bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of the Algal Biomass to Biodiesel Supply Chain: Case Studies of the State of Oklahoma and the United States
Authors: Yadala Soumya , Smith Justin D. , Young David , Crunkleton Daniel W. , Cremaschi Selen
Published: April 2020

The goal of this work is to design a supply chain network that distributes algae biomass from supply locations to meet biodiesel demand at specified demand locations, given a specified algae species, cultivation (i.e., supply) locations, demand locations, and demand requirements. The final supply chain topology includes the optimum sites to grow biomass, to extract algal oil from the biomass, and to convert the algae oil into biodiesel. The objective is to minimize the overall cost of the supply chain, which includes production, operation, and transportation costs over a planning horizon of ten years. Algae production was modeled both within the U.S. State of Oklahoma, as well as the entire contiguous United States. The biodiesel production cost was estimated at $7.07 per U.S. gallon ($1.87 per liter) for the State of Oklahoma case. For the contiguous United States case, a lower bound on costs of $13.68 per U.S. gallon ($3.62 per liter) and an upper bound of $61.69 ($16.32 per liter) were calculated, depending on the transportation distance of algal biomass from production locations.
Quadratic Interpolation Based Simultaneous Heat Transfer Search Algorithm and Its Application to Chemical Dynamic System Optimization
Authors: Alnahari Ebrahim , Shi Hongbo , Alkebsi Khalil
Published: April 2020

Dynamic optimization problems (DOPs) are widely encountered in complex chemical engineering processes. However, due to the existence of highly constrained, nonlinear, and nonsmooth environment in chemical processes, which usually causes nonconvexity, multimodality and discontinuity, handling DOPs is not a straightforward task. Heat transfer search (HTS) algorithm is a relative novel metaheuristic approach inspired by the natural law of thermodynamics and heat transfer. In order to solve DOPs efficiently, a new variant of HTS algorithm named quadratic interpolation based simultaneous heat transfer search (QISHTS) algorithm is proposed in this paper. The QISHTS algorithm introduces three modifications into the original HTS algorithm, namely the effect of simultaneous heat transfer search, quadratic interpolation method, and population regeneration mechanism. These three modifications are employed to provide lower computational complexity, as well as to enhance the exploration and exploitation capabilities. Therefore, the ensemble of these modifications can provide a more efficient optimization algorithm with well-balanced exploration and exploitation capabilities. The proposed variant is firstly investigated by well-defined benchmark problems and then applied to solve four chemical DOPs. Moreover, it is compared with different well-established methods existing in the literature. The results demonstrate that QISHTS algorithm has the greatest robustness and precision than other competitors.
Similar but Superior: Rhetoric of Coexistence Employed by Religions in Jeju Island, Korea
Authors: Yoo Yohan
Published: April 2020

Religions in Jeju, South Korea, have sometimes been in conflict with each other, but have generally coexisted peacefully. In a situation where diverse religions share an island that is isolated from the mainland, they have emphasized that they are similar yet superior to their rivals. Religions that were imported to Jeju, including Confucianism, Buddhism, and Christianity, have tried to make themselves look familiar to Jeju people on the basis of people’s knowledge of preexisting religions. These religions sometimes embraced rituals of preexisting religions to which people were strongly attached. The Jeju indigenous religion has also acknowledged that the ideas and practices of Buddhism and Confucianism have remarkable similarities to those of its own. Simultaneously, each religion in Jeju has claimed its superiority over others. Religions in Jeju have argued that other religions’ partial truth and limited value are in sharp contrast with the complete truth and superior value of their own. They have asserted that only they can provide the proper way of keeping the order of the universe or attaining salvation of human beings. This common rhetoric that “my religion is similar but superior to other religions” has been repeated in Jeju, in order to persuade people outside the religion to accept or at least approve it on the one hand, and to maintain the peaceful coexistence with other religions on the other hand.
Juana of Castile’s Book of Hours: An Archduchess at Prayer
Authors: Twomey Lesley K.
Published: April 2020

This article examines one of Juana of Castile’s books of hours (London, BL Add. MS 18852) comparing it with those written for members of Juana’s family and seeking to discern how it was used, in order to reassess her peers’ evaluation of her spiritual affinities. It considers how Juana customized her book of hours with a miniature of the Virgin and Child, comparing it with a gifted panel painted by Rogier van der Weyden that Juana treasured to show how she placed herself under the protection of the Virgin. Numbered precepts would be intended for her to instruct any future children and are replicated in Isabel, her daughter’s, book. The office of the Guardian Angel is compared with similar ones in Spain and Burgundy and, like devotion to St Veronica, such prayer is another means of protection. The striking mirror of conscience with its reflected skull, like other similar objects decorated with a skull that Juana possessed, sought to lift her from the decay and sinfulness of the world to the spiritual realm.
Reading the Book of Nature after Nature
Authors: Sherman Jacob Holsinger
Published: April 2020

