bluuebunny/crossref_metadata_2025 huggingface dataset:
Transparent Polyimide/Organoclay Nanocomposite Films Containing Different Diamine Monomers
Authors: Shin Hyeon Il , Chang Jin-Hae
Published: January 2020

Poly (amic acid) s (PAAs) were synthesized using 4,4′-(hexafluoroisopropyl-idene) diphthalic anhydride (6FDA) and two types of diamines—bis(3-aminophenyl) sulfone (BAS) and bis(3-amino-4-hydroxyphenyl) sulfone (BAS-OH). Two series of transparent polyimide (PI) hybrid films were synthesized by solution intercalation polymerization and thermal imidization using various concentrations (from 0 to 1 wt%) of organically modified clay Cloisite 30B in PAA solution. The thermo-mechanical properties, morphology, and optical transparency of the hybrid films were observed. The transmission electronic microscopy (TEM) results showed that some of the clays were agglomerated, but most of them showed dispersed nanoscale clay. The effects of -OH groups on the properties of the two PI hybrids synthesized using BAS and BAS-OH monomers were compared. The BAS PI hybrids were superior to the BAS-OH PI hybrids in terms of thermal stability and optical transparency, but the BAS-OH PI hybrids exhibited higher glass transition temperatures (Tg) and mechanical properties. Analysis of the thermal properties and tensile strength showed that the highest critical concentration of organoclay was 0.50 wt%.
Biodegradable Poly(ε-Caprolactone) Active Films Loaded with MSU-X Mesoporous Silica for the Release of α-Tocopherol
Authors: Mellinas Cristina , Ramos Marina , Grau-Atienza Aida , Jordà Anna , Burgos Nuria , Jiménez Alfonso , Serrano Elena , Garrigós María del Carmen
Published: January 2020

In this study, new active PCL (poly(ε-caprolactone)) films containing α-tocopherol (TOC) and MSU-X mesoporous silica were prepared by melt blending. The studied additives were directly incorporated into the polymer matrix or by impregnating TOC into MSU-X silica (PCL-IMP). Thermal, optical, oxygen and water barrier properties as well as oxidation onset parameters, were studied. Films containing MSU-X and/or TOC showed a significant increase in oxidative onset temperature (OOT) and oxidative induction time (OIT), improving thermal stability against materials oxidation by the addition of mesoporous silica and TOC into the polymer matrix. In addition, the effect of MSU-X addition on the migration behaviour of α-tocopherol from active films was investigated at 40 °C using 50% (v/v) ethanol as fatty food simulant, showing PCL-IMP films the lower release content and diffusion coefficient (3.5 × 10−15 cm2 s−1). Moreover, radical scavenging (DPPH and ABTS) and antibacterial activity against E. coli and S. aureus were favoured by the release of α-tocopherol in the developed films. The obtained results have demonstrated the potential of the new PCL-based active formulations for TOC controlled release in antioxidant and antibacterial food packaging applications.
TiO2 Sol-Gel Coated PAN/O-MMT Multi-Functional Composite Nanofibrous Membrane Used as the Support for Laccase Immobilization: Synergistic Effect between the Membrane Support and Enzyme for Dye Degradation
Authors: Wang Qingqing , Wang Tingting , Lv Zihao , Cui Mengting , Zhao Ziqiang , Cao Xiuming , Wei Qufu
Published: January 2020

Removal of a triphenylmethane dye (crystal violet, CV) by a simultaneous enzymatic-photocatalytic-adsorption treatment was investigated in this work. A desirable synergistic effect on dye treatment was achieved by decorating laccase (Lac) onto the surface of TiO2 sol-gel coated polyacrylonitrile/organically modified montmorillonite (PAN/O-MMT) nanofibers prepared by electrospinning. The assembly of Lac on the surface of PAN/O-MMT/TiO2 nanofibers was confirmed by confocal laser scanning microscope (CLSM). In comparison with free Lac, the immobilized Lac showed better pH, temperature and operational stabilities, reaching highest relative activity at an optimum pH of 3 and optimum temperature of 50 °C. Therefore, the immobilized Lac displayed a higher degradation efficiency of CV at an initial dye concentration of 100 mg/L, an optimum pH of 4.5 and temperature at 60 °C. Under UV illumination, the CV removal efficiency was further improved by ~20%. These results demonstrated that the Lac-immobilized PAN/O-MMT/TiO2 composite nanofibers with a combined effect between the immobilized enzyme and the polymeric support have potential for industrial dye degradation.
Friction and Wear Performance of Staple Carbon Fabric-Reinforced Composites: Effects of Surface Topography
Authors: Wu Chang-Mou , Cheng Yi-Ching , Lai Wen-You , Chen Po-Hsun , Way Tzong-Der
Published: January 2020

