bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis and Characterization of Alkali Metal Ion-Binding Copolymers Bearing Dibenzo-24-crown-8 Ether Moieties
Authors: Wang Da-Ming , Aso Yuji , Ohara Hitomi , Tanaka Tomonari
Published: October 2018

Dibenzo-24-crown-8 (DB24C8)-bearing copolymers were synthesized by radical copolymerization using a DB24C8-carrying acrylamide derivative and N-isopropylacrylamide monomers. The cloud point of the resulting copolymers changed in aqueous solution in the presence of cesium ions. In addition, the 1H NMR signals of DB24C8-bearing copolymers shifted in the presence of alkali metal. This shift was more pronounced following the addition of Cs+ compared to Rb+, K+, Na+, and Li+ ions due to recognition of the Cs+ ion by DB24C8.
A Process Study of Lactic Acid Production from Phragmites australis Straw by a Thermophilic Bacillus coagulans Strain under Non-Sterilized Conditions
Authors: Zhang Yuming , Li Mengran , Nie Tian , Ni Zhihua
Published: October 2018

Phragmites australis straw (PAS) is an abundant and renewable wetland lignocellulose. Bacillus coagulans IPE22 is a robust thermophilic strain with pentose-utilizing capability and excellent resistance to growth inhibitors. This work is focused on the process study of lactic acid (LA) production from P. australis lignocellulose which has not been attempted previously. By virtue of thermophilic feature of strain IPE22, two fermentation processes (i.e., separated process and integrated process), were developed and compared under non-sterilized conditions. The integrated process combined dilute-acid pretreatment, hemicellulosic hydrolysates fermentation, and cellulose utilization. Sugars derived from hemicellulosic hydrolysates and cellulose enzymatic hydrolysis were efficiently fermented to LA in a single vessel. Using the integrated process, 41.06 g LA was produced from 100 g dry PAS. The established integrated process results in great savings in terms of time and labor, and the fermentation process under non-sterilized conditions is easy to scale up for economical production of lactic acid from PAS.
Glycosylation Flux Analysis of Immunoglobulin G in Chinese Hamster Ovary Perfusion Cell Culture
Authors: Hutter Sandro , Wolf Moritz , Papili Gao Nan , Lepori Dario , Schweigler Thea , Morbidelli Massimo , Gunawan Rudiyanto
Published: October 2018

The terminal sugar molecules of the N-linked glycan attached to the fragment crystalizable (Fc) region is a critical quality attribute of therapeutic monoclonal antibodies (mAbs) such as immunoglobulin G (IgG). There exists naturally-occurring heterogeneity in the N-linked glycan structure of mAbs, and such heterogeneity has a significant influence on the clinical safety and efficacy of mAb drugs. We previously proposed a constraint-based modeling method called glycosylation flux analysis (GFA) to characterize the rates (fluxes) of intracellular glycosylation reactions. One contribution of this work is a significant improvement in the computational efficiency of the GFA, which is beneficial for analyzing large datasets. Another contribution of our study is the analysis of IgG glycosylation in continuous perfusion Chinese Hamster Ovary (CHO) cell cultures. The GFA of the perfusion cell culture data indicated that the dynamical changes of IgG glycan heterogeneity are mostly attributed to alterations in the galactosylation flux activity. By using a random forest regression analysis of the IgG galactosylation flux activity, we were further able to link the dynamics of galactosylation with two process parameters: cell-specific productivity of IgG and extracellular ammonia concentration. The characteristics of IgG galactosylation dynamics agree well with what we previously reported for fed-batch cultivations of the same CHO cell strain.
Natural Products Extraction of the Future—Sustainable Manufacturing Solutions for Societal Needs
Authors: Uhlenbrock Lukas , Sixt Maximilian , Tegtmeier Martin , Schulz Hartwig , Hagels Hansjörg , Ditz Reinhard , Strube Jochen
Published: October 2018

The production of plant-based extracts is significantly influenced by traditional techniques and the natural variability of feedstock. For that reason, the discussion of innovative approaches to improve the manufacturing of established products and the development of new products within the regulatory framework is essential to adapt to shifting quality standards. This perspective of members of the DECHEMA/ProcessNet working group on plant-based extracts outlines extraction business models and the regulatory framework regarding the extraction of traditional herbal medicines as complex extracts. Consequently, modern approaches to innovative process design methods like QbD (Quality by Design) and quality control in the form of PAT (Process Analytical Technology) are necessary. Further, the benefit of standardized laboratory equipment combined with physico-chemical predictive process modelling and innovative modular, flexible batch or continuous manufacturing technologies which are fully automated by advanced process control methods are described. A significant reduction of the cost of goods, i.e., by a factor of 4–10, and decreased investments of about 1–5 mil. € show the potential for new products which are in line with market requirements.
Special Issue: Fluid Flow in Fractured Porous Media
Authors: Liu Richeng , Jiang Yujing
Published: October 2018