Early modernity tended to appeal to the trope of the book of nature as a way of securing knowledge—including knowledge about God—against the exigencies of history and culture, but as theorists such as Timothy Morton, Bruno Latour, and others have argued, today this assumed dualism of nature and culture is both ecologically and critically suspect. What might it mean to read the book of nature in a time of ecological precarity, what many have called the Anthropocene? I will argue that premodern theological traditions of the book of nature, such as one finds in the twelfth century Hugh of Saint Victor, have something extremely important to add to a postmodern ‘terrestrial’ hermeneutics of nature, precisely because the premodern book of nature already performs the construal of nature as culture (and of culture as nature) so often recommended today by critics such as Latour, Haraway, and others. On such an account, nature is neither a fantasy object to be ignored or fled, nor a stable text to be tamed, rationalized, and epistemically leveraged, but rather the changing concept and experience of nature is a symbol illuminated in a book we half receive, and half create, a symbol open to both critique and contemplation, which gives rise to thought, action, and the sort of novel moral intuitions we need now more than ever.
Religious Faith as Cultural Heritage at the Refuge for World Truths
Authors: Raverty Aaron
Published: April 2020

Faith undergirds the Refuge for World Truths, a multireligious heritage-scape that emerged out of an old Spanish land grant adjacent to the Wild West mining and ranching town of Crestone, Colorado. Established by an entrepreneurial husband-and-wife team in the late twentieth century, the Refuge’s spiritual centers were founded upon different faith commitments. Christian, [Sufi] Muslim, and Baha’i centers adhere to a monotheistic faith and claim divine revelation as the source of their presence in the Refuge. New Age, polytheistic, and nontheistic groups base their faith claim on the personal mystical revelations of “Glenn,” a local peripatetic and self-described prophet who hailed the arrival of the original couple. Two stints of ethnographic research point to the spiritual centers’ public ritual performances as both invitations to pilgrims to intensify this faith and as functional cogs in the integration and continuity of the heritage-scape’s ritual economy. Finally, the faith expressions underlying the Refuge for World Truths allow this unique locality to champion interreligious dialogue as a method for addressing diversity and negotiating potential onsite conflict on the path to peaceful mutuality.
Remnant Coal Detection System
Authors: Hargrave Chad , McPhee Paul
Published: April 2020

This paper describes the development and successful implementation of a system designed to detect coal deposits remaining in coal train wagons after unloading (dumping). These undesirable coal deposits constitute both small residual amounts of “carryback”, but also larger “hang-ups” of significant volume that have failed to discharge. The system was originally developed simply to detect and record volumes of carryback, as part of an effort to characterise the extent of the problem for the coal transport industry, but was then enhanced to provide real-time feedback of large hang-ups so that they could be discharged prior to the wagons exiting the dump station. The paper describes the hardware and processing systems used in the system, including the different strategies employed to ensure a reliable detection system. The system has now been installed and operated in a production environment at three dump stations across two different coal terminals, and a case study of the results from one of these dump stations is presented. Automating remnant coal detection at dump stations provides short interval control to minimise potential hazards and downtime, and historical data that may be integrated into existing data platforms and analysed for productivity, environmental, and safety planning insights.
Information Sharing, Bank Penetration and Tax Evasion in Emerging Markets
Authors: Vo Duc Hong , Nguyen Ha Minh , Vo Tan Manh , McAleer Michael
Published: April 2020

Tax evasion, which is typically considered an illegal activity, is a critical problem and is considered a barrier to economic growth. A review of the literature shows that tax and social security contributions, regulations, public sector services, the quality of institutions and tax compliance, play important roles in determining the degree to which firms attempt to evade taxes. Measuring tax evasion is problematic due to data requirements and inadequacies. Few tax evasion indices have been estimated but it appears that they cannot be used for international comparisons across countries. This important issue has largely been ignored in the literature, in particular for emerging markets. Consequently, this paper is conducted to develop a new tax evasion index (TEI) using the most substantial and recent data from the standardized World Bank Enterprises Survey 2006–2017. In addition, using the newly developed TEI, the paper examines the importance and contribution of information sharing and bank penetration to the degree of tax evasion in emerging markets. The paper uses a sample of 112 emerging markets from 2006–2017 and the Tobit model in estimation. The empirical findings from the paper indicate that the average TEI during the 2006–2017 period for emerging markets is 0.62, with a range of (0.25, 0.75). In addition, we find that information sharing and bank penetration negatively affect the degree of tax evasion, as proxied by the TEI, in emerging markets. The empirical results also confirm the view that large firms are considered to have adopted good tax compliance practices, while firms located in remote areas are more likely to evade taxes. Policy implications have emerged on the basis of the empirical findings from the paper.
An Analysis of Joint Assembly Geometric Errors Affecting End-Effector for Six-Axis Robots
Authors: Raksiri Chana , Pa-im Krittiya , Rodkwan Supasit
Published: April 2020

