bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Kaolin Flocculation in Seawater by Optical Backscattering Measurements: Effect of Flocculant Management and Liquor Conditions
Authors: Jeldres Ricardo I. , Jeldres Matías , MacIver Michael R. , Pawlik Marek , Robles Pedro , Toro Norman
Published: April 2020

Optical backscattering (OBS) signal values were used to evaluate the flocculation of kaolin slurries and seek the implications of using seawater. Two anionic flocculants were applied to kaolin suspensions at several dosages and in water of varying pH and electrolyte concentration. An OBS height scan method was used to estimate the degree of aggregation, supernatant quality, and solids concentration of the sediments. The residual solids of the supernatant depended of the extent of particle coagulation before flocculant was added, where the pH and salinity displayed a significant impact on flocculation. The OBS results were highly sensitive to the presence of fine particles, which was estimated in parallel from the focused beam reflectance measurements (FBRM.) In seawater, without flocculant added, the samples had increased root-mean-square scattering (Frms) values and larger final sediment volume than samples prepared in water with lower electrolyte concentration. This indicates a higher initial state of aggregation of the particles in seawater. Then, the aggregation degree was best linked to the square-weighted chord length distribution of the FBRM data, which intensifies the sensitivity to coarse aggregates.
Trace-Element and Pb Isotope Evidence on Extracting Sulfides from Potassic Melts beneath Longmenshan and Molabushan Volcanoes, Wudalianchi, Northeast China
Authors: Rasskazov Sergei , Sun Yi-Min , Chuvashova Irina , Yasnygina Tatyana , Yang Chen , Xie Zhenhua , Saranina Elena , Gerasimov Nikolay , Vladimirova Tatyana
Published: April 2020

In the Wudalianchi volcanic field, eruptions started with low-Mg potassic lava flows 2.5–2.0 Ma ago and later changed to both low- and moderate-Mg potassic compositions. Volcanic rocks from the Molabushan and Longmenshan volcanoes record an unusually wide range of Pb abundances (from 3.7 ppm to 21 ppm relative to predominant range of 10–15 ppm). To determine the cause of these, we performed a comparative trace-element and Pb isotope study of rocks from these volcanoes and older lava flows. On a uranogenic lead diagram, older low-Mg lavas from lithospheric mantle sources plot on a secondary isochron with a slope corresponding to an age of 1.88 Ga. This contrasts with moderate-Mg volcanic rocks from the Molabushan cone, interpreted to have been derived from a recent convective mantle source, which define a flat linear pattern. Low-Mg rocks from the Molabushan flow have lead isotopic compositions that indicate mixed Gelaqiu and Molabu sources. Relative to rocks from the Molabushan cone, moderate-Mg lavas and slags from the East Longmenshan volcano have modified compositions characterized by Pb, S, and Ni abundances, Ni/Co, Ni/MgO ratios as well as 206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb, Ce/Pb, Th/Pb, and U/Pb ratios. We infer that the older Wudalianchi magmas were likely derived from a Paleoproterozoic lithospheric fragment, related to the evolved primordial mantle, and that later magmas were generated in the convecting mantle. These were influenced by segregation of small amounts of sulfides.
Reactive Pulsed Laser Deposition of Clustered-Type MoSx (x ~ 2, 3, and 4) Films and Their Solid Lubricant Properties at Low Temperature
Authors: Fominski V. , Demin M. , Nevolin V. , Fominski D. , Romanov R. , Gritskevich M. , Smirnov N.
Published: April 2020