The fluid flow in fractured porous media plays a significant role in the characteristic/assessment of deep underground reservoirs such as CO2 sequestration [1–3], enhanced oil recovery [4,5] and geothermal energy development [...]
Systematic and Model-Assisted Process Design for the Extraction and Purification of Artemisinin from Artemisia annua L.—Part II: Model-Based Design of Agitated and Packed Columns for Multistage Extraction and Scrubbing
Authors: Schmidt Axel , Sixt Maximilian , Huter Maximilian Johannes , Mestmäcker Fabian , Strube Jochen
Published: October 2018

Liquid-liquid extraction (LLE) is an established unit operation in the manufacturing process of many products. However, development and integration of multistage LLE for new products and separation routes is often hindered and is probably more cost intensive due to a lack of robust development strategies and reliable process models. Even today, extraction columns are designed based on pilot plant experiments. For dimensioning, knowledge of phase equilibrium, hydrodynamics and mass transport kinetics are necessary. Usually, those must be determined experimentally for scale-up, at least in scales of DN50-150 (nominal diameter). This experiment-based methodology is time consuming and it requires large amounts of feedstock, especially in the early phase of the project. In this study the development for the integration of LLE in a new manufacturing process for artemisinin as an anti-malaria drug is presented. For this, a combination of miniaturized laboratory and mini-plant experiments supported by mathematical modelling is used. System data on extraction and washing distributions were determined by means of shaking tests and implemented as a multi-stage extraction in a process model. After the determination of model parameters for mass transfer and plant hydrodynamics in a droplet measurement apparatus, a distributed plug-flow model is used for scale-up studies. Operating points are validated in a mini-plant system. The mini-plant runs are executed in a Kühni-column (DN26) for extraction and a packed extraction column (DN26) for the separation of side components with a throughput of up to 3.6 L/h, yield of up to 100%, and purity of 41% in the feed mixture to 91% after washing.
Statistical Analysis of Circulating Water Quality Parameters under Variable-Frequency Vertical Electromagnetic Fields
Authors: He Fang , Wang Jianguo
Published: October 2018

No unified electromagnetic anti-fouling mechanism is currently available. Most research has focused on the effects of structural parameters and water quality parameters on electromagnetic fields; variations in water quality parameters under the influence of electromagnetic fields have not been reported. A variable-frequency vertical electromagnetic field is proposed in this study. Relationships between conductivity, pH value, dissolved oxygen, turbidity, fouling resistance, and magnetic acting time were carefully analyzed using statistical analysis. Results show that the conductivity difference was the most explanatory predictive variable on magnetic acting time in the multiple stepwise regression model. Magnetic acting time has a greater impact on conductivity than pH value and dissolved oxygen. Conductivity is used as an adaptive feedback control parameter for the optimum anti-fouling state. Fouling resistance on the heat-exchanging surface of the magnetic experiment was smaller than that of the contrast experiment. The anti-fouling efficiency in 1 kHz and 5 kHz magnetic and contrast experiments was 91.23% and 46.97%, respectively. Better anti-fouling performance was realized under the influence of low-frequency electromagnetic fields, confirming that physical water treatment is an effective and environmentally friendly method to eliminate heat exchanger fouling. This research serves as a reference for the development of an electromagnetic-adaptive closed-loop water treatment device.
Early Responses to Severe Drought Stress in the Arabidopsis thaliana Cell Suspension Culture Proteome
Authors: Alqurashi May , Chiapello Marco , Bianchet Chantal , Paolocci Francesco , Lilley Kathryn S. , Gehring Christoph
Published: October 2018

Abiotic stresses are considered the most deleterious factor affecting growth and development of plants worldwide. Such stresses are largely unavoidable and trigger adaptive responses affecting different cellular processes and target different compartments. Shotgun proteomic and mass spectrometry-based approaches offer an opportunity to elucidate the response of the proteome to abiotic stresses. In this study, the severe drought or water-deficit response in Arabidopsis thaliana was mimicked by treating cell suspension callus with 40% polyethylene glycol for 10 and 30 min. Resulting data demonstrated that 310 proteins were differentially expressed in response to this treatment with a strict ±2.0-fold change. Over-representation was observed in the gene ontology categories of ‘ribosome’ and its related functions as well as ‘oxidative phosphorylation’, indicating both structural and functional drought responses at the cellular level. Proteins in the category ‘endocytosis’ also show significant enrichment and this is consistent with increased active transport and recycling of membrane proteins in response to abiotic stress. This is supported by the particularly pronounced enrichment in proteins of the endosomal sorting complexes that are required for membrane remodelling. Taken together, the findings point to rapid and complex physiological and structural changes essential for survival in response to sudden severe drought stress.
A Robust General Multivariate Chain Ladder Method
Authors: Peremans Kris , Van Aelst Stefan , Verdonck Tim
Published: October 2018

