High-resolution Data Collection Research Articles

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

Small unmanned aerial systems (UAS) have emerged as high-throughput platforms for the collection of high-resolution image data over large crop fields to support precision agriculture and plant breeding research. At the same time, the improved efficiency in image capture is leading to massive datasets, which pose analysis challenges in providing needed phenotypic data. To complement these high-throughput platforms, there is an increasing need in crop improvement to develop robust image analysis methods to analyze large amount of image data. Analysis approaches based on deep learning models are currently the most promising and show unparalleled performance in analyzing large image datasets. This study developed and applied an image analysis approach based on a SegNet deep learning semantic segmentation model to estimate sorghum panicles counts, which are critical phenotypic data in sorghum crop improvement, from UAS images over selected sorghum experimental plots. The SegNet model was trained to semantically segment UAS images into sorghum panicles, foliage and the exposed ground using 462, 250 × 250 labeled images, which was then applied to field orthomosaic to generate a field-level semantic segmentation. Individual panicle locations were obtained after post-processing the segmentation output to remove small objects and split merged panicles. A comparison between model panicle count estimates and manually digitized panicle locations in 60 randomly selected plots showed an overall detection accuracy of 94%. A per-plot panicle count comparison also showed high agreement between estimated and reference panicle counts (Spearman correlation ρ = 0.88, mean bias = 0.65). Misclassifications of panicles during the semantic segmentation step and mosaicking errors in the field orthomosaic contributed mainly to panicle detection errors. Overall, the approach based on deep learning semantic segmentation showed good promise and with a larger labeled dataset and extensive hyper-parameter tuning, should provide even more robust and effective characterization of sorghum panicle counts.

RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices.

BackgroundWith a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field.ObjectiveWide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy.MethodsRADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided.ResultsGeneral principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts.ConclusionsRADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

New Approaches to Late Bronze Age Urban Landscapes on Cyprus: Investigations at Kalavasos-Ayios Dhimitrios, 2012–2016

The Late Bronze Age was a period of profound transformation on the island of Cyprus. Through investigations at the Maroni complex and at Kalavasos-Ayios Dhimitrios—two urban centers in south-central Cyprus—the Kalavasos and Maroni Built Environments (KAMBE) Project seeks to understand the relationship between these changes and the coeval rise of the island's first cities. Here we discuss the results of new work at Ayios Dhimitrios, where the collection of high-resolution data is providing new insights into the emergence and development of this Late Bronze Age urban landscape. Our work has focused on two areas: (1) a monumental court-centered building (Building XVI) with possible evidence for feasting and (2) the approach to the city's administrative core, which was monumentalized through a series of construction phases. We argue that cities are produced by the place-making activities of their inhabitants at various scales, and our investigations in these two areas of Ayios Dhimitrios provide compelling evidence for elite place making through which the urban environment shaped, and was shaped by, new patterns of movement, social interaction, and daily practice. Comparison with the nearby and largely contemporaneous Maroni complex reveals that the first cities on Cyprus took rather divergent paths to becoming urban.

Examination of the Expendable Digital Dropsonde–Derived Vertical Velocities from the Tropical Cyclone Intensity (TCI) Experiment

Abstract The newly developed expendable digital dropsonde (XDD) allows for high spatial and temporal resolution data collection in tropical cyclones (TCs). In 2015, a total of 725 XDDs were launched into Hurricanes Marty (27–28 September), Joaquin (2–5 October), and Patricia (20–23 October) as part of the Tropical Cyclone Intensity (TCI) experiment. These dropsondes were launched from a NASA WB-57 at altitudes above 18 km, capturing the full depth of the TCs to the tropopause. This study documents the vertical velocity distributions observed in TCI using the XDDs and examines the distributions altitudinally, radially, and azimuthally. The strongest mean or median XDD-derived vertical velocities observed during TCI occurred in the upper levels and within the cores of the three TCs. There was little azimuthal signal in the vertical velocity distribution, likely due to sampling asymmetries and noise in the data. Downdrafts were strongest in Joaquin, while updrafts were strongest in Patricia, especially within the eyewall on 23 October. Patricia also had an impressive low-level (<2 km) updraft that exceeded 10 m s−1 associated with a shallow, overturning, radial circulation in the secondary eyewall.