Here, staple carbon fiber fabric-reinforced polycarbonate (PC)- and epoxy (EP)-based composites with different impregnating resin levels were fabricated using a modified film stacking process. The effects of surface topographies and resin types on the tribological properties of stable carbon fabric composites (sCFC) were investigated. Friction and wear tests on the carbon composites were conducted under unlubricated sliding using a disk-on-disk wear test machine. Experimental results showed that the coefficient of friction (COF) of the sCFC was dominated by matrix type, followed by peak material portion (Smr1) values, and finalized with core height (Sk) values. The COF of composites decreased by increasing the sliding speed and applied pressure. This also relied on surface topography and temperature generated at the worn surface. However, the specific wear rate was strongly affected by resin impregnation. Partially-impregnated composites showed lower specific wear rate, whereas fully-impregnated composites showed a higher wear rate. This substantially increased by increasing the sliding speed and applied pressure. Scanning electron microscopy observations of the worn surfaces revealed that the primary wear mechanisms were abrasion, adhesion, and fatigue for PC-based composites. For EP-based composites, this was primarily abrasion and fatigue. Results proved that partially-impregnated composites exhibited better tribological properties under severe conditions.
Erratum: Effect of Porosity and Crystallinity on 3D Printed PLA Properties. Polymers 2019, 11, 1487
Authors: Liao Yuhan , Liu Chang , Coppola Bartolomeo , Barra Giuseppina , Di Maio Luciano , Incarnato Loredana , Lafdi Khalid
Published: January 2020

The authors wish to make a change to the published paper [...]
In Situ Preparation of Amphibious ZnO Quantum Dots with Blue Fluorescence Based on Hyperbranched Polymers and their Application in Bio-Imaging
Authors: Lei Gaiying , Yang Shu , Cao Ranran , Zhou Peng , Peng Han , Peng Rui , Zhang Xiaoming , Yang Yujiao , Li Yueyang , Wang Mengyue , He Yaru , Zhou Linzhu , Du Jimin , Du Weimin , Shi Yunfeng , Wu Hankui
Published: January 2020

A new strategy for preparing amphibious ZnO quantum dots (QDs) with blue fluorescence within hyper-branched poly(ethylenimine)s (HPEI) was proposed in this paper. By changing [Zn2+]/[OH−] molar ratio and heating time, ZnO QDs with a quantum yields (QY) of 30% in ethanol were obtained. Benefiting from the amphibious property of HPEI, the ZnO/HPEI nanocomposites in ethanol could be dissolved in chloroform and water, acquiring a QY of 53%, chloroform and 11% in water. By this strategy, the ZnO/HPEI nano-composites could be applied in not only in optoelectronics, but also biomedical fields (such as bio-imaging and gene transfection). The bio-imaging application of water-soluble ZnO/HPEI nanocomposites was investigated and it was found that they could easily be endocytosed by the COS-7 cells, without transfection reagent, and they exhibited excellent biological imaging behavior.
First Comparisons of Surface Temperature Estimations between ECOSTRESS, ASTER and Landsat 8 over Italian Volcanic and Geothermal Areas
Authors: Silvestri Malvina , Romaniello Vito , Hook Simon , Musacchio Massimo , Teggi Sergio , Buongiorno Maria Fabrizia
Published: January 2020