The chain ladder method is a popular technique to estimate the future reserves needed to handle claims that are not fully settled. Since the predictions of the aggregate portfolio (consisting of different subportfolios) do not need to be equal to the sum of the predictions of the subportfolios, a general multivariate chain ladder (GMCL) method has already been proposed. However, the GMCL method is based on the seemingly unrelated regression (SUR) technique which makes it very sensitive to outliers. To address this issue, we propose a robust alternative that estimates the SUR parameters in a more outlier resistant way. With the robust methodology it is possible to automatically flag the claims with a significantly large influence on the reserve estimates. We introduce a simulation design to generate artificial multivariate run-off triangles based on the GMCL model and illustrate the importance of taking into account contemporaneous correlations and structural connections between the run-off triangles. By adding contamination to these artificial datasets, the sensitivity of the traditional GMCL method and the good performance of the robust GMCL method is shown. From the analysis of a portfolio from practice it is clear that the robust GMCL method can provide better insight in the structure of the data.
Bond Yields, Sovereign Risk and Maturity Structure
Authors: González-Fernández Marcos , González-Velasco Carmen
Published: October 2018

The aim of this paper is to analyze the relation between maturity structure, sovereign bond yields and sovereign risk in the Economic and Monetary Union for the period of 1990–2013. The results confirm the existence of an inverse relationship between sovereign bond yields, sovereign risk and the maturity structure of sovereign debt, regardless of the proxy that is used to measure sovereign risk and the time variance of the variables employed. The results indicate that risk shortens the maturity structure of sovereign debt because it reduces the stock of long-term debt. The relationship between maturity structure and sovereign bond yields differs depending on the risk of the countries analyzed (non-monotonic relationship) and the differences between peripheral and core countries are greater for higher levels of the yields. If we control for the indebtedness level of these countries, the results show that the relationship between the sovereign bond yields and maturity strengthens as the debt level increases.
Numerical Ruin Probability in the Dual Risk Model with Risk-Free Investments
Authors: Loke Sooie-Hoe , Thomann Enrique
Published: October 2018

In this paper, a dual risk model under constant force of interest is considered. The ruin probability in this model is shown to satisfy an integro-differential equation, which can then be written as an integral equation. Using the collocation method, the ruin probability can be well approximated for any gain distributions. Examples involving exponential, uniform, Pareto and discrete gains are considered. Finally, the same numerical method is applied to the Laplace transform of the time of ruin.
Pléiades Tri-Stereo Data for Glacier Investigations—Examples from the European Alps and the Khumbu Himal
Authors: Rieg Lorenzo , Klug Christoph , Nicholson Lindsey , Sailer Rudolf
Published: October 2018

In this study, we use Pléiades tri-stereo data to generate a digital elevation model (DEM) from the Pléiades images using a workflow employing semi-global matching (SGM). We examine the DEM accuracy in complex mountain glaciated terrain by comparing the new DEMs with an independent high-quality DEM based on airborne laser scanning (ALS) data for a study area in the Austrian Alps, and with ground control points for a study area in the Khumbu Himal of Nepal. The DEMs derived using the SGM algorithm compare well to the independent high-quality ALS DEM, and the workflow produces models of sufficient quality to resolve ground control points, which are based on Pléiades imagery that are of sufficient quality to perform high spatio-temporal resolution assessments of remote areas for which no field data is available. The relative accuracy is sufficient to investigate glacier surface elevation changes below one meter, and can therefore be applied over relatively short periods of time, such as those required for annual and seasonal assessments of change. The annual geodetic mass balance for the Alpine case derived from our DEM compares well to the glaciological mass balance, and multitemporal DEM analysis is used to resolve the seasonal changes of five glaciers in the Khumbu Himal, revealing that glaciological processes such as accumulation, ablation, and glacier movement mainly take place during the summer season, with the winter season being largely inactive in the year sampled.
Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral Data
Authors: Bradley Patrick Erik , Keller Sina , Weinmann Martin
Published: October 2018

In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available. This is motivated by the fact that unsupervised feature selection techniques combine the advantages of standard dimensionality reduction techniques (which only rely on the given feature vectors and not on the corresponding labels) and supervised feature selection techniques (which retain a subset of the original set of features). Thus, feature selection becomes independent of the given classification task and, consequently, a subset of generally versatile features is retained. We present different techniques relying on the topology of the given sparse training data. Thereby, the topology is described with an ultrametricity index. For the latter, we take into account the Murtagh Ultrametricity Index (MUI) which is defined on the basis of triangles within the given data and the Topological Ultrametricity Index (TUI) which is defined on the basis of a specific graph structure. In a case study addressing the classification of high-dimensional hyperspectral data based on sparse training data, we demonstrate the performance of the proposed unsupervised feature selection techniques in comparison to standard dimensionality reduction and supervised feature selection techniques on four commonly used benchmark datasets. The achieved classification results reveal that involving supervised feature selection techniques leads to similar classification results as involving unsupervised feature selection techniques, while the latter perform feature selection independently from the given classification task and thus deliver generally versatile features.
Flood Hazard Assessment Supported by Reduced Cost Aerial Precision Photogrammetry
Authors: Zazo Santiago , Rodríguez-Gonzálvez Pablo , Molina José-Luis , González-Aguilera Diego , Agudelo-Ruiz Carlos Andrés , Hernández-López David
Published: October 2018

