bluuebunny/crossref_metadata_2025 huggingface dataset:
Some Second-Order σ Schemes Combined with an H1-Galerkin MFE Method for a Nonlinear Distributed-Order Sub-Diffusion Equation
Authors: Hou Yaxin , Wen Cao , Li Hong , Liu Yang , Fang Zhichao , Yang Yining
Published: February 2020

In this article, some high-order time discrete schemes with an H 1 -Galerkin mixed finite element (MFE) method are studied to numerically solve a nonlinear distributed-order sub-diffusion model. Among the considered techniques, the interpolation approximation combined with second-order σ schemes in time is used to approximate the distributed order derivative. The stability and convergence of the scheme are discussed. Some numerical examples are provided to indicate the feasibility and efficiency of our schemes.
On Boundary Layer Expansions for a Singularly Perturbed Problem with Confluent Fuchsian Singularities
Authors: Malek Stephane
Published: February 2020

We consider a family of nonlinear singularly perturbed PDEs whose coefficients involve a logarithmic dependence in time with confluent Fuchsian singularities that unfold an irregular singularity at the origin and rely on a single perturbation parameter. We exhibit two distinguished finite sets of holomorphic solutions, so-called outer and inner solutions, by means of a Laplace transform with special kernel and Fourier integral. We analyze the asymptotic expansions of these solutions relatively to the perturbation parameter and show that they are (at most) of Gevrey order 1 for the first set of solutions and of some Gevrey order that hinges on the unfolding of the irregular singularity for the second.
Computational Modeling and Constructal Design Theory Applied to the Geometric Optimization of Thin Steel Plates with Stiffeners Subjected to Uniform Transverse Load
Authors: Troina Grégori , Cunha Marcelo , Pinto Vinícius , Rocha Luiz , dos Santos Elizaldo , Fragassa Cristiano , Isoldi Liércio
Published: February 2020

Stiffened thin steel plates are structures widely employed in aeronautical, civil, naval, and offshore engineering. Considering a practical application where a transverse uniform load acts on a simply supported stiffened steel plate, an approach associating computational modeling, Constructal Design method, and Exhaustive Search technique was employed aiming to minimize the central deflections of these plates. To do so, a non-stiffened plate was adopted as reference from which all studied stiffened plate’s geometries were originated by the transformation of a certain amount of steel of its thickness into longitudinal and transverse stiffeners. Different values for the stiffeners volume fraction (φ) were analyzed, representing the ratio between the volume of the stiffeners’ material and the total volume of the reference plate. Besides, the number of longitudinal (Nls) and transverse (Nts) stiffeners and the aspect ratio of stiffeners shape (hs/ts, being hs and ts, respectively, the height and thickness of stiffeners) were considered as degrees of freedom. The optimized plates were determined for all studied φ values and showed a deflection reduction of over 90% in comparison with the reference plate. Lastly, the influence of the φ parameter regarding the optimized plates was evaluated defining a configuration with the best structural performance among all analyzed cases.
Femtosecond Laser-Micromachining of Glass Micro-Chip for High Order Harmonic Generation in Gases
Authors: Ciriolo Anna G. , Vázquez Rebeca Martínez , Roversi Alice , Frezzotti Aldo , Vozzi Caterina , Osellame Roberto , Stagira Salvatore
Published: February 2020

We report on the application of femtosecond laser micromachining to the fabrication of complex glass microdevices, for high-order harmonic generation in gas. The three-dimensional capabilities and extreme flexibility of femtosecond laser micromachining allow us to achieve accurate control of gas density inside the micrometer interaction channel. This device gives a considerable increase in harmonics’ generation efficiency if compared with traditional harmonic generation in gas jets. We propose different chip geometries that allow the control of the gas density and driving field intensity inside the interaction channel to achieve quasi phase-matching conditions in the harmonic generation process. We believe that these glass micro-devices will pave the way to future downscaling of high-order harmonic generation beamlines.
Origin of the Photoluminescence of Metal Nanoclusters: From Metal-Centered Emission to Ligand-Centered Emission
Authors: Yang Tai-Qun , Peng Bo , Shan Bing-Qian , Zong Yu-Xin , Jiang Jin-Gang , Wu Peng , Zhang Kun
Published: February 2020

Recently, metal nanoclusters (MNCs) emerged as a new class of luminescent materials and have attracted tremendous interest in the area of luminescence-related applications due to their excellent luminous properties (good photostability, large Stokes shift) and inherent good biocompatibility. However, the origin of photoluminescence (PL) of MNCs is still not fully understood, which has limited their practical application. In this mini-review, focusing on the origin of the photoemission emission of MNCs, we simply review the evolution of luminescent mechanism models of MNCs, from the pure metal-centered quantum confinement mechanics to ligand-centered p band intermediate state (PBIS) model via a transitional ligand-to-metal charge transfer (LMCT or LMMCT) mechanism as a compromise model.
Infants’ Vitamin D Nutritional Status in the First Year of Life in Northern Taiwan
Authors: Chen Chiao-Ming , Mu Shu-Ci , Chen Yi-Ling , Tsai Li-Yi , Kuo Yung-Ting , Cheong In-Mei , Chang Mei-Ling , Li Sing-Chung
Published: February 2020