This paper presents an analysis of the geometric errors of joint assembly that affect the end-effector for a six-axis industrial robot. The errors were composed of 30 parameters that come from the Geometric Dimensioning and Tolerancing (GD&T) design, which is not the normal way to describe them. Three types of manufacturing tolerancing—perpendicularity, parallelism and position—were introduced and investigated. These errors were measured by the laser tracker. The measurement data were calculated with an analysis of the circle fitting method. The kinematic model and error model based on a combination of translations methods were used. The experiment was carried out in order to calculate the tolerancing of geometric error. Then, the positions of the end-effector in the actual measurement from laser tracker and exact performance were compared. The discrepancy was compensated by offline programming. As a result, the position errors were reduced by 90%.
Wheeled Robot Dedicated to the Evaluation of the Technical Condition of Large-Dimension Engineering Structures
Authors: Domin Jarosław , Górski Marcin , Białecki Ryszard , Zając Jakub , Grzyb Krzysztof , Kielan Paweł , Adamczyk Wojciech , Ostrowski Ziemowit , Wienchol Paulina , Lamkowski Kamil , Kamiński Jakub , Doledutko Mateusz , Rosiek Radosław
Published: April 2020

There are many reasons why engineering structures are at risk of losing their loading capacity during their long-term exploitation, which may lead to hazardous states. In such cases, structures must be strengthened. The most popular technique of strengthening is based on the use of composite materials—fiber-reinforced polymer (FRP) elements attached to the structure with the special resins. FRP elements are applied externally, often in hard to reach places, which makes it difficult to diagnose the durability and quality of such a connection. In this study, a combination of a modern thermographic method was proposed, which makes it possible to assess the degree of damage to the contact of the structure with the composite material along with the running platform (wheeled robot) equipped with a set of diagnostic sensors. The development potential of such a solution for subsequent projects was also indicated.
Improved Inference and Prediction for Imbalanced Binary Big Data Using Case-Control Sampling: A Case Study on Deforestation in the Amazon Region
Authors: Valle Denis , Hyde Jacy , Marsik Matthew , Perz Stephen
Published: April 2020

It is computationally challenging to fit models to big data. For example, satellite imagery data often contain billions to trillions of pixels and it is not possible to use a pixel-level analysis to identify drivers of land-use change and create predictions using all the data. A common strategy to reduce sample size consists of drawing a random sample but this approach is not ideal when the outcome of interest is rare in the landscape because it leads to very few pixels with this outcome. Here we show that a case-control (CC) sampling approach, in which all (or a large fraction of) pixels with the outcome of interest and a subset of the pixels without this outcome are selected, can yield much better inference and prediction than random sampling (RS) if the estimated parameters and probabilities are adjusted with the equations that we provide. More specifically, we show that a CC approach can yield unbiased inference with much less uncertainty when CC data are analyzed with logistic regression models and its semiparametric variants (e.g., generalized additive models). We also show that a random forest model, when fitted to CC data, can generate much better predictions than when fitted to RS data. We illustrate this improved performance of the CC approach, when used together with the proposed bias-correction adjustments, with extensive simulations and a case study in the Amazon region focused on deforestation.
Mapping Winter Wheat in North China Using Sentinel 2A/B Data: A Method Based on Phenology-Time Weighted Dynamic Time Warping
Authors: Dong Qi , Chen Xuehong , Chen Jin , Zhang Chishan , Liu Licong , Cao Xin , Zang Yunze , Zhu Xiufang , Cui Xihong
Published: April 2020