Smart road stud based two-lane traffic surveillance

This paper develops the two-lane traffic surveillance function of a smart road stud (SRS), which has the capabilities of high-resolution traffic data collection and transmission, and a light-based traffic guidance. The SRS addresses the limitations of the current systems with the potential for high performance and usage for a variety of applications. SRS nodes are mounted on the road surface along the lane separation lines instead of the intrusive middle of the lane detection sensors. The key innovation in SRS design is its ability to detect vehicles in two adjacent lanes in real time by employing a three-axis geomagnetic sensor mounting on the lane line, which is the basis of advanced applications such as traffic surveillance, driving guidance, and congestion monitoring. Multiple vehicles are recognized based on the superposition of the disturbance of the geomagnetic fields. Magnetic field changes along the axes are simulated and analyzed with vehicles modeled as magnet dipoles to determine the criteria for the detection of vehicles in adjacent lanes, which are verified by actual waveforms. Extended dual-window and two-lane vehicle detection algorithms are developed, enabling to distinguish five detection scenarios and count vehicles in two lanes. Field test results show that one SRS can detect traffic in adjacent lanes with a correct vehicle detection rate of over 98%, the same level achievable by intrusive middle of the lane detectors. Based on the enhanced features, high performance, cost-effectiveness, and value-added services, the SRS is a significant improvement on the conventional studs for intelligent transport system applications.

Integration of Wireless Sensor Networks and Smart UAVs for Precision Viticulture

Precision viticulture (PV) aims to improve the grapevine production efficiency, quality, and profitability, while reducing the environmental impact. The promises of PV are realized only if large areas are monitored with high spatial and temporal resolutions. This paper considers the integration of a wireless sensor network and a smart unmanned aerial vehicle platform. To this end, local variations of factors that influence grape yield and quality are measured and site-specific management practices are applied. This approach achieves real-time, uninterrupted monitoring of the vine growth environment, and on-demand imaging and high-resolution data collection from any specific location, thereby optimizing the production efficiencies and the application of inputs in a cost-effective way.

Point cloud filtering on UAV based point cloud

Nowadays, Unmanned Aerial Vehicles (UAVs) have been attracted wide attentions such as a new measurement equipment and mapping, which are capable of the high-resolution point cloud data collection. In addition, a massive point cloud data has brought about the data filtering and irregular data organization for the generation of digital terrain models. Filtering of point clouds contains vegetations and artificial objects play a crucial role for bare earth terrain modelling. Topographical maps rely on the data structures which are built on bare ground terrain points. The bare earth surface extraction is not the only crucial to the topographical maps but also decision-making processes such as natural hazards management, deformation analysis and interpretation.In order to filter a UAV-based 3D raw point cloud data, in this paper, filtering performance of four different algorithms using open source and commercial software’s have been investigated, (1) curvature based (Multiscale Curvature Classification-MCC), (2) surface-based filtering (FUSION), (3) progressive TIN based (LasTool-LasGround module-commercial) and (4) physical simulation processing (Cloth Simulation Filtering-CSF). The applied filtering results were validated with the reference data set classified by operator. Although different filtering methodologies implemented on point clouds, these methods demonstrated similar results to extract ground on distinctive terrain feature such as dense vegetated, flat surface, rough and complex landscapes. The filtering algorithms’ results revealed that UAV-generated data suitable for extraction of bare earth surface feature on the different type of a terrain. Accuracy of the filtered point cloud reached the 93% true classification on flat surfaces from CSF filtering method.

High resolution single particle cryo-electron microscopy using beam-image shift.

Automated data acquisition is used widely for single-particle reconstruction of three-dimensional (3D) volumes of biological complexes preserved in vitreous ice and imaged in a transmission electron microscope. Automation has become integral to this method because of the very large number of particle images required in order to overcome the typically low signal-to-noise ratio of these images. For optimal efficiency, automated data acquisition software packages typically employ some beam-image shift targeting as this method is both fast and accurate (±0.1 µm). In contrast, using only stage movement, relocation to a targeted area under low-dose conditions can only be achieved in combination with multiple iterations or long relaxation times, both reducing efficiency. Nevertheless it is well known that applying beam-image shift induces beam-tilt and with it a potential structure phase error with a phase error π/4 the highest acceptable value. This theory has been used as an argument against beam-image shift for high resolution data collection. Nevertheless, in practice many small beam-image shift datasets have resulted in 3D reconstructions beyond the π/4 phase error limit. To address this apparent contradiction, we performed cryo-EM single-particle reconstructions on a T20S proteasome sample using applied beam-image shifts corresponding to beam tilts from 0 to 10 mrad. To evaluate the results we compared the FSC values, and examined the water density peaks in the 3D map. We conclude that the phase error does not limit the validity of the 3D reconstruction from single-particle averaging beyond the π/4 resolution limit.