The ECO System Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a new space mission developed by NASA-JPL which launched on July 2018. It includes a multispectral thermal infrared radiometer that measures the radiances in five spectral channels between 8 and 12 μm. The primary goal of the mission is to study how plants use water by measuring their temperature from the vantage point of the International Space Station. However, as ECOSTRESS retrieves the surface temperature, the data can be used to measure other heat-related phenomena, such as heat waves, volcanic eruptions, and fires. We have cross-compared the temperatures obtained by ECOSTRESS, the Advanced Spaceborne Thermal Emission and Reflectance radiometer (ASTER) and the Landsat 8 Thermal InfraRed Sensor (TIRS) in areas where thermal anomalies are present. The use of ECOSTRESS for temperature analysis as well as ASTER and Landsat 8 offers the possibility of expanding the availability of satellite thermal data with very high spatial and temporal resolutions. The Temperature and Emissivity Separation (TES) algorithm was used to retrieve surface temperatures from the ECOSTRESS and ASTER data, while the single-channel algorithm was used to retrieve surface temperatures from the Landsat 8 data. Atmospheric effects in the data were removed using the moderate resolution atmospheric transmission (MODTRAN) radiative transfer model driven with vertical atmospheric profiles collected by the University of Wyoming. The test sites used in this study are the active Italian volcanoes and the Parco delle Biancane geothermal area (Italy). In order to test and quantify the difference between the temperatures retrieved by the three spaceborne sensors, a set of coincident imagery was acquired and used for cross comparison. Preliminary statistical analyses show a very good agreement in terms of correlation and mean values among sensors over the test areas.
Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models
Authors: Hu Yang , Xu Xuelei , Wu Fayun , Sun Zhongqiu , Xia Haoming , Meng Qingmin , Huang Wenli , Zhou Hua , Gao Jinping , Li Weitao , Peng Daoli , Xiao Xiangming
Published: January 2020

The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R2 = 0.91, RMSE = 35.13 m3 ha−1, n = 321) and in the test phase (i.e., R2 = 0.58, RMSE = 65.03 m3 ha−1, and n = 138). This model was followed by the SVR model (R2 = 0.54, RMSE = 65.60 m3 ha−1, n = 321 in training; R2 = 0.54, RMSE = 66.00 m3 ha−1, n = 138 in testing), which was slightly better than the MLR model (R2 = 0.38, RMSE = 75.74 m3 ha−1, and n = 321 in training; R2 = 0.49, RMSE = 70.22 m3 ha−1, and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 108 m3) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 108 m3). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.
Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification
Authors: Myroniuk Viktor , Kutia Mykola , J. Sarkissian Arbi , Bilous Andrii , Liu Shuguang
Published: January 2020

Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15%.
Combination of Linear Regression Lines to Understand the Response of Sentinel-1 Dual Polarization SAR Data with Crop Phenology—Case Study in Miyazaki, Japan
Authors: Wali Emal , Tasumi Masahiro , Moriyama Masao
Published: January 2020

This study investigated the relationship between backscattering coefficients of a synthetic aperture radar (SAR) and the four biophysical parameters of rice crops—plant height, green vegetation cover, leaf area index, and total dry biomass. A paddy rice field in Miyazaki, Japan was studied from April to July of 2018, which is the rice cultivation season. The SAR backscattering coefficients were provided by Sentinel-1 satellite. Backscattering coefficients of two polarization settings—VH (vertical transmitting, horizontal receiving) and VV (vertical transmitting, vertical receiving)—were investigated. Plant height, green vegetation cover, leaf area index, and total dry biomass were measured at ground level, on the same dates as satellite image acquisition. Polynomial regression lines indicated relationships between backscattering coefficients and plant biophysical parameters of the rice crop. The biophysical parameters had stronger relationship to VH than to VV polarization. A disadvantage of adopting polynomial regression equations is that the equation can have two biophysical parameter solutions for a particular backscattering coefficient value, which prevents simple conversion from backscattering coefficients to plant biophysical parameters. To overcome this disadvantage, the relationships between backscattering coefficients and the plant biophysical parameters were expressed using a combination of two linear regression lines, one line for the first sub-period and the other for the second sub-period during the entire cultivation period. Following this approach, all four plant biophysical parameters were accurately estimated from the SAR backscattering coefficient, especially with VH polarization, from the date of transplanting to about two months, until the mid-reproductive stage. However, backscattering coefficients saturate after two months from the transplanting, and became insensitive to the further developments in plant biophysical parameters. This research indicates that SAR can effectively and accurately monitor rice crop biophysical parameters, but only up to the mid reproductive stage.
Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes
Authors: Cao Ruyin , Feng Yan , Liu Xilong , Shen Miaogen , Zhou Ji
Published: January 2020

Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.
Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks
Authors: Gao Jianhao , Yuan Qiangqiang , Li Jie , Zhang Hai , Su Xin
Published: January 2020

