bluuebunny/crossref_metadata_2025 huggingface dataset:
Detecting Tree Stems from Volumetric TLS Data in Forest Environments with Rich Understory
Authors: Heinzel Johannes , Huber Markus
Published: December 2016

The present study introduces a method to identify tree stems from terrestrial laser scanning (TLS) data. We focused on forest environments of diverse and layered structure, which were technically characterized by strong occlusion effects with regards to laser scanning. The number and distribution of tree stems are important information for the management of protective forests against natural hazards, for forest inventory, and for ecological studies. Our approach builds upon a three-dimensional (3D) voxel grid transformation of the original point cloud data, followed by two major steps of processing. Firstly, a series of morphological operations removed leaves and branches and left only potential stem segments. Secondly, the stem segments of each tree were combined by a multipart workflow, which uses shape and neighborhood criteria. At the same time, erroneous fragments and noise were removed from the dataset. As a result, each object in the voxel grid was represented by a single connected component referring to one specific tree stem. Testing the method on nine spatially independent plots provided detection rates of 97% for the number and location of stems from mature trees with a diameter >= 12 cm and 84% for smaller trees with a minimum of 130 cm total tree height. In summary, we obtained a dataset covering the number and locations of the stems from both mature and understory trees, while not aiming at a precise reconstruction of the stem shape.
Multi-Task Joint Sparse and Low-Rank Representation for the Scene Classification of High-Resolution Remote Sensing Image
Authors: Qi Kunlun , Liu Wenxuan , Yang Chao , Guan Qingfeng , Wu Huayi
Published: December 2016

Scene classification plays an important role in the intelligent processing of High-Resolution Satellite (HRS) remotely sensed images. In HRS image classification, multiple features, e.g., shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS images. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structures across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.
Gap-Filling of Landsat 7 Imagery Using the Direct Sampling Method
Authors: Yin Gaohong , Mariethoz Gregoire , McCabe Matthew
Published: December 2016

The failure of the Scan Line Corrector (SLC) on Landsat 7 imposed systematic data gaps on retrieved imagery and removed the capacity to provide spatially continuous fields. While a number of methods have been developed to fill these gaps, most of the proposed techniques are only applicable over relatively homogeneous areas. When they are applied to heterogeneous landscapes, retrieving image features and elements can become challenging. Here we present a gap-filling approach that is based on the adoption of the Direct Sampling multiple-point geostatistical method. The method employs a conditional stochastic resampling of known areas in a training image to simulate unknown locations. The approach is assessed across a range of both homogeneous and heterogeneous regions. Simulation results show that for homogeneous areas, satisfactory results can be obtained by simply adopting non-gap locations in the target image as baseline training data. For heterogeneous landscapes, bivariate simulations using an auxiliary variable acquired at a different date provides more accurate results than univariate simulations, especially as land cover complexity increases. Apart from recovering spatially continuous fields, one of the key advantages of the Direct Sampling is the relatively straightforward implementation process that relies on relatively few parameters.
A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements
Authors: Shi Yuechan , Wang Jindi , Wang Jian , Qu Yonghua
Published: December 2016

High-resolution leaf area index (LAI) maps from remote sensing data largely depend on empirical models, which link field LAI measurements to the vegetation index. The existing empirical methods often require the field measurements to be sufficient for constructing a reliable model. However, in many regions of the world, there are limited field measurements available. This paper presents a prior knowledge-based (PKB) method to derivate LAI with limited field measurements, in an effort to improve the accuracy of empirical model. Based on the assumption that the experimental sites with the same vegetation type can be represented by similar models, a priori knowledge for crops was extracted from the published models in various cropland sites. The knowledge, composed of an initial guess of each model parameter with the associated uncertainty, was then combined with the local field measurements to determine a semi-empirical model using the Bayesian inversion method. The proposed method was evaluated at a cropland site in the Huailai region of Hebei Province, China. Compared with the regression method, the proposed PKB method can effectively improve the accuracy of empirical model and LAI estimation, when the field measurements were limited. The results demonstrate that a priori knowledge extracted from the universal sites can provide important auxiliary information to improve the representativeness of the empirical model in a given study area.
Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space
Authors: Yang Bisheng , Huang Ronggang , Li Jianping , Tian Mao , Dai Wenxia , Zhong Ruofei
Published: December 2016