Approaches to altering particle distributions in cryo-electron microscopy sample preparation.

Cryo-electron microscopy (cryo-EM) can now be used to determine high-resolution structural information on a diverse range of biological specimens. Recent advances have been driven primarily by developments in microscopes and detectors, and through advances in image-processing software. However, for many single-particle cryo-EM projects, major bottlenecks currently remain at the sample-preparation stage; obtaining cryo-EM grids of sufficient quality for high-resolution single-particle analysis can require the careful optimization of many variables. Common hurdles to overcome include problems associated with the sample itself (buffer components, labile complexes), sample distribution (obtaining the correct concentration, affinity for the support film), preferred orientation, and poor reproducibility of the grid-making process within and between batches. This review outlines a number of methodologies used within the electron-microscopy community to address these challenges, providing a range of approaches which may aid in obtaining optimal grids for high-resolution data collection.

KAMO: towards automated data processing for microcrystals.

In protein microcrystallography, radiation damage often hampers complete and high-resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5-10°) separately from multiple crystals. The data from these crystals can then be merged into a complete reflection-intensity set. However, data processing of multiple small-wedge data sets is challenging. Here, a new open-source data-processing pipeline, KAMO, which utilizes existing programs, including the XDS and CCP4 packages, has been developed to automate whole data-processing tasks in the case of multiple small-wedge data sets. Firstly, KAMO processes individual data sets and collates those indexed with equivalent unit-cell parameters. The space group is then chosen and any indexing ambiguity is resolved. Finally, clustering is performed, followed by merging with outlier rejections, and a report is subsequently created. Using synthetic and several real-world data sets collected from hundreds of crystals, it was demonstrated that merged structure-factor amplitudes can be obtained in a largely automated manner using KAMO, which greatly facilitated the structure analyses of challenging targets that only produced microcrystals.

Ground-State Charge-Density Distribution in a Crystal of the Luminescent ortho-Phenylenediboronic Acid Complex with 8-Hydroxyquinoline.

This contribution is devoted to the first electron density studies of a luminescent oxyquinolinato boron complex in the solid state. ortho-Phenylenediboronic acid mixed with 8-hydroxyquinoline in dioxane forms high-quality single crystals via slow solvent evaporation, which allows successful high resolution data collection (sinθ/λ = 1.2 Å-1) and charge density distribution modeling. Particular attention has been paid to the boron-oxygen fragment connecting the two parts of the complex, and to the solvent species exhibiting anharmonic thermal motion. The experiment and theory compared rather well in terms of atomic charges and volumes, except for the boron centers. Boron atoms, as expected, constitute the most electron-deficient species in the complex molecule, whereas the neighboring oxygen and carbon atoms are the most significantly negatively charged ones. This part of the molecule appears to be very much involved in the charge transfer occurring between the acid fragment and oxyquinoline moiety leading to the observed fluorescence, as supported by the time-dependent density functional theory (TDDFT) results and the generated transition density maps. TDDFT calculations indicated that p-type atomic orbitals contributing to the HOMO-1, HOMO, and LUMO play the major role in the lowest energy transitions, and enabled further comparison with the charge density features, which is discussed in details. Furthermore, the results confirmed the known fact the Q ligand character is most important for the spectroscopic properties of this class of complexes.

Charge density studies of an inorganic-organic hybrid p-phenylenediammonium tetrachlorocuprate

High quality single crystals of an inorganic-organic hybrid, p-phenylenediammonium tetrachlorocuprate (pPDA:CuCl 4 ), suitable for high-resolution X-ray data collection, have been crystallized. pPDA:CuCl 4 crystallizes at special positions in the P21/c space group of the monoclinic crystal system with only halves of the moieties in the asymmetric part of the unit cell. This compound forms a hybrid structure consisting of separate inorganic anion and organic cation layers linked by weak N-H∙∙∙Cl hydrogen bonds. The Cu atoms are located at the centers of symmetry and each of them is surrounded by six chlorine atoms thus forming a tetragonal bipyramid. Two pairs of the chlorine atoms form short Cu-Cl bonds (2.27964(4) and 2.29765(4) A), whereas the third pair forms the longest Cu-Cl bond (2.90452(4) A). Experimental and theoretical electron density distributions have been established. There is an excellent agreement between theoretical and experimental properties at bond critical points. The total charge of the organic cation is equal to +1.64 $$ \overset{-}{\boldsymbol{e}} $$ and is neutralized by the charge of the inorganic anion. The orbital population analysis of the copper 3d electrons and analysis of geometry of the tetrachlorocuprate have been performed and they clearly show presence of the Jahn-Teller effect. Comparison of some experimentally derived parameters such as area and linear density distribution with those calculated on the base of relations derived from literature shows that a longer series of experimental charge density investigations of the hybrid structures should be measured. It should allow for obtaining more reliable relations among different parameters for the hybrid structures with a strong predictive power of such relations. Analysis of residuals of measured intensities of reflections and those resulting from the refined model shows a serious underestimation of esds of measured reflections used in the refinement.