We studied the tribological properties of amorphous molybdenum sulfide (MoSx) thin-film coatings during sliding friction in an oxidizing environment at a low temperature (−100 °C). To obtain films with different sulfur contents (x ~ 2, 3, and 4), we used reactive pulsed laser deposition, where laser ablation of the Mo target was performed in H2S at various pressures. The lowest coefficient of friction (0.08) was observed during tribo-testing of the MoS3 coating. This coating had good ductility and low wear; the wear of a steel counterbody was minimal. The MoS2 coating had the best wear resistance, due to the tribo-film adhering well to the coating in the wear track. Tribo-modification of the MoS2 coating, however, caused a higher coefficient of friction (0.16) and the most intensive wear of the counterbody. The MoS4 coating had inferior tribological properties. This study explored the mechanisms of possible tribo-chemical changes and structural rearrangements in MoSx coatings upon contact with a counterbody when exposed to oxygen and water. The properties of the tribo-film and the efficiency of its transfer onto the coating and/or the counterbody largely depended on local atomic packing of the nanoclusters that formed the structure of the amorphous MoSx films.
Osteoblastic Differentiation on Graphene Oxide-Functionalized Titanium Surfaces: An In Vitro Study
Authors: Di Carlo Roberta , Di Crescenzo Antonello , Pilato Serena , Ventrella Alessia , Piattelli Adriano , Recinella Lucia , Chiavaroli Annalisa , Giordani Silvia , Baldrighi Michele , Camisasca Adalberto , Zavan Barbara , Falconi Mirella , Cataldi Amelia , Fontana Antonella , Zara Susi
Published: April 2020

Background: Titanium implant surfaces are continuously modified to improve biocompatibility and to promote osteointegration. Graphene oxide (GO) has been successfully used to ameliorate biomaterial performances, in terms of implant integration with host tissue. The aim of this study is to evaluate the Dental Pulp Stem Cells (DPSCs) viability, cytotoxic response, and osteogenic differentiation capability in the presence of GO-coated titanium surfaces. Methods: Two titanium discs types, machined (control, Crtl) and sandblasted and acid-etched (test, Test) discs, were covalently functionalized with GO. The ability of the GO-functionalized substrates to allow the proliferation and differentiation of DPSCs, as well as their cytotoxic potential, were assessed. Results: The functionalization procedures provide a homogeneous coating with GO of the titanium surface in both control and test substrates, with unchanged surface roughness with respect to the untreated surfaces. All samples show the deposition of extracellular matrix, more pronounced in the test and GO-functionalized test discs. GO-functionalized test samples evidenced a significant viability, with no cytotoxic response and a remarkable early stage proliferation of DPSCs cells, followed by their successful differentiation into osteoblasts. Conclusions: The described protocol of GO-functionalization provides a novel not cytotoxic biomaterial that is able to stimulate cell viability and that better and more quickly induces osteogenic differentiation with respect to simple titanium discs. Our findings pave the way to exploit this GO-functionalization protocol for the production of novel dental implant materials that display improved integration with the host tissue.
TGF: A New MATLAB-based Software for Terrain-related Gravity Field Calculations
Authors: Yang Meng , Hirt Christian , Pail Roland
Published: April 2020

With knowledge of geometry and density-distribution of topography, the residual terrain modelling (RTM) technique has been broadly applied in geodesy and geophysics for the determination of the high-frequency gravity field signals. Depending on the size of investigation areas, challenges in computational efficiency are encountered when using an ultra-high-resolution digital elevation model (DEM) in the Newtonian integration. For efficient and accurate gravity forward modelling in the spatial domain, we developed a new MATLAB-based program called, terrain gravity field (TGF). Our new software is capable of calculating the gravity field generated by an arbitrary topographic mass-density distribution. Depending on the attenuation character of gravity field with distance, the adaptive algorithm divides the integration masses into four zones, and adaptively combines four types of geometries (i.e., polyhedron, prism, tesseroid and point-mass) and DEMs with different spatial resolutions. Compared to some publicly available algorithms depending on one type of geometric approximation, this enables accurate modelling of gravity field and greatly reduces the computation time. Besides, the TGF software allows to calculate ten independent gravity field functionals, supports two types of density inputs (constant density value and digital density map), and considers the curvature of the Earth by involving spherical approximation and ellipsoidal approximation. Further to this, the TGF software is also capable of delivering the gravity field of full-scale topographic gravity field implied by masses between the Earth’s surface and mean sea level. In this contribution, the TGF software is introduced to the geoscience community and its capabilities are explained. Results from internal and external numerical validation experiments of TGF confirmed its accuracy at the sub-mGal level. Based on TGF, the trade-off between accuracy and efficiency, values for the spatial resolution and extension of topography models are recommended. The TGF software has been extensively tested and recently been applied in the SRTM2gravity project to convert the global 3” SRTM topography to implied gravity effects at 28 billion computation points. This confirms the capability of TGF for dealing with large datasets. Together with this paper, the TGF software will be released in the public domain for free use in geodetic and geophysical forward modelling computations.
Feasibility of Estimating Turbulent Heat Fluxes via Variational Assimilation of Reference-Level Air Temperature and Specific Humidity Observations
Authors: Tajfar Elahe , Bateni Sayed M. , Heggy Essam , Xu Tongren
Published: April 2020