Reconstructing building models at different levels of detail (LoDs) from airborne laser scanning point clouds is urgently needed for wide application as this method can balance between the user’s requirements and economic costs. The previous methods reconstruct building LoDs from the finest 3D building models rather than from point clouds, resulting in heavy costs and inflexible adaptivity. The scale space is a sound theory for multi-scale representation of an object from a coarser level to a finer level. Therefore, this paper proposes a novel method to reconstruct buildings at different LoDs from airborne Light Detection and Ranging (LiDAR) point clouds based on an improved morphological scale space. The proposed method first extracts building candidate regions following the separation of ground and non-ground points. For each building candidate region, the proposed method generates a scale space by iteratively using the improved morphological reconstruction with the increase of scale, and constructs the corresponding topological relationship graphs (TRGs) across scales. Secondly, the proposed method robustly extracts building points by using features based on the TRG. Finally, the proposed method reconstructs each building at different LoDs according to the TRG. The experiments demonstrate that the proposed method robustly extracts the buildings with details (e.g., door eaves and roof furniture) and illustrate good performance in distinguishing buildings from vegetation or other objects, while automatically reconstructing building LoDs from the finest building points.
Using MODIS Data to Predict Regional Corn Yields
Authors: Ban Ho-Young , Kim Kwang , Park No-Wook , Lee Byun-Woo
Published: December 2016

A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data, which are eight-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season (end of DOY (EOD)) was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the LAD-logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield. The yield model YP used LAD from predicted emergence to maturity, and the yield model YF used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model, including the predicted phenology, performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed similar or better performance, even for the much earlier pre-harvest yield prediction at DOY 257. In addition, the model performance showed no difference between the two study regions with very different climates and cultivation methods, including cultivar and irrigation management. These results suggested that the approach described in this paper has potential for application to relatively wide agroclimatic regions with different cultivation methods and for extension to the other crops. However, it needs to be examined further in tropical and sub-tropical regions, which are very different from the two study regions with respect to agroclimatic constraints and agrotechnologies.
Advancing NASA’s AirMOSS P-Band Radar Root Zone Soil Moisture Retrieval Algorithm via Incorporation of Richards’ Equation
Authors: Sadeghi Morteza , Tabatabaeenejad Alireza , Tuller Markus , Moghaddam Mahta , Jones Scott
Published: December 2016

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar observations, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For this reason, an empirical quadratic function (second order polynomial) is currently applied in the AirMOSS inversion algorithm to retrieve the SMP. The three free parameters of the polynomial are retrieved for each AirMOSS pixel using three backscatter observations (i.e., one frequency at three polarizations of Horizontal-Horizontal, Vertical-Vertical and Horizontal-Vertical). In this paper, a more realistic, physically-based SMP model containing three free parameters is derived, based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.
Compound Event Barrier Coverage in Wireless Sensor Networks under Multi-Constraint Conditions
Authors: Zhuang Yaoming , Wu Chengdong , Zhang Yunzhou , Jia Zixi
Published: December 2016

It is important to monitor compound event by barrier coverage issues in wireless sensor networks (WSNs). Compound event barrier coverage (CEBC) is a novel coverage problem. Unlike traditional ones, the data of compound event barrier coverage comes from different types of sensors. It will be subject to multiple constraints under complex conditions in real-world applications. The main objective of this paper is to design an efficient algorithm for complex conditions that can combine the compound event confidence. Moreover, a multiplier method based on an active-set strategy (ASMP) is proposed to optimize the multiple constraints in compound event barrier coverage. The algorithm can calculate the coverage ratio efficiently and allocate the sensor resources reasonably in compound event barrier coverage. The proposed algorithm can simplify complex problems to reduce the computational load of the network and improve the network efficiency. The simulation results demonstrate that the proposed algorithm is more effective and efficient than existing methods, especially in the allocation of sensor resources.
Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method
Authors: Shen Yueqian , Lindenbergh Roderik , Wang Jinhu
Published: December 2016

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis.
A Visual Analytics Approach for Station-Based Air Quality Data
Authors: Du Yi , Ma Cuixia , Wu Chao , Xu Xiaowei , Guo Yike , Zhou Yuanchun , Li Jianhui
Published: December 2016