Is substrate composition a suitable predictor for deep-water coral occurrence on fine scales?

Species distribution modelling can be applied to identify potentially suitable habitat for species with largely unknown distributions, such as many deep-water corals. Important variables influencing species occurrence in the deep sea, e.g. substrate composition, are often not included in these modelling approaches because high-resolution data are unavailable. We investigated the relationship between substrate composition and the occurrence of the two deep-water octocoral species Primnoa resedaeformis and Paragorgia arborea, which require hard substrate for attachment. On a scale of 10s of metres, we analysed images of the seafloor taken at two locations inside the Northeast Channel Coral Conservation Area in the Northwest Atlantic. We interpolated substrate composition over the sampling areas and determined the contribution of substrate classes, depth and slope to describe habitat suitability using maximum entropy modelling (Maxent). Substrate composition was similar at both sites - dominated by pebbles in a matrix of sand (>80%) with low percentages of suitable substrate for coral occurrence. Coral abundance was low at site 1 (0.9 colonies of P. resedaeformis per 100m2) and high at site 2 (63 colonies of P. resedaeformis per 100m2) indicating that substrate alone is not sufficient to explain varying patterns in coral occurrence. Spatial interpolations of substrate classes revealed the difficulty to accurately resolve sparsely distributed boulders (3–5% of substrate). Boulders were by far the most important variable in the habitat suitability model (HSM) for P. resedaeformis at site 1, indicating the fundamental influence of a substrate class that is the least abundant. At site 2, HSMs identified cobbles and sand/pebble as the most important variables for habitat suitability. However, substrate classes were correlated making it difficult to determine the influence of individual variables. To provide accurate information on habitat suitability for the two coral species, substrate composition needs to be quantified so that small fractions (<20% contribution of certain substrate class) of suitable substrate are resolved. While the collection and analysis of high-resolution data is costly and spatially limited, the required resolution is unlikely to be achieved in coarse-scale interpolations of substrate data.

Quality-Guaranteed Event-Sensitive Data Collection and Monitoring in Vibration Sensor Networks

High-resolution vibration data collection with data quality guaranteeing is important in a class of applications like industrial machine and structural health monitoring. Applying wireless vibration sensor networks (WVSNs) to this class is challenging due to severe resource constraints (e.g., bandwidth and energy). State-of-the-art data reduction approaches (e.g., signal processing, in-network aggregation) suggested to improve these constraints do not satisfy application-specific requirements, e.g., high quality of data (QoD) collection or quality of monitoring (QoM). In this paper, we propose $v\text{Collector}$ , a general approach to vibration data collection and monitoring in a resource-constrained WVSN. We enable each sensor to reduce the amount of data (before transmission) in a decentralized manner in two stages: the data acquisition stage and data transmission stage. In the first, we propose a solution to low-complexity signal processing; each sensor analyzes signals using the fast Fourier transform (FFT) under the quadrature amplitude modulation (QAM) and then applies an idea from the Goertzel algorithm (first proposed by Goertzel in 1958) so that the sensor can reduce a significant amount of data without sacrificing the QoD. In the second stage, we propose a decision-making algorithm by which each sensor can make a decision on its acquired data (considered event-sensitive data if it has information about harmful vibrations ) so that event-insensitive data communication is reduced. Evaluation results (obtained by simulations using our empirical data traces and by a real system deployment) demonstrate that $v\text{Collector}$ significantly reduces energy consumption and guarantees QoM in a WVSN.