This study investigated the feasibility of partitioning the available energy between sensible (H) and latent (LE) heat fluxes via variational assimilation of reference-level air temperature and specific humidity. For this purpose, sequences of reference-level air temperature and specific humidity were assimilated into an atmospheric boundary layer model (ABL) within a variational data assimilation (VDA) framework to estimate H and LE. The VDA approach was tested at six sites (namely, Arou, Audubon, Bondville, Brookings, Desert, and Willow Creek) with contrasting climatic and vegetative conditions. The unknowns of the VDA system were the neutral bulk heat transfer coefficient (CHN) and evaporative fraction (EF). EF estimates were found to agree well with observations in terms of magnitude and day-to-day fluctuations in wet/densely vegetated sites but degraded in dry/sparsely vegetated sites. Similarly, in wet/densely vegetated sites, the variations in the CHN estimates were found to be consistent with those of the leaf area index (LAI) while this consistency deteriorated in dry/sparely vegetated sites. The root mean square errors (RMSEs) of daily H and LE estimates at the Arou site (wet) were 25.43 (Wm−2) and 55.81 (Wm−2), which are respectively 57.6% and 45.4% smaller than those of 60.00 (Wm−2) and 102.21 (Wm−2) at the Desert site (dry). Overall, the results show that the VDA system performs well at wet/densely vegetated sites (e.g., Arou and Willow Creek), but its performance degrades at dry/slightly vegetated sites (e.g., Desert and Audubon). These outcomes show that the sequences of reference-level air temperature and specific humidity have more information on the partitioning of available energy between the sensible and latent heat fluxes in wet/densely vegetated sites than dry/slightly vegetated sites.
Prediction of Individual Tree Diameter Using a Nonlinear Mixed-Effects Modeling Approach and Airborne LiDAR Data
Authors: Fu Liyong , Duan Guangshuang , Ye Qiaolin , Meng Xiang , Luo Peng , Sharma Ram P. , Sun Hua , Wang Guangxing , Liu Qingwang
Published: April 2020

Rapidly advancing airborne laser scanning technology has become greatly useful to estimate tree- and stand-level variables at a large scale using high spatial resolution data. Compared with that of ground measurements, the accuracy of the inferred information of diameter at breast height (DBH) from a remotely sensed database and the models developed with traditional regression approaches (e.g., ordinary least square regression) may not be sufficient. Thus, this regression approach is no longer appropriate to develop accurate models and predict DBH from remotely sensed-related variables because DBH is subject to the random effects of forest stands. This study developed a generalized nonlinear mixed-effects DBH estimation model from remotely sensed imagery data. The light detection and ranging (LiDAR)-derived stand canopy density, crown projection area, and tree height were used as predictors in the DBH estimation model. These variables can be more readily measured over an extensive forest area with higher accuracy compared to the conventional field-based methods. The airborne LiDAR data for a total of 402 Picea crassifolia Kom trees on a sample plot that were divided into 16 sub-sample plots and located in the most important distribution region of western China were used. The leave-one sub-sample plot-out cross-validation method was applied to evaluate the model’s prediction accuracy. The results indicated that the random effects of the sub-sample plot on the prediction of DBH were large and their inclusion into the DBH model significantly improved the prediction accuracy. The prediction accuracy of the proposed model at the mean (M) response was also substantially improved relative to the accuracy obtained from the base model. Among several tree selection alternatives evaluated, a sample size of the two largest trees per sub-sample plot used in estimating the random effects showed a significantly higher accuracy compared to other sampling alternatives. This sample size would balance both the measurement cost and potential prediction errors. The nonlinear mixed-effects DBH estimation model at the M response can also be applied if obtaining the estimates of individual tree DBH with a relatively lower cost, and a lower prediction accuracy was the purpose of the study.
Seismo-Deformation Anomalies Associated with the M6.1 Ludian Earthquake on August 3, 2014
Authors: Chen Chieh-Hung , Su Xiaoning , Cheng Kai-Chien , Meng Guojie , Wen Strong , Han Peng
Published: April 2020