With the deployment of multi-modality and large-scale sensor networks for monitoring air quality, we are now able to collect large and multi-dimensional spatio-temporal datasets. For these sensed data, we present a comprehensive visual analysis approach for air quality analysis. This approach integrates several visual methods, such as map-based views, calendar views, and trends views, to assist the analysis. Among those visual methods, map-based visual methods are used to display the locations of interest, and the calendar and the trends views are used to discover the linear and periodical patterns. The system also provides various interaction tools to combine the map-based visualization, trends view, calendar view and multi-dimensional view. In addition, we propose a self-adaptive calendar-based controller that can flexibly adapt the changes of data size and granularity in trends view. Such a visual analytics system would facilitate big-data analysis in real applications, especially for decision making support.
A Cu2+-Selective Probe Based on Phenanthro-Imidazole Derivative
Authors: Cheng Dandan , Liu Xingliang , Yang Hongzhi , Zhang Tian , Han Aixia , Zang Ling
Published: December 2016

A novel fluorescent Probe 1, based on phenanthro-imidazole has been developed as an efficient chemosensor for the trace detection of copper ions (Cu2+). Probe 1 demonstrated sensitive fluorescence quenching upon binding with Cu2+ through 1:1 stoichiometric chelation. The detection limit for Cu2+ ions was projected through linear quenching fitting to be as low as 2.77 × 10−8 M (or 1.77 ppb). The sensing response was highly selective towards Cu2+ with minimal influence from other common metal ions, facilitating the practical application of Probe 1 in trace detection.
An Indoor Positioning System Based on Wearables for Ambient-Assisted Living
Authors: Belmonte-Fernández Óscar , Puertas-Cabedo Adrian , Torres-Sospedra Joaquín , Montoliu-Colás Raúl , Trilles-Oliver Sergi
Published: December 2016

The urban population is growing at such a rate that by 2050 it is estimated that 84% of the world’s population will live in cities, with flats being the most common living place. Moreover, WiFi technology is present in most developed country urban areas, with a quick growth in developing countries. New Ambient-Assisted Living applications will be developed in the near future having user positioning as ground technology: elderly tele-care, energy consumption, security and the like are strongly based on indoor positioning information. We present an Indoor Positioning System for wearable devices based on WiFi fingerprinting. Smart-watch wearable devices are used to acquire the WiFi strength signals of the surrounding Wireless Access Points used to build an ensemble of Machine Learning classification algorithms. Once built, the ensemble algorithm is used to locate a user based on the WiFi strength signals provided by the wearable device. Experimental results for five different urban flats are reported, showing that the system is robust and reliable enough for locating a user at room level into his/her home. Another interesting characteristic of the presented system is that it does not require deployment of any infrastructure, and it is unobtrusive, the only device required for it to work is a smart-watch.
Enhancement of Fluorescence-Based Sandwich Immunoassay Using Multilayered Microplates Modified with Plasma-Polymerized Films
Authors: Yano Kazuyoshi , Iwasaki Akira
Published: December 2016

A functional modification of the surface of a 96-well microplate coupled with a thin layer deposition technique is demonstrated for enhanced fluorescence-based sandwich immunoassays. The plasma polymerization technique enabling the deposition of organic thin films was employed for the modification of the well surface of a microplate. A silver layer and a plasma-polymerized film were consecutively deposited on the microplate as a metal mirror and the optical interference layer, respectively. When Cy3-labeled antibody was applied to the wells of the resulting multilayered microplate without any immobilization step, greatly enhanced fluorescence was observed compared with that obtained with the unmodified one. The same effect could be also exhibited for an immunoassay targeting antigen directly adsorbed on the multilayered microplate. Furthermore, a sandwich immunoassay for the detection of interleukin 2 (IL-2) was performed with the multilayered microplates, resulting in specific and 88-fold–enhanced fluorescence detection.
Fiber-Optic Fabry-Pérot Interferometers for Axial Force Sensing on the Tip of a Needle
Authors: Beekmans Steven , Lembrechts Thomas , Van den Dobbelsteen John , Van Gerwen Dennis
Published: December 2016

