3D MT inversion to have a better understanding of its magmatic and hydrothermal system. We selected data from 54 MT stations for 3D inversion. We performed a series of 3D MT inversion tests by changing the type of data to be inverted, as well as the starting model to obtain a model in agreement with the geology. The final 3D MT model presents a conductive body (<20 Ωm) located 2 km below the summit of Tolhuaca volcano, inferred as a shallow magmatic storage compartment. We also distinguish a ~300 m thick layer of high conductivity (<10 Ωm) corresponding to argillic hydrothermal alteration. The MT model includes two resistive bodies (~200 Ωm) in the upper crust below the laterally displaced argillic alteration layer to the west beneath the extinct Tolhuaca, which would correspond to a shallow reservoir (~1000 m from the surface) and a deep reservoir (>1800 m from the surface) that had so far not been identified by previous resistivity models. The result of this study provides new insights into the complexity of the Tolhuaca geothermal system.
DTM-Based Comparative Geomorphometric Analysis of Four Scoria Cone Areas—Suggestions for Additional Approaches
Authors: Vörös Fanni , van Wyk de Vries Benjamin , Guilbaud Marie-Noëlle , Görüm Tolga , Karátson Dávid , Székely Balázs
Published: December 2022

Morphometric studies of scoria cones have a long history in research. Their geometry and shape are believed to be related to evolution by erosion after their formation, and hence the morphometric parameters are supposed to be related with age. We analysed 501 scoria cones of four volcanic fields: San Francisco Volcanic Field (Arizona, USA), Chaîne des Puys (France), Sierra Chichinautzin (Mexico), and Kula Volcanic Field (Turkey). All morphometric parameters (cone height, cone width, crater width, slope angles, ellipticity) were derived using DTMs. As new parameters, we calculated Polar Coordinate Transformed maps, Spatial Elliptical Fourier Descriptors to study the asymmetries. The age groups of the four volcanic fields were created and their slope distributions were analysed. The age groups of individual volcanic fields show a statistically significant decreasing tendency of slope angles tested by Mann–Whitney tests. By mixing the age groups of the volcanic fields and sorting them by age interval, we can also observe a general, statistically significant decrease. The interquartile ranges of the distributions also tend to decrease with time. These observations support the hypothesis that whereas the geometry of individual scoria cones differs initially (just after formation), general trends may exist for their morphological evolution with time in the various volcanic fields.
Exploring the Ability of Solar-Induced Chlorophyll Fluorescence for Drought Monitoring Based on an Intelligent Irrigation Control System
Authors: Zhao Wenhui , Wu Jianjun , Shen Qiu , Yang Jianhua , Han Xinyi
Published: December 2022

Drought is one of the most devastating disasters and a serious constraint on agricultural development. The reflectance-based vegetation indices (VIs), such as Normalized Difference Vegetation Index (NDVI), have been widely used for drought monitoring, but there is a lag in the response of VIs to the changes of photosynthesis induced by drought. Solar-induced chlorophyll fluorescence (SIF) is closely related to photosynthesis of vegetation and can capture changes induced by drought timely. This study investigated the capability of SIF for drought monitoring. An intelligent irrigation control system (IICS) utilizing the Internet of Things was designed and constructed. The soil moisture of the experiment plots was controlled at 60–80% (well-watered, T1), 50–60% (mild water stress, T2), 40–50% (moderate water stress, T3) and 30–40% (severe water stress, T4) of the field water capacity using the IICS based on data collected by soil moisture sensors. Meanwhile, SIF, NDVI, Normalized Difference Red Edge (NDRE) and Optimized Soil Adjusted Vegetation Index (OSAVI) were collected for a long time series using an automated spectral monitoring system. The differences in the responses of SIF, NDVI, NDRE and OSAVI to different drought intensities were fully analyzed. This study illustrates that the IICS can realize precise irrigation management strategies and the construction of regulated deficit irrigation treatments. SIF significantly decreased under mild stress, while NDVI, NDRE and OSAVI only significantly decreased under moderate and severe stress, indicating that SIF is more sensitive to drought. This study demonstrates the excellent ability of SIF for drought monitoring and lays the foundation for the future application of SIF in agricultural drought monitoring.
Dual-Branch Attention-Assisted CNN for Hyperspectral Image Classification
Authors: Huang Wei , Zhao Zhuobing , Sun Le , Ju Ming
Published: December 2022