Increasing flood hazards worldwide due to the intensification of hydrological events and the development of adaptation-mitigation strategies are key challenges that society must address. To minimize flood damages, one of the crucial factors is the identification of flood prone areas through fluvial hydraulic modelling in which a detailed knowledge of the terrain plays an important role for reliable results. Recent studies have demonstrated the suitability of the Reduced Cost Aerial Precision Photogrammetry (RC-APP) technique for fluvial applications by accurate-detailed-reliable Digital Terrain Models (DTMs, up to: ≈100 point/m2; vertical-uncertainty: ±0.06 m). This work aims to provide an optimal relationship between point densities and vertical-uncertainties to generate more reliable fluvial hazard maps by fluvial-DTMs. This is performed through hydraulic models supported by geometric models that are obtained from a joint strategy based on Structure from Motion and Cloth Simulation Filtering algorithms. Furthermore, to evaluate vertical-DTM, uncertainty is proposed as an alternative approach based on the method of robust estimators. This offers an error dispersion value analogous to the concept of standard deviation of a Gaussian distribution without requiring normality tests. This paper reinforces the suitability of new geomatic solutions as a reliable-competitive source of accurate DTMs at the service of a flood hazard assessment.
Inter-Comparison and Evaluation of the Four Longest Satellite-Derived Cloud Climate Data Records: CLARA-A2, ESA Cloud CCI V3, ISCCP-HGM, and PATMOS-x
Authors: Karlsson Karl-Göran , Devasthale Abhay
Published: October 2018

Results from four global cloud climate data records (ISCCP-HGM, ESA Cloud CCI V3, CLARA-A2 and PATMOS-x) have been inter-compared in global time series plots, in global maps and in zonal region plots covering the period in common, 1984–2009. The investigated cloud parameters were total cloud fraction and cloud top pressure. Averaged seasonal cycles of cloud cover, as observed by the CALIPSO-CALIOP sensor over the 2007–2015 period, were also used as an additional independent and high-quality reference for the study of global cloud cover. All CDRs show good agreement on global cloud amounts (~65%) and also a weak negative trend (0.5–1.9% per decade) over the period of investigation. Deviations between the CDRs are seen especially over the southern mid-latitude region and over the poles. Particularly good results are shown by PATMOS-x and by ESA Cloud CCI V3 when compared to the CALIPSO-CALIOP reference. Results for cloud top pressure show large differences (~60 hPa) between ISCCP-HGM and the other CDRs for the global mean. The two CDR groups show also opposite signs in the trend over the period.
Assessing Impacts of Urban Form on Landscape Structure of Urban Green Spaces in China Using Landsat Images Based on Google Earth Engine
Authors: Huang Conghong , Yang Jun , Jiang Peng
Published: October 2018

The structure of urban green spaces (UGS) plays an important role in determining the ecosystem services that they support. Knowledge of factors shaping landscape structure of UGS is imperative for planning and management of UGS. In this study, we assessed the influence of urban form on the structure of UGS in 262 cities in China based on remote sensing data. We produced land cover maps for 262 cities in 2015 using 6673 scenes of Landsat ETM+/OLI images based on the Google Earth Engine platform. We analyzed the impact of urban form on landscape structure of UGS in these cities using boosted regression tree analysis with the landscape and urban form metrics derived from the land cover maps as response and prediction variables, respectively. The results showed that the three urban form metrics—perimeter area ratio, road density, and compound terrain complexity index—were all significantly correlated with selected landscape metrics of UGS. Cities with high road density had less UGS area and the UGS in those cities was more fragmented. Cities with complex built-up boundaries tended to have more fragmented UGS. Cities with high terrain complexity had more UGS but the UGS were more fragmented. Our results for the first time revealed the importance of urban form on shaping landscape structure of UGS in 262 cities at a national scale.
Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets
Authors: Qiu Chunping , Schmitt Michael , Mou Lichao , Ghamisi Pedram , Zhu Xiao Xiang
Published: October 2018