The existence of clouds is one of the main factors that contributes to missing information in optical remote sensing images, restricting their further applications for Earth observation, so how to reconstruct the missing information caused by clouds is of great concern. Inspired by the image-to-image translation work based on convolutional neural network model and the heterogeneous information fusion thought, we propose a novel cloud removal method in this paper. The approach can be roughly divided into two steps: in the first step, a specially designed convolutional neural network (CNN) translates the synthetic aperture radar (SAR) images into simulated optical images in an object-to-object manner; in the second step, the simulated optical image, together with the SAR image and the optical image corrupted by clouds, is fused to reconstruct the corrupted area by a generative adversarial network (GAN) with a particular loss function. Between the first step and the second step, the contrast and luminance of the simulated optical image are randomly altered to make the model more robust. Two simulation experiments and one real-data experiment are conducted to confirm the effectiveness of the proposed method on Sentinel 1/2, GF 2/3 and airborne SAR/optical data. The results demonstrate that the proposed method outperforms state-of-the-art algorithms that also employ SAR images as auxiliary data.
Calibration and Impact of BeiDou Satellite-Dependent Timing Group Delay Bias
Authors: Zhang Yize , Wang Hu , Chen Junping , Wang Ahao , Meng Lingdong , Wang Ershen
Published: January 2020

The accuracy of the timing group delay (TGD) transmitted in the broadcast ephemeris is an important factor that affects the service performance of a GNSS system. In this contribution, an apparent bias is found by comparing the orbit and clock difference using half-year data of the BeiDou navigation satellite system (BDS) broadcast ephemeris and precise post-processed products. The bias differs at each satellite on each frequency and shows a general systematic difference between BDS-2 and BDS-3. We attribute this to the satellite-dependent TGD bias of the BDS broadcast ephemeris, which is subsequently calibrated. Moreover, to calibrate the bias independently, a network solution strategy is proposed based on 87 globally distributed multi-GNSS experiment (MGEX) stations spanning 25 weeks. The estimated bias shows good agreement with the values observed from the orbit and clock comparison. For the validation of the bias, we compared the signal-in-space range error (SISRE) performance with and without the TGD bias correction. The results show that the SISRE of the BDS improved from 0.71, 0.81, and 1.40 m to 0.64, 0.66, and 0.64 m in the B1I, B3I, and B1I/B3I frequencies. For BDS-3, the SISRE is well within 0.50 m after the bias correction. To further validate the bias, a week’s data were collected at 97 globally distributed MGEX stations. When the TGD bias is corrected, the root mean square (RMS) of single point positioning (SPP) can be improved by 5.6, 8.4, and 21.6% in the B1I, B3I, and B1I/B3I frequencies. Meanwhile, the SISRE and SPP assessment results also indicate that the TGD bias should be corrected by each satellite rather than only corrected between BDS-2 and BDS-3.
Monitoring River Basin Development and Variation in Water Resources in Transboundary Imjin River in North and South Korea Using Remote Sensing
Authors: Kim Donghwan , Lee Hyongki , Jung Hahn Chul , Hwang Euiho , Hossain Faisal , Bonnema Matthew , Kang Do-Hyuk , Getirana Augusto
Published: January 2020

This paper presents methods of monitoring river basin development and water variability for the transboundary river in North and South Korea. River basin development, such as dams and water infrastructure in transboundary rivers, can be a potential factor of tensions between upstream and downstream countries since dams constructed upstream can adversely affect downstream riparians. However, because most of the information related to North Korea has been limited to the public, the information about dams constructed and their locations were inaccurate in many previous studies. In addition, water resources in transboundary rivers can be exploited as a political tool. Specifically, due to the unexpected water release from the Hwanggang Dam, upstream of the transboundary Imjin River in North and South Korea, six South Koreans died on 6 September 2009. The Imjin River can be used as a political tool by North Korea, and seven events were reported as water conflicts in the Imjin River from 2001 to 2016. In this paper, firstly, we have updated the information about the dams constructed over the Imjin River in North Korea using multi-temporal images with a high spatial resolution (15–30 cm) obtained from Google Earth. Secondly, we analyzed inter- and intra-water variability over the Hwanggang Reservoir using open-source images obtained from the Global Surface Water Explorer. We found a considerable change in water surface variability before and after 2008, which might result from the construction of the Hwanggang Dam. Thirdly, in order to further investigate intra-annual water variability, we present a method monitoring water storage changes of the Hwanggang Reservoir using the area-elevation curve (AEC), which was derived from multi-sensor Synthetic Aperture Radar (SAR) images (Sentinel-1A and -1B) and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). Since many previous studies for estimating water storage change have depended on satellite altimetry dataset and optical images for deriving AEC, the method adopted in this study is the only application for such inaccessible areas since no altimetry ground track exists for the Hwanggang Reservoir and because clouds can block the study area for wet seasons. Moreover, this study has newly proven that unexpected water release can occur in dry seasons because the water storage in the Hwanggang Reservoir can be high enough to conduct a release that can be used as a geopolitical tool. Using our method, potential risks can be mitigated, not in response to a water release, but based on pre-event water storage changes in the Hwanggang Reservoir.
Editorial for Special Issue “Remote Sensing for Target Object Detection and Identification”
Authors: Vivone Gemine , Addesso Paolo , Ziemann Amanda
Published: January 2020