Accurate mapping of winter wheat over a large area is of great significance for guiding policy formulation related to food security, farmland management, and the international food trade. Due to the complex phenological features of winter wheat, the cloud contamination in time-series imagery, and the influence of the soil/snow background on vegetation indices, there remains no effective method for mapping winter wheat at a medium spatial resolution (10–30 m). In this study, we proposed a novel method called phenology-time weighted dynamic time warping (PT-DTW) for identifying winter wheat based on Sentinel 2A/B time-series data. The main advantages of PT-DTW include (1) the use of phenological features in two periods, i.e., the greenness increase before winter and greenness decrease after heading, which are common to all winter wheat and are distinct from the features of other land cover types, and (2) the use of the normalized differential phenology index (NDPI) instead of traditional vegetation indices to provide more robust vegetation information and to suppress the adverse impacts of soil and snow cover, especially during the before-winter growth period. The proposed PT-DTW method was employed for winter wheat mapping based on Sentinel 2A/B data on the Huang-Huai Plain, China. Validation with visually interpreted samples showed that the produced winter wheat map achieved an overall classification accuracy of 89.98% and a kappa coefficient of 0.7978, outperforming previous winter wheat classification methods. Moreover, the planting area derived from PT-DTW agreed well with census data at the municipal level, with a coefficient of determination of 0.8638, indicating that the winter wheat map produced at 20 m resolution was reliable overall. Therefore, the PT-DTW method is recommended for winter wheat mapping over large areas.
Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy
Authors: Falanga Bolognesi Salvatore , Pasolli Edoardo , Belfiore Oscar , De Michele Carlo , D’Urso Guido
Published: April 2020

Lack of accurate and up-to-date data associated with irrigated areas and related irrigation amounts is hampering the full implementation and compliance of the Water Framework Directive (WFD). In this paper, we describe the framework that we developed and implemented within the DIANA project to map the actual extent of irrigated areas in the Campania region (Southern Italy) during the 2018 irrigation season. For this purpose, we considered 202 images from the Harmonized Landsat Sentinel-2 (HLS) products (57 images from Landsat 8 and 145 images from Sentinel-2). Such data were preprocessed in order to extract a multitemporal Normalized Difference Vegetation Index (NDVI) map, which was then smoothed through a gap-filling algorithm. We further integrated data coming from high-resolution (4 km) global satellite precipitation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) products. We collected an extensive ground truth in the field represented by 2992 data points coming from three main thematic classes: bare soil and rainfed (class 0), herbaceous (class 1), and tree crop (class 2). This information was exploited to generate irrigated area maps by adopting a machine learning classification approach. We compared six different types of classifiers through a cross-validation approach and found that, in general, random forests, support vector machines, and boosted decision trees exhibited the best performances in terms of classification accuracy and robustness to different tested scenarios. We found an overall accuracy close to 90% in discriminating among the three thematic classes, which highlighted promising capabilities in the detection of irrigated areas from HLS products.
Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique
Authors: Mananze Sosdito , Pôças Isabel , Cunha Mário
Published: April 2020

Land cover maps obtained at high spatial and temporal resolutions are necessary to support monitoring and management applications in areas with many smallholder and low-input agricultural systems, as those characteristic in Mozambique. Various regional and global land cover products based on Earth Observation data have been developed and made publicly available but their application in regions characterized by a large variety of agro-systems with a dynamic nature is limited by several constraints. Challenges in the classification of spatially heterogeneous landscapes, as in Mozambique, include the definition of the adequate spatial resolution and data input combinations for accurately mapping land cover. Therefore, several combinations of variables were tested for their suitability as input for random forest ensemble classifier aimed at mapping the spatial dynamics of smallholder agricultural landscape in Vilankulo district in Mozambique. The variables comprised spectral bands from Landsat 7 ETM+ and Landsat 8 OLI/TIRS, vegetation indices and textural features and the classification was performed within the Google Earth Engine cloud computing for the years 2012, 2015, and 2018. The study of three different years aimed at evaluating the temporal dynamics of the landscape, typically characterized by high shifting nature. For the three years, the best performing variables included three selected spectral bands and textural features extracted using a window size of 25. The classification overall accuracy was 0.94 for the year 2012, 0.98 for 2015, and 0.89 for 2018, suggesting that the produced maps are reliable. In addition, the areal statistics of the class classified as agriculture were very similar to the ground truth data as reported by the Serviços Distritais de Actividades Económicas (SDAE), with an average percentage deviation below 10%. When comparing the three years studied, the natural vegetation classes are the predominant covers while the agriculture is the most important cause of land cover changes.
Remote Detection of Uranium Using Self-Focusing Intense Femtosecond Laser Pulses
Authors: Burger Miloš , Skrodzki Patrick J. , Finney Lauren A. , Nees John , Jovanovic Igor
Published: April 2020