A time-frequency method retrieving the acceleration changes in the terminal stage of theM6.1 Ludian earthquake in China is discussed in this article. The non-linear, non-stationaryseismo-demformation was obtained by using the Hilbert–Huang transform and followed by aband-pass filter. We found that the temporal evolution of the residual GNSS-derived orientationexhibits a unique disorder-alignment-disorder sequence days before the earthquake whichcorresponds well with the four stages of an earthquake: elastic strain buildup, crack developments,deformation, and the terminal stage of material failure. The disordering orientations are graduallyaligned with a common direction a few days before the terminal stage. This common direction isconsistent with the most compressive axis derived from the seismological method. In addition, theregion of the stress accumulation, as identified by the size of the disordered orientation, isgenerally consistent with the earthquake preparation zones estimated by using numerical models.
Correction: Koga, Y., et al. A Method for Vehicle Detection in High-Resolution Satellite Images That Uses a Region-Based Object Detector and Unsupervised Domain Adaptation. Remote Sensing 2020, 12, 575
Authors: Koga Yohei , Miyazaki Hiroyuki , Shibasaki Ryosuke
Published: April 2020

The authors wish to make the following corrections to this paper [...]
Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks
Authors: Nezami Somayeh , Khoramshahi Ehsan , Nevalainen Olli , Pölönen Ilkka , Honkavaara Eija
Published: April 2020

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, Red-Green-Blue (RGB) channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.
The Quality Control and Rain Rate Estimation for the X-Band Dual-Polarization Radar: A Study of Propagation of Uncertainty
Authors: Wen Guang , Fox Neil I. , Market Patrick S.
Published: April 2020

In this study, the specific differential phase ( K d p ) is applied to attenuation correction for radar reflectivity Z H and differential reflectivity Z D R , and then the corrected Z H , Z D R , and K d p are studied in the rain rate (R) estimation at the X-band. The statistical uncertainties of Z H , Z D R , and R are propagated from the uncertainty of K d p , leading to variability in their error characteristics. For the attenuation correction, a differential phase shift ( Φ d p )-based method is adopted, while the statistical uncertainties σ ( Z H ) and σ ( Z D R ) are related to σ ( K d p ) via the relations of K d p -specific attenuation ( A H ) and K d p -specific differential attenuation ( A D P ), respectively. For the rain rate estimation, the rain rates are retrieved by the power-law relations of R ( K d p ) , R ( Z h ) , R ( Z h , Z d r ) , and R ( Z h , Z d r , K d p ) . The statistical uncertainty σ ( R ) is propagated from Z H , Z D R , and K d p via the Taylor series expansion of the power-law relations. A composite method is then proposed to reduce the σ ( R ) effect. When compared to the existing algorithms, the composite method yields the best performance in terms of root mean square error and Pearson correlation coefficient, but shows slightly worse normalized bias than R ( K d p ) and R ( Z h , Z d r , K d p ) . The attenuation correction and rain rate estimation are evaluated by analyzing a squall line event and a prolonged rain event. It is clear that Z H , Z D R , and K d p show the storm structure consistent with the theoretical model, while the statistical uncertainties σ ( Z H ) , σ ( Z D R ) and σ ( K d p ) are increased in the transition region. The scatterplots of Z H , Z D R , and K d p agree with the self-consistency relations at X-band, indicating a fairly good performance. The rain rate estimation algorithms are also evaluated by the time-series of the prolonged rain event, yielding strong correlations between the composite method and rain gauge data. In addition, the statistical uncertainty σ ( R ) is propagated from Z H , Z D R , and K d p , showing higher uncertainty when the large gradient presents.
An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery
Authors: Pyo JongCheol , Duan Hongtao , Ligaray Mayzonee , Kim Minjeong , Baek Sangsoo , Kwon Yong Sung , Lee Hyuk , Kang Taegu , Kim Kyunghyun , Cha YoonKyung , Cho Kyung Hwa
Published: April 2020