This special issue gathers fourteen papers focused on the application of a variety of target object detection and identification techniques for remotely-sensed data. These data are acquired by different types of sensors (both passive and active) and are located on various platforms, ranging from satellites to unmanned aerial vehicles. This editorial provides an overview of the contributed papers, briefly presenting the technologies and algorithms employed as well as the related applications.
Remote Sensing of Mangroves and Estuarine Communities in Central Queensland, Australia
Authors: Chamberlain Debbie , Phinn Stuart , Possingham Hugh
Published: January 2020

Great Barrier Reef catchments are under pressure from the effects of climate change, landscape modifications, and hydrology alterations. With the use of remote sensing datasets covering large areas, conventional methods of change detection can expose broad transitions, whereas workflows that excerpt data for time-series trends divulge more subtle transformations of land cover modification. Here, we combine both these approaches to investigate change and trends in a large estuarine region of Central Queensland, Australia, that encompasses a national park and is adjacent to the Great Barrier Reef World Heritage site. Nine information classes were compiled in a maximum likelihood post classification change analysis in 2004–2017. Mangroves decreased (1146 hectares), as was the case with estuarine wetland (1495 hectares), and saltmarsh grass (1546 hectares). The overall classification accuracies and Kappa coefficient for 2004, 2006, 2009, 2013, 2015, and 2017 land cover maps were 85%, 88%, 88%, 89%, 81%, and 92%, respectively. The cumulative area of open forest, estuarine wetland, and saltmarsh grass (1628 hectares) was converted to pasture in a thematic change analysis showing the “from–to” change. We generated linear regression relationships to examine trends in pixel values across the time series. Our findings from a trend analysis showed a decreasing trend (p value range = 0.001–0.099) in the vegetation extent of open forest, fringing mangroves, estuarine wetlands, saltmarsh grass, and grazing areas, but this was inconsistent across the study site. Similar to reports from tropical regions elsewhere, saltmarsh grass is poorly represented in the national park. A severe tropical cyclone preceding the capture of the 2017 Landsat 8 Operational Land Imager (OLI) image was likely the main driver for reduced areas of shoreline and stream vegetation. Our research contributes to the body of knowledge on coastal ecosystem dynamics to enable planning to achieve more effective conservation outcomes.
A Power Spectrum Maps Estimation Algorithm Based on Generative Adversarial Networks for Underlay Cognitive Radio Networks
Authors: Han Xu , Xue Lei , Shao Fucai , Xu Ying
Published: January 2020

In the underlay cognitive radio networks, the main challenge in detecting the idle radio resources is to estimate the power spectrum maps (PSMs), where the radio propagation characteristics are hard to obtain. For this reason, we propose a novel PSMs estimation algorithm based on the generative adversarial networks (GANs). First, we constructed the PSMs estimation model as a regression model in deep learning. Then, we converted the estimation task into an image reconstruction task by image color mapping. We fulfilled the above task by designing an image generator and an image discriminator in the proposed maps’ estimation GANs (MEGANs). The generator is trained to extract the radio propagation characteristics and generate the PSMs images. However, the discriminator is trained to identify the generated images and help to improve the generator’s performance. With the training process of MEGANs, the abilities of the generator and the discriminator are enhanced continually until reaching a balance, which means a high-accuracy PSMs estimation is achieved. The proposed MEGANs algorithm learns and utilizes accurate radio propagation features from the training process rather than making direct imprecise or biased propagation assumptions as in the traditional methods. Simulation results demonstrate that the MEGANs algorithm provides a more accurate estimation performance than the conventional methods.
Regionalization of Agricultural Nonpoint Source Pollution over China with a Combination of Qualitative and Quantitative Method
Authors: Fu Qiang , Zhu Yunqiang , Huang Shengli
Published: January 2020