Global Local Climate Zone (LCZ) maps, indicating urban structures and land use, are crucial for Urban Heat Island (UHI) studies and also as starting points to better understand the spatio-temporal dynamics of cities worldwide. However, reliable LCZ maps are not available on a global scale, hindering scientific progress across a range of disciplines that study the functionality of sustainable cities. As a first step towards large-scale LCZ mapping, this paper tries to provide guidance about data/feature choice. To this end, we evaluate the spectral reflectance and spectral indices of the globally available Sentinel-2 and Landsat-8 imagery, as well as the Global Urban Footprint (GUF) dataset, the OpenStreetMap layers buildings and land use and the Visible Infrared Imager Radiometer Suite (VIIRS)-based Nighttime Light (NTL) data, regarding their relevance for discriminating different Local Climate Zones (LCZs). Using a Residual convolutional neural Network (ResNet), a systematic analysis of feature importance is performed with a manually-labeled dataset containing nine cities located in Europe. Based on the investigation of the data and feature choice, we propose a framework to fully exploit the available datasets. The results show that GUF, OSM and NTL can contribute to the classification accuracy of some LCZs with relatively few samples, and it is suggested that Landsat-8 and Sentinel-2 spectral reflectances should be jointly used, for example in a majority voting manner, as proven by the improvement from the proposed framework, for large-scale LCZ mapping.
Ecosystem Services in a Protected Mountain Range of Portugal: Satellite-Based Products for State and Trend Analysis
Authors: Carvalho-Santos Claudia , Monteiro António , Arenas-Castro Salvador , Greifeneder Felix , Marcos Bruno , Portela Ana , Honrado João
Published: October 2018

Mountains are facing strong environmental pressures, which may jeopardize the supply of various ecosystem services. For sustainable land management, ecosystem services and their supporting functions should thus be evaluated and monitored. Satellite products have been receiving growing attention for monitoring ecosystem functioning, mainly due to their increasing temporal and spatial resolutions. Here, we aim to illustrate the high potential of satellite products, combined with ancillary in situ and statistical data, to monitor the current state and trend of ecosystem services in the Peneda-Gerês National Park, a protected mountain range in Portugal located in a transition climatic zone (Atlantic to Mediterranean). We focused on three ecosystem services belonging to three broad categories: provisioning (reared animals), regulating (of water flows), and cultural (conservation of an endemic and iconic species). These services were evaluated using a set of different satellite products, namely grassland cover, soil moisture, and ecosystem functional attributes. In situ and statistical data were also used to compute final indicators of ecosystem services. We found a decline in the provision of reared animals since year 2000, although the area of grasslands had remained stable. The regulation of water flows had been maintained, and a strong relationship with interannual precipitation pattern was noted. In the same period, conservation of the focal iconic species might have been affected by interannual fluctuations of suitable habitat areas, with a possible influence of wildfires and precipitation. We conclude that satellite products can efficiently provide information about the current state and trend in the supply of various categories of ecosystem services, especially when combined with in situ or statistical data in robust modeling frameworks.
Self-Dictionary Regression for Hyperspectral Image Super-Resolution
Authors: Gao Dongsheng , Hu Zhentao , Ye Renzhen
Published: October 2018

Due to sensor limitations, hyperspectral images (HSIs) are acquired by hyperspectral sensors with high-spectral-resolution but low-spatial-resolution. It is difficult for sensors to acquire images with high-spatial-resolution and high-spectral-resolution simultaneously. Hyperspectral image super-resolution tries to enhance the spatial resolution of HSI by software techniques. In recent years, various methods have been proposed to fuse HSI and multispectral image (MSI) from an unmixing or a spectral dictionary perspective. However, these methods extract the spectral information from each image individually, and therefore ignore the cross-correlation between the observed HSI and MSI. It is difficult to achieve high-spatial-resolution while preserving the spatial-spectral consistency between low-resolution HSI and high-resolution HSI. In this paper, a self-dictionary regression based method is proposed to utilize cross-correlation between the observed HSI and MSI. Both the observed low-resolution HSI and MSI are simultaneously considered to estimate the endmember dictionary and the abundance code. To preserve the spectral consistency, the endmember dictionary is extracted by performing a common sparse basis selection on the concatenation of observed HSI and MSI. Then, a consistent constraint is exploited to ensure the spatial consistency between the abundance code of low-resolution HSI and the abundance code of high-resolution HSI. Extensive experiments on three datasets demonstrate that the proposed method outperforms the state-of-the-art methods.
AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data
Authors: Fang Bin , Lakshmi Venkat , Bindlish Rajat , Jackson Thomas
Published: October 2018