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash–Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application.
Deciphering Circular Anthropogenic Anomalies in PALSAR Data—Using L-Band SAR for Analyzing Archaeological Features on the Steppe
Authors: Caspari Gino , Blochin Jegor , Sadykov Timur , Balz Timo
Published: April 2020

Synthetic aperture radar has been employed for archaeological purposes for nearly forty years: nonetheless, its application among archaeological practitioners has remained limited. We analyzed circular anthropogenic anomalies in a steppe environment in PALSAR-2 data, which appeared as a homogeneous group of signatures. Each anomaly was examined using additional SAR and optical data, as well as investigated through extensive ground truth and, in one case, excavation. We found the anomalies to originate from a wide range of processes and structural characteristics showing the non-intuitive complexity of SAR data interpretation. We found that this is likely the reason for the limited application SAR has seen within the archaeological community. In order to improve the usage of SAR for archaeological purposes beyond change detection and digital elevation models, specific products that are more readily understandable and superior to optical data in a narrow frame of application should be developed.
Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging
Authors: Xu Jianhui , Zhang Feifei , Jiang Hao , Hu Hongda , Zhong Kaiwen , Jing Wenlong , Yang Ji , Jia Binghao
Published: April 2020

Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.
Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images
Authors: Zhang Weixing , Liljedahl Anna K. , Kanevskiy Mikhail , Epstein Howard E. , Jones Benjamin M. , Jorgenson M. Torre , Kent Kelcy
Published: April 2020

State-of-the-art deep learning technology has been successfully applied to relatively small selected areas of very high spatial resolution (0.15 and 0.25 m) optical aerial imagery acquired by a fixed-wing aircraft to automatically characterize ice-wedge polygons (IWPs) in the Arctic tundra. However, any mapping of IWPs at regional to continental scales requires images acquired on different sensor platforms (particularly satellite) and a refined understanding of the performance stability of the method across sensor platforms through reliable evaluation assessments. In this study, we examined the transferability of a deep learning Mask Region-Based Convolutional Neural Network (R-CNN) model for mapping IWPs in satellite remote sensing imagery (~0.5 m) covering 272 km2 and unmanned aerial vehicle (UAV) (0.02 m) imagery covering 0.32 km2. Multi-spectral images were obtained from the WorldView-2 satellite sensor and pan-sharpened to ~0.5 m, and a 20 mp CMOS sensor camera onboard a UAV, respectively. The training dataset included 25,489 and 6022 manually delineated IWPs from satellite and fixed-wing aircraft aerial imagery near the Arctic Coastal Plain, northern Alaska. Quantitative assessments showed that individual IWPs were correctly detected at up to 72% and 70%, and delineated at up to 73% and 68% F1 score accuracy levels for satellite and UAV images, respectively. Expert-based qualitative assessments showed that IWPs were correctly detected at good (40–60%) and excellent (80–100%) accuracy levels for satellite and UAV images, respectively, and delineated at excellent (80–100%) level for both images. We found that (1) regardless of spatial resolution and spectral bands, the deep learning Mask R-CNN model effectively mapped IWPs in both remote sensing satellite and UAV images; (2) the model achieved a better accuracy in detection with finer image resolution, such as UAV imagery, yet a better accuracy in delineation with coarser image resolution, such as satellite imagery; (3) increasing the number of training data with different resolutions between the training and actual application imagery does not necessarily result in better performance of the Mask R-CNN in IWPs mapping; (4) and overall, the model underestimates the total number of IWPs particularly in terms of disjoint/incomplete IWPs.
Mapping Kenyan Grassland Heights Across Large Spatial Scales with Combined Optical and Radar Satellite Imagery
Authors: Spagnuolo Olivia S.B. , Jarvey Julie C. , Battaglia Michael J. , Laubach Zachary M. , Miller Mary Ellen , Holekamp Kay E. , Bourgeau-Chavez Laura L.
Published: April 2020

Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve.
How Can Remote Sensing Help Monitor Tropical Moist Forest Degradation?—A Systematic Review
Authors: Dupuis Chloé , Lejeune Philippe , Michez Adrien , Fayolle Adeline
Published: April 2020

In the context of the climate and biodiversity crisis facing our planet, tropical forests playing a key role in global carbon flux and containing over half of Earth’s species are important to preserve. They are today threatened by deforestation but also by forest degradation, which is more difficult to study. Here, we performed a systematic review of studies on moist tropical forest degradation using remote sensing and fitting indicators of forest resilience to perturbations. Geographical repartition, spatial extent and temporal evolution were analyzed. Indicators of compositional, structural and regeneration criteria were noted as well as remote sensing indices and metrics used. Tropical moist forest degradation is not extensively studied especially in the Congo basin and in southeast Asia. Forest structure (i.e., canopy gaps, fragmentation and biomass) is the most widely and easily measured criteria with remote sensing, while composition and regeneration are more difficult to characterize. Mixing LiDAR/Radar and optical data shows good potential as well as very high-resolution satellite data. The awaited GEDI and BIOMASS satellites data will fill the actual gap to a large extent and provide accurate structural information. LiDAR and unmanned aerial vehicles (UAVs) form a good bridge between field and satellite data. While the performance of the LiDAR is no longer to be demonstrated, particular attention should be brought to the UAV that shows great potential and could be more easily used by local communities and stakeholders.
DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data
Authors: Bao Hanqing , Ming Dongping , Guo Ya , Zhang Kui , Zhou Keqi , Du Shigao
Published: April 2020

The urban functional zone, as a special fundamental unit of the city, helps to understand the complex interaction between human space activities and environmental changes. Based on the recognition of physical and social semantics of buildings, combining remote sensing data and social sensing data is an effective way to quickly and accurately comprehend urban functional zone patterns. From the object level, this paper proposes a novel object-wise recognition strategy based on very high spatial resolution images (VHSRI) and social sensing data. First, buildings are extracted according to the physical semantics of objects; second, remote sensing and point of interest (POI) data are combined to comprehend the spatial distribution and functional semantics in the social function context; finally, urban functional zones are recognized and determined by building with physical and social functional semantics. When it comes to building geometrical information extraction, this paper, given the importance of building boundary information, introduces the deeper edge feature map (DEFM) into the segmentation and classification, and improves the result of building boundary recognition. Given the difficulty in understanding deeper semantics and spatial information and the limitation of traditional convolutional neural network (CNN) models in feature extraction, we propose the Deeper-Feature Convolutional Neural Network (DFCNN), which is able to extract more and deeper features for building semantic recognition. Experimental results conducted on a Google Earth image of Shenzhen City show that the proposed method and model are able to effectively, quickly, and accurately recognize urban functional zones by combining building physical semantics and social functional semantics, and are able to ensure the accuracy of urban functional zone recognition.
Improving Vertical Wind Speed Extrapolation Using Short-Term Lidar Measurements
Authors: Basse Alexander , Pauscher Lukas , Callies Doron
Published: April 2020

This study investigates how short-term lidar measurements can be used in combination with a mast measurement to improve vertical extrapolation of wind speed. Several methods are developed and analyzed for their performance in estimating the mean wind speed, the wind speed distribution, and the energy yield of an idealized wind turbine at the target height of the extrapolation. These methods range from directly using the wind shear of the short-term measurement to a classification approach based on commonly available environmental parameters using linear regression. The extrapolation strategies are assessed using data of ten wind profiles up to 200 m measured at different sites in Germany. Different mast heights and extrapolation distances are investigated. The results show that, using an appropriate extrapolation strategy, even a very short-term lidar measurement can significantly reduce the uncertainty in the vertical extrapolation of wind speed. This observation was made for short as well as for very large extrapolation distances. Among the investigated methods, the linear regression approach yielded better results than the other methods. Integrating environmental variables into the extrapolation procedure further increased the performance of the linear regression approach. Overall, the extrapolation error in (theoretical) energy yield was decreased by around 50% to 70% on average for a lidar measurement of approximately one to two months depending on the extrapolation height and distance. The analysis of seasonal patterns revealed that appropriate extrapolation strategies can also significantly reduce the seasonal bias that is connected to the season during which the short-term measurement is performed.
PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing
Authors: Browne David , Giering Michael , Prestwich Steven
Published: April 2020