Optical measurement techniques can address certain important challenges associated with nuclear safety and security. Detection of uranium over long distances presents one such challenge that is difficult to realize with traditional ionizing radiation detection, but may benefit from the use of techniques based on intense femtosecond laser pulses. When a high-power laser pulse propagates in air, it experiences collapse and confinement into filaments over an extended distance even without external focusing. In our experiments, we varied the initial pulse chirp to optimize the emission signal from the laser-produced uranium plasma at an extended distance. While the ablation efficiency of filaments formed by self-focusing is known to be significantly lower when compared to filaments produced by external focusing, we show that filaments formed by self-focusing can still generate luminous spectroscopic signatures of uranium detectable within seconds over a 10-m range. The intensity of uranium emission varies periodically with laser chirp, which is attributed to the interplay among self-focusing, defocusing, and multi-filament fragmentation along the beam propagation axis. Grouping of multi-filaments incident on target is found to be correlated with the uranium emission intensity. The results show promise towards long-range detection, advancing the diagnostics and analytical capabilities in ultrafast laser-based spectroscopy of high-Z elements.
Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter
Authors: Legleiter Carl J. , Kinzel Paul J.
Published: April 2020

The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement ( R 2 > 0.9 ) between remotely sensed velocities and field measurements. Similarly, analysis of the sensitivity of PIV accuracy to image sequence duration showed that dwell times as short as 16 s would be sufficient at a frame rate of 1 Hz and could be cut in half if the frame rate were doubled. The results of this investigation indicate that helicopter-based remote sensing of velocities in sediment-laden rivers could contribute to noncontact streamgaging programs and enable reach-scale mapping of flow fields.
Fast and Automatic Registration of Terrestrial Point Clouds Using 2D Line Features
Authors: Tao Wuyong , Hua Xianghong , Chen Zhiping , Tian Pengju
Published: April 2020

Point cloud registration, as the first step for the use of point cloud data, has attracted increasing attention. In order to obtain the entire point cloud of a scene, the registration of point clouds from multiple views is necessary. In this paper, we propose an automatic method for the coarse registration of point clouds. The 2D lines are first extracted from the two point clouds being matched. Then, the line correspondences are established and the 2D transformation is calculated. Finally, a method is developed to calculate the displacement along the z-axis. With the 2D transformation and displacement, the 3D transformation can be easily achieved. Thus, the two point clouds are aligned together. The experimental results well demonstrate that our method can obtain high-precision registration results and is computationally very efficient. In the experimental results obtained by our method, the biggest rotation error is 0.5219o, and the biggest horizontal and vertical errors are 0.2319 m and 0.0119 m, respectively. The largest total computation time is only 713.4647 s.
Comparison of Cloud Cover Detection Algorithms on Sentinel–2 Images of the Amazon Tropical Forest
Authors: Sanchez Alber Hamersson , Picoli Michelle Cristina A. , Camara Gilberto , Andrade Pedro R. , Chaves Michel Eustaquio D. , Lechler Sarah , Soares Anderson R. , Marujo Rennan F. B. , Simões Rolf Ezequiel O. , Ferreira Karine R. , Queiroz Gilberto R.
Published: April 2020

Tropical forests regulate the global water and carbon cycles and also host most of the world’s biodiversity. Despite their importance, they are hard to survey due to their location, extent, and particularly, their cloud coverage. Clouds hinder the spatial and radiometric correction of satellite imagery and also diminishing the useful area on each image, making it difficult to monitor land change. For this reason, our purpose is to identify the cloud detection algorithm best suited for the Amazon rainforest on Sentinel–2 images. To achieve this, we tested four cloud detection algorithms on Sentinel–2 images spread in five areas of the Amazonia. Using more than eight thousand validation points, we compared four cloud detection methods: Fmask 4, MAJA, Sen2Cor, and s2cloudless. Our results point out that FMask 4 has the best overall accuracy on images of the Amazon region (90%), followed by Sen2Cor’s (79%), MAJA (69%), and S2cloudless (52%). We note the choice of method depends on the intended use. Since MAJA reduces the number of false positives by design, users that aim to improve the producer’s accuracy should consider its use.
Evaluation of Atmospheric Correction Algorithms for Sentinel-2-MSI and Sentinel-3-OLCI in Highly Turbid Estuarine Waters
Authors: Renosh Pannimpullath Remanan , Doxaran David , Keukelaere Liesbeth De , Gossn Juan Ignacio
Published: April 2020