Agricultural nonpoint source pollution has been a serious problem in China; however, currently a lack of basic data and quantitative analysis hinders control and reduction of agricultural nonpoint source pollution. Therefore, it is necessary to explore a regionalization method in the study of nationwide agricultural nonpoint source pollution over China. This paper proposes a method of combining both quantitative calculation and qualitative analysis. Based on agricultural nonpoint source pollution mechanism, we first proposed the natural environment index, which was calculated from relief degree of land surface, thermal humidity index, water resources quantity and precipitation index, and land cover index. Second, we proposed basic agricultural environment index, which was calculated based on the area of cultivated land use and the quality of integrated soil fertility. Third, we simplified the spatial distribution of natural environment and basic agricultural environment with the method of choropleth map classification, thematic map series, and gravity centers curve. Fourth, we conducted a qualitative analysis for both the natural environment and basic agricultural environment by overlaying the classification and existing regionalization maps to reveal the intra-region homogeneity and inter-region heterogeneity with a high reliability. The regionalization method used in this study resulted in a nationwide regional zoning of agricultural nonpoint source pollution over China, and China can be divided into 10 regions, which can be a trustworthy reference for agricultural nonpoint source pollution study and management.
Daily Precipitation Threshold for Rainstorm and Flood Disaster in the Mainland of China: An Economic Loss Perspective
Authors: Liu Wenhui , Wu Jidong , Tang Rumei , Ye Mengqi , Yang Jing
Published: January 2020

Exploring precipitation threshold from an economic loss perspective is critical for rainstorm and flood disaster risk assessment under climate change. Based on the daily gridded precipitation dataset and direct economic losses (DELs) of rainstorm and flood disasters in the mainland of China, this paper first filtered a relatively reasonable disaster-triggering daily precipitation threshold (DDPT) combination according to the relationship between extreme precipitation days and direct economic loss (DEL) rates at province level and then comprehensively analyzed the spatial landscape of DDPT across China. The results show that (1) the daily precipitation determined by the combination of a 10 mm fixed threshold and 99.3th percentile is recognized as the optimal DDPT of rainstorm and flood disasters, and the correlation coefficient between annual extreme precipitation days and DEL rates reached 0.45 (p < 0.01). (2) The optimal DDPT decreases from southeast (up to 87 mm) to northwest (10 mm) across China, and the DDPTs of 7 out of 31 provinces are lower than 25 mm, while 5 provinces are higher than 50 mm on average. These results suggest that DDPTs exist with large spatial heterogeneity across China, and adopting regional differentiated DDPT is helpful for conducting effective disaster risk analysis.
Life Expectancy at Birth in Europe: An Econometric Approach Based on Random Forests Methodology
Authors: Martín Cervantes Pedro Antonio , Rueda López Nuria , Cruz Rambaud Salvador
Published: January 2020

The objective of this work is to identify and classify the relative importance of several socioeconomic factors which explain life expectancy at birth in the European Union (EU) countries in the period 2008–2017, paying special attention to greenhouse gas emissions and public environmental expenditures. Methods: The Random Forests methodology was employed, which allows classification of the socioeconomic variables considered in the analysis according to their relative importance to explain health outcomes. Results: Per capita income, the educational level of the population, and the variable AREA (which reflects the subdivision of Europe into four relatively homogeneous areas), followed by the public expenditures on environmental and social protection, are the variables with the highest relevance in explaining life expectancy at birth in Europe over the perip.1 he peusto el correo e inciod 2008–2017. Conclusions: We have identified seven sectors as the main sources of greenhouse gas emissions: Electricity, gas, steam, and air conditioning supply; manufacturing; transportation and storage; agriculture, forestry, and fishing; construction; wholesale and retail trade, repair of motor vehicles and motorcycles; and mining and quarrying. Therefore, any public intervention related to environmental policy should be aimed at these economic sectors. Furthermore, it will be more effective to focus on public programs with higher relevance to the health status of the population, such as environmental and social protection expenditures.
Does China’s Outward Direct Investment Improve the Institutional Quality of the Belt and Road Countries?
Authors: Pan Chunyang , Wei William X. , Muralidharan Etayankara , Liao Jia , Andreosso-O’Callaghan Bernadette
Published: January 2020