Integrating data from multiple time-location measurement methods for use in exposure assessment: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

Tools to assess time-location patterns related to environmental exposures have expanded from reliance on time-location diaries (TLDs) and questionnaires to use of geospatial location devices such as data-logging Global Positioning System (GPS) equipment. The Multi-Ethnic Study of Atherosclerosis and Air Pollution obtained typical time-location patterns via questionnaire for 6424 adults in six US cities. At a later time (mean 4.6 years after questionnaire), a subset (n=128) participated in high-resolution data collection for specific 2-week periods resulting in concurrent GPS and detailed TLD data, which were aggregated to estimate time spent in various microenvironments. During these 2-week periods, participants were observed to spend the most time at home indoors (mean of 78%) and a small proportion of time in-vehicle (mean of 4%). Similar overall patterns were reported by these participants on the prior questionnaire (mean home indoors: 75%; mean in-vehicle: 4%). However, individual micro-environmental time estimates measured over specific 2-week periods were not highly correlated with an individual's questionnaire report of typical behavior (Spearman's ρ of 0.43 for home indoors and 0.39 for in-vehicle). Although questionnaire data about typical time-location patterns can inform interpretation of long-term epidemiological analyses and risk assessment, they may not reliably represent an individual's short-term experience.

Crystallization of a fungal lytic polysaccharide monooxygenase expressed from glycoengineered Pichia pastoris for X-ray and neutron diffraction.

Lytic polysaccharide monooxygenases (LPMOs) are carbohydrate-disrupting enzymes secreted by bacteria and fungi that break glycosidic bonds via an oxidative mechanism. Fungal LPMOs typically act on cellulose and can enhance the efficiency of cellulose-hydrolyzing enzymes that release soluble sugars for bioethanol production or other industrial uses. The enzyme PMO-2 from Neurospora crassa (NcPMO-2) was heterologously expressed in Pichia pastoris to facilitate crystallographic studies of the fungal LPMO mechanism. Diffraction resolution and crystal morphology were improved by expressing NcPMO-2 from a glycoengineered strain of P. pastoris and by the use of crystal seeding methods, respectively. These improvements resulted in high-resolution (1.20 Å) X-ray diffraction data collection at 100 K and the production of a large NcPMO-2 crystal suitable for room-temperature neutron diffraction data collection to 2.12 Å resolution.

IoT business models in an industrial context

Abstract There is a broad consensus that the transformative power of the Internet of Things (IoT) will affect all kinds of industries; or, to put it in a more optimistic light, that almost no domain is excluded from the opportunities to leverage the IoT. But, what does this mean for the future of industrial processes? This article introduces the concept of high-resolution management (HRM). IoT enables the collection of high-resolution data for the physical world where, as in the digital world, every aspect of business operations can be measured in real-time. This capability facilitates high-resolution management, such as short optimization cycles in industrial production, logistics and equipment efficiency, comparable to methods like A/B-Testing or Search Engine Optimization, which are state of the art in digital business. We take the following two perspectives on leveraging high-resolution management. First, through greater insights into their industrial processes, companies that apply HRM in their operations are able to achieve higher efficiency, quality and flexibility. The example of vehicle fleet management illustrates this effect. Second, we build upon the St. Gallen Business Model Navigator in order to look in greater detail on how the IoT affects industrial processes. Gassmann et al. Gassmann, O., Frankenberger, K., and Csik, M. (2014). The business model navigator: 55 models that will revolutionise your business. Financial Times. introduce 55 generic business model patterns, of which our extended research identified 20 that could profit significantly from the IoT Fleisch, E., Weinberger, M., and Wortmann, F. (2014). Geschäftsmodelle im Internet der Dinge. HMD Praxis der Wirtschaftsinformatik, 51(6), 812–826; Gassmann, O., Frankenberger, K., and Csik, M. (2014). The business model navigator: 55 models that will revolutionise your business. Financial Times. . Analyzing these 20 patterns allowed for the identification of six key components: Remote Usage and Condition Monitoring, Object Self Service, Digital Add-on, Digital Lock-in, Product as a Point of Sales and Physical Freemium. These building blocks help companies to supply HRM-supported offerings. Finally, the example of remote monitoring of process parameters shows that these business model components can also be deployed to create offerings that enable others to apply HRM.

High-resolution photo-mosaic time-series imagery for monitoring human use of an artificial reef.

Successful marine management relies on understanding patterns of human use. However, obtaining data can be difficult and expensive given the widespread and variable nature of activities conducted. Remote camera systems are increasingly used to overcome cost limitations of conventional labour‐intensive methods. Still, most systems face trade‐offs between the spatial extent and resolution over which data are obtained, limiting their application. We trialed a novel methodology, CSIRO Ruggedized Autonomous Gigapixel System (CRAGS), for time series of high‐resolution photo‐mosaic (HRPM) imagery to estimate fine‐scale metrics of human activity at an artificial reef located 1.3 km from shore. We compared estimates obtained using the novel system to those produced with a web camera that concurrently monitored the site. We evaluated the effect of day type (weekday/weekend) and time of day on each of the systems and compared to estimates obtained from binocular observations. In general, both systems delivered similar estimates for the number of boats observed and to those obtained by binocular counts; these results were also unaffected by the type of day (weekend vs. weekday). CRAGS was able to determine additional information about the user type and party size that was not possible with the lower resolution webcam system. However, there was an effect of time of day as CRAGS suffered from poor image quality in early morning conditions as a result of fixed camera settings. Our field study provides proof of concept of use of this new cost‐effective monitoring tool for the remote collection of high‐resolution large‐extent data on patterns of human use at high temporal frequency.

The Promise and Perils of Wearable Sensors in Organizational Research

Rapid advances in mobile computing technology have the potential to revolutionize organizational research by facilitating new methods of data collection. The emergence of wearable electronic sensors in particular harbors the promise of making the large-scale collection of high-resolution data related to human interactions and social behavior economically viable. Popular press and practitioner-oriented research outlets have begun to tout the game-changing potential of wearable sensors for both researchers and practitioners. We systematically examine the utility of current wearable sensor technology for capturing behavioral constructs at the individual and team levels. In the process, we provide a model for performing validation work in this new domain of measurement. Our findings highlight the need for organizational researchers to take an active role in the development of wearable sensor systems to ensure that the measures derived from these devices and sensors allow us to leverage and extend the extant knowledge base. We also offer a caution regarding the potential sources of error arising from wearable sensors in behavioral research.

URBAN MORPHOLOGICAL DYNAMICS IN SANTIAGO (CHILE): PROPOSING SUSTAINABLE INDICATORS FROM REMOTE SENSING

Latin America is one of the world’s most urbanised regions, with more than 80% of inhabitants living in urban areas and over 50 cities with at least 1 million inhabitants. The concept of urban structure types (UST) allows the dynamics of a growing urban environment to be captured in its quantity and quality. They are defined as areas of homogenous appearance in the urban matrix with a recognisable mixture of built-up areas and open spaces. We used the vegetation-impervious-soil (V-I-S) model approach to classify and monitor different types of USTs in Santiago (~800 km2), Chile between 1985 and 2015. The V-I-S model is based on a simplification of the large diversity of urban land cover types in three general categories: vegetation, impervious surfaces and soil. These categories were obtained by processing Landsat-5 TM and Landsat-8 OLI images. First, we applied standard radiometric calibration and co-registration methods to all datasets. Second, using a linear spectral unmixing algorithm we performed a soft classification of urban land cover types (&amp;lt;i&amp;gt;end members&amp;lt;/i&amp;gt;): trees, shrubs, herbaceous plants, soils, buildings, roads and water bodies. All &amp;lt;i&amp;gt;end members&amp;lt;/i&amp;gt; were validated using a combination of photointerpretation on high-resolution images (~1 m) and field data collection (only for 2015). In each pixel we used the resulting probability scores, logically grouped, to obtain final values for each V-I-S component. Third, we used statistical clustering of V-I-S values to create a set of eight pixel groups, which we interpreted as USTs and mapped them for each date. The overall accuracy for V-I-S components in 1985 and 2015 were 78% and 82%, respectively, and errors did not exhibit any spatial correlation. The main sources of differentiation between USTs were the trade-off proportions between vegetation and impervious components, whereas soil proportions remained near 5% across the city in both dates. To analyse the change in UST spatial configuration between dates, we used a set of selected landscape metrics and discussed their use as indicators for sustainable urban development. These indicators relate to the dispersion pattern of urban growth, the connectivity of open green space and the complexity in the composition of the UST types within the different sectors of the city. We were able to identify, using the dynamics exhibited by the USTs, three main zones: (1) city centre, where USTs of high-intensity development predominate, (2) eastern high-income areas whose spatial structure is marked by a relatively high urbanisation intensity with a very large proportion of vegetated spaces, and (3) peripheral areas, with significant changes in composition and configuration of USTs, in recent decades, showing high rates of urbanisation, shifting from low-medium to high densities. We concluded that these patterns and their dynamics are mainly determined by the spatial socio-economic stratification of the population.