The present study assesses the performance of state-of-the-art atmospheric correction (AC) algorithms applied to Sentinel-2-MultiSpectral Instrument (S2-MSI) and Sentinel-3-Ocean and Land Color Instrument (S3-OLCI) data recorded over moderately to highly turbid estuarine waters, considering the Gironde Estuary (SW France) as a test site. Three spectral bands of water-leaving reflectance ( R h o w ) are considered: green (560 nm), red (655 or 665 nm) and near infrared (NIR) (865 nm), required to retrieve the suspended particulate matter (SPM) concentrations in clear to highly turbid waters (SPM ranging from 1 to 2000 mg/L). A previous study satisfactorily validated Acolite short wave infrared (SWIR) AC algorithm for Landsat-8-Operational Land Imager (L8-OLI) in turbid estuarine waters. The latest version of Acolite Dark Spectrum Fitting (DSF) is tested here and shows very good agreement with Acolite SWIR for OLI data. L8-OLI satellite data corrected for atmospheric effects using Acolite DSF are then used as a reference to assess the validity of atmospheric corrections applied to other satellite data recorded over the same test site with a minimum time difference. Acolite DSF and iCOR (image correction for atmospheric effects) are identified as the best performing AC algorithms among the tested AC algorithms (Acolite DSF, iCOR, Polymer and C2RCC (case 2 regional coast color)) for S2-MSI. Then, the validity of six different AC algorithms (OLCI Baseline Atmospheric Correction (BAC), iCOR, Polymer, Baseline residual (BLR), C2RCC-V1 and C2RCC-V2) applied to OLCI satellite data is assessed based on comparisons with OLI and/or MSI Acolite DSF products recorded on a same day with a minimum time lag. Results show that all the AC algorithms tend to underestimate R h o w in green, red and NIR bands except iCOR in green and red bands. The iCOR provides minimum differences in green (slope = 1.0 ± 0.15, BIAS = 1.9 ± 4.5% and mean absolute percentage error (MAPE) = 12 ± 5%) and red (slope = 1.0 ± 0.17, BIAS = −9.8 ± 9% and MAPE = 28 ± 20%) bands with Acolite DSF products from OLI and MSI data. For the NIR band, BAC provides minimum differences (slope = 0.7 ± 0.13, BIAS = −33 ± 17% and MAPE = 55 ± 20%) with Acolite DSF products from OLI and MSI data. These results based on comparisons between almost simultaneous satellite products are supported by match-ups between satellite-derived and field-measured SPM concentrations provided by automated turbidity stations. Further validation of satellite products based on rigorous match-ups with in-situ R h o w measurements is still required in highly turbid waters.
Computer Vision and Deep Learning Techniques for the Analysis of Drone-Acquired Forest Images, a Transfer Learning Study
Authors: Kentsch Sarah , Lopez Caceres Maximo Larry , Serrano Daniel , Roure Ferran , Diez Yago
Published: April 2020

Unmanned Aerial Vehicles (UAV) are becoming an essential tool for evaluating the status and the changes in forest ecosystems. This is especially important in Japan due to the sheer magnitude and complexity of the forest area, made up mostly of natural mixed broadleaf deciduous forests. Additionally, Deep Learning (DL) is becoming more popular for forestry applications because it allows for the inclusion of expert human knowledge into the automatic image processing pipeline. In this paper we study and quantify issues related to the use of DL with our own UAV-acquired images in forestry applications such as: the effect of Transfer Learning (TL) and the Deep Learning architecture chosen or whether a simple patch-based framework may produce results in different practical problems. We use two different Deep Learning architectures (ResNet50 and UNet), two in-house datasets (winter and coastal forest) and focus on two separate problem formalizations (Multi-Label Patch or MLP classification and semantic segmentation). Our results show that Transfer Learning is necessary to obtain satisfactory outcome in the problem of MLP classification of deciduous vs evergreen trees in the winter orthomosaic dataset (with a 9.78% improvement from no transfer learning to transfer learning from a a general-purpose dataset). We also observe a further 2.7% improvement when Transfer Learning is performed from a dataset that is closer to our type of images. Finally, we demonstrate the applicability of the patch-based framework with the ResNet50 architecture in a different and complex example: Detection of the invasive broadleaf deciduous black locust (Robinia pseudoacacia) in an evergreen coniferous black pine (Pinus thunbergii) coastal forest typical of Japan. In this case we detect images containing the invasive species with a 75% of True Positives (TP) and 9% False Positives (FP) while the detection of native trees was 95% TP and 10% FP.
Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery
Authors: Bachhofner Stefan , Loghin Ana-Maria , Otepka Johannes , Pfeifer Norbert , Hornacek Michael , Siposova Andrea , Schmidinger Niklas , Hornik Kurt , Schiller Nikolaus , Kähler Olaf , Hochreiter Ronald
Published: April 2020

We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one time solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads.
A Refined Four-Stream Radiative Transfer Model for Row-Planted Crops
Authors: Ma Xu , Wang Tiejun , Lu Lei
Published: April 2020