Soil moisture (SM) applications in terrestrial hydrology require higher spatial resolution soil moisture products than those provided by passive microwave remote sensing instruments (grid resolution of 9 km or larger). In this investigation, an innovative algorithm that uses visible/infrared remote sensing observations to downscale Advanced Microwave Scanning Radiometer 2 (AMSR2) coarse spatial resolution SM products was developed and implemented for use with data provided by the Advanced Microwave Scanning Radiometer 2 (AMSR2). The method is based on using the Normalized Difference Vegetation Index (NDVI) modulated relationships between day/night SM and temperature change at corresponding times. Land surface model output variables from the North America Land Data Assimilation System (NLDAS), remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) were used in this methodology. The functional relationships developed using NLDAS data at a grid resolution of 12.5 km were applied to downscale AMSR2 JAXA (Japan Aerospace Exploration Agency) SM product (25 km) using MODIS land surface temperature (LST) and NDVI observations (1 km) to produce the 1 km SM estimates. The downscaled SM estimates were validated by comparing them with ISMN (International Soil Moisture Network) in situ SM in the Black Bear–Red Rock watershed, central Oklahoma between 2015–2017. The overall statistical variables of the downscaled AMSR2 SM validation R2, slope, RMSE and bias, demonstrate good accuracy. The downscaled SM better characterized the spatial and temporal variability of SM at watershed scales than the original SM product.
Modeling the Directional Clumping Index of Crop and Forest
Authors: Peng Jingjing , Fan Wenjie , Wang Lizhao , Xu Xiru , Li Jvcai , Zhang Beitong , Tian Dingfang
Published: October 2018

The Clumping Index (Ω) was introduced to quantify the spatial distribution pattern of vegetation elements. It is crucial to improve the estimation accuracy of vital vegetation parameters, such as Leaf Area Index (LAI) and Gross Primary Production (GPP). Meanwhile, the parameterization of Ω is challenging partly due to the varying observations of canopy gaps from different view angles. Many previous studies have shown the increase of Ω with view zenith angle through samples of gap size distribution from in situ measurements. In contrast, remote sensing retrieval algorithms only assign a constant value for each biome type to roughly correct the clumping effect as a compromise between the accuracy and efficiency. In this paper, analytical models are proposed that estimate the directional clumping index (Ω(θ)) of crop and forest at canopy level. The angular variation trend and magnitude of crop Ω(θ) was analyzed within row structure where vegetation elements are randomly spaced along rows. The forest model predicts Ω(θ) with tree density, distribution pattern, crown shape, trunk size, and leaf area and angle distribution function. The models take into account the main directional characteristics of clumping index using easy-to-measure parameters. Test cases showed that Ω(θ) magnitude variation for black spruce forest was 102.3% of the hemispherical average clumping index ( Ω ˜ ), whereas the Larch forest had 48.7% variation, and row crop variation reached 32.4%. This study provided tools to assess Ω(θ) of discontinuous canopies.
Permafrost Soil Moisture Monitoring Using Multi-Temporal TerraSAR-X Data in Beiluhe of Northern Tibet, China
Authors: Wang Chao , Zhang Zhengjia , Paloscia Simonetta , Zhang Hong , Wu Fan , Wu Qingbai
Published: October 2018

Global change has significant impact on permafrost region in the Tibet Plateau. Soil moisture (SM) of permafrost is one of the most important factors influencing the energy flux, ecosystem, and hydrologic process. The objectives of this paper are to retrieve the permafrost SM using time-series SAR images, without the need of auxiliary survey data, and reveal its variation patterns. After analyzing the characteristics of time-series radar backscattering coefficients of different landcover types, a two-component SM retrieval model is proposed. For the alpine meadow area, a linear retrieving model is proposed using the TerraSAR-X time-series images based on the assumption that the lowest backscattering coefficient is measured when the soil moisture is at its wilting point and the highest backscattering coefficient represents the water-saturated soil state. For the alpine desert area, the surface roughness contribution is eliminated using the dual SAR images acquired in the winter season with different incidence angles when retrieving soil moisture from the radar signal. Before the model implementation, landcover types are classified based on their backscattering features. 22 TerraSAR-X images are used to derive the soil moisture in Beiluhe, Northern Tibet with different incidence angles. The results obtained from the proposed method have been validated using in-situ soil moisture measurements, thus obtaining RMSE and Bias of 0.062 cm3/cm3 and 4.7%, respectively. The retrieved time-series SM maps of the study area point out the spatial and temporal SM variation patterns of various landcover types.
A Modified Change Vector Approach for Quantifying Land Cover Change
Authors: Xu Ru , Lin Hui , Lü Yihe , Luo Ying , Ren Yanjiao , Comber Alexis
Published: October 2018

This paper develops and applies a novel method for inferring land cover/land use (LCLU) change that combines direct multi-date classification with measures from a change vector analysis. The model predicts change directly rather than the land cover at either time, although these could be inferred. Unsupervised classifications of bi-temporal imagery were manually labeled and used to generate reference data for class-to-class changes. These were used to train a Random Forest model with inputs from the bi-temporal image bands and change vector measures (change vector direction, angle and the spectral angle) and used to generate a predicted surface of land cover change for a case study in the Pearl River Delta, China. The overall accuracy of LCLU change prediction was 96% and specific class-to-class changes had errors rates of 0–12.8%. Some errors were related the semi-automated labeling of the training data. The spectral angle variables and Near Infra-Red image bands for both years were found to be strong predictors of change. Odd ratios were used to quantify regional differences in land cover change rates in urban and peri-urban areas. The regional differences and origins of the observed errors are discussed, along with some areas of further work. The key contributions of this paper are the focus on change rather than LCLU through the construction of a model to predict changes directly and the development of an approach that provides quick, effective and informative analysis of LCLU change in support of policy and planning in rapidly urbanizing areas.
Thermokarst Development Detected from High-Definition Topographic Data in Central Yakutia
Authors: Saito Hitoshi , Iijima Yoshihiro , Basharin Nikolay , Fedorov Alexander , Kunitsky Viktor
Published: October 2018

