bluuebunny/crossref_metadata_2025 huggingface dataset:
Contrasting Post-Fire Dynamics between Africa and South America based on MODIS Observations
Authors: Zhou Lei , Wang Yuhang , Chi Yonggang , Wang Shaoqiang , Wang Quan
Published: May 2019

Fire is an important driver of land cover change throughout the world, affecting processes such as deforestation, forest recovery and vegetation transition. Little attention has been given to the role of fire in shaping the temporal and spatial land cover changes among continents. This study has integrated two MODIS products (MCD64A1: Burned area and MCD12Q1: Land cover) over Africa and South America from 2001–2013 to explore the vegetation dynamics after fires. The results indicated that while Africa suffered from repeated fires, more than 50% of the total burned area in South America experienced only one fire. The vegetation dynamics of the high-density vegetated regions in the 10 years after a fire showed that the forest losses in the first year after a fire in Africa were slightly larger than that in South America (Africa: 17.2% vs. South America: 14.5% in the Northern Hemisphere). The continental comparison suggested that early successional forests in Africa recovered relatively fast (northern part: 10.2 years; southern part: 12.8 years) than in South America, which recovered (18.4 years) slowly in Northern Hemisphere or ever with no recovery in the Southern Hemisphere. No clear information of the recoveries of other vegetation types (i.e., shrub, grass and crop) in Africa or South America could be identified from the satellite data. In addition, we also analyzed the changes of high-density vegetation in non-burned regions in both continents. These findings highlighted the impact of the fire regime on the vegetation changes in Africa, which appear resilient to fire, but there were complex systems in South America related to fires.
Step-Frequency Ground Penetrating Radar for Agricultural Soil Morphology Characterisation
Authors: Lombardi Federico , Lualdi Maurizio
Published: May 2019

Soil morphology plays a fundamental role in the vertical and lateral movements of solutes and water transport, providing knowledge regarding spatial distribution of its textural properties and subsurface dynamics. In this framework, the measured values of electrical conductivity are able to reveal the heterogeneity of soil that is present in a particular agricultural field and they are affected by more than one important physical characteristic: soil texture, organic matter, moisture content, and the depth of the clay pan. In the microwave region, these dynamics are known to exhibit a frequency dependent behaviour. This study explores the application of a Step Frequency Continuous Wave Ground Penetrating Radar (SFCW GPR) to shed light on the practical impact that these dependencies have on the imaging results, not only regarding the electrical characterisation of the subsurface morphology, but also in its correct interpretation. This information is of notable importance for determining water-use efficiency and planning precision-agriculture programs. The results clearly show visible and significant fluctuations of the amplitude levels, depending on the considered central frequency, demonstrating that the frequency dependence of electromagnetic properties of heterogeneous soil are significant and cannot be ignored if the aim is to properly define the subsurface attributes. The measurements also suggest that correlating the delineated variations might help in the identification of extended features and the classification of areas that possess similar properties in order to increase the confidence in monitoring soil resources.
High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform
Authors: Ma Xiaodan , Zhu Kexin , Guan Haiou , Feng Jiarui , Yu Song , Liu Gang
Published: May 2019

Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.
Kalahari Wildfires Drive Continental Post-Fire Brightening in Sub-Saharan Africa
Authors: Saha Michael V. , D’Odorico Paolo , Scanlon Todd M.
Published: May 2019

Fire can induce long-lived changes to land-surface albedo, an important aspect of the Earth’s energy budget, but the temporal evolution of these anomalies is poorly understood. Due to the widespread presence of fire in Africa, this represents uncertainty in the continental energy budget, which has important implications for regional climate and hydrologic cycling. In this study, we present the first object-based accounting of albedo anomalies induced by larger (>1 km2) individual wildfires in sub-Saharan Africa. We group spatially contiguous wildfire pixels into fire objects and track the albedo anomaly for five years after the burn. We find that albedo anomalies all have the same general temporal signature: An immediate, brief period of darkening followed by persistent brightening. The strongest brightening is found in the Kalahari region while more intense and long-lived initial darkening is found in the Sahel region. The average southern hemisphere albedo anomaly is +1.50 × 10−3 in the year following wildfire, representing a statistically significant negative surface energy balance forcing on a continental scale. This study challenges an existing paradigm surrounding the physical effects of fire on the landscape. Our results suggest that models of albedo that assume a darkening and recovery to baseline are overly simplistic in almost all circumstances. Furthermore, the presumption that immediate darkening is the only meaningful effect on albedo is incorrect for the majority of the African continent.
Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network
Authors: Li Lu , Wang Chao , Zhang Hong , Zhang Bo , Wu Fan
Published: May 2019