A range of complex percutaneous procedures, such as biopsy or regional anesthesia, rely heavily on accurate needle insertion. Small variations in the mechanical properties of the pierced tissue can however cause deviations from the projected needle path and can thus result in inaccurate placement of the needle. Navigation of a rigid needle towards the target tissue is traditionally based on the surgeons capacity to interpret small variations in the needle insertion force. A more accurate measurement of these small force variations enables improvement in needle targeting, can potentially aid in enhancing force feedback in robotic needle placement and can provide valuable information on tissue-tool interaction. In this study we investigated several concepts for the design of a force sensor based on a fiber-optic Fabry-Pérot interferometer to measure needle-tissue interaction forces on the tip of a 18 G needle, where special attention was given to concepts for a sensor with (1), an intrinsic low cross-sensitivity to temperature and (2), elementary design and fabrication. Three concepts, using either a quartz capillary, an Invar capillary or a thin polyimide film as the force sensitive element were prototyped and subjected to both static and dynamic testing. The force transducer based on a quartz capillary presented the lowest cross-sensitivity to temperature ( 12 m N / ∘ C) and good accuracy (maximum measurement error of 65 m N /10 N ) in a measurement of static forces. However, limited strength of the sensor is expected to prevent usage of the quartz capillary in small diameter needles. The concepts for a sensor based on an Invar capillary or a thin polyimide film proved a higher cross-sensitivity to temperature ( 50 m N / ∘ C and 220 m N / ∘ C, respectively) and higher maximum measurement error (350 m N /10 N , 800 m N /10 N ), comparable to those of FBG-based sensors reported in literature, but are likely to be more suitable for integration in very small biopsy needles.
Multi-User Identification-Based Eye-Tracking Algorithm Using Position Estimation
Authors: Kang Suk-Ju
Published: December 2016

This paper proposes a new multi-user eye-tracking algorithm using position estimation. Conventional eye-tracking algorithms are typically suitable only for a single user, and thereby cannot be used for a multi-user system. Even though they can be used to track the eyes of multiple users, their detection accuracy is low and they cannot identify multiple users individually. The proposed algorithm solves these problems and enhances the detection accuracy. Specifically, the proposed algorithm adopts a classifier to detect faces for the red, green, and blue (RGB) and depth images. Then, it calculates features based on the histogram of the oriented gradient for the detected facial region to identify multiple users, and selects the template that best matches the users from a pre-determined face database. Finally, the proposed algorithm extracts the final eye positions based on anatomical proportions. Simulation results show that the proposed algorithm improved the average F1 score by up to 0.490, compared with benchmark algorithms.
Feasibility Test of a Liquid Film Thickness Sensor on a Flexible Printed Circuit Board Using a Three-Electrode Conductance Method
Authors: Lee Kyu , Kim Jong , Park Goon , Cho Hyoung
Published: December 2016

Liquid film thickness measurements under temperature-varying conditions in a two-phase flow are of great importance to refining our understanding of two-phase flows. In order to overcome the limitations of the conventional electrical means of measuring the thickness of a liquid film, this study proposes a three-electrode conductance method, with the device fabricated on a flexible printed circuit board (FPCB). The three-electrode conductance method offers the advantage of applicability under conditions with varying temperatures in principle, while the FPCB has the advantage of usability on curved surfaces and in relatively high-temperature conditions in comparison with sensors based on a printed circuit board (PCB). Two types of prototype sensors were fabricated on an FPCB and the feasibility of both was confirmed in a calibration test conducted at different temperatures. With the calibrated sensor, liquid film thickness measurements were conducted via a falling liquid film flow experiment, and the working performance was tested.
TPLE: A Reliable Data Delivery Scheme for On-Road WSN Traffic Monitoring
Authors: Wang Rui , Chang Fei , Ren Suli
Published: December 2016

In an on-road environment, motor-engines severely disturb the wireless link of a sensor node, leading to high package loss rate, high delivery delay, and poor radio communication quality. The existing data delivery mechanisms, such as the ACK-based retransmission mechanism and window-based link quality estimation mechanism, could not handle these challenges well. To solve this challenge, we propose a Target-Prediction-based Link quality Estimation scheme (TPLE) to realize high quality data delivery in an on-road environment. To perform on-road link quality estimation, TPLE dynamically calculates the track of a nearby vehicle target and estimates target impact on wireless link. Based on the local estimation of link quality, TPLE schedules radio communication tasks effectively. Simulations indicate that our proposed TPLE scheme produces a 94% data delivery rate, its average retransmission number is around 0.8. Our conducted on-road data delivery experiments also indicated a similar result as the computer simulation.
Influence of Individual Differences on the Calculation Method for FBG-Type Blood Pressure Sensors
Authors: Koyama Shouhei , Ishizawa Hiroaki , Fujimoto Keisaku , Chino Shun , Kobayashi Yuka
Published: December 2016