Vitamin D deficiency (VDD) and insufficiency (VDI) are common among exclusively breastfeeding infants. However, epidemiological evidence for the prevalence of VDD in infants during their first year of life in Taiwan has never been found. This trial determined the prevalence of VDD and VDI and the association between dietary vitamin D and vitamin D nutritional status in Northern Taiwan. A cross-sectional study was conducted on infants who returned to well-baby examinations from October 2012 to January 2014 in three hospitals: Shin Kong Wu Ho-Su Memorial Hospital, Taipei Medical University Hospital, and Shuang Ho Hospital. The specific vitamin D cut-off concentrations for VDD, VDI, and VDS are 25(OH)D3 levels ≤20, 21–29, and ≥30 (ng/mL). Overall, 481 infants’ parents completed a questionnaire comprising questions related to vitamin D nutritional status, including weekly time outdoors, breastfeeding status, anthropometric measurement, and assessment of dietary intake, including milk and complementary food. The results revealed that 197 (41%) and 212 (44%) of infants in their first year of life had VDI and VDD, respectively, by the Endocrine Society guidelines. Breastfed infants had a higher prevalence of VDI (86.1%) than did mixed-fed (51.9%) and formula-fed (38.5%) infants (p < 0.001). The prevalence of VDD was 55.4% in infants aged under six months but increased to 61.6% in infants aged over six months. Infants in the VDI and VDD groups had the same anthropometrics as those in the vitamin D sufficiency (VDS) group. Our results revealed that 25(OH)D3 had a negative correlation with the intact parathyroid hormone (iPTH) when the serum 25(OH)D3 level ≤20 ng/mL (r = −0.21, p = 0.001). The VDS group had a higher total vitamin D intake than did the VDI and VDD groups, which was mainly obtained from infant formula. Our data revealed that dietary vitamin D intake and birth season were major indicators in predicting VDD. Lower dietary vitamin D intake and born in winter and spring significantly increased the odds ratio (OR) for VDI by 1.15 (95% CI 1.09–1.20) and 2.02 (95% CI 1.10–3.70), respectively, and that for VDD by 1.23 (95% CI 1.16–1.31) and 2.37 (95% CI 1.35–4.17) without covariates adjustment, respectively. Furthermore, ORs for VDI and VDD significantly differed after adjustment for covariates. In conclusion, the prevalence of VDI and VDD were high in infants during the first year of life. Breastfeeding infants had difficulty in obtaining sufficient vitamin D from diet. In cases where the amount of sun exposure that is safe and sufficient to improve vitamin D status is unclear, breastfed infants aged below one year old are recommended to be supplemented with vitamin D.
Consumption of Lactose, Other FODMAPs and Diarrhoea during Adjuvant 5-Fluorouracil Chemotherapy for Colorectal Cancer
Authors: Holma Reetta , Laatikainen Reijo , Orell Helena , Joensuu Heikki , Peuhkuri Katri , Poussa Tuija , Korpela Riitta , Österlund Pia
Published: February 2020

Chemotherapy-induced mucosal injury of the small intestine may interfere with the enzymes and transporters responsible for the hydrolysis and absorption of dietary carbohydrates causing diarrhoea, abdominal discomfort and pain. The aim of this study was to investigate the association between the consumption of foods rich in FODMAPs (fermentable oligo-, di- and monosaccharides and polyols) and gastrointestinal symptoms in patients receiving adjuvant therapy for colorectal cancer. The patients (n = 52) filled in a 4-day food diary at baseline and during therapy and kept a symptom diary. The intakes of FODMAP-rich foods were calculated as portions and the intakes were divided into two consumption categories. Patients with high consumption of FODMAP-rich foods had diarrhoea more frequently than those with low consumption (for lactose-rich foods the odds ratio (OR) was 2.63, P = 0.03; and for other FODMAP-rich foods 1.82, P = 0.20). Patients with high consumption of both lactose-rich and other FODMAP-rich foods had an over 4-fold risk of developing diarrhoea as compared to those with low consumption of both (OR, 4.18; P = 0.02). These results were confirmed in multivariate models. Conclusion: Consumption of lactose-rich foods results in an increased risk of diarrhoea during adjuvant therapy for colorectal cancer, especially when the consumption of other FODMAP-rich foods is also high.
Can Remote Sensing Technologies Capture the Extreme Precipitation Event and Its Cascading Hydrological Response? A Case Study of Hurricane Harvey Using EF5 Modeling Framework
Authors: Chen Mengye , Nabih Soumaya , Brauer Noah S. , Gao Shang , Gourley Jonathan J. , Hong Zhen , Kolar Randall L. , Hong Yang
Published: February 2020