Eastern Siberia is characterized by widespread permafrost thawing and subsequent thermokarst development. Estimation of the impacts of the predicted rise in precipitation and air temperatures under climate change requires quantitative knowledge about the spatial distribution of thermokarst development. In the last few years, unmanned aerial systems (UAS) and structure-from-motion multi-view stereo (SfM-MVS) photogrammetry attracted a tremendous amount of interest for acquiring high-definition topographic data. This study detected characteristics of thermokarst landforms using UAS and SfM-MVS photogrammetry at a disused airfield (3.0 ha) and for arable land that was previously used for farming (6.3 ha) in the Churapcha area, located on the right bank of the Lena River in central Yakutia. Orthorectified photographs and digital terrain models with spatial resolutions of 4.0 cm and 8.0 cm, respectively, were obtained for this study. At the disused airfield site and the abandoned arable land, 174 and 867 high-centered polygons that developed after the 1990s were detected, respectively. The data showed that the average diameter and average area of the polygons at the disused airfield site were 11.6 m and 111.2 m2, respectively, while those of the polygons in the abandoned arable land were 7.4 m and 46.8 m2, respectively. The abandoned arable land was characterized by smaller polygons and a higher polygon density. The differences in polygon size for the abandoned arable land and the disused airfield site indicate a difference in the ice wedge distributions and thermokarst developments. The subsidence rate was estimated as 2.1 cm/year for the disused airfield site and 3.9 cm/year for the abandoned arable land.
Enhanced Measurements of Leaf Area Density with T-LiDAR: Evaluating and Calibrating the Effects of Vegetation Heterogeneity and Scanner Properties
Authors: Soma Maxime , Pimont François , Durrieu Sylvie , Dupuy Jean-Luc
Published: October 2018

Reliable measurements of the 3D distribution of Leaf Area Density (LAD) in forest canopy are crucial for describing and modelling microclimatic and eco-physiological processes involved in forest ecosystems functioning. To overcome the obvious limitations of direct measurements, several indirect methods have been developed, including methods based on Terrestrial LiDAR scanning (TLS). This work focused on various LAD estimators used in voxel-based approaches. LAD estimates were compared to reference measurements at branch scale in laboratory, which offered the opportunity to investigate in controlled conditions the sensitivity of estimations to various factors such as voxel size, distance to scanner, leaf morphology (species), type of scanner and type of estimator. We found that all approaches to retrieve LAD estimates were highly sensitive to voxel size whatever the species or scanner and to distance to the FARO scanner. We provided evidence that these biases were caused by vegetation heterogeneity and variations in the effective footprint of the scanner. We were able to identify calibration functions that could be readily applied when vegetation and scanner are similar to those of the present study. For different vegetation and scanner, we recommend replicating our method, which can be applied at reasonable cost. While acknowledging that the test conditions in the laboratory were very different from those of the measurements taken in the forest (especially in terms of occlusion), this study revealed existence of strong biases, including spatial biases. Because the distance between scanner and vegetation varies in field scanning, these biases should occur in a similar manner in the field and should be accounted for in voxel-based methods but also in gap-fraction methods.
An Explorative Study on Estimating Local Accuracies in Land-Cover Information Using Logistic Regression and Class-Heterogeneity-Stratified Data
Authors: Zhang Jingxiong , Yang Wenjing , Zhang Wangle , Wang Yu , Liu Di , Xiu Yingchang
Published: October 2018