With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar images quickly with fine spatiotemporal resolution and are not affected by weather conditions, making multi-temporal SAR images suitable for change detection. In this paper, a new urban building change detection method based on an improved difference image and residual U-Net network is proposed. In order to overcome the intensity compression problem of the traditional log-ratio method, the spatial distance and intensity similarity are combined to generate a weighting function to obtain a weighted difference image. By fusing the weighted difference image and the bitemporal original images, the three-channel color difference image is generated for building change detection. Due to the complexity of urban environments and the small scale of building changes, the residual U-Net network is used instead of fixed statistical models and the construction and classifier of the network are modified to distinguish between different building changes. Three scenes of Sentinel-1 interferometric wide swath data are used to validate the proposed method. The experimental results and comparative analysis show that our proposed method is effective for urban building change detection and is superior to the original U-Net and SVM method.
Extending ALS-Based Mapping of Forest Attributes with Medium Resolution Satellite and Environmental Data
Authors: Luther Joan E. , Fournier Richard A. , van Lier Olivier R. , Bujold Mélodie
Published: May 2019

Airborne laser scanner (ALS) data are used to map a range of forest inventory attributes at operational scales. However, when wall-to-wall ALS coverage is cost prohibitive or logistically challenging, alternative approaches are needed for forest mapping. We evaluated an indirect approach for extending ALS-based maps of forest attributes using medium resolution satellite and environmental data. First, we developed ALS-based models and predicted a suite of forest attributes for a 950 km2 study area covered by wall-to-wall ALS data. Then, we used samples extracted from the ALS-based predictions to model and map these attributes with satellite and environmental data for an extended 5600 km2 area with similar forest and ecological conditions. All attributes were predicted well with the ALS data (R2 ≥ 0.83; RMSD% < 26). The satellite and environmental models developed using the ALS-based predictions resulted in increased correspondence between observed and predicted values by 13–49% and decreased prediction errors by 8–28% compared with models developed directly with the ground plots. Improvements were observed for both multiple regression and random forest models, and for the suite of forest attributes assessed. We concluded that the use of ALS-based predictions in this study improved the estimation of forest attributes beyond an approach linking ground plots directly to the satellite and environmental data.
A Three-Step Method for Determining Unhealthy Time Period of GPS Satellite Orbit in Broadcast Ephemeris and Its Preliminary Applications for Precise Orbit Determination
Authors: Ye Fei , Yuan Yunbin , Zhang Baocheng , Tan Bingfeng , Ou Jikun
Published: May 2019

Abnormal information of satellite orbits inevitably appears in the broadcast ephemeris. Failure to obtain unhealthy information on GPS satellite orbits in precise orbit determination (POD) degrades GPS service performance. At present, the reliable unhealthy information published by the Center for Orbit Determination in Europe (CODE) is usually used, but it has at least one-day latency, and the current level of unhealthy information cannot fully meet the requirements of rapid and real-time geodetic applications, especially for non-IGS (International global navigation satellite systems (GNSS) Service) analysis centers and BeiDou navigation satellite system (BDS) users. Furthermore, the unhealthy orbit information detected by the traditional method, which is based on the synchronized pseudo-range residuals and regional observation network, cannot meet the requirement of setting separate sub-arcs in POD. In view of these problems, we propose a three-step method for determining unhealthy time periods of GPS satellite orbit in broadcast ephemeris during POD to provide reliable unhealthy information in near-real time. This method is a single-epoch solution, and it can detect unhealthy time periods in each sampling of observation in theory. It was subsequently used to detect unhealthy time periods for satellites G09 and G01 based on the 111 globally distributed tracking stations in the IGS. The performance of the new method was evaluated using cross-validation. Based on the test results, it detected an orbital leap for G09 in the broadcast ephemeris from 09:59:42 to 14:00:42 on 25 August 2017. Compared to the traditional method, the unhealthy start time using the three-step method was in better agreement with the information provided by CODE’s satellite crux files. G01 did not appear to have an orbital leap on the specified date, but it was misjudged by the traditional method. Furthermore, compared to the traditional method, the three-step method can perform unhealthy time period detection for a satellite all day long. In addition, precise orbit determination for unhealthy satellites is realized successfully with the unhealthy orbit arc information identified in this study. Compared to the CODE orbit, the root mean square and standard deviation of the new method for G09 are less than 2 cm, and the three-step method shows an improvement in accuracy compared with the traditional method. From the above results, it can be seen that this study can provide a feasible approach to meet the real-time unhealthy time period detection requirements of a satellite orbit in a broadcast ephemeris during POD. Furthermore, compared to waiting for updates of CODE’s satellite crux files or for accumulating delayed observation data, it has the potential to provide additional information in the process of generating ultra-rapid/real-time orbits.
Laboratory Intercomparison of Radiometers Used for Satellite Validation in the 400–900 nm Range
Authors: Vabson Viktor , Kuusk Joel , Ansko Ilmar , Vendt Riho , Alikas Krista , Ruddick Kevin , Ansper Ave , Bresciani Mariano , Burmester Henning , Costa Maycira , D’Alimonte Davide , Dall’Olmo Giorgio , Damiri Bahaiddin , Dinter Tilman , Giardino Claudia , Kangro Kersti , Ligi Martin , Paavel Birgot , Tilstone Gavin , Van Dommelen Ronnie , Wiegmann Sonja , Bracher Astrid , Donlon Craig , Casal Tânia
Published: May 2019

An intercomparison of radiance and irradiance ocean color radiometers (The Second Laboratory Comparison Exercise—LCE-2) was organized within the frame of the European Space Agency funded project Fiducial Reference Measurements for Satellite Ocean Color (FRM4SOC) May 8–13, 2017 at Tartu Observatory, Estonia. LCE-2 consisted of three sub-tasks: 1) SI-traceable radiometric calibration of all the participating radiance and irradiance radiometers at the Tartu Observatory just before the comparisons; 2) Indoor intercomparison using stable radiance and irradiance sources in controlled environment; and 3) Outdoor intercomparison of natural radiation sources over terrestrial water surface. The aim of the experiment was to provide one link in the chain of traceability from field measurements of water reflectance to the uniform SI-traceable calibration, and after calibration to verify whether different instruments measuring the same object provide results consistent within the expected uncertainty limits. This paper describes the activities and results of the first two phases of LCE-2: the SI-traceable radiometric calibration and indoor intercomparison, the results of outdoor experiment are presented in a related paper of the same journal issue. The indoor experiment of the LCE-2 has proven that uniform calibration just before the use of radiometers is highly effective. Distinct radiometers from different manufacturers operated by different scientists can yield quite close radiance and irradiance results (standard deviation s < 1%) under defined conditions. This holds when measuring stable lamp-based targets under stationary laboratory conditions with all the radiometers uniformly calibrated against the same standards just prior to the experiment. In addition, some unification of measurement and data processing must be settled. Uncertainty of radiance and irradiance measurement under these conditions largely consists of the sensor’s calibration uncertainty and of the spread of results obtained by individual sensors measuring the same object.
Automatic Recognition of Common Structural Elements from Point Clouds for Automated Progress Monitoring and Dimensional Quality Control in Reinforced Concrete Construction
Authors: Maalek Reza , Lichti Derek D. , Ruwanpura Janaka Y.
Published: May 2019

This manuscript provides a robust framework for the extraction of common structural components, such as columns, from terrestrial laser scanning point clouds acquired at regular rectangular concrete construction projects. The proposed framework utilizes geometric primitive as well as relationship-based reasoning between objects to semantically label point clouds. The framework then compares the extracted objects to the planned building information model (BIM) to automatically identify the as-built schedule and dimensional discrepancies. A novel method was also developed to remove redundant points of a newly acquired scan to detect changes between consecutive scans independent of the planned BIM. Five sets of point cloud data were acquired from the same construction site at different time intervals to assess the effectiveness of the proposed framework. In all datasets, the framework successfully extracted 132 out of 133 columns and achieved an accuracy of 98.79% for removing redundant surfaces. The framework successfully determined the progress of concrete work at each epoch in both activity and project levels through earned value analysis. It was also shown that the dimensions of 127 out of the 132 columns and all the slabs complied with those in the planned BIM.
Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region
Authors: Viana Cláudia M. , Girão Inês , Rocha Jorge
Published: May 2019

The increasing availability and volume of remote sensing data, such as Landsat satellite images, have allowed the multidimensional analysis of land use/land cover (LULC) changes. However, the performance of image classification is highly dependent on the quality and quantity of the training set and its temporal continuity, which may affect the accuracy of the classification and bias the analysis of the LULC changes. In this study, we intended to apply a long-term LULC analysis in a rural region based on a Landsat time series of 21 years (1995 to 2015). Here, we investigated the use of open LULC source data to provide training samples and the application of the K-means clustering technique to refine the broad range of spectral signatures for each LULC class. Experiments were conducted on a predominantly rural region characterized by a mixed agro-silvo-pastoral environment. The open source data of the official Portuguese LULC map (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, and 2015 were integrated to generate the training samples for the entire period of analysis. The time series was computed from Landsat data based on the normalized difference vegetation index and normalized difference water index, using 221 Landsat images. The Time-Weighted Dynamic Time Warping (TWDTW) classifier was used, since it accounts for LULC-type seasonality and has already achieved promising overall accuracy values for classifications based on time series. The results revealed that the proposed method was efficient in classifying a long-term satellite time-series with an overall accuracy of 76%, providing insights into the main LULC changes that occurred over 21 years.
3D-Modelling of Charlemagne’s Summit Canal (Southern Germany)—Merging Remote Sensing and Geoarchaeological Subsurface Data
Authors: Schmidt Johannes , Rabiger-Völlmer Johannes , Werther Lukas , Werban Ulrike , Dietrich Peter , Berg Stefanie , Ettel Peter , Linzen Sven , Stele Andreas , Schneider Birgit , Zielhofer Christoph
Published: May 2019

The Early Medieval Fossa Carolina is the first hydro-engineering construction that bridges the Central European Watershed. The canal was built in 792/793 AD on order of Charlemagne and should connect the drainage systems of the Rhine-Main catchment and the Danube catchment. In this study, we show for the first time, the integration of Airborne LiDAR (Light Detection and Ranging) and geoarchaeological subsurface datasets with the aim to create a 3D-model of Charlemagne’s summit canal. We used a purged Digital Terrain Model that reflects the pre-modern topography. The geometries of buried canal cross-sections are derived from three archaeological excavations and four high-resolution direct push sensing transects. By means of extensive core data, we interpolate the trench bottom and adjacent edges along the entire canal course. As a result, we are able to create a 3D-model that reflects the maximum construction depth of the Carolingian canal and calculate an excavation volume of approx. 297,000 m3. Additionally, we compute the volume of the present dam remnants by Airborne LiDAR data. Surprisingly, the volume of the dam remnants reveals only 120,000 m3 and is much smaller than the computed Carolingian excavation volume. The difference reflects the erosion and anthropogenic overprint since the 8th century AD.
A Two-Stage Localization Scheme with Partition Handling for Data Tagging in Underwater Acoustic Sensor Networks
Authors: Islam Tariq , Lee Yong Kyu
Published: May 2019

Knowledge about the geographic coordinates of underwater sensor nodes is of primary importance for many applications and protocols of under water sensor networks (UWSNs) thus making localization of sensor nodes a crucial part of underwater network design. In case of mobile underwater sensor network, location estimation becomes challenging not only due to the need for periodic tracking of nodes, but also due to network partitioning caused by the pseudo-random mobility of nodes. Our proposed technique accomplishes the task of localization in two stages: (1) relative localization of sensor nodes with respect to a reference node at regular intervals during sensing operation. (2) Offline absolute localization of sensor nodes using absolute coordinates of the reference node and relative locations estimated during stage 1. As our protocol deals with mobile underwater sensor networks that may introduce network partitioning, we also propose a partition handling routine to deal with network partitions to achieve high localization coverage. The major design goal of our work is to maximize localization coverage while keeping communication overhead minimum, thus achieving better energy efficiency. Major contributions of this paper are: (1) Two energy efficient relative localization techniques, and (2) A partition handling strategy that ensures localization of partitioned nodes.
A Rare Event Modelling Approach to Assess Injury Severity Risk of Vulnerable Road Users
Authors: Vilaça Mariana , Macedo Eloísa , Coelho Margarida C.
Published: May 2019

Vulnerable road users (VRUs) represent a large portion of fatalities and injuries occurring on European Union roads. It is therefore important to address the safety of VRUs, particularly in urban areas, by identifying which factors may affect the injury severity level that can be used to develop countermeasures. This paper aims to identify the risk factors that affect the severity of a VRU injured when involved in a motor vehicle crash. For that purpose, a comparative evaluation of two machine learning classifiers—decision tree and logistic regression—considering three different resampling techniques (under-, over- and synthetic oversampling) is presented, comparing both imbalanced and balanced datasets. Crash data records were analyzed involving VRUs from three different cities in Portugal and six years (2012–2017). The main conclusion that can be drawn from this study is that oversampling techniques improve the ability of the classifiers to identify risk factors. On the one hand, this analysis revealed that road markings, road conditions and luminosity affect the injury severity of a pedestrian. On the other hand, age group and temporal variables (month, weekday and time period) showed to be relevant to predict the severity of a cyclist injury when involved in a crash.
Too Many Cooks: Multiple International Principals Can Spoil the Quality of Governance
Authors: Abouharb M. Rodwan , Cingranelli David , Filippov Mikhail
Published: May 2019

We contribute to the research stream emphasizing the competition between international organizations and citizens for influence over the domestic policy choices of national politicians. Drawing upon previous theoretical and empirical work on the common agency problem, we contend that the joint influence of a country’s memberships in multiple international governmental organizations (IGOs) generates consistent, unintended, disruptive effects, which reduces domestic accountability and can worsen the quality of a domestic government. Even if we assume that joining any particular IGO is beneficial for member states, the competing demands of multiple IGO memberships could undermine the quality of their governments. Our comparative, cross-national empirical findings support this theoretical expectation. Countries participating in a larger number of IGOs tend to have poorer scores on five widely used indicators of the quality of a domestic government. Future research should identify the types of policies and countries where the negative externalities of international cooperation on domestic accountability are greatest.
Economic, Social Impacts and Operation of Smart Factories in Industry 4.0 Focusing on Simulation and Artificial Intelligence of Collaborating Robots
Authors: Benotsmane Rabab , Kovács György , Dudás László
Published: May 2019

Smart Factory is a complex system that integrates the main elements of the Industry 4.0 concept (e.g., autonomous robots, Internet of Things, and Big data). In Smart Factories intelligent robots, tools, and smart workpieces communicate and collaborate with each other continuously, which results in self-organizing and self-optimizing production. The significance of Smart Factories is to make production more competitive, efficient, flexible and sustainable. The purpose of the study is not only the introduction of the concept and operation of the Smart Factories, but at the same time to show the application of Simulation and Artificial Intelligence (AI) methods in practice. The significance of the study is that the economic and social operational requirements and impacts of Smart Factories are summarized and the characteristics of the traditional factory and the Smart Factory are compared. The most significant added value of the research is that a real case study is introduced for Simulation of the operation of two collaborating robots applying AI. Quantitative research methods are used, such as numerical and graphical modeling and Simulation, 3D design, furthermore executing Tabu Search in the space of trajectories, but in some aspects the work included fundamental methods, like suggesting an original whip-lashing analog for designing robot trajectories. The conclusion of the case study is that—due to using Simulation and AI methods—the motion path of the robot arm is improved, resulting in more than five percent time-savings, which leads to a significant improvement in productivity. It can be concluded that the establishment of Smart Factories will be essential in the future and the application of Simulation and AI methods for collaborating robots are needed for efficient and optimal operation of production processes.
What is the Best Inference Trajectory for Mapping Soil Functions: An Example of Mapping Soil Available Water Capacity over Languedoc Roussillon (France)
Authors: Styc Quentin , Lagacherie Philippe
Published: May 2019

Extending digital soil mapping to the mapping of soil functions that can support end-user decisions comes to coupling a digital soil mapping procedure and a soil function assessment method. This can be done following various possible inference trajectories following the order with which “combining primary soil properties”, “aggregating soil layers across depths” and “mapping” are executed to provide the targeted output. Eighteen inference trajectories, designed for computing soil available water capacity maps in the Languedoc–Roussillon region (France), were compared with regard to their mapping performances. The best performance (SSMSE = 0.42) was obtained by a trajectory that, before mapping, combined the three first GlobalSoilMap soil layers and computed the available water capacity of each layer. The worst (SSMSE = 0.07) was observed when all the soil layers and soil properties were combined prior to mapping. We explain the observed differences between trajectories by examining the differences in mapping errors and in error propagation between the compared trajectories, which involve both the correlations between the soil properties and between their mapping errors. This paves the way to spatial soil inference systems that could perform an ex ante selection of the best possible inference trajectory for mapping a soil function.
Business Models and Performance of International Construction Companies
Authors: Jang Youjin , Ahn Yonghan , Park Moonseo , Lee Hyun-Soo , Kwon Nahyun
Published: May 2019

As the construction business environment becomes ever more competitive and intense, business models are receiving considerable attention as potential sources of sustainable survival and growth. In order to design sustainable business models in today’s global construction market, it is important to understand the business models that would make a construction company achieve higher performance in terms of profitability, growth and market competitiveness. Therefore, this study identifies the business model variables of international construction and statistically analyzes the relationship between business model variables and firm performance guiding 72 international construction companies over a six-year period from 2009 to 2014. We examine the effect of business model variables on firm performance and how different business model variables can lead to different outcomes. The results show that business models play significant roles in determining the performance of international construction companies, with financial resources being a major determinant of profitability and regional diversification a major determinant of revenue growth and market competitiveness. Each business model variable had a different effect on profitability, growth and market competitiveness. This confirms that there are ideal combinations of business model variables that can help firms achieve higher performance. These findings are expected to provide useful guidance to assist executives’ decision making when designing a business model that will enable their firm to thrive in the global marketplace.
Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images
Authors: Guimarães Tainá T. , Veronez Maurício R. , Koste Emilie C. , Souza Eniuce M. , Brum Diego , Gonzaga Luiz , Mauad Frederico F.
Published: May 2019

The concentration of suspended solids in water is one of the quality parameters that can be recovered using remote sensing data. This paper investigates the data obtained using a sensor coupled to an unmanned aerial vehicle (UAV) in order to estimate the concentration of suspended solids in a lake in southern Brazil based on the relation of spectral images and limnological data. The water samples underwent laboratory analysis to determine the concentration of total suspended solids (TSS). The images obtained using the UAV were orthorectified and georeferenced so that the values referring to the near, green, and blue infrared channels were collected at each sampling point to relate with the laboratory data. The prediction of the TSS concentration was performed using regression analysis and artificial neural networks. The obtained results were important for two main reasons. First, although regression methods have been used in remote sensing applications, they may not be adequate to capture the linear and/or non-linear relationships of interest. Second, results show that the integration of UAV in the mapping of water bodies together with the application of neural networks in the data analysis is a promising approach to predict TSS as well as their temporal and spatial variations.
Hybrid Simulation of Soil Station System Response to Two-Dimensional Earthquake Excitation
Authors: Yang Cheng-Yu , Cai Xue-Song , Yuan Yong , Ma Yuan-Chi
Published: May 2019

Soil station system seismic issues have been highly valued in recent years. In order to investigate the dynamic seismic behaviors of the intermediate column in soil station systems, a hybrid test of a soil station system was conducted. The soil station model was performed with OpenSees. Virtual hybrid simulation was fulfilled with adapter elements. A hybrid model, composed of the steel column specimen and the remainder numerical model, was assembled using the OpenFresco framework. An intermediate column was treated as the physical substructure, while the rest of the soil station system was treated as the numerical substructure in a hybrid simulation. The hybrid test results are compared with the analytical results. The data obtained from such tests show that the system can accurately reflect the mechanical properties of intermediate columns in soil station systems. A hybrid simulation would be a proper way to assess the seismic performance of a soil station system.
Shedding Light on Realized Sustainable Consumption Behavior and Perceived Barriers of Young Adults for Creating Stimulating Teaching–Learning Situations
Authors: Kreuzer Christine , Weber Susanne , Off Mona , Hackenberg Tobias , Birk Chiara
Published: May 2019

Sustainable consumption behavior has a central impact on climate balance. Research on sustainable consumption behavior of young adults is still in its infancy. To boost sustainable consumption behavior for young adults in vocational education training, it is necessary to reveal insights on their behavior during their sustainable consumption processes and in its barriers. To get insights of young adults’ sustainable consumption intention, we used a questionnaire (N = 60). To identify young adults’ consumption behavior as well as the perceived barriers, we also conducted a semi-structured interview (N = 14). Results show that young adults primarily consider the acquisition and bear less in mind the phases of use and disposal during their realized sustainable consumption behavior. The main barriers identified are high price, lack of information, as well as knowledge and abilities. They do not want to spend time for housekeeping and are not ready to disclaim from fashion or habits. Based on that, stimulating teaching–learning situations can be constructed to broaden the view on all consumption phases. Strategies of already realized behavior can be assured.
A Study on the Sustainable Development Strategy of Firms: Niche and Social Network Theory
Authors: Chen Yung-Shuan , Lei Han-Sheng , Hsu Wei-Che
Published: May 2019

In the industrial environment, individual enterprises survive by obtaining specific resources. Niche theory uses adaptations of the organizational niche and the environment to explain the survival opportunities of generalist and specialist organizations. However, it does not explain in depth the key to the sustainable development of many enterprises with poor conditions, which have productive social relationships and can obtain the resources that are needed for sustainable development from these relationships. In this study, 106 valid questionnaires were obtained through e-mail. Taiwanese firms investing in China were taken as the enterprise groups, and SPSS statistical software was used to test the empirical relationships among niche characteristics, network relationships, and enterprise performance. The research draws the following conclusions. (1) When the external environment changes unfavorably, the financial performance of firms with a high degree of network embeddedness and large niche width is poorer than that of firms with a low degree of network embeddedness. (2) When the external environment changes unfavorably, the financial performance of firms with a low degree of network centrality and high niche specificity is better than that of firms with a high degree of network centrality. These results can provide suggestions for management theory and practice.
Forest Certification: More Than a Market-Based Tool, Experiences from the Asia Pacific Region
Authors: Lewin Allison , Mo Karen , Scheyvens Henry , Gabai Sara
Published: May 2019

Over the last 25 years, the global area of certified forests has grown rapidly and voluntary forest certification has become recognized as an effective tool to engage international markets in improving sustainability within forest management units. However, the bulk of this growth has occurred in North America, Northern Europe, Australia, and New Zealand, with relatively limited uptake in the tropics. Since its creation, forest certification has been largely understood as a “market-based” mechanism, in contrast to government-led policies and regulations. Through the experience of the Responsible Asia Forestry and Trade (RAFT) partnership in the Asia Pacific region, we find that the framing of forest certification as voluntary and market-based, and as a mechanism to overcome governance failure, has created an artificial dichotomy. In this dichotomy, voluntary certification and regulatory measures to promote sustainable forest management are conceived of and pursued largely independently. We argue that it is more constructive to view them as complementary approaches that share a common goal of increasing sustainability across the forestry sector. In practice, forest certification interacts with conventional governance institutions and mechanisms. Understanding these interactions and their implications, as well as additional possibilities for interaction, will help in realizing the full potential of forest certification.
Reviewing the Poyang Lake Hydraulic Project Based on Humans’ Changing Cognition of Water Conservancy Projects
Authors: Wu Yao , Guo Lidan , Xia Ziqiang , Jing Peiran , Chunyu Xunzhou
Published: May 2019

Water conservancy projects occupy an extremely important status in human development history. Human cognition about water conservancy projects has been in constant evolution along with the changing relationship between humans and nature (water). Based on a literature review, this study provides a systemic summary of the evolution of the human need for water resources and of water conservancy projects, as well as the equilibrium between humans and nature (river and lake ecosystems). More importantly, the transformation of cognition concerning the governance of nature and river and lake ecosystems is reviewed. Taking the perspective of modern rationalism on water conservancy projects, four major changes in human cognition are identified. Further, the most salient water-related issues arising from China’s largest freshwater lake, Poyang Lake, are analyzed and the history and predicament of its governance are described. This is accompanied by a discussion on the countermeasures. Finally, based on the evolution of the human–water relationship and cognitive transformation concerning water conservancy projects, several suggestions are proposed for the planned construction of the Poyang Lake Hydraulic Project. The conclusions of this paper shed new light on the principles of water governance and water conservancy project construction in a new era that has witnessed a growing conflict between socioeconomic development and the water environment. Our suggestions are also valuable for the governance of Poyang Lake.
Visible Near Infrared Spectroscopy as a Green Technology: An Environmental Impact Comparative Study on Olive Oil Analyses
Authors: Casson Andrea , Beghi Roberto , Giovenzana Valentina , Fiorindo Ilaria , Tugnolo Alessio , Guidetti Riccardo
Published: May 2019

The olive oil industry is a significant productive sector in the European Union and the related production process is characterised by practices and techniques associated with several adverse effects on the environment. In the literature, many works on the environmental impact assessment of the olive oil chain have been carried out but the effects of the analytical analyses procedures were considered to be negligible. Currently, the reduction of solvents and of energy consumption in the laboratory has become a crucial aspect to be investigated. In this scenario, non-destructive optical methods based on visible/near-infrared (vis/NIR) spectroscopy represent a simple, rapid, and easy-to-use method to predict olive and olive oil quality parameters. Therefore, the aim of the work was to evaluate the environmental impact of the use of optical vis/NIR technologies for analytical assessment in comparison to chemical analyses on olive oil. The life cycle assessment method (LCA) was used. The functional unit defined for this study was the analysis and a “from cradle to grave” approach was applied. The vis/NIR technology results were distinctly better, by 36 times on average, than the chemical methods. Attention must be paid to the calibration phase of the vis/NIR instrumentation: In this case, the two methods must coexist for this initial procedure to obtain the required reference data for a reliable chemometric model. In conclusion, the vis/NIR spectroscopy gives very reliable results and can be considered a green technology, representing a choice among applications of low environmental impact analytical technologies.
Paradoxes of the Italian Historic Centres between Underutilisation and Planning Policies for Sustainability
Authors: Pellegrini Paola , Micelli Ezio
Published: May 2019

The paper presents the analysis of the statistical data on population and real estate in 20 small-to-medium-sized cities in Northern Italy and shows a high rate of vacancy of housing and significant shrinkage of businesses and institutions in the historic centres, where urban heritage is concentrated. Given these findings, the paper analyses the official city plans of the cities with the worst underutilisation conditions, to understand how the plans have reacted to the decline of the centre. The result shows the extensive planning and regulation activity has very limitedly registered the phenomenon and failed to propose the empty inner cores as resources to reduce land consumption and recycle valuable assets in a circular economic vision. Combining the statistical data and the findings from the city plans, the paper concludes that Italian historic centres are living paradoxes—a collection of beauty, icon of well-being, model of sustainability, but abandoned—and therefore, the dense regulatory mechanisms that were necessary to conserve urban heritage during the decades of economic and demographic growth must be reframed to implement a circular economy and adapt to new requirements for living conditions.
Comparison of Health and Well-Being Aspects in Building Certification Schemes
Authors: Potrč Obrecht Tajda , Kunič Roman , Jordan Sabina , Dovjak Mateja
Published: May 2019

The quality of the indoor environment is becoming increasingly important because people are spending the majority of their time indoors. This has led to an increased interest in the field of health and well-being of users of buildings and to the development of various assessment schemes dealing with this issue. In this study, LEED O+M Building Operations and Maintenance (hereinafter LEED O+M), BREEAM In-Use and DGNB Buildings In-Use were compared with a specialized BCS WELL that is focused on the health and well-being of building occupants. The aim of the comparison was to evaluate to what extent the selected building certification schemes (BCSs) cover the aspects of health and well-being in buildings. Based on the analysis, it was found that the observed BCSs mostly pay attention to indoor air quality, light and thermal comfort. In other areas, only certain topics are covered or the topics have a different focus. Another important finding of the study was that certain aspects that are important for the health and well-being of the occupants are not dependent only on the building design but also on the management of the building and the services provided within the building. This kind of information is especially valuable for building developers, managers and owners so they know how to improve health and well-being in a building. The present study provides them with a comprehensive overview of the certification schemes that are widely used in current practice.
Mountain Farming Systems’ Exposure and Sensitivity to Climate Change and Variability: Agroforestry and Conventional Agriculture Systems Compared in Ecuador’s Indigenous Territory of Kayambi People
Authors: Córdova Raúl , Hogarth Nicholas J. , Kanninen Markku
Published: May 2019

Smallholder farming is considered one of the most vulnerable sectors to the impacts of climate change, variability, and extremes, especially in the developing world. This high vulnerability is due to the socioeconomic limitations and high environmental sensitivity which affect the biophysical and socioeconomic components of their farming systems. Therefore, systems’ functionality and farmers’ livelihoods will also be affected, with significant implications for global food security, land-use/land-cover change processes and agrobiodiversity conservation. Thus, less vulnerable and more resilient smallholder farming systems constitute an important requisite for sustainable land management and to safeguard the livelihoods of millions of rural and urban households. This study compares a comprehensive socioeconomic and environmental dataset collected in 2015–2016 based on household interviews of 30 farmers of highland agroforestry systems and 30 farmers of conventional agriculture systems, to determine which system provides better opportunities to reduce exposure and sensitivity. A modified Climate Change Questionnaire Version 2 of the World Overview of Conservation Approaches and Technologies (WOCAT) was applied to collect the data. The interview data are based on the perceptions of Kayambi indigenous farmers about the levels of exposure and sensitivity of their farming systems during the last decade. Descriptive statistics were applied to analyze the data from the 60 farms. Results indicate that both agroforesters and conventional farmers clearly perceived increases in temperature and reductions in precipitation for the last decade, and expected this trend to continue in the next decade. Furthermore, conventional farmers perceived greater exposure to droughts (20%), solar radiation (43%), and pests, weeds and disease outbreaks (40%) than agroforesters. Additionally, results emphasize the better ability of agroforestry systems to reduce exposure and sensitivity to climate change and variability. These findings support the well-known assumptions about the key role played by agroforestry systems for climate change adaptation and mitigation, especially in developing countries.