Convolutional neural network (CNN)-based hyperspectral image (HSI) classification models have developed rapidly in recent years due to their superiority. However, recent deep learning methods based on CNN tend to be deep networks with multiple parameters, which inevitably resulted in information redundancy and increased computational cost. We propose a dual-branch attention-assisted CNN (DBAA-CNN) for HSI classification to address these problems. The network consists of spatial-spectral and spectral attention branches. The spatial-spectral branch integrates multi-scale spatial information with cross-channel attention by extracting spatial–spectral information jointly utilizing a 3-D CNN and a pyramid squeeze-and-excitation attention (PSA) module. The spectral branch maps the original features to the spectral interaction space for feature representation and learning by adding an attention module. Finally, the spectral and spatial features are combined and input into the linear layer to generate the sample label. We conducted tests with three common hyperspectral datasets to test the efficacy of the framework. Our method outperformed state-of-the-art HSI classification algorithms based on classification accuracy and processing time.
ArithFusion: An Arithmetic Deep Model for Temporal Remote Sensing Image Fusion
Authors: Hoque Md Reshad Ul , Wu Jian , Kwan Chiman , Koperski Krzysztof , Li Jiang
Published: December 2022

Different satellite images may consist of variable numbers of channels which have different resolutions, and each satellite has a unique revisit period. For example, the Landsat-8 satellite images have 30 m resolution in their multispectral channels, the Sentinel-2 satellite images have 10 m resolution in the pan-sharp channel, and the National Agriculture Imagery Program (NAIP) aerial images have 1 m resolution. In this study, we propose a simple yet effective arithmetic deep model for multimodal temporal remote sensing image fusion. The proposed model takes both low- and high-resolution remote sensing images at t1 together with low-resolution images at a future time t2 from the same location as inputs and fuses them to generate high-resolution images for the same location at t2. We propose an arithmetic operation applied to the low-resolution images at the two time points in feature space to take care of temporal changes. We evaluated the proposed model on three modality pairs for multimodal temporal image fusion, including downsampled WorldView-2/original WorldView-2, Landsat-8/Sentinel-2, and Sentinel-2/NAIP. Experimental results show that our model outperforms traditional algorithms and recent deep learning-based models by large margins in most scenarios, achieving sharp fused images while appropriately addressing temporal changes.
Remote Monitoring of Mediterranean Hurricanes Using Infrasound
Authors: Listowski Constantino , Forestier Edouard , Dafis Stavros , Farges Thomas , De Carlo Marine , Grimaldi Florian , Le Pichon Alexis , Vergoz Julien , Heinrich Philippe , Claud Chantal
Published: December 2022

Mediterranean hurricanes, or medicanes, are tropical-like cyclones forming once or twice per year over the waters of the Mediterranean Sea. These mesocyclones pose a serious threat to coastal infrastructure and lives because of their strong winds and intense rainfall. Infrasound technology has already been employed to investigate the acoustic signatures of severe weather events, and this study aims at characterizing, for the first time, the infrasound detections that can be related to medicanes. This work also contributes to infrasound source discrimination efforts in the context of the Comprehensive Nuclear-Test-Ban Treaty. We use data from the infrasound station IS48 of the International Monitoring System in Tunisia to investigate the infrasound signatures of mesocyclones using a multi-channel correlation algorithm. We discuss the detections using meteorological fields to assess the presence of stratospheric waveguides favoring propagation. We corroborate the detections by considering other datasets, such as satellite observations, a surface lightning detection network, and products mapping the simulated intensity of the swell. High- and low-frequency detections are evidenced for three medicanes at distances ranging between 250 and 1100 km from the station. Several cases of non-detection are also discussed. While deep convective systems, and mostly lightning within them, seem to be the main source of detections above 1 Hz, hotspots of swell (microbarom) related to the medicanes are evidenced between 0.1 and 0.5 Hz. In the latter case, simulations of microbarom detections are consistent with the observations. Multi-source situations are highlighted, stressing the need for more resilient detection-estimation algorithms. Cloud-to-ground lightning seems not to explain all high-frequency detections, suggesting that additional sources of electrical or dynamical origin may be at play that are related to deep convective systems.
Trend Analysis and Driving Factors of Vegetation Dynamics in Northern China from 1982 to 2015
Authors: Sun Rui , Chen Shaohui , Su Hongbo
Published: December 2022

Under the background of global warming, understanding the dynamic of vegetation plays a key role in revealing the structure and function of an ecosystem. Assessing the impact of climate change and human activities on vegetation dynamics is crucial for policy formulation and ecological protection. Based on the Global Inventory Monitoring and Modeling System (GIMMS) third generation of Normalized Difference Vegetation Index (NDVI3g), meteorological data and land cover data, this study analyzed the linear and nonlinear trends of vegetation in northern China from 1982 to 2015, and quantified the relative impact of climate change and human activities on vegetation change. The results showed that more than 53% of the vegetation had changed significantly, and 36.64% of the vegetation had a reverse trend. There were potential risks of vegetation degradation in the southwestern, northwestern and northeastern parts of the study’s area. The linear analysis method cannot disclose the reversal of the vegetation growth trend, which will underestimate or overestimate the risk of vegetation degradation or restoration. Climate change and human activities promoted 76.54% of the vegetation growth in the study area, with an average contribution rate of 51.22% and 48.78%, respectively, while the average contribution rate to the vegetation degradation area was 47.43% and 52.57%, respectively. Vegetation restoration of grassland and woodland was mainly affected by climate change, and human activities dominated their degradation, while cropland vegetation was opposite. The contribution rate of human activities to vegetation change in the southeastern and eastern parts of the study area was generally higher than that of climate change, but it was the opposite in the high altitude area, with obvious spatial heterogeneity. These results are helpful to understand the dynamic mechanism of vegetation in northern China, and provide a scientific basis for vegetation restoration and protection of regional ecosystems.
Spatial Dynamics and Predictive Analysis of Vegetation Cover in the Ouémé River Delta in Benin (West Africa)
Authors: Osseni Abdel Aziz , Dossou-Yovo Hubert Olivier , Gbesso Gbodja Houéhanou François , Lougbegnon Toussaint Olou , Sinsin Brice
Published: December 2022

The vegetation cover of the Ouémé Delta constitutes a biodiversity hotspot for the wetlands in southern Benin. However, the overexploitation of natural resources in addition to the intensification of agricultural practices led to the degradation of the natural ecosystems in this region. The present work aims to reconstruct, using remote sensing, the spatial dynamics of land use in the Ouémé Delta in order to assess the recent changes and predict the trends in its vegetation cover. The methodology was based on remote sensing and GIS techniques. Altogether, this process helped us carry out the classification of Landsat images for a period of 30 years (stating year 1990, 2005, and 2020) via the Envi software. The spatial statistics resulting from this processing were combined using ArcGIS software to establish the transition matrices in order to monitor the conversion rates of the land cover classes obtained. Then, the prediction of the plant landscape by the year 2035 was performed using the “Land Change Modeler” extension available under IDRISI. The results showed seven (07) classes of occupation and land use. There were agglomerations, mosaics of fields and fallow land, water bodies, dense forests, gallery forests, swamp forests, and shrubby wooded savannahs. The observation of the vegetation cover over the period of 15 years from 1990 to 2005 showed a decrease from 71.55% to 63.42% in the surface area of the Ouémé Delta. A similar trend was noticed from 2005 to 2020 when it reached 55.19%, entailing a loss of 16.37% of the surface area of natural habitats in 30 years. The two drivers of such changes are the fertility of alluvial soils for agriculture along and urbanization. The predictive modeling developed for 2035 reveals a slight increase in the area of dense forests and shrubby wooded savannas, contrary to the lack of significant decrease in the area of gallery forests and swamp forests. This is key information that is expected to be useful to both policy and decision makers involved in the sustainable management and conservation of natural resources in the study area.
Spatial Heterogeneity and Temporal Variation in Urban Surface Albedo Detected by High-Resolution Satellite Data
Authors: Wu Hantian , Huang Bo , Zheng Zhaoju , Ma Zonghan , Zeng Yuan
Published: December 2022

Albedo is one of the key parameters in the surface energy balance and it has been altered due to urban expansion, which has significant impacts on local and regional climate. Many previous studies have demonstrated that changes in the urban surface albedo are strongly related to the city’s heterogeneity and have significant spatial-temporal characteristics but fail to address the albedo of the urban surface as a unique variable in urban thermal environment research. This study selects Beijing as the experimental area for exploring the spatial-temporal characteristics of the urban surface albedo and the albedo’s uniqueness in environmental research on urban spaces. Our results show that the urban surface albedo at high spatial resolution can better represent the urban spatial heterogeneity, seasonal variation, building canyon, and pixel adjacency effects. Urban surface albedo is associated with building density and height, land surface temperature (LST), and fractional vegetation cover (FVC). Furthermore, albedo can reflect livability and environmental rating due to the variances of building materials and architectural formats in the urban development. Hence, we argue that the albedo of the urban surface can be considered as a unique variable for improving the acknowledgment of the urban environment and human livability with wider application in urban environmental research.
X-Band Radar System to Detect Bathymetric Changes at River Mouths during Storm Surges: A Case Study of the Arno River
Authors: Raffa Francesco , Alberico Ines , Serafino Francesco
Published: December 2022

Storm surges are natural events that influence the dispersion of sediment along coasts, leading to sudden morphological changes in the seabed. From this perspective, we focused our study on the analysis of measurements from a mobile X-band radar system to survey the sea state and the changes in the seabed depth during storm surges. This analysis was supported by additional information from Sentinel 2 satellite images, the Gorgona wave buoy, the San Giovanni alla Vena hydrometric station, and an echosounder survey. The survey period was from 26 to 28 February and 3 March 2020. During these days, the simultaneous occurrence of a storm surge and flooding of the Arno River was monitored. The analysis of the marine X-band radar mobile images determined the formation and dismantling of seabed shapes. An elongated shoal and a bar-like shape are visible on the right side of the Arno River in the radar image of 26 February and at the Arno mouth on that of 28 February, respectively. The radar image of 3 March shows, near the mouth of the Arno, a delta shape probably due to the deposition of sediment favoured by the interaction between the river flow and storm waves. X-band coastal radar is a detection system that improves the effectiveness and reliability of coastal monitoring because it has a high temporal and spatial resolution. It can be considered a valuable warning system to monitor the sea-bed depth changes in strategic sites, such as harbour areas, during sea storms. Moreover, this system, together with a satellite observing system, is a valid tool for shedding light on the environmental drivers that reshape coastal areas.
Electrochemical DNA Biosensor Based on Immobilization of a Non-Modified ssDNA Using Phosphoramidate-Bonding Strategy and Pencil Graphite Electrode Modified with AuNPs/CB and Self-Assembled Cysteamine Monolayer
Authors: Moustakim Hamza , Mohammadi Hasna , Amine Aziz
Published: December 2022

The present paper describes an alternative approach to the traditionally used covalent immobilization methods that require cost-intensive and complicated chemistry modification of a single-stranded DNA (ssDNA) capture probe. The low-cost pencil graphite electrode (PGE) modified with carbon black (CB) and gold nanoparticles (AuNPs) was used as an electrochemical platform and the non-modified ssDNA was immobilized on a self-assembled cysteamine modified AuNPs/CB–PGE through a phosphoramidate bond between the 5′-terminal phosphate group of ssDNA and the primary amine group of cysteamine. The microRNA-21 was used as a target model in the fabrication of this electrochemical DNA biosensor and the hybridization process with the complementary probe was monitored by differential pulse voltammetry using methylene blue (MB) as an electrochemical hybridization indicator. The decreased reduction peak current of MB shows a good linear correlation with the increased concentration of microRNA-21 target sequences because the MB signal is determined by the amount of exposed guanine bases. The linear range of the fabricated DNA biosensor was from 1.0 × 10−8 to 5.0 × 10−7 M with a detection limit of 1.0 × 10−9 M. These results show that the covalent immobilization of a non-modified ssDNA capture probe through a phosphoramidate-bonding strategy could serve as a cost-effective and versatile approach for the fabrication of DNA biosensors related to a wide range of applications that cover the fields of medical diagnostic and environmental monitoring. The fabricated electrochemical DNA biosensor was used to analyze microRNA-21 in a (spiked) human serum sample and it showed satisfactory and encouraging results as an electrochemical DNA biosensor platform.
Frame Structure Fault Diagnosis Based on a High-Precision Convolution Neural Network
Authors: Xue Yingfang , Cai Chaozhi , Chi Yaolei
Published: December 2022

Structural health monitoring and fault diagnosis are important scientific issues in mechanical engineering, civil engineering, and other disciplines. The basic premise of structural health work is to be able to accurately diagnose the fault in the structure. Therefore, the accurate fault diagnosis of structure can not only ensure the safe operation of mechanical equipment and the safe use of civil construction, but also ensure the safety of people’s lives and property. In order to improve the accuracy fault diagnosis of frame structure under noise conditions, the existing Convolutional Neural Network with Training Interference (TICNN) model is improved, and a new convolutional neural network model with strong noise resistance is proposed. In order to verify THE superiority of the proposed improved TICNN in anti-noise, comparative experiments are carried out by using TICNN, One Dimensional Convolution Neural Network (1DCNN) and First Layer Wide Convolution Kernel Deep Convolution Neural Network (WDCNN). The experimental results show that the improved TICNN has the best anti-noise ability. Based on the improved TICNN, the fault diagnosis experiment of a four-story steel structure model is carried out. The experimental results show that the improved TICNN can obtain high diagnostic accuracy under strong noise conditions, which verifies the advantages of the improved TICNN.
Inhalation Injury Grading Using Transfer Learning Based on Bronchoscopy Images and Mechanical Ventilation Period
Authors: Li Yifan , Pang Alan W. , Zeitouni Jad , Zeitouni Ferris , Mateja Kirby , Griswold John A. , Chong Jo Woon
Published: December 2022

The abbreviated injury score (AIS) is commonly used as a grading system for inhalation injuries. While inhalation injury grades have inconsistently been shown to correlate positively with the time mechanical ventilation is needed, grading is subjective and relies heavily on the clinicians’ experience and expertise. Additionally, no correlation has been shown between these patients’ inhalation injury grades and outcomes. In this paper, we propose a novel inhalation injury grading method which uses deep learning algorithms in bronchoscopy images to determine the injury grade from the carbonaceous deposits, blistering, and fibrin casts in the bronchoscopy images. The proposed method adopts transfer learning and data augmentation concepts to enhance the accuracy performance to avoid overfitting. We tested our proposed model on the bronchoscopy images acquired from eighteen patients who had suffered inhalation injuries, with the degree of severity 1, 2, 3, 4, 5, or 6. As performance metrics, we consider accuracy, sensitivity, specificity, F-1 score, and precision. Experimental results show that our proposed method, with both transfer learning and data augmentation components, provides an overall 86.11% accuracy. Moreover, the experimental results also show that the performance of the proposed method outperforms the method without transfer learning or data augmentation.
Analytical Modeling and Analysis of Permanent-Magnet Motor with Demagnetization Fault
Authors: Shi Cenwei , Peng Lin , Zhang Zhen , Shi Tingna
Published: December 2022

Factors such as insufficient heat dissipation and excessively high temperature can easily lead to demagnetization of the PMs in permanent-magnet (PM) motors. As a result, the magnetic field distribution of the motor will not be uniform, producing fault harmonics and lowering the operational performance of the motor. An essential stage in the diagnosis of faults and the monitoring of motor condition is the establishment of an accurate model of motors with demagnetization faults. In this paper, demagnetization faults are modeled by changing the Fourier coefficients in the Fourier expansion of the magnetization of PMs. This model can be used to determine the motor performance under various types of demagnetization, including radial air gap flux density, back electromotive force (EMF), and torque. On this basis, the corresponding relationship between the demagnetization degree and the fault signature is established, to provide a theoretical foundation for the subsequent demagnetization fault diagnosis. The finite element analysis (FEA) verifies the effectiveness and superiority of the proposed analytical model. The modeling method proposed in this paper can be applied to PM motors with PMs having different magnetization directions and shapes because it is based on the demagnetization region of PMs.
HyScreen: A Ground-Based Imaging System for High-Resolution Red and Far-Red Solar-Induced Chlorophyll Fluorescence
Authors: Peng Huaiyue , Cendrero-Mateo Maria Pilar , Bendig Juliane , Siegmann Bastian , Acebron Kelvin , Kneer Caspar , Kataja Kari , Muller Onno , Rascher Uwe
Published: December 2022

Solar-induced chlorophyll fluorescence (SIF) is used as a proxy of photosynthetic efficiency. However, interpreting top-of-canopy (TOC) SIF in relation to photosynthesis remains challenging due to the distortion introduced by the canopy’s structural effects (i.e., fluorescence re-absorption, sunlit-shaded leaves, etc.) and sun–canopy–sensor geometry (i.e., direct radiation infilling). Therefore, ground-based, high-spatial-resolution data sets are needed to characterize the described effects and to be able to downscale TOC SIF to the leafs where the photosynthetic processes are taking place. We herein introduce HyScreen, a ground-based push-broom hyperspectral imaging system designed to measure red (F687) and far-red (F760) SIF and vegetation indices from TOC with single-leaf spatial resolution. This paper presents measurement protocols, the data processing chain and a case study of SIF retrieval. Raw data from two imaging sensors were processed to top-of-canopy radiance by dark-current correction, radiometric calibration, and empirical line correction. In the next step, the improved Fraunhofer line descrimination (iFLD) and spectral-fitting method (SFM) were used for SIF retrieval, and vegetation indices were calculated. With the developed protocol and data processing chain, we estimated a signal-to-noise ratio (SNR) between 50 and 200 from reference panels with reflectance from 5% to 95% and noise equivalent radiance (NER) of 0.04 (5%) to 0.18 (95%) mW m−2 sr−1 nm−1. The results from the case study showed that non-vegetation targets had SIF values close to 0 mW m−2 sr−1 nm−1, whereas vegetation targets had a mean F687 of 1.13 and F760 of 1.96 mW m−2 sr−1 nm−1 from the SFM method. HyScreen showed good performance for SIF retrievals at both F687 and F760; nevertheless, we recommend further adaptations to correct for the effects of noise, varying illumination and sensor optics. In conclusion, due to its high spatial resolution, Hyscreen is a promising tool for investigating the relationship between leafs and TOC SIF as well as their relationship with plants’ photosynthetic capacity.
Exploring Machine Learning-Based Fault Monitoring for Polymer-Based Additive Manufacturing: Challenges and Opportunities
Authors: Sampedro Gabriel Avelino R. , Rachmawati Syifa Maliah , Kim Dong-Seong , Lee Jae-Min
Published: December 2022

Three-dimensional printing, often known as additive manufacturing (AM), is a groundbreaking technique that enables rapid prototyping. Monitoring AM delivers benefits, as monitoring print quality can prevent waste and excess material costs. Machine learning is often applied to automating fault detection processes, especially in AM. This paper explores recent research on machine learning-based mechanical fault monitoring systems in fused deposition modeling (FDM). Specifically, various machine learning-based algorithms are applied to measurements extracted from different parts of a 3D printer to diagnose and identify faults. The studies often use mechanical-based fault analysis from data gathered from sensors that measure attitude, acoustic emission, acceleration, and vibration signals. This survey examines what has been achieved and opens up new opportunities for further research in underexplored areas such as SLM-based mechanical fault monitoring.
Estimation of Lens Stray Light with Regard to the Incapacitation of Imaging Sensors—Part 2: Validation
Authors: Schwarz Bastian , Ritt Gunnar , Eberle Bernd
Published: December 2022

Recently, we developed a simple theoretical model for the estimation of the irradiance distribution at the focal plane of commercial off-the-shelf (COTS) camera lenses in case of laser illumination. The purpose of such a model is to predict the incapacitation of imaging sensors when irradiated by laser light. The model is based on closed-form equations that comprise mainly standard parameters of the laser dazzle scenario and those of the main devices involved (laser source, camera lens and imaging sensor). However, the model also includes three non-standard parameters, which describe the scattering of light within the camera lens. In previous work, we have performed measurements to derive these typically unknown scatter parameters for a collection of camera lenses of the Double-Gauss type. In this publication, we compare calculations based on our theoretical model and the measured scatter parameters with the outcome of stray light simulations performed with the optical design software FRED in order to validate the reliability of our theoretical model and of the derived scatter parameters.
BBNSF: Blockchain-Based Novel Secure Framework Using RP2-RSA and ASR-ANN Technique for IoT Enabled Healthcare Systems
Authors: Kumar Mohit , Mukherjee Priya , Verma Sahil , Kavita  , Kaur Maninder , Singh S. , Kobielnik Martyna , Woźniak Marcin , Shafi Jana , Ijaz Muhammad Fazal
Published: December 2022

The wearable healthcare equipment is primarily designed to alert patients of any specific health conditions or to act as a useful tool for treatment or follow-up. With the growth of technologies and connectivity, the security of these devices has become a growing concern. The lack of security awareness amongst novice users and the risk of several intermediary attacks for accessing health information severely endangers the use of IoT-enabled healthcare systems. In this paper, a blockchain-based secure data storage system is proposed along with a user authentication and health status prediction system. Firstly, this work utilizes reversed public-private keys combined Rivest–Shamir–Adleman (RP2-RSA) algorithm for providing security. Secondly, feature selection is completed by employing the correlation factor-induced salp swarm optimization algorithm (CF-SSOA). Finally, health status classification is performed using advanced weight initialization adapted SignReLU activation function-based artificial neural network (ASR-ANN) which classifies the status as normal and abnormal. Meanwhile, the abnormal measures are stored in the corresponding patient blockchain. Here, blockchain technology is used to store medical data securely for further analysis. The proposed model has achieved an accuracy of 95.893% and is validated by comparing it with other baseline techniques. On the security front, the proposed RP2-RSA attains a 96.123% security level.