In modeling the canopy reflectance of row-planted crops, neglecting horizontal radiative transfer may lead to an inaccurate representation of vegetation energy balance and further cause uncertainty in the simulation of canopy reflectance at larger viewing zenith angles. To reduce this systematic deviation, here we refined the four-stream radiative transfer equations by considering horizontal radiation through the lateral “walls”, considered the radiative transfer between rows, then proposed a modified four-stream (MFS) radiative transfer model using single and multiple scattering. We validated the MFS model using both computer simulations and in situ measurements, and found that the MFS model can be used to simulate crop canopy reflectance at different growth stages with an accuracy comparable to the computer simulations (RMSE < 0.002 in the red band, RMSE < 0.019 in NIR band). Moreover, the MFS model can be successfully used to simulate the reflectance of continuous (RMSE = 0.012) and row crop canopies (RMSE < 0.023), and therefore addressed the large viewing zenith angle problems in the previous row model based on four-stream radiative transfer equations. Our results demonstrate that horizontal radiation is an important factor that needs to be considered in modeling the canopy reflectance of row-planted crops. Hence, the refined four-stream radiative transfer model is applicable to the real world.
Potato Late Blight Detection at the Leaf and Canopy Levels Based in the Red and Red-Edge Spectral Regions
Authors: Fernández Claudio Ignacio , Leblon Brigitte , Haddadi Ata , Wang Keri , Wang Jinfei
Published: April 2020

Potato late blight, caused by Phytophthora infestans, is a major disease worldwide that has a significant economic impact on potato crops, and remote sensing might help to detect the disease in early stages. This study aims to determine changes induced by potato late blight in two parameters of the red and red-edge spectral regions: the red-well point (RWP) and the red-edge point (REP) as a function of the number of days post-inoculation (DPI) at the leaf and canopy levels. The RWP or REP variations were modelled using linear or exponential regression models as a function of the DPI. A Support Vector Machine (SVM) algorithm was used to classify healthy and infected leaves or plants using either the RWP or REP wavelength as well as the reflectances at 668, 705, 717 and 740 nm. Higher variations in the RWP and REP wavelengths were observed for the infected leaves compared to healthy leaves. The linear and exponential models resulted in higher adjusted R2 for the infected case than for the healthy case. The SVM classifier applied to the reflectance of the red and red-edge bands of the Micasense® Dual-X camera was able to sort healthy and infected cases with both the leaf and canopy measurements, reaching an overall classification accuracy of 89.33% at 3 DPI when symptoms were visible for the first time with the leaf measurements and of 89.06% at 5 DPI, i.e., two days after the symptoms became apparent, with the canopy measurements. The study shows that RWP and REP at leaf and canopy levels allow detecting potato late blight, but these parameters are less efficient to sort healthy and infected leaves or plants than the reflectance at 668, 705, 717 and 740 nm. Future research should consider larger samples, other cultivars and the test of unmanned aerial vehicle (UAV) imagery for field-based detection.
Optimization of UAV Flight Missions in Steep Terrain
Authors: Kozmus Trajkovski Klemen , Grigillo Dejan , Petrovič Dušan
Published: April 2020

Unmanned aerial vehicle (UAV) photogrammetry is one of the most effective methods for capturing a terrain in smaller areas. Capturing a steep terrain is more complex than capturing a flat terrain. To fly a mission in steep rugged terrain, a ground control station with a terrain following mode is required, and a quality digital elevation model (DEM) of the terrain is needed. The methods and results of capturing such terrain were analyzed as part of the Belca rockfall surveys. In addition to the national digital terrain model (NDTM), two customized DEMs were developed to optimize the photogrammetric survey of the steep terrain with oblique images. Flight heights and slant distances between camera projection centers and terrain are analyzed in the article. Some issues were identified and discussed, namely the vertical images in steep slopes and the steady decrease of UAV heights above ground level (AGL) with the increase of height above take-off (ATO) at 6%-8% rate. To compensate for the latter issue, the custom DEMs and NDTM were tilted. Based on our experience, the proposed optimal method for capturing the steep terrain is a combination of vertical and oblique UAV images.
Near-Field Remote Sensing of Surface Velocity and River Discharge Using Radars and the Probability Concept at 10 U.S. Geological Survey Streamgages
Authors: Fulton John W. , Mason Christopher A. , Eggleston John R. , Nicotra Matthew J. , Chiu Chao-Lin , Henneberg Mark F. , Best Heather R. , Cederberg Jay R. , Holnbeck Stephen R. , Lotspeich R. Russell , Laveau Christopher D. , Moramarco Tommaso , Jones Mark E. , Gourley Jonathan J. , Wasielewski Daniel
Published: April 2020