This article investigates the effects of China’s outward direct investment (ODI) on the institutional quality of the Belt and Road (B&R) countries. Based on a panel data set of 63 B&R countries during the period 2003 to 2016, we find that China’s ODI improves the institutional quality of B&R countries not only in the short run but also in the long run. Further, although China’s ODI exerts no differential impacts on host country institutional dimensions of “control of corruption,” “government effectiveness,” and “political stability” in countries with different natural resource endowments, it improves their institutional dimensions of “regulatory quality” and “rule of law,” implying that China’s ODI may help the host B&R countries minimize the “resource curse”. As one of the most important strategies for China’s opening-up development in the current era, the B&R initiative serves as means to promote sustainable development of B&R countries. The article therefore contributes to existing scholarship on the institutional effects of China’s ODI and sheds light on the mechanisms that drive sustainable development.
A Tool to Analyze, Ideate and Develop Circular Innovation Ecosystems
Authors: Konietzko Jan , Bocken Nancy , Hultink Erik Jan
Published: January 2020

The circular economy may help firms to maximize the value of their material resources and minimize the overall resource use, waste, pollution and emissions of their business activities. Implementing a circular economy program requires radical changes in product, business model and ecosystem innovation. Most research on circular oriented innovation takes a product or business model perspective. Few publications have explored how to innovate in ecosystems: how a group of loosely coupled organizations can change how they interact with each other to achieve a collective outcome. This study proposes the Circularity Deck: a card deck-based tool that can help firms to analyze, ideate and develop the circularity potential of their innovation ecosystems. The tool is based on a literature review of circular oriented innovation principles, and of practical examples that show how these principles have been applied. The principles are organized according to the intended circular strategy outcome that they pursue (i.e., narrow, slow, close, regenerate and inform material and energy flows), and the extent of the innovation perspective that is needed to operationalize a principle (i.e., product, business model, or ecosystem innovation). This review and categorization process first produced a novel analysis of the circular economy innovation landscape, using an ecosystem perspective. Second, these results served to develop the Circularity Deck, which was further developed and tested for ease of use and perceived usefulness in 12 workshops with 136 participants from 62 different organizations. The Circularity Deck provides an approach for future research and practice to integrate new principles and examples that can help firms to analyze, ideate and develop circular innovation ecosystems.
Local Entrepreneurship in the Context of Food Production: A Review
Authors: Kwil Izabela , Piwowar-Sulej Katarzyna , Krzywonos Małgorzata
Published: January 2020

Local food production is meaningful not only for a single producer but also for the consumer, and finally for the entire region. Therefore, it would be beneficial to take up the issue of local entrepreneurship in the context of food production. The aim of the study was to analyze important terms, research topics, and research results related to the issue of local entrepreneurship in the context of food production. Literature review revealed definitional discrepancies related to the subject of the study. Thus, the need to create an unambiguous definition of local food and local entrepreneurship was emphasized. Own definitions of these issues were provided. Most of the available publications are devoted to local food produced in selected countries. In the analyzed research papers, the problem of local food is most often correlated with marketing or health-promoting properties of local food. A research gap was identified: suggestions were made in regards to the research problems worth bringing up in empirical research in the interest of activating local entrepreneurship.
A Time-Efficiency Study of Medium-Duty Trucks Delivering in Urban Environments
Authors: Sanchez-Diaz Ivan , Palacios-Argüello Laura , Levandi Anders , Mardberg Jimmy , Basso Rafael
Published: January 2020

This paper uses data from a major logistics service provider in Gothenburg (Sweden) to (i) identify the different activities in a typical urban distribution tour, (ii) quantify the time required by drivers to perform each of these activities, and (iii) identify potential initiatives to improve time efficiency. To do so, the authors collected GPS data, conducted a time-study of the activities performed by the drivers for a week, conducted a focus group with the drivers, and a set of interviews with managers. The results show that driving represents only 30% of the time, another 15% is spent on breaks, and the remaining 55% is used to perform activities related to customer service, freight handling, and planning. The latter are subdivided into multiple activities, each taking a small amount of time. A focus group with the drivers and some interviews revealed several initiatives to improve time efficiency. Most initiatives can bring small gains, but when aggregating all potential time savings there is a big potential to improve overall time efficiency. Initiatives with highest potential and low cost are: providing better pre-advice on upcoming customers, improving route planning, having hand-free cell phone use, and enhancing handling equipment.
The Impact of Management Methods on Employee Engagement
Authors: Baran Małgorzata , Sypniewska Barbara
Published: January 2020