In this paper, we propose a blood pressure calculation and associated measurement method that by using a fiber Bragg grating (FBG) sensor. There are several points at which the pulse can be measured on the surface of the human body, and when a FBG sensor located at any of these points, the pulse wave signal can be measured. The measured waveform is similar to the acceleration pulse wave. The pulse wave signal changes depending on several factors, including whether or not the individual is healthy and/or elderly. The measured pulse wave signal can be used to calculate the blood pressure using a calibration curve, which is constructed by a partial least squares (PLS) regression analysis using a reference blood pressure and the pulse wave signal. In this paper, we focus on the influence of individual differences from calculated blood pressure based on each calibration curve. In our study, the calculated blood pressure from both the individual and overall calibration curves were compared, and our results show that the calculated blood pressure based on the overall calibration curve had a lower measurement accuracy than that based on an individual calibration curve. We also found that the influence of the individual differences on the calculated blood pressure when using the FBG sensor method were very low. Therefore, the FBG sensor method that we developed for measuring the blood pressure was found to be suitable for use by many people.
Literature Review: Theory and Application of In-Line Inspection Technologies for Oil and Gas Pipeline Girth Weld Defection
Authors: Feng Qingshan , Li Rui , Nie Baohua , Liu Shucong , Zhao Lianyu , Zhang Hong
Published: December 2016

Girth weld cracking is one of the main failure modes in oil and gas pipelines; girth weld cracking inspection has great economic and social significance for the intrinsic safety of pipelines. This paper introduces the typical girth weld defects of oil and gas pipelines and the common nondestructive testing methods, and systematically generalizes the progress in the studies on technical principles, signal analysis, defect sizing method and inspection reliability, etc., of magnetic flux leakage (MFL) inspection, liquid ultrasonic inspection, electromagnetic acoustic transducer (EMAT) inspection and remote field eddy current (RFDC) inspection for oil and gas pipeline girth weld defects. Additionally, it introduces the new technologies for composite ultrasonic, laser ultrasonic, and magnetostriction inspection, and provides reference for development and application of oil and gas pipeline girth weld defect in-line inspection technology.
Relative Estimation of Water Content for Flat-Type Inductive-Based Oil Palm Fruit Maturity Sensor
Authors: Misron Norhisam , Aliteh Nor , Harun Noor , Tashiro Kunihisa , Sato Toshiro , Wakiwaka Hiroyuki
Published: December 2016

The paper aims to study the sensor that identifies the maturity of oil palm fruit bunches by using a flat-type inductive concept based on a resonant frequency technique. Conventionally, a human grader is used to inspect the ripeness of the oil palm fresh fruit bunch (FFB) which can be inconsistent and inaccurate. There are various new methods that are proposed with the intention to grade the ripeness of the oil palm FFB, but none has taken the inductive concept. In this study, the resonance frequency of the air coil is investigated. Samples of oil palm FFB are tested with frequencies ranging from 20 Hz to 10 MHz and the results obtained show a linear relationship between the graph of the resonance frequency (MHz) against time (Weeks). It is observed that the resonance frequencies obtained for Week 10 (pre-mature) and Week 18 (mature) are around 8.5 MHz and 9.8 MHz, respectively. These results are compared with the percentage of the moisture content. Hence, the inductive method of the oil palm fruit maturity sensor can be used to detect the change in water content for ripeness detection of the oil palm FFB.
Towards Transgressive Learning through Ontological Politics: Answering the “Call of the Mountain” in a Colombian Network of Sustainability
Authors: Chaves Martha , Macintyre Thomas , Verschoor Gerard , Wals Arjen
Published: December 2016

In line with the increasing calls for more transformative and transgressive learning in the context of sustainability studies, this article explores how encounters between different ontologies can lead to socio-ecological sustainability. With the dominant one-world universe increasingly being questioned by those who advocate the existence of many worlds—a so-called pluriverse—there lays the possibility of not only imagining other human–nature realities, but also engaging with them in practice. Moving towards an understanding of what happens when a multiplicity of worlds encounter one another, however, entails a sensitivity to the negotiations between often competing ontologies—or ontological politics. Based on an ethnographic methodology and narrative methods, data were collected from two consecutive intercultural gatherings called El Llamado de la Montaña (The Call of the Mountain), which take place for five days every year in different parts of Colombia. By actively participating in these gatherings of multiplicity, which address complex socio-ecological challenges such as food sovereignty and defence of territory, results show how encounters between different ontologies can result in transformative and potentially transgressive learning in terms of disrupting stubborn routines, norms and hegemonic powers which tend to accelerate unsustainability. Although we argue that a fundamental part of the wicked sustainability puzzle lies in supporting more relational ontologies, we note that such learning environments also lead to conflicts through inflexibility and (ab)use of power which must be addressed if sustained socio-ecological learning is to take place.
Car Tourism in Xinjiang: The Mediation Effect of Perceived Value and Tourist Satisfaction on the Relationship between Destination Image and Loyalty
Authors: Wang Bin , Yang Zhaoping , Han Fang , Shi Hui
Published: December 2016

This study aims to test a model linking destination image, perceived value, tourist satisfaction, and tourist loyalty. Based on a sample of 300 tourists travelling by car from the World Natural Heritage Site of Tianchi, China, a new model of destination image was explored and data were analysed using partial least squares structural equation modelling (PLS-SEM). The results show that perceived value and satisfaction are direct antecedents of destination loyalty. Above all, perceived value and tourist satisfaction mediate the relationship between destination image and loyalty. Finally, this study discusses the theoretical and management implications of the findings in order to boost the tourism industry in the context of car trips.
Public Interest in Microclimate Data in Knoxville, Tennessee, USA
Authors: Mason Lisa , Hathaway Jon , Ellis Kelsey , Harrison Taylor
Published: December 2016

New technologies can sense urban environmental conditions at finer scales than previously possible. This has paved the way for monitoring microclimates between and within neighborhoods. Equally vital, though much less studied, is stakeholder engagement in understanding and using such data. This study examines interests and preferences for accessing neighborhood-scale microclimate data among residents of Knoxville, Tennessee, USA. Data are from randomly sampled phone surveys (N = 200) and purposively sampled focus group participants (N = 25). Survey participants expressed high interest in neighborhood air quality, temperature, and rainfall. Focus groups revealed four themes for designing smartphone applications or websites for neighborhood-scale data: easy access to integrated data, clear and intuitive design, information for everyday living and healthy behavior, and tools for civic engagement. Results support the value of creating meaningful, usable science interfaces with which the public can readily engage.
Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models
Authors: Dong Jiefang , Deng Chun , Li Rongrong , Huang Jieyu
Published: December 2016

With the rapid economic development of the Xinjiang Uygur Autonomous Region, the area’s transport sector has witnessed significant growth, which in turn has led to a large increase in carbon dioxide emissions. As such, calculating of the carbon footprint of Xinjiang’s transportation sector and probing the driving factors of carbon dioxide emissions are of great significance to the region’s energy conservation and environmental protection. This paper provides an account of the growth in the carbon emissions of Xinjiang’s transportation sector during the period from 1989 to 2012. We also analyze the transportation sector’s trends and historical evolution. Combined with the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and ridge regression, this study further quantitatively analyzes the factors that influence the carbon emissions of Xinjiang’s transportation sector. The results indicate the following: (1) the total carbon emissions and per capita carbon emissions of Xinjiang’s transportation sector both continued to rise rapidly during this period; their average annual growth rates were 10.8% and 9.1%, respectively; (2) the carbon emissions of the transportation sector come mainly from the consumption of diesel and gasoline, which accounted for an average of 36.2% and 2.6% of carbon emissions, respectively; in addition, the overall carbon emission intensity of the transportation sector showed an “S”-pattern trend within the study period; (3) population density plays a dominant role in increasing carbon dioxide emissions. Population is then followed by per capita GDP and, finally, energy intensity. Cargo turnover has a more significant potential impact on and role in emission reduction than do private vehicles. This is because road freight is the primary form of transportation used across Xinjiang, and this form of transportation has low energy efficiency. These findings have important implications for future efforts to reduce the growth of transportation-based carbon dioxide emissions in Xinjiang and for any effort to construct low-carbon and sustainable environments.
Sustainability Analysis and Buy-Back Coordination in a Fashion Supply Chain with Price Competition and Demand Uncertainty
Authors: Wang Fan , Zhuo Xiaopo , Niu Baozhuang
Published: December 2016

Supply chain sustainability has become significantly important in the fashion industry, and more and more fashion brands have invested in developing sustainable supply chains. We note that dual channel system comprising a brand-owned direct channel and retail outsourcing channel is quite common in the fashion industry, and in the latter, buy-back contract is popular between brands and retailers. Therefore, we build a stylized dual channel model with price competition and demand uncertainty to characterize the main properties of a fashion supply chain. Our foci are the sustainability analysis and the channel coordination mechanism. We first design a buy-back contract with return cost to coordinate the channel. We then study supply chain sustainability and examine the effect of two key influencing factors, i.e., price competition and demand uncertainty. Interestingly, we find that a fiercer price competition will lead to a more sustainable supply chain. From the perspective of supply chain managers, we conclude that (1) if managers care about environmental sustainability, fierce price competition is not a suggested strategy; (2) if managers care about economic sustainability, fierce price competition is an advantageous strategy. We also find that high demand uncertainty results in a less sustainable supply chain, in both an environmental and economic sustainability sense.
Assessment of the Carbon Footprint, Social Benefit of Carbon Reduction, and Energy Payback Time of a High-Concentration Photovoltaic System
Authors: Hu Allen , Huang Lance , Lou Sylvia , Kuo Chien-Hung , Huang Chin-Yao , Chian Ke-Jen , Chien Hao-Ting , Hong Hwen-Fen
Published: December 2016

Depleting fossil fuel sources and worsening global warming are two of the most serious world problems. Many renewable energy technologies are continuously being developed to overcome these challenges. Among these technologies, high-concentration photovoltaics (HCPV) is a promising technology that reduces the use of expensive photovoltaic materials to achieve highly efficient energy conversion. This reduction process is achieved by adopting concentrating and tracking technologies. This study intends to understand and assess the carbon footprint and energy payback time (EPBT) of HCPV modules during their entire life cycles. The social benefit of carbon reduction is also evaluated as another indicator to assess the energy alternatives. An HCPV module and a tracker from the Institute of Nuclear Energy Research (INER) were applied, and SimaPro 8.0.2 was used for the assessment. The functional unit used in this study was 1 kWh, which is produced by HCPV, and inventory data was sourced from Ecoinvent 3.0 and the Taiwan carbon footprint calculation database. The carbon footprint, EPBT, and social benefit of carbon reduction were evaluated as 107.69 g CO2eq/kWh, 2.61 years, and 0.022 USD/kWh, respectively. Direct normal irradiation (DNI), life expectancy, and the degradation rate of HCPV system were subjected to sensitivity analysis. Results show that the influence of lifetime assumption under a low DNI value is greater than those under high DNI values. Degradation rate is also another important factor when assessing the carbon footprint of HCPV under a low DNI value and a long lifetime assumption. The findings of this study can provide several insights for the development of the Taiwanese solar industry.
From a Systematic Literature Review to a Classification Framework: Sustainability Integration in Fashion Operations
Authors: Karaosman Hakan , Morales-Alonso Gustavo , Brun Alessandro
Published: December 2016

Sustainability management in global fashion operations is an area of growing concern. This can be seen by the number of research articles and industrial reports published. To establish a further debate, this study pursues two objectives. Firstly, it provides a systematic literature review pertaining to environmental and social sustainability management in fashion operations by encompassing 38 research articles indexed in Scopus from 2006 to 2016. Secondly, it presents a classification framework in which sustainability practices are categorized according to a three-dimensional concurrent engineering framework by focusing on product, process and supply chain levels. Results address that the breakdown of environmental and social sustainability practices identified in earlier research is not homogenous. For instance, some critical social aspects such as human rights are not widely covered in production processes. Similarly, serious environmental aspects such as biodiversity are not entirely focused on at the chain level. Last, this study concludes with a framework illustrating strategic priorities to be taken to advance sustainability in fashion operations.
Exploring Socio-Technical Features of Green Interior Design of Residential Buildings: Indicators, Interdependence and Embeddedness
Authors: Ning Yan , Li Yadi , Yang Shuangshuang , Ju Chuanjing
Published: December 2016

This research aims to develop indicators for assessing green interior design of new residential buildings in China, grounded in the socio-technical systems approach. The research was carried out through a critical literature review and two focus group studies. The results show that the boundaries of green interior design were identified with respect to three dimensions, namely performance, methodology and stakeholders. The socio-technical systems approach argues for the recognition of the interdependence between the systems elements and the feature of embeddedness. The interdependence of the systems elements exists within each of these three dimensions and across them. It is also found that the socio-technical systems of green interior design are embedded in the social, regulatory and geographic context. Taking interior design of residential buildings as the empirical setting, this study contributes to the literature of green building assessment by presenting a socio-technical systems approach.
A Meta-Analysis of Human–Wildlife Conflict: South African and Global Perspectives
Authors: Seoraj-Pillai Nimmi , Pillay Neville
Published: December 2016