Near-field remote sensing of surface velocity and river discharge (discharge) were measured using coherent, continuous wave Doppler and pulsed radars. Traditional streamgaging requires sensors be deployed in the water column; however, near-field remote sensing has the potential to transform streamgaging operations through non-contact methods in the U.S. Geological Survey (USGS) and other agencies around the world. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from fixed platforms such as bridges and cable stays. Radar gages were collocated with 10 USGS streamgages in river reaches of varying hydrologic and hydraulic characteristics, where basin size ranged from 381 to 66,200 square kilometers. Radar-derived mean-channel (mean) velocity and discharge were computed using the probability concept and were compared to conventional instantaneous measurements and time series. To test the efficacy of near-field methods, radars were deployed for extended periods of time to capture a range of hydraulic conditions and environmental factors. During the operational phase, continuous time series of surface velocity, radar-derived discharge, and stage-discharge were recorded, computed, and transmitted contemporaneously and continuously in real time every 5 to 15 min. Minimum and maximum surface velocities ranged from 0.30 to 3.84 m per second (m/s); minimum and maximum radar-derived discharges ranged from 0.17 to 4890 cubic meters per second (m3/s); and minimum and maximum stage-discharge ranged from 0.12 to 4950 m3/s. Comparisons between radar and stage-discharge time series were evaluated using goodness-of-fit statistics, which provided a measure of the utility of the probability concept to compute discharge from a singular surface velocity and cross-sectional area relative to conventional methods. Mean velocity and discharge data indicate that velocity radars are highly correlated with conventional methods and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and discharge.
Recovery of Rapid Water Mass Changes (RWMC) by Kalman Filtering of GRACE Observations
Authors: Ramillien Guillaume , Seoane Lucía , Schumacher Maike , Forootan Ehsan , Frappart Frédéric , Darrozes José
Published: April 2020

We demonstrate a new approach to recover water mass changes from GRACE satellite data at a daily temporal resolution. Such a product can be beneficial in monitoring extreme weather events that last a few days and are missing by conventional monthly GRACE data. The determination of the distribution of these water mass sources over networks of juxtaposed triangular tiles was made using Kalman Filtering (KF) of daily GRACE geopotential difference observations that were reduced for isolating the continental hydrology contribution of the measured gravity field. Geopotential differences were obtained from the along-track K-Band Range Rate (KBRR) measurements according to the method of energy integral. The recovery approach was validated by inverting synthetic GRACE geopotential differences simulated using GLDAS/WGHM global hydrology model outputs. Series of daily regional and global KF solutions were estimated from real GRACE KBRR data for the period 2003–2012. They provide a realistic description of hydrological fluxes at monthly time scales, which are consistent with classical spherical harmonics and mascons solutions provided by the GRACE official centers but also give an intra-month/daily continuity of these variations.
Fine-Scale Coastal Storm Surge Disaster Vulnerability and Risk Assessment Model: A Case Study of Laizhou Bay, China
Authors: Liu Yueming , Lu Chen , Yang Xiaomei , Wang Zhihua , Liu Bin
Published: April 2020

In the assessment of storm surge vulnerability, existing studies have often selected several types of disaster-bearing bodies and assessed their exposure. In reality, however, storm surges impact all types of disaster-bearing bodies in coastal and estuarine areas. Therefore, all types of disaster-bearing bodies exposed to storm surges should be considered when assessing exposure. In addition, geographical factors will also have an impact on the exposure of the affected bodies, and thus need to be fully considered. Hence, we propose a fine-scale coastal storm surge disaster vulnerability and risk assessment model. First, fine-scale land-use data were obtained based on high-resolution remote sensing images. Combined with natural geographic factors, such as the digital elevation model (DEM), slope, and distance to water, the exposure of the disaster-bearing bodies in each geographic unit of the coastal zone was comprehensively determined. A total of five indicators, such as the percentage of females and ratio of fishery products to the gross domestic product (GDP), were then selected to assess sensitivity. In addition, six indicators, including GDP and general public budget expenditure, were selected to assess adaptability. Utilizing the indicators constructed from exposure, sensitivity, and adaptability, a vulnerability assessment was performed in the coastal area of Laizhou Bay, China, which is at high risk from storm surges. Furthermore, the storm surge risk assessment was achieved in combination with storm water statistics. The results revealed that the Kenli District, Changyi City, and the Hanting District have a higher risk of storm surge and require more attention during storm surges. The storm surge vulnerability and risk assessment model proposed in this experiment fully considers the impact of the natural environment on the exposure indicators of the coastal zone’s disaster-bearing bodies, and combines sensitivity, adaptability indicators, and storm water record data to conduct vulnerability and risk assessment. At the same time, the model proposed in this study can also realize multi-scale assessment of storm surge vulnerability and risk based on different scales of socioeconomic statistical data, which has the advantages of flexibility and ease of operation.