A new generation of precipitation measurement products has emerged, and their performances have gained much attention from the scientific community, such as the Multi-Radar Multi-Sensor system (MRMS) from the National Severe Storm Laboratory (NSSL) and the Global Precipitation Measurement Mission (GPM) from the National Aeronautics and Space Administration (NASA). This study statistically evaluated the MRMS and GPM products and investigated their cascading hydrological response in August of 2017, when Hurricane Harvey brought historical and record-breaking precipitation to the Gulf Coast (>1500 mm), causing 107 fatalities along with about USD 125 billion worth of damage. Rain-gauge observations from Harris County Flood Control District (HCFCD) and stream-gauge measurements by the United States Geological Survey (USGS) were used as ground truths to evaluate MRMS, GPM and National Centers for Environmental Prediction (NCEP) gauge-only data by using statistical metrics and hydrological simulations using the Ensemble Framework for Flash Flooding Forecast (EF5) model. The results indicate that remote sensing technologies can accurately detect and estimate the unprecedented precipitation event with their near-real-time products, and all precipitation products produced good hydrological simulations, where the Nash–Sutcliff model efficiency coefficients (NSCE) were close to 0.9 for both the MRMS and GPM products. With the timeliness and seamless coverage of MRMS and GPM, the study also demonstrated the capability and efficiency of the EF5 framework for flash flood modeling over the United States and potentially additional international domains.
Statistical Assessment of Sea-Surface Salinity from SMAP: Arabian Sea, Bay of Bengal and a Promising Red Sea Application
Authors: Menezes Viviane V.
Published: February 2020

Sea-surface salinity (SSS) is an essential climate variable connected to Earth’s hydrological cycle and a dynamical component of ocean circulation, but its variability is not well-understood. Thanks to Argo floats, and the first decade of salinity remote sensing, this is changing. While satellites can retrieve salinity with some confidence, accuracy is regionally dependent and challenging within 500–1000 km offshore. The present work assesses the first four years of the National Aeronautics and Space Administration’s Soil Moisture Active Passive (SMAP) satellite in the North Indian Ocean. SMAP’s improved spatial resolution, better mitigation for radio-frequency interference, and land contamination make it particularly attractive to study coastal areas. Here, regions of interest are the Bay of Bengal, the Arabian Sea, and the extremely salty Red Sea (the last of which has not yet received attention). Six SMAP products, which include Levels 2 and 3 data, were statistically evaluated against in situ measurements collected by a variety of instruments. SMAP reproduced SSS well in both the Arabian Sea and the Bay of Bengal, and surprisingly well in the Red Sea. Correlations there were 0.81–0.93, and the root-mean-square difference was 0.38–0.67 for Level 3 data.
A Multi-Observation Least-Squares Inversion for GNSS-Acoustic Seafloor Positioning
Authors: Sakic Pierre , Ballu Valérie , Royer Jean-Yves
Published: February 2020

Monitoring deformation on the seafloor is a major challenge for modern geodesy and a key to better understanding tectonic processes and assess related hazards. The extension of the geodetic networks offshore can be achieved by combining satellite positioning (GNSS) of a surface platform with acoustic ranging to seafloor transponders. This approach is called GNSS-Acoustic (GNSS-A). The scope of this work is to provide a tool to identify and quantify key points in the error budget of such experiment. For this purpose, we present a least-squares inversion method to determine the absolute position of a seafloor transponder array. Assuming the surface platform is accurately positioned by GNSS, the main observables are the two-way travel time in water between the transponders on the seafloor and the surface platform acoustic head. To better constrain transponder positions, we also consider the baseline lengths and the relative depth-differences between different pairs of them. We illustrate the usefulness of our forward modeling approach and least-square inversion by simulating different experimental protocols (i.e., platform trajectories, with or without information on the distance and depth between transponders). We find that the overall accuracy of a GNSS-A experiment is significantly improved with additional information about the relative depths of the instruments. Baseline lengths also improve the accuracy, but only when combined with depth differences. The codes in Python3 used in this article are freely available online.
Crop-Type Classification for Long-Term Modeling: An Integrated Remote Sensing and Machine Learning Approach
Authors: Momm Henrique G. , ElKadiri Racha , Porter Wesley
Published: February 2020