It is increasingly recognized that classification accuracy should be characterized locally at the level of individual pixels to depict its spatial variability to better inform users and producers of land-cover information than by conventional error-matrix-based methods. Local or per-pixel accuracy is usually estimated through empirical modelling, such as logistic regression, which often proceeds in a class-aggregated or a class-stratified way, with the latter being generally more accurate due to its accommodation for between-class inhomogeneity in accuracy-context relations. As an extension to class-stratified modelling, class-heterogeneity-stratified modelling, in which logistic models are built separately for contextually heterogeneous vs. homogeneous sub-strata in individual strata of map classes, is proposed in this paper for proper handling of within-class inhomogeneity in accuracy-context relations to increase accuracy of estimation. Unlike in existing literature where sampling is usually approached separately, the double-stratification method is also adopted in sampling design so that more sample data are likely allocated to heterogeneous sub-strata (which are more prone to misclassifications than homogeneous ones). This class-heterogeneity-stratified method furnished for sampling and modelling jointly thus constitutes an integrative framework for accuracy estimation and information refinement. As the first step in building up such a framework, this paper investigates the proposed double-stratification method’s performance and sensitivity to sample size regarding local accuracy estimation in comparison with those of existing methods through a case study concerning Globeland30 2010 land cover over Wuhan, China. A detailed review of existing methods for analyses, estimation, and use of local accuracy was provided, helping to put the proposed research in a broader context. Candidate explanatory variables for logistic regression included sample pixels’ map classes, positions, and contextual features that were computed in different-sized moving windows. Relative performances of these methods were evaluated based on an independent reference sample, with all methods found reliable. It was confirmed that the proposed method is in general the most accurate, as observed with varying sample sizes. The proposed method’s competitive performance is thus proved, reinforcing its potential for information refinement. Extensions to and uncertainty aspects of the proposed method were discussed, with further research proposed.
Calibrating the Severity of Forest Defoliation by Pine Processionary Moth with Landsat and UAV Imagery
Authors: Otsu Kaori , Pla Magda , Vayreda Jordi , Brotons Lluís
Published: October 2018

The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we attempted to estimate the damage severity by capturing the maximum defoliation period over winter between pre-outbreak and post-outbreak images. The difference in vegetation index (dVI) derived from Landsat 8 was used as the change detection indicator and was further calibrated with Unmanned Aerial Vehicle (UAV) imagery. Regression models between predicted dVIs and observed defoliation degrees by UAV were compared among five selected dVIs for the coefficient of determination. Our results found the highest R-squared value (0.815) using Moisture Stress Index (MSI), with an overall accuracy of 72%, as a promising approach for estimating the severity of defoliation in affected areas where ground-truth data is limited. We concluded with the high potential of using UAVs as an alternative method to obtain ground-truth data for cost-effectively monitoring forest health. In future studies, combining UAV images with satellite data may be considered to validate model predictions of the forest condition for developing ecosystem service tools.
A Multifunctional Molecular Probe for Detecting Hg2+ and Ag+ Based on Ion-Mediated Base Mismatch
Authors: Wang Luhui , Zhang Yingying , Dong Yafei
Published: October 2018

In this paper, a multifunctional biosensing platform for sensitively detecting Hg2+ and Ag+, based on ion-mediated base mismatch, fluorescent labeling, and strand displacement, is introduced. The sensor can also be used as an OR logic gate, the multifunctional design of sensors is realized. Firstly, orthogonal experiments with three factors and three levels were carried out on the designed sensor, and preliminary optimization of conditions was performed for subsequent experiments. Next, the designed sensor was tested the specificity and target selectivity under the optimized conditions, and the application to actual environmental samples further verified the feasibility. Generally, this is a convenient, fast, stable, and low-cost method that provides a variety of ideas and an experimental basis for subsequent research.
Birefringent Bragg Grating in C-Shaped Optical Fiber as a Temperature-Insensitive Refractometer
Authors: Tan Rex Xiao , Ho Daryl , Tse Chun Ho , Tan Yung Chuen , Yoo Seong Woo , Tjin Swee Chuan , Ibsen Morten
Published: October 2018

We demonstrate a simple-to-fabricate refractometer based on the inscription of fiber Bragg gratings in a special C-shaped optical fiber. The C-shaped fiber was drawn into shape using a quarter cladding removed preform of a commercial standard single-mode fiber by simple machining. The sensor did not suffer from cross-sensitivity of the refractive index with ambient temperature fluctuations, commonly occurring with many optical fiber refractometers. A refractive index sensitivity of 1300 pm per refractive index unit (RIU) was achieved without employing any additional sensitization techniques such as tapering or etching.
Domain Adaptation and Adaptive Information Fusion for Object Detection on Foggy Days
Authors: Chen Zhe , Li Xiaofang , Zheng Hao , Gao Hongmin , Wang Huibin
Published: October 2018

Foggy days pose many difficulties for outdoor camera surveillance systems. On foggy days, the optical attenuation and scattering effects of the medium significantly distort and degenerate the scene radiation, making it noisy and indistinguishable. Aiming to solve this problem, in this paper we propose a novel object detection method that has the ability to exploit the information in the color and depth domains. To prevent the error propagation problem, we clean the depth information before the training process and remove false samples from the database. A domain adaptation strategy is employed to adaptively fuse the decisions obtained in the color and depth domains. In the experiments, we evaluate the contribution of the depth information for object detection on foggy days. Moreover, the advantages of the multiple-domain adaptation strategy are experimentally demonstrated via comparison with other methods.
A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer
Authors: Aich Satyabrata , Pradhan Pyari Mohan , Park Jinse , Sethi Nitin , Vathsa Vemula Sai Sri , Kim Hee-Cheol
Published: October 2018

One of the most common symptoms observed among most of the Parkinson’s disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as “freezing of gait (FoG)”. To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson’s correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG.