High-resolution Spatiotemporal Data Research Articles

Small Phytoplankton Shapes Colored Dissolved Organic Matter Dynamics in the North Atlantic Subtropical Gyre.

The North Atlantic subtropical gyre (NASTG) is a model of the future ocean under climate change. Ocean warming signals are hidden within the blue color of these clear waters and can be tracked by understanding the dynamics among phytoplankton chlorophyll ([Chl]) and colored dissolved organic matter (CDOM). In NASTG, [Chl] and CDOM are strongly correlated. Yet, this unusual correlation for open oceans remains unexplained. Here, we test main hypotheses by analyzing high spatiotemporal resolution data collected by Biogeochemical‐Argo floats between 2012 and 2018. The direct production of CDOM via phytoplankton metabolism is the main occurring mechanism. More importantly, CDOM dynamics strongly depend on the abundance of picophytoplankton. Our findings thus highlight the critical role of these small organisms under the ocean warming scenario. Picophytoplankton will enhance the production of colored dissolved compounds and, ultimately, impact on the ocean carbon cycle.

Combined geospatial and techno-economic analysis of deep building envelope retrofit

In this study, the Swiss residential building stock model (SwissRes model) is used to conduct a geospatial analysis of the techno-economic potential of deep building envelope retrofit packages in Switzerland. Element and building characteristics of 8242 archetype buildings are combined with high-resolution spatio-temporal weather data. We estimate final energy demand before and after retrofit, while correcting for the energy performance gap. Levelized costs of energy savings are calculated in two ways, i.e. based on full costs and investment cost related only to thermal improvement of the envelope. Results show that, technically, a 57% reduction of the final energy demand for space heating could be achieved with the deep envelope retrofit. For full costs, less than 1% of these measures would be cost-effective, whereas for improvement costs nearly the full retrofit potential is cost-effective. We identified primarily rural and mountainous regions as well as historic city centres to show highest cost-effectiveness.

A Low-Cost, Multi-Sensor System to Monitor Temporary Stream Dynamics in Mountainous Headwater Catchments.

While temporary streams account for more than half of the global discharge, high spatiotemporal resolution data on the three main hydrological states (dry streambed, standing water, and flowing water) of temporary stream remains sparse. This study presents a low-cost, multi-sensor system to monitor the hydrological state of temporary streams in mountainous headwaters. The monitoring system consists of an Arduino microcontroller board combined with an SD-card data logger shield, and four sensors: an electrical resistance (ER) sensor, temperature sensor, float switch sensor, and flow sensor. The monitoring system was tested in a small mountainous headwater catchment, where it was installed on multiple locations in the stream network, during two field seasons (2016 and 2017). Time-lapse cameras were installed at all monitoring system locations to evaluate the sensor performance. The field tests showed that the monitoring system was power efficient (running for nine months on four AA batteries at a five-minute logging interval) and able to reliably log data (<1% failed data logs). Of the sensors, the ER sensor (99.9% correct state data and 90.9% correctly timed state changes) and flow sensor (99.9% correct state data and 90.5% correctly timed state changes) performed best (2017 performance results). A setup of the monitoring system with these sensors can provide long-term, high spatiotemporal resolution data on the hydrological state of temporary streams, which will help to improve our understanding of the hydrological functioning of these important systems.

Hierarchical measurement strategy for cost-effective interpolation of spatiotemporal data in manufacturing

High-resolution spatiotemporal data is crucial for characterizing, modeling, and monitoring the space–time dynamics of complex systems in manufacturing. However, the acquisition of such data is generally expensive and time-consuming. Spatiotemporal interpolation aims to predict the values at unmeasured locations using measured data, and emerges as a promising solution to cost-effectively characterizing spatiotemporal processes. Since the interpolation performance is largely influenced by the available measurement data, an intelligent measurement strategy is an important prerequisite to the success of interpolation methods. In this paper, a hierarchical measurement strategy is developed to achieve a balance between interpolation precision and measurement cost in spatiotemporal interpolation. A hierarchical decision-making problem is formulated to determine the observation times and measurement locations at each observation. To expedite the solution search process, hierarchical genetic algorithm is adopted and implemented using high-performance computing. Moreover, a new form of the covariance function is developed using a Bessel additive periodic variogram to more accurately model the periodic spatial variations in spatiotemporal processes. Case studies using real-world data collected from ultrasonic metal welding are reported to demonstrate the effectiveness of the proposed method.

A new stochastic oil spill risk assessment model for Persian Gulf: Development, application and evaluation

Persian Gulf is a semi-enclosed highly saline reverse estuary that is exposed to the risk of oil spills in offshore oil and gas activities. In the early 2000s, a specific version of NOAA's Trajectory Analysis Planner (TAP II) was developed for Persian Gulf to assist regional organizations in preparing oil spill contingency plans. In this research, a new stochastic model is developed to cover the limitations of TAP II. The new model is based on an advanced trajectory model, which is now linked with high resolution spatiotemporal data of the wind and sea current. In a case study, the developed model is compared with TAP II, and evaluated by multiple tests designed for analysis of uncertainty, sensitivity, reliability and variability. The case study proved the applicability of the new model, and the evaluation tests provided useful information for the future development of the model.

Post-storm geomorphic recovery and resilience of a prograding coastal dune system

Geomorphic resilience is the capacity of a system to recover to pre-disturbance conditions following a perturbation. The 2013/14 Atlantic winter storm period had extensive geomorphological impacts and provides an opportunity to assess coastline resilience. This paper uses high spatio-temporal resolution data to quantify the beach-dune response and subsequent recovery of a prograding coastline following the 5 December 2013 North Sea storm surge. It demonstrates that despite the high water levels and destructive nature of the storm, the beach-dune system recovered sediment rapidly over the first post-storm year. Within four years the dune advance had exceeded the seawards position expected based on long-term coastal trends but had not yet recovered the pre-storm foredune profile. Cumulative evidence from numerous European locations suggests one of the stormiest periods on record triggered only a minor disturbance to what appear to be highly resilient beach-dune systems.

RETRACTED: Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China

Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie–Ames–Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m2·yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas.

Thermal stratification in liquid metal pools under influence of penetrating colder jets

An experimental study is presented on the evolution of thermal stratification in a liquid gallium pool when a colder jet is introduced resulting in mixed convection currents. Different injection flow rates are considered for this experimental work with an objective to find the transition where flow fluctuations are able to overcome the restoring buoyant forces. At low flow rates, clearly stratified thermal regimes are observed and a diffuse planar front evolves during the injection experiments. However, at the higher end of the flow rates, thermal fluctuations accompany the stratified interface. This scenario is expected in some of the advanced liquid metal-cooled nuclear reactor designs where these fluctuations can lead to fatigue in adjacent solid structures and can be detrimental for reactor safety. The Gallium Thermal-hydraulic Experiment (GaTE) used for this study was designed with a moving magnet pump to inject gallium at controlled flow rates and a distributed temperature measurement system, based on the principles of Rayleigh back-scattering, to obtain high resolution spatio-temporal data. These capabilities allow the maneuvering and monitoring of the flux Richardson number (Rif), which signifies the transition from planar to fluctuating thermally stratified front. Experimental results exhibit thermal fluctuations near the stratified interface for Rif⩽1.3, which is close to the theoretically expected criteria i.e. Rif=1. In terms of the global Richardson number, these liquid gallium experiments show the transition criteria to be Ri<8.

Spatial Coherence of Water Vapor and Rainfall over the Indian Subcontinent during Different Monsoon Seasons

Abstract Spatial coherency in atmospheric water vapor and rainfall and their association have been studied over the Indian subcontinent utilizing high spatiotemporal resolution data. Total column water vapor (TCWV) values derived from the Indian National Satellite (INSAT) system series (INSAT-3D) are first evaluated against data from an International GNSS Service (IGS) GPS receiver at Bangalore and the Global Precipitation Measurement Microwave Imager (GMI). The bias, correlation coefficient, and RMSE of TCWV between INSAT-3D and GMI show that, except for the south Bay of Bengal, the datasets compare well. The seasonal mean TCWV shows large values with lower standard deviation during the southwest monsoon (SWM) than in the northeast monsoon (NEM). Different temporal scales that contribute to the TCWV variance at a given point are quantified, and the variability due to 30–60-day oscillations is found to be dominant during both the monsoon seasons. TCWV and rainfall show good correspondence over the whole Indian subcontinent during both monsoon seasons except over the Arabian Sea and southern Myanmar regions, where large TCWV values show less rainfall during the SWM. On the whole, the spatial homogeneity and intergrid correlations in TCWV and rainfall are higher in NEM than in SWM. The decorrelation distance d0 for TCWV is found to be 10 times larger than that for rainfall, indicating that the rainfall homogeneity is generally limited to smaller areas. The large d0 values of TCWV are mainly due to the occurrence of source and sink processes at large spatial scales over those regions.