Long-term temporal and spatial information of crop type supports a wide range of applications including hydrological and climatological studies. In the U.S., yearly crop data layers (CDLs) are available starting in the early 2000s and have been developed using combined field information and sets of temporal imagery from multiple sensors. Development of long-term crop-type layers similar to CDLs is restricted by reduced accessibility to imagery and the necessary auxiliary datasets. In this study, a procedure to generate a historical crop type was developed and evaluated. Time series of Normalized Difference Vegetation Index (NDVI) datasets from Landsat 5 TM sensor for the Lower Bear Creek watershed were collected and processed. Object-based pseudo phenology curves, represented by the NDVI time series, were generated using noise filtering and dimensionality standardization procedures for the years 1985, 1990, 1995, 2000, and 2005. Classifiers were developed and evaluated using random-forest machine learning algorithms and CDL datasets as the reference. Increased generalization performance was obtained when the model was developed using multi-year datasets. This can be attributed to improved crop type representation during the training phase coupled with characterization of yearly variations due to natural (weather) and anthropogenic factors (farming management). Source of uncertainties were the presence of multiple crops within objects, phenological similarities between soybean and corn/maize, and the accuracy of CDL itself. The proposed procedure supports the development of historic crop types for long-term studies at the field scale in agricultural watersheds.
A Cloud Detection Approach Based on Hybrid Multispectral Features with Dynamic Thresholds for GF-1 Remote Sensing Images
Authors: Xiong Quan , Wang Yuan , Liu Diyou , Ye Sijing , Du Zhenbo , Liu Wei , Huang Jianxi , Su Wei , Zhu Dehai , Yao Xiaochuang , Zhang Xiaodong
Published: February 2020

Nowadays, GF-1 (GF is the acronym for GaoFen which means high-resolution in Chinese) remote sensing images are widely utilized in agriculture because of their high spatio-temporal resolution and free availability. However, due to the transferrable rationale of optical satellites, the GF-1 remote sensing images are inevitably impacted by clouds, which leads to a lack of ground object’s information of crop areas and adds noises to research datasets. Therefore, it is crucial to efficiently detect the cloud pixel of GF-1 imagery of crop areas with powerful performance both in time consumption and accuracy when it comes to large-scale agricultural processing and application. To solve the above problems, this paper proposed a cloud detection approach based on hybrid multispectral features (HMF) with dynamic thresholds. This approach combined three spectral features, namely the Normalized Difference Vegetation Index (NDVI), WHITENESS and the Haze-Optimized Transformation (HOT), to detect the cloud pixels, which can take advantage of the hybrid Multispectral Features. Meanwhile, in order to meet the variety of the threshold values in different seasons, a dynamic threshold adjustment method was adopted, which builds a relationship between the features and a solar altitude angle to acquire a group of specific thresholds for an image. With the test of GF-1 remote sensing datasets and comparative trials with Random Forest (RF), the results show that the method proposed in this paper not only has high accuracy, but also has advantages in terms of time consumption. The average accuracy of cloud detection can reach 90.8% and time consumption for each GF-1 imagery can reach to 5 min, which has been reduced by 83.27% compared with RF method. Therefore, the approach presented in this work could serve as a reference for those who are interested in the cloud detection of remote sensing images.
Driving Factors of Land Change in China’s Loess Plateau: Quantification Using Geographically Weighted Regression and Management Implications
Authors: Ren Yanjiao , Lü Yihe , Fu Bojie , Comber Alexis , Li Ting , Hu Jian
Published: February 2020

Land change is a key topic in research on global environmental change, and the restoration of degraded land is the core component of the global Land Degradation Neutrality target under the UN 2030 Agenda for Sustainable Development. In this study, remote-sensing-derived land-use data were used to characterize the land-change processes in China’s Loess Plateau, which is experiencing large-scale ecological restoration. Geographically Weighted Regression was applied to capture the spatiotemporal variations in land change and driving-force relationships. First, we explored land-use change in the Loess Plateau for the period 1990–2015. Grassland, cropland and forestland were dominant land cover in the region, with a total percentage area of 88%. The region experienced dramatic land-use transitions during the study period: degraded grassland and wetland, expansion of cropland and built-up land and weak restoration of forestland during 1990–2000; and increases in grassland, built-up land, forestland and wetland, concurrent with shrinking cropland during 2000–2015. A Geographically Weighted Regression (GWR) analysis revealed altitude to be the common dominant factor associated with the four major land-use types (forestland, grassland, cropland and built-up land). Altitude and slope were found to be positively associated with forestland, while being negatively associated with cropland in the high, steep central region. For both forestland and grassland, temperature and precipitation behaved in a similar manner, with a positive hotspot in the northwest. Altitude, slope and distance to road were all negatively associated with built-up land across the region. The GWR captured the spatial non-stationarity on different socioeconomic driving forces. Spatial heterogeneity and temporal variation of the impact of socioeconomic drivers indicate that the ecological restoration projects positively affected the region’s greening trend with hotspots in the center and west, and also improved farmer well-being. Notably, urban population showed undesired effects, expressed in accelerating grassland degradation in central and western regions for 1990–2000, hindering forestland and grassland restoration in the south during 2000–2015, and highlighting the long-term sustainability of the vegetation restoration progress. Such local results have the potential to provide a methodological contribution (e.g., nesting local-level approaches, i.e., GWR, within land system research) and spatially explicit evidence for context-related and proactive land management (e.g., balancing urbanization and ecological restoration processes and advancing agricultural development and rural welfare improvement).
Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes
Authors: Zeng Zhengxin , Amin Moeness G. , Shan Tao
Published: February 2020

Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man–machine interfaces and smart environments. In this paper, we use the time-series analysis method to accurately measure the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply two machine learning (ML) techniques, namely, the dynamic time warping (DTW) method and the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and characteristics of the time-series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.
A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model
Authors: Cui Yaokui , Chen Xi , Xiong Wentao , He Lian , Lv Feng , Fan Wenjie , Luo Zengliang , Hong Yang
Published: February 2020

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.
Discriminative Feature Learning Constrained Unsupervised Network for Cloud Detection in Remote Sensing Imagery
Authors: Xie Weiying , Yang Jian , Li Yunsong , Lei Jie , Zhong Jiaping , Li Jiaojiao
Published: February 2020

Cloud detection is a significant preprocessing step for increasing the exploitability of remote sensing imagery that faces various levels of difficulty due to the complexity of underlying surfaces, insufficient training data, and redundant information in high-dimensional data. To solve these problems, we propose an unsupervised network for cloud detection (UNCD) on multispectral (MS) and hyperspectral (HS) remote sensing images. The UNCD method enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. The UNCD enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. First, a compact representation of the original imagery is obtained by a latent adversarial learning constrained encoder. Meanwhile, the majority class with sufficient samples (i.e., background pixels) is more accurately reconstructed than the clouds with limited samples by the decoder. An image discriminator is used to prevent the generalization of out-of-class features caused by latent adversarial learning. To further highlight the background information in the deep latent space, a multivariate Gaussian distribution is introduced. In particular, the residual error with clouds highlighted and background samples suppressed is applied in the cloud detection in deep latent space. To evaluate the performance of the proposed UNCD method, experiments were conducted on both MS and HS datasets that were captured by various sensors over various scenes, and the results demonstrate its state-of-the-art performance. The sensors that captured the datasets include Landsat 8, GaoFen-1 (GF-1), and GaoFen-5 (GF-5). Landsat 8 was launched at Vandenberg Air Force Base in California on 11 February 2013, in a mission that was initially known as the Landsat Data Continuity Mission (LDCM). China launched the GF-1 satellite. The GF-5 satellite captures hyperspectral observations in the Chinese Key Projects of High-Resolution Earth Observation System. The overall accuracy (OA) values for Images I and II from the Landsat 8 dataset were 0.9526 and 0.9536, respectively, and the OA values for Images III and IV from the GF-1 wide field of view (WFV) dataset were 0.9957 and 0.9934, respectively. Hence, the proposed method outperformed the other considered methods.
Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images
Authors: Alganci Ugur , Soydas Mehmet , Sertel Elif
Published: February 2020

Object detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution (VHR) satellite images. This article provides a comparative evaluation of the state-of-the-art convolutional neural network (CNN)-based object detection models, which are Faster R-CNN, Single Shot Multi-box Detector (SSD), and You Look Only Once-v3 (YOLO-v3), to cope with the limited number of labeled data and to automatically detect airplanes in VHR satellite images. Data augmentation with rotation, rescaling, and cropping was applied on the test images to artificially increase the number of training data from satellite images. Moreover, a non-maximum suppression algorithm (NMS) was introduced at the end of the SSD and YOLO-v3 flows to get rid of the multiple detection occurrences near each detected object in the overlapping areas. The trained networks were applied to five independent VHR test images that cover airports and their surroundings to evaluate their performance objectively. Accuracy assessment results of the test regions proved that Faster R-CNN architecture provided the highest accuracy according to the F1 scores, average precision (AP) metrics, and visual inspection of the results. The YOLO-v3 ranked as second, with a slightly lower performance but providing a balanced trade-off between accuracy and speed. The SSD provided the lowest detection performance, but it was better in object localization. The results were also evaluated in terms of the object size and detection accuracy manner, which proved that large- and medium-sized airplanes were detected with higher accuracy.
Structure Label Matrix Completion for PolSAR Image Classification
Authors: Wu Qian , Hou Biao , Wen Zaidao , Ren Zhongle , Ren Bo , Jiao Licheng
Published: February 2020