Scene classification is an important aspect of image/video understanding and segmentation. However, remote-sensing scene classification is a challenging image recognition task, partly due to the limited training data, which causes deep-learning Convolutional Neural Networks (CNNs) to overfit. Another difficulty is that images often have very different scales and orientation (viewing angle). Yet another is that the resulting networks may be very large, again making them prone to overfitting and unsuitable for deployment on memory- and energy-limited devices. We propose an efficient deep-learning approach to tackle these problems. We use transfer learning to compensate for the lack of data, and data augmentation to tackle varying scale and orientation. To reduce network size, we use a novel unsupervised learning approach based on k-means clustering, applied to all parts of the network: most network reduction methods use computationally expensive supervised learning methods, and apply only to the convolutional or fully connected layers, but not both. In experiments, we set new standards in classification accuracy on four remote-sensing and two scene-recognition image datasets.
Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space
Authors: Taghizadeh-Mehrjardi Ruhollah , Schmidt Karsten , Amirian-Chakan Alireza , Rentschler Tobias , Zeraatpisheh Mojtaba , Sarmadian Fereydoon , Valavi Roozbeh , Davatgar Naser , Behrens Thorsten , Scholten Thomas
Published: April 2020

Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge of carbon gains and losses due to climatic change. However, little is known about the SOC content in the contrasting arid and sub-humid regions of Iran, whose complex SOC–landscape relationships pose a challenge to spatial analysis. Machine learning (ML) models with a digital soil mapping framework can solve such complex relationships. Current research focusses on ensemble ML models to increase the accuracy of prediction. The usual ensemble method is boosting or weighted averaging. This study proposes a novel ensemble technique: the stacking of multiple ML models through a meta-learning model. In addition, we tested the ensemble through rescanning the covariate space to maximize the prediction accuracy. We first applied six state-of-the-art ML models (i.e., Cubist, random forests (RF), extreme gradient boosting (XGBoost), classical artificial neural network models (ANN), neural network ensemble based on model averaging (AvNNet), and deep learning neural networks (DNN)) to predict and map the spatial distribution of SOC content at six soil depth intervals for both regions. In addition, the stacking of multiple ML models through a meta-learning model with/without rescanning the covariate space were tested and applied to maximize the prediction accuracy. Out of six ML models, the DNN resulted in the best modeling accuracies, followed by RF, XGBoost, AvNNet, ANN, and Cubist. Importantly, the stacking of models indicated a significant improvement in the prediction of SOC content, especially when combined with rescanning the covariate space. For instance, the RMSE values for SOC content prediction of the upper 0–5 cm of the soil profiles of the arid site and the sub-humid site by the proposed stacking approaches were 17% and 9% respectively, less than that obtained by the DNN models—the best individual model. This indicates that rescanning the original covariate space by a meta-learning model can extract more information and improve the SOC content prediction accuracy. Overall, our results suggest that the stacking of diverse sets of models could be used to more accurately estimate the spatial distribution of SOC content in different climatic regions.
Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery
Authors: Lee Junghee , Han Daehyeon , Shin Minso , Im Jungho , Lee Junghye , Quackenbush Lindi J.
Published: April 2020