Human–wildlife conflict (HWC), due to competition for shared natural resources between people and wildlife, influences food security of people and the well-being of people and animals. HWC is a major concern in developing countries, affecting people of different socio-economic classes. We conducted a meta-analysis of the occurrence of published scientific reports on HWC globally and South Africa particularly, to identify vulnerable human communities and their farming practices in developing and developed countries, and vulnerable wildlife guilds. We accessed Institute for Scientific Information publications from 1994 to 2015. Local communities (people living contiguous with protected natural areas) and commercial farmers jointly experienced the highest HWC incidences compared to subsistence farmers, possibly due to reporting bias for commercial farmers. Rural people in Africa and Asia experienced conflict with a diversity of mammals, confirming our expectation that developing countries could potentially experience regular encounters with wildlife. South Africa had more HWC cases than developed countries (e.g., in Australia and North America), yet the dichotomy between first world and third world economies in South Africa provides a regional exemplar of global patterns in HWC. Globally, HWC involved mainly mammals and birds, with carnivores and primates as the most high-scale conflict species and thus were a severely persecuted group. Our foundational research provides the first global assessment of HWC and showed that people in developing countries are vulnerable to HWC, perhaps related to reduced protection of livestock and crops against a larger guild of problem mammals. We suggest that a wider range of literature, including governmental and non-governmental publications, be surveyed to contribute to further research in this area of study.
Identification and Prediction of Large Pedestrian Flow in Urban Areas Based on a Hybrid Detection Approach
Authors: Zhang Kaisheng , Wang Mei , Wei Bangyang , Sun Daniel(Jian)
Published: December 2016

Recently, population density has grown quickly with the increasing acceleration of urbanization. At the same time, overcrowded situations are more likely to occur in populous urban areas, increasing the risk of accidents. This paper proposes a synthetic approach to recognize and identify the large pedestrian flow. In particular, a hybrid pedestrian flow detection model was constructed by analyzing real data from major mobile phone operators in China, including information from smartphones and base stations (BS). With the hybrid model, the Log Distance Path Loss (LDPL) model was used to estimate the pedestrian density from raw network data, and retrieve information with the Gaussian Progress (GP) through supervised learning. Temporal-spatial prediction of the pedestrian data was carried out with Machine Learning (ML) approaches. Finally, a case study of a real Central Business District (CBD) scenario in Shanghai, China using records of millions of cell phone users was conducted. The results showed that the new approach significantly increases the utility and capacity of the mobile network. A more reasonable overcrowding detection and alert system can be developed to improve safety in subway lines and other hotspot landmark areas, such as the Bundle, People’s Square or Disneyland, where a large passenger flow generally exists.
Measuring Urban Redevelopment Sustainability: Exploring Challenges from Downtown Seoul
Authors: Schuetze Thorsten , Chelleri Lorenzo , Je June-Hyung
Published: December 2016

A main challenge for the design and building of green and smart cities is the redevelopment of existing built environments. This research investigates the usefulness of urban district sustainability evaluation systems in informing and guiding urban redevelopment projects. The German DGNB Urban District (DGNB UD) sustainability assessment and certification system has been chosen among others due to its comprehensive method to evaluate eight awarded projects from the “Re-structuring Seunsangga Citywalk International Competition” organized for Seoul’s downtown redevelopment. The results have been compared with the sustainability assessment of the district status quo and the competition call text, noticing a generic enhancement of sustainability. However, sustainability categories and criteria were not addressed sufficiently by any project in order to meet the integrated and holistic certification requirements of DGNB UD. The findings from this research emphasize the need for integrated assessment and measurements of urban sustainability, beyond projects’ self-promotion emphasizing greening or single sustainability facets. The research findings particularly exemplify how awarded project proposals might still be stuck in a specific, limited framework of sustainability. This paper concludes that balanced sustainability could be achieved by utilization of comprehensive assessment and evaluation systems in the preparation and assessment of plans.
Extreme Weather Impacts on Maize Yield: The Case of Shanxi Province in China
Authors: Wei Taoyuan , Zhang Tianyi , De Bruin Karianne , Glomrød Solveig , Shi Qinghua
Published: December 2016

Extreme weather can have negative impacts on crop production. In this study, we statistically estimate the impacts of dry days, heat waves, and cold days on maize yield based on household survey data from 1993 to 2011 in ten villages of Shanxi province, China. Our results show that dry days, heat waves, and cold days have negative effects on maize yield, although these effects are marginal if these extreme events do not increase dramatically. Specifically, a one percent increase in extreme-heat-degree-days and consecutive-dry-days results in a maize yield declines of 0.2% and 0.07%, respectively. Maize yield also is reduced by 0.3% for cold days occurring during the growing season from May to September. However, these extreme events can increase dramatically in a warmer world and result in considerable reduction in maize yields. If all the historical temperatures in the villages are shifted up by 2 degrees Celsius, total impacts of these extreme events would lead to a reduction of maize yield by over 30 percent. The impacts may be underestimated since we did not exclude the offset effect of adaptation measures adopted by farmers to combat these extreme events.