Spatio-Temporal Variations in Foredune Dynamics Determined with Mobile Laser Scanning

Coastal foredunes are highly dynamic landforms because of rapid erosion by waves and currents during storm surges in combination with gradual accretion by aeolian transport during more quiescent conditions. While our knowledge into the mechanisms behind foredune erosion have reached considerable maturity, this is not the case for foredune growth. High resolution spatio-temporal data sets of beach and foredune topography, which are needed to increase our understanding of mechanisms behind aeolian transport in coastal environments and to develop predictive dune-accretion models, are scarce. Here we aim to illustrate that repeated Mobile Laser Scanning (MLS) surveys provide an accurate and robust method to study detailed changes in dune volume on the timescales of months to years. An MLS system attached to an inertial navigation system with RTK-GPS (INS-GPS) was used to carry out 13 surveys along a 3.5-km Dutch beach over a 2.5-year period. The height observations were post-processed and averaged into 1 × 1 m Digital Elevation Models (DEMs). Comparison with airborne LiDAR and RTK-GPS data revealed that the obtained DEMs were accurate and robust up to a height of 15 m in the foredune above which dense vegetation hampers the MLS to see the sand surface. Estimates of dune volume change of the lower 13 m of the foredune have an uncertainty of about 0.25 m 3 /m. Time series of dune volume change show that at our study site the foredune accretes throughout the year at similar rates (10 m 3 /m/year), while marine erosion is obviously confined to storm surges. Foredune accretion and erosion vary spatially, which can, in part, be related to variations in beach width.

High spatio-temporal resolution pollutant measurements of on-board vehicle emissions using ultra-fast response gas analyzers

Abstract. Existing ultra-fast response engine exhaust emissions analyzers have been adapted for on-board vehicle use combined with GPS data. We present, for the first time, how high spatio-temporal resolution data products allow transient features associated with internal combustion engines to be examined in detail during on-road driving. Such data are both useful to examine the circumstances leading to high emissions, and reveals the accurate position of urban air quality “hot spots” as deposited by the candidate vehicle, useful for source attribution and dispersion modelling. The fast response time of the analyzers, which results in 100 Hz data, makes accurate time-alignment with the vehicle's engine control unit (ECU) signals possible. This enables correlation with transient air fuel ratio, engine speed, load, and other engine parameters, which helps to explain the causes of the emissions “spikes” that portable emissions measurement systems (PEMS) and conventional slow response analyzers would miss or smooth out due to mixing within their sampling systems. The data presented is from NO and NOx analyzers, but other fast analyzers (e.g. total hydrocarbons (THC), CO and CO2) can be used similarly. The high levels of NOx pollution associated with accelerating on entry ramps to motorways, driving over speed bumps, accelerating away from traffic lights, are explored in detail. The time-aligned ultra-fast analyzers offer unique insight allowing more accurate quantification and better interpretation of engine and driver activity and the associated emissions impact on local air quality.

Wind comfort assessment by means of large eddy simulation with lattice Boltzmann method in full scale city area

Large-eddy simulations based on the Lattice-Boltzmann method of the flow in a realistic, full scale urban area are performed to compare several wind comfort criteria. It is observed that popular criteria for pedestrian comfort lead to very different conclusions, due to the access to high spatio-temporal resolution data. Different mixed strategies based on the combination of several criteria are proposed and compared to enhance pedestrian wind comfort assessment in practical cases.

Observed Variability of Surface Layer in the Central Bay of Bengal:Results of Measurements Using Glider

Underwater gliders measure high-resolution spatiotemporal oceanographic data. In April 2014, the National Institute of Ocean Technology, Chennai operated an underwater glider ‘Barathi’, for 127 days for observation of Bay of Bengal (BoB). In this article we present the effectiveness of the glider Barathi for high resolution temporal sampling of the surface layer in the central BoB for studying variation of temperature, salinity and density structures and acoustic characteristics on 26–27 May 2014. The results showed ‘afternoon effect’ on acoustic characteristics and formation of secondary sound channel. Our data set is strongly correlated (coefficient of determination r 2 > 0.96) with data from a nearby Array for real-time geostrophic Oceanography (Argo) float.