This study compares some different types of spectral domain transformations for convolutional neural network (CNN)-based land cover classification. A novel approach was proposed, which transforms one-dimensional (1-D) spectral vectors into two-dimensional (2-D) features: Polygon graph images (CNN-Polygon) and 2-D matrices (CNN-Matrix). The motivations of this study are that (1) the shape of the converted 2-D images is more intuitive for human eyes to interpret when compared to 1-D spectral input; and (2) CNNs are highly specialized and may be able to similarly utilize this information for land cover classification. Four seasonal Landsat 8 images over three study areas—Lake Tapps, Washington, Concord, New Hampshire, USA, and Gwangju, Korea—were used to evaluate the proposed approach for nine land cover classes compared to several other methods: Random forest (RF), support vector machine (SVM), 1-D CNN, and patch-based CNN. Oversampling and undersampling approaches were conducted to examine the effect of the sample size on the model performance. The CNN-Polygon had better performance than the other methods, with overall accuracies of about 93%–95 % for both Concord and Lake Tapps and 80%–84% for Gwangju. The CNN-Polygon particularly performed well when the training sample size was small, less than 200 per class, while the CNN-Matrix resulted in similar or higher performance as sample sizes became larger. The contributing input variables to the models were carefully analyzed through sensitivity analysis based on occlusion maps and accuracy decreases. Our result showed that a more visually intuitive representation of input features for CNN-based classification models yielded higher performance, especially when the training sample size was small. This implies that the proposed graph-based CNNs would be useful for land cover classification where reference data are limited.
An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model
Authors: Mateus Pedro , Catalão João , Mendes Virgílio B. , Nico Giovanni
Published: April 2020

The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth’s atmosphere’s global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.
Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data
Authors: Song Ahram , Kim Yongil
Published: April 2020

Change detection (CD) networks based on supervised learning have been used in diverse CD tasks. However, such supervised CD networks require a large amount of data and only use information from current images. In addition, it is time consuming to manually acquire the ground truth data for newly obtained images. Here, we proposed a novel method for CD in case of a lack of training data in an area near by another one with the available ground truth data. The proposed method automatically entails generating training data and fine-tuning the CD network. To detect changes in target images without ground truth data, the difference images were generated using spectral similarity measure, and the training data were selected via fuzzy c-means clustering. Recurrent fully convolutional networks with multiscale three-dimensional filters were used to extract objects of various sizes from unmanned aerial vehicle (UAV) images. The CD network was pre-trained on labeled source domain data; then, the network was fine-tuned on target images using generated training data. Two further CD networks were trained with a combined weighted loss function. The training data in the target domain were iteratively updated using he prediction map of the CD network. Experiments on two hyperspectral UAV datasets confirmed that the proposed method is capable of transferring change rules and improving CD results based on training data extracted in an unsupervised way.
Editorial for the Special Issue “Soil Moisture Retrieval using Radar Remote Sensing Sensors”
Authors: Zribi Mehrez , Albergel Clément , Baghdadi Nicolas
Published: April 2020

Soil moisture is a key parameter when it comes to understanding the processes related to the water cycle on continental surfaces (infiltration, evapotranspiration, runoff, etc [...]
An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products
Authors: Zou Bin , Liu Ning , Wang Wei , Feng Huihui , Liu Xiangping , Lin Yan
Published: April 2020

Current reported spatiotemporal solutions for fusing multisensor aerosol optical depth (AOD) products used to recover gaps either suffer from unacceptable accuracy levels, i.e., fixed rank smooth (FRS), or high time costs, i.e., Bayesian maximum entropy (BME). This problem is generally more serious when dealing with multiple AOD products in a long time series or over large geographic areas. This study proposes a new, effective, and efficient enhanced FRS method (FRS-EE) to fuse satellite AOD products with uncertainty constraints. AOD products used in the fusion experiment include Moderate Resolution Imaging SpectroRadiometer (MODIS) DB/DT_DB_Combined AOD and Multiangle Imaging SpectroRadiometer (MISR) AOD across mainland China from 2016 to 2017. Results show that the average completeness of original, initial FRS fused, and FRS-EE fused AODs with uncertainty constraints are 22.80%, 95.18%, and 65.84%, respectively. Although the correlation coefficient (R = 0.77), root mean square error (RMSE = 0.30), and mean bias (Bias = 0.023) of the initial FRS fused AODs are relatively lower than those of original AODs compared to Aerosol Robotic Network (AERONET) AOD records, the accuracy of FRS-EE fused AODs, which are R = 0.88, RMSE = 0.20, and Bias = 0.022, is obviously improved. More importantly, in regions with fully missing original AODs, the accuracy of FRS-EE fused AODs is close to that of original AODs in regions with valid retrievals. Meanwhile, the time cost of FRS-EE for AOD fusion was only 2.91 h; obviously lower than the 30.46 months taken for BME.