The aim of the paper is to present the findings of our own questionnaire-based quantitative study carried out in 2018. The research questionnaire was sent to companies in the databases of two universities (the database of enterprises cooperating with each university), which were selected according to the criterion of the number of employees (micro, small, medium, and large companies). The study attempted to identify the correlations among the following variables: people-oriented management, non-people-oriented management, direct active and passive participation, and engagement in work. Two research questions drove the research process: (RQ1) What are the links between people-oriented management and non-people-oriented management, direct (active and passive) participation, and work engagement? (RQ2) Does direct participation (active and passive) mediate the relationship between people and non-people-oriented management and employees’ engagement? To this end, 1037 employees of companies operating in Poland reported the intensity of people-oriented management, non-people-oriented management, and direct (active and passive) participation. Research findings revealed that people-oriented management and active participation (i.e., co-deciding) are the most significant for work engagement. Not only does non-people-oriented management entail a low level of engagement but a lower level of direct participation as well. As far as the dimensions of engagement are concerned (i.e., vigour, dedication, and absorption), if one of them is more intense, the other are intense as well. People-oriented management translates into active participation and the latter into engagement in all the three dimensions. A structural equation model demonstrated that perceived people-oriented management and active participation were strong, positive, and significant predictors of work engagement.
The Responses of the Ecosystems in the Tianshan North Slope under Multiple Representative Concentration Pathway Scenarios in the Middle of the 21st Century
Authors: Cai Peng , Li Chaofan , Luo Geping , Zhang Chi , Ochege Friday Uchenna , Caluwaerts Steven , De Cruz Lesley , De Troch Rozemien , Top Sara , Termonia Piet , De Maeyer Philippe
Published: January 2020

The arid ecosystem is fragile and sensitive to the changes in climate and CO2 concentration. Exploring the responses of the arid ecosystem to the changes under different representative concentration pathways (RCPs) is of particular significance for the sustainable development of the ecosystem. In this study, the dynamics of net primary productivity (NPP), evapotranspiration (ET), and water use efficiency (WUE) for arid ecosystems in Tianshan North Slope are explored by running the arid ecosystem model at 25 km resolution under RCP2.6, RCP4.5, and RCP8.5. The climate in Tianshan North Slope presents a wet-warming trend during 2006–2055 under each RCP scenario with temporal and spatial heterogeneity. In response to the changes in climate and CO2, the regional annual NPP and ET increased during 2006–2055 by a respectively maximum rate of 2.15 g C m−2 year−1 and 0.52 mm year−1 under RCP8.5. Both the NPP and ET share a similar temporal and spatial heterogeneity with climate change. Different vegetation types respond differently to the changes under different RCP scenarios with increasing WUE. Under each RCP, the non-phreatophyte, phreatophyte, and grass are more sensitive to the changes than in the others, and the broadleaf forest and cropland are less sensitive to the changes.
Does Fertilizer Use Intensity Respond to the Urban-Rural Income Gap? Evidence from a Dynamic Panel-Data Analysis in China
Authors: Zhang Chao , Hu Ruifa
Published: January 2020

This study aims to investigate the impact of the urban-rural income gap on fertilizer use intensity in China. A theoretical analysis of the relationship among per capita rural income, the urban-rural income gap, and fertilizer use intensity is developed, which is similar to the environmental Kuznets curve hypothesis. Both the Theil index and urban-rural income ratio are employed to measure the urban-rural income gap using a provincial-level panel dataset covering 25 provincial-level administrative regions over the period 1995–2017. The estimation results of the system Generalized Method of Moments show that the expansion of the urban-rural income gap significantly increases fertilizer use intensity. While an inverted U-shaped relationship exists between fertilizer use intensity and per capita rural income, the peak turning point is much higher than the actual per capita rural income of all provinces in China. This demonstrates that fertilizer use intensity would further increase with the growth of rural income over a period of time. In addition, a lower growth rate of the agricultural product price, larger total sown size, and technological progress are likely to reduce fertilizer use intensity. This study has several important policy implications for promoting the sustainable development of agriculture and rural areas in China. Specifically, efforts must be made to narrow the urban-rural income gap, encourage agricultural research and extension, and promote land conversion and appropriately scaled-up agricultural business.