Dynamic Sampling Design for Characterizing Spatiotemporal Processes in Manufacturing

Fine-scale characterization and monitoring of spatiotemporal processes are crucial for high-performance quality control of manufacturing processes, such as ultrasonic metal welding and high-precision machining. However, it is generally expensive to acquire high-resolution spatiotemporal data in manufacturing due to the high cost of the three-dimensional (3D) measurement system or the time-consuming measurement process. In this paper, we develop a novel dynamic sampling design algorithm to cost-effectively characterize spatiotemporal processes in manufacturing. A spatiotemporal state-space model and Kalman filter are used to predictively determine the measurement locations using a criterion considering both the prediction performance and the measurement cost. The determination of measurement locations is formulated as a binary integer programming problem, and genetic algorithm (GA) is applied for searching the optimal design. In addition, a new test statistic is proposed to monitor and update the surface progression rate. Both simulated and real-world spatiotemporal data are used to demonstrate the effectiveness of the proposed method.

Satellite remote sensing of fine particulate air pollutants over Indian mega cities

In the backdrop of the need for high spatio-temporal resolution data on PM2.5 mass concentrations for health and epidemiological studies over India, empirical relations between Aerosol Optical Depth (AOD) and PM2.5 mass concentrations are established over five Indian mega cities. These relations are sought to predict the surface PM2.5 mass concentrations from high resolution columnar AOD datasets. Current study utilizes multi-city public domain PM2.5 data (from US Consulate and Embassy’s air monitoring program) and MODIS AOD, spanning for almost four years. PM2.5 is found to be positively correlated with AOD. Station-wise linear regression analysis has shown spatially varying regression coefficients. Similar analysis has been repeated by eliminating data from the elevated aerosol prone seasons, which has improved the correlation coefficient. The impact of the day to day variability in the local meteorological conditions on the AOD-PM2.5 relationship has been explored by performing a multiple regression analysis. A cross-validation approach for the multiple regression analysis considering three years of data as training dataset and one-year data as validation dataset yielded an R value of ∼0.63. The study was concluded by discussing the factors which can improve the relationship.

UAV Localization in Row Crops

High‐flying unmanned aerial vehicles (UAVs) are transforming industrial and research agriculture by delivering high spatiotemporal resolution data on a field environment. While current UAVs fly high above fields collecting aerial imagery, future low‐flying aircraft will directly interact with the environment and will utilize a wider variety of sensors. Safely and reliably operating close to unstructured environments requires improving UAVs' sensing, localization, and control algorithms. To this end, we investigate localizing a micro‐UAV in corn phenotyping trials using a laser scanner and IMU to control the altitude and position of the vehicle relative to the plant rows. In this process, the laser scanner is not only a means of localization, but also a scientific instrument for measuring plant properties. Experimental evaluations demonstrate that the is capable of safely and reliably operating in real‐world phenotyping trials. We experimentally validate the system in both low and high wind conditions in fully mature corn fields. Using test data from 18 test flights, we show that the UAV is capable of localizing its position to within one field row of the true position.

Combining physiological, environmental and locational sensors for citizen-oriented health applications

This work investigates the potential of combining the outputs of multiple low-cost sensor technologies for the direct measurement of spatio-temporal variations in phenomena that exist at the interface between our bodies and the environment. The example used herein is the measurement of personal exposure to traffic pollution, which may be considered as a function of the concentration of pollutants in the air and the frequency and volume of that air which enters our lungs. The sensor-based approach described in this paper removes the ‘traditional’ requirements either to model or interpolate pollution levels or to make assumptions about the physiology of an individual. Rather, a wholly empirical analysis into pollution exposure is possible, based upon high-resolution spatio-temporal data drawn from sensors for NO2, nasal airflow and location (GPS). Data are collected via a custom smartphone application and mapped to give an unprecedented insight into exposure to traffic pollution at the individual level. Whilst the quality of data from low-cost miniaturised sensors is not suitable for all applications, there certainly are many applications for which these data would be well suited, particularly those in the field of citizen science. This paper demonstrates both the potential and limitations of sensor-based approaches and discusses the wider relevance of these technologies for the advancement of citizen science.

Crop Phenology Detection Using High Spatio-Temporal Resolution Data Fused from SPOT5 and MODIS Products.

Timely and efficient monitoring of crop phenology at a high spatial resolution are crucial for the precise and effective management of agriculture. Recently, satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), have been widely used for the phenology detection of terrestrial ecosystems. In this paper, a framework is proposed to detect crop phenology using high spatio-temporal resolution data fused from Systeme Probatoire d'Observation de la Tarre5 (SPOT5) and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The framework consists of a data fusion method to produce a synthetic NDVI dataset at SPOT5’s spatial resolution and at MODIS’s temporal resolution and a phenology extraction algorithm based on NDVI time-series analysis. The feasibility of our phenology detection approach was evaluated at the county scale in Shandong Province, China. The results show that (1) the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm can accurately blend SPOT5 and MODIS NDVI, with an R2 of greater than 0.69 and an root mean square error (RMSE) of less than 0.11 between the predicted and referenced data; and that (2) the estimated phenology parameters, such as the start and end of season (SOS and EOS), were closely correlated with the field-observed data with an R2 of the SOS ranging from 0.68 to 0.86 and with an R2 of the EOS ranging from 0.72 to 0.79. Our research provides a reliable approach for crop phenology mapping in areas with high fragmented farmland, which is meaningful for the implementation of precision agriculture.

From subsidy evaluation to effort estimation: Advancing the function of voyage data recorders for offshore trawl fishery management

The overfishing of large vertebrates and shellfish is the first major disturbance to all the valuable coastal ecosystems that have been studied. However, usable logbook data has not been required or of concern in many small-scale fisheries operating in the coastal or offshore regions, rendering impact evaluation and further management difficult. Various studies have taken advantage of vessel monitoring systems (VMSs) that were originally developed for purposes such as surveillance, to derive high-resolution spatiotemporal effort data or further develop logbook-like data. These systems are usually installed on large-scale fishing vessels but seldom on small-scale vessels. Taiwan provides fuel subsidies to fishery operations, evaluated according to active moving hours at sea, as calculated with customized voyage data recorders (VDRs) that have been installed on most small-scale offshore fishing vessels. The device provides temporal position and speed data similar to that of VMSs, not in real time but with cheaper device cost, no data transmission fee and higher resolution at 3-min intervals. This paper takes the offshore trawl fishery of southwestern Taiwan as example to demonstrate that the VDR data used for subsidy evaluation could also be used for high-resolution effort estimation. After briefly documenting the development of Taiwanese trawl fisheries and the application of VDR in Taiwan, the paper proposes a simple five-step procedure for managers to categorize major fishing patterns of fisheries by reviewing the speed and track profiles of vessels on a trip-by-trip basis, and finally to develop speed criteria for defining fishing efforts.

“Spatial Energetics”

To address the current obesity and inactivity epidemics, public health researchers have attempted to identify spatial factors that influence physical inactivity and obesity. Technologic and methodologic developments have led to a revolutionary ability to examine dynamic, high-resolution measures of temporally matched location and behavior data through GPS, accelerometry, and GIS. These advances allow the investigation of spatial energetics, high-spatiotemporal resolution data on location and time-matched energetics, to examine how environmental characteristics, space, and time are linked to activity-related health behaviors with far more robust and detailed data than in previous work. Although the transdisciplinary field of spatial energetics demonstrates promise to provide novel insights on how individuals and populations interact with their environment, there remain significant conceptual, technical, analytical, and ethical challenges stemming from the complex data streams that spatial energetics research generates. First, it is essential to better understand what spatial energetics data represent, the relevant spatial context of analysis for these data, and if spatial energetics can establish causality for development of spatially relevant interventions. Second, there are significant technical problems for analysis of voluminous and complex data that may require development of spatially aware scalable computational infrastructures. Third, the field must come to agreement on appropriate statistical methodologies to account for multiple observations per person. Finally, these challenges must be considered within the context of maintaining participant privacy and security. This article describes gaps in current practice and understanding and suggests solutions to move this promising area of research forward.