Terrain classification is a hot topic in polarimetric synthetic aperture radar (PolSAR) image interpretation that aims at assigning a label to every pixel and forms a label matrix for a PolSAR image. From the perspective of human interpretation, classification is not in terms of pixels, but decomposed perceptual groups and structures. Therefore, a new perspective of label matrix completion is proposed to treat the classification task as a label matrix inversion process. Firstly, a matrix completion framework is built to avoid processing the large-scale PolSAR image data for traditional feature and classifier learning. Secondly, a light network is used to obtain the known labels for the complete task by uniform down-sampling of the entire image that aims to keep the shape of regions in a PolSAR image and reduce the computational complexity. Finally, the zeroth- and first-order label information is proposed as the prior distribution for label matrix completion to keep the structure and texture. The experiments are tested on real PolSAR images, which demonstrates that the proposed method can realize excellent classification results with less computation time and labeled pixels. Classification results with different down-sampling rates in the light network also prove the robustness of this method.
Wind Farm Wakes from SAR and Doppler Radar
Authors: Ahsbahs Tobias , Nygaard Nicolai Gayle , Newcombe Alexander , Badger Merete
Published: February 2020

We retrieve atmospheric wake characteristics at the wind farm Westermost Rough from Sentinel-1 Synthetic Aperture Radar (SAR) images. For the first time, co-located reference measurements of the full flow field around the wind farm are available from Doppler radars. One case with a reference measurement of up to 10 km downstream of the wind farms shows that SAR images depict the wake better close to the wind farm than further downstream. The comparison of two cases with similar wind speed and direction indicate that under unstable atmospheric stratification, we can retrieve the structure of the wake field close to the wind farm from SAR, while this was not possible for a case with stable stratification. We find that openly available Sentinel-1 image archives can be used to study the structure of wind farm wakes depending on the atmospheric stability conditions. From an average of twelve available co-located cases, we find that velocity deficits at the wind turbine hub height are 8% from Doppler radar measurements and 4% from SAR wind retrievals.
Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition
Authors: Liu Shuang , Li Mei , Zhang Zhong , Xiao Baihua , Durrani Tariq S.
Published: February 2020

In recent times, deep neural networks have drawn much attention in ground-based cloud recognition. Yet such kind of approaches simply center upon learning global features from visual information, which causes incomplete representations for ground-based clouds. In this paper, we propose a novel method named multi-evidence and multi-modal fusion network (MMFN) for ground-based cloud recognition, which could learn extended cloud information by fusing heterogeneous features in a unified framework. Namely, MMFN exploits multiple pieces of evidence, i.e., global and local visual features, from ground-based cloud images using the main network and the attentive network. In the attentive network, local visual features are extracted from attentive maps which are obtained by refining salient patterns from convolutional activation maps. Meanwhile, the multi-modal network in MMFN learns multi-modal features for ground-based cloud. To fully fuse the multi-modal and multi-evidence visual features, we design two fusion layers in MMFN to incorporate multi-modal features with global and local visual features, respectively. Furthermore, we release the first multi-modal ground-based cloud dataset named MGCD which not only contains the ground-based cloud images but also contains the multi-modal information corresponding to each cloud image. The MMFN is evaluated on MGCD and achieves a classification accuracy of 88.63% comparative to the state-of-the-art methods, which validates its effectiveness for ground-based cloud recognition.
Rapid Identification and Prediction of Cadmium-Lead Cross-Stress of Different Stress Levels in Rice Canopy Based on Visible and Near-Infrared Spectroscopy
Authors: Zhang Shuangyin , Li Jun , Wang Siying , Huang Yingjing , Li Yizhuo , Chen Yiyun , Fei Teng
Published: February 2020

Accurate detection of cadmium (Cd) and lead (Pb)-induced cross-stress on crops is essential for agricultural, ecological environment, and food security. The feasibility to diagnose and predict Cd–Pb cross-stress in agricultural soil was explored by measuring the visible and near-infrared reflectance of rice leaves. In this study, two models were developed—namely a diagnostic model and a prediction model. The diagnostic model was established based on visible and near-infrared reflectance spectroscopy (VNIRS) datasets with Support Vector Machine (SVM), followed by leave-one-out cross-validation (LOOCV). A partial least-squares (PLS) regression, as the prediction model was employed to predict the foliar concentration of Cd and Pb contents. To accurately calibrate the two models, a rigorous greenhouse experiment was designed and implemented, with 4 levels of treatments on each of the Cd and Pb stress on rice. Results show that with the appropriate pre-processing, the diagnostic model can identify 79% of Cd and 85% of Pb stress of any levels. The significant bands that have been used mainly distributed between 681–776 nm and 1224–1349 nm for Cd stress and 712–784 nm for Pb stress. The prediction model can estimate Cd with coefficient of determination of 0.7, but failed to predict Pb accurately. The results illustrated the feasibility to diagnose Cd stress accurately by measuring the visible and near-infrared reflectance of rice canopy in a cross-contamination soil environment. This study serves as one step forward to heavy metal pollutant detection in a farmland environment.
Recent Accelerating Glacier Mass Loss of the Geladandong Mountain, Inner Tibetan Plateau, Estimated from ZiYuan-3 and TanDEM-X Measurements
Authors: Liu Lin , Jiang Liming , Zhang Zhimin , Wang Hansheng , Ding Xiaoli
Published: February 2020

The headwaters of many Asian rivers are at mountain glaciers of the Tibetan Plateau. Glacier melt-water is a non-negligible contributor of river runoff, especially for a drought year. However, the observation of mass glacier changes was scarce in recent years. Here, we estimated the recent glacier mass change of the Geladandong mountain, by differencing the digital elevation models (DEMs) produced from ZiYuan-3 images and TanDEM-X data. Moreover, we compared the SRTM-C DEM with TanDEM-X DEMs to retrieve glacier mass balances since 2000. The annual mass loss rates of −0.11 ± 0.03 and −0.47 ± 0.09 m w.e. yr−1 were derived in 2000–2012 and 2012−2018, respectively. This result revealed an accelerating rate of negative glacier mass changes during recent years, which is mainly caused by the significant increase of mass loss over non-surge glaciers, rather than surge-type glaciers, which held a slight increase of mass loss. In addition, we found a pronounced discrepancy of glacier mass change between non-surge and surge-type glaciers during 2012−2018, and suggested that this difference may be caused by the heterogeneous responses of surge-type glaciers to climate variations, because of the different timing and type of surge events.
Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran
Authors: Arabameri Alireza , Saha Sunil , Roy Jagabandhu , Chen Wei , Blaschke Thomas , Tien Bui Dieu
Published: February 2020

This analysis aims to generate landslide susceptibility maps (LSMs) using various machine learning methods, namely random forest (RF), alternative decision tree (ADTree) and Fisher’s Linear Discriminant Function (FLDA). The results of the FLDA, RF and ADTree models were compared with regard to their applicability for creating an LSM of the Gallicash river watershed in the northern part of Iran close to the Caspian Sea. A landslide inventory map was created using GPS points obtained in a field analysis, high-resolution satellite images, topographic maps and historical records. A total of 249 landslide sites have been identified to date and were used in this study to model and validate the LSMs of the study region. Of the 249 landslide locations, 70% were used as training data and 30% for the validation of the resulting LSMs. Sixteen factors related to topographical, hydrological, soil type, geological and environmental conditions were used and a multi-collinearity test of the landslide conditioning factors (LCFs) was performed. Using the natural break method (NBM) in a geographic information system (GIS), the LSMs generated by the RF, FLDA, and ADTree models were categorized into five classes, namely very low, low, medium, high and very high landslide susceptibility (LS) zones. The very high susceptibility zones cover 15.37% (ADTree), 16.10% (FLDA) and 11.36% (RF) of the total catchment area. The results of the different models (FLDA, RF, and ADTree) were explained and compared using the area under receiver operating characteristics (AUROC) curve, seed cell area index (SCAI), efficiency and true skill statistic (TSS). The accuracy of models was calculated considering both the training and validation data. The results revealed that the AUROC success rates are 0.89 (ADTree), 0.92 (FLDA) and 0.97 (RF) and predication rates are 0.82 (ADTree), 0.79 (FLDA) and 0.98 (RF), which justifies the approach and indicates a reasonably good landslide prediction. The results of the SCAI, efficiency and TSS methods showed that all models have an excellent modeling capability. In a comparison of the models, the RF model outperforms the boosted regression tree (BRT) and ADTree models. The results of the landslide susceptibility modeling could be useful for land-use planning and decision-makers, for managing and controlling the current and future landslides, as well as for the protection of society and the ecosystem.
A Healthy Park Needs Healthy Vegetation: The Story of Gorongosa National Park in the 21st Century
Authors: Herrero Hannah , Waylen Peter , Southworth Jane , Khatami Reza , Yang Di , Child Brian
Published: February 2020

Understanding trends or changes in biomass and biodiversity around conservation areas in Africa is important and has economic and societal impacts on the surrounding communities. Gorongosa National Park, Mozambique was established under unique conditions due to its complex history. In this study, we used a time-series of Normalized Difference Vegetation Index (NDVI) to explore seasonal trends in biomass between 2000 and 2016. In addition, vegetation directional persistence was created. This product is derived from the seasonal NDVI time series-based analysis and represents the accumulation of directional change in NDVI relative to a fixed benchmark (2000–2004). Trends in precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) was explored from 2000–2016. Different vegetation covers are also considered across various landscapes, including a comparison between the Lower Gorongosa (savanna), Mount Gorongosa (rainforest), and surrounding buffer zones. Important findings include a decline in precipitation over the time of study, which most likely drives the observed decrease in NDVI. In terms of vegetation persistence, Lower Gorongosa had stronger positive trends than the buffer zone, and Mount Gorongosa had higher negative persistence overall. Directional persistence also varied by vegetation type. These are valuable findings for park managers and conservationists across the world.
Detecting Frost Stress in Wheat: A Controlled Environment Hyperspectral Study on Wheat Plant Components and Implications for Multispectral Field Sensing
Authors: Murphy Mary E. , Boruff Bryan , Callow J. Nikolaus , Flower Ken C.
Published: February 2020

Radiant frost during the reproductive stage of plant growth can result in considerable wheat (Triticum aestivum L.) yield loss. Much effort has been spent to prevent and manage these losses, including post-frost remote sensing of damage. This study was done under controlled conditions to examine the effect of imposed frost stress on the spectral response of wheat plant components (heads and flag leaves). The approach used hyperspectral profiling to determine whether changes in wheat components were evident immediately after a frost (up to 5 days after frosting (DAF)). Significant differences were found between frost treatments, irrespective of DAF, in the Blue/Green (419–512 nanometers (nm)), Red (610–675 nm) and Near Infrared (NIR; 749–889 nm) regions of the electromagnetic spectrum (EMS) in head spectra, and in the Blue (415–494 nm), Red (670–687 nm) and NIR (727–889 nm) regions in the leaf spectra. Significant differences were found for an interaction between time and frost treatment in the Green (544–575 nm) and NIR (756–889 nm) in head spectra, and in the UV (394–396 nm) and Green/Red (564–641 nm) in leaf spectra. These findings were compared with spectral and temporal resolutions of commonly used field agricultural multispectral sensors to examine their potential suitability for frost damage studies at the canopy scale, based on the correspondence of their multispectral bands to the results from this laboratory-based hyperspectral study.
Bidirectional Segmented Detection of Land Use Change Based on Object-Level Multivariate Time Series
Authors: Hao Yuzhu , Chen Zhenjie , Huang Qiuhao , Li Feixue , Wang Beibei , Ma Lei
Published: February 2020

High-precision information regarding the location, time, and type of land use change is integral to understanding global changes. Time series (TS) analysis of remote sensing images is a powerful method for land use change detection. To address the complexity of sample selection and the salt-and-pepper noise of pixels, we propose a bidirectional segmented detection (BSD) method based on object-level, multivariate TS, that detects the type and time of land use change from Landsat images. In the proposed method, based on the multiresolution segmentation of objects, three dimensions of object-level TS are constructed using the median of the following indices: the normalized difference vegetation index (NDVI), the normalized difference built index (NDBI), and the modified normalized difference water index (MNDWI). Then, BSD with forward and backward detection is performed on the segmented objects to identify the types and times of land use change. Experimental results indicate that the proposed BSD method effectively detects the type and time of land use change with an overall accuracy of 90.49% and a Kappa coefficient of 0.86. It was also observed that the median value of a segmented object is more representative than the commonly used mean value. In addition, compared with traditional methods such as LandTrendr, the proposed method is competitive in terms of time efficiency and accuracy. Thus, the BSD method can promote efficient and accurate land use change detection.
Oil Spill Discrimination by Using General Compact Polarimetric SAR Features
Authors: Yin Junjun , Yang Jian , Zhou Liangjiang , Xu Liying
Published: February 2020

Ocean surveillance is one of the important applications of synthetic aperture radar (SAR). Polarimetric SAR provides multi-channel information and shows great potential for monitoring ocean dynamic environments. Oil spills are a form of pollution that can seriously affect the marine ecosystem. Dual-polarimetric SAR systems are usually used for routine ocean surface monitoring. The hybrid dual-pol SAR imaging mode, known as compact polarimetry, can provide more information than the conventional dual-pol imaging modes. However, backscatter measurements of the hybrid dual-pol mode depend on the transmit wave polarization, which results in lacking consistent interpretation for various compact polarimetric (CP) images. In this study, we will explore the capability of different CP modes for oil spill detection and discrimination. Firstly, we introduce the general CP formalism method to formulate an arbitrary CP backscattered wave, such that the target scattering vector is characterized in the same framework for all CP modes. Then, a recently proposed CP decomposition method is investigated to reveal the backscattering properties of oil spills and their look-alikes. Both intensity and polarimetric features are studied to analyze the optimal CP mode for oil spill observation. Spaceborne polarimetric SAR data sets collected over natural oil slicks and experimental biogenic slicks are used to demonstrate the capability of the general CP mode for ocean surface surveillance.