High-resolution Spatiotemporal Data Research Articles

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.

A sensor package for mapping pH and oxygen from mobile platforms

A novel chemical sensor package named “WavepHOx” was developed in order to facilitate measurement of surface ocean pH, dissolved oxygen, and temperature from mobile platforms. The system comprises a Honeywell Durafet pH sensor, Aanderaa optode oxygen sensor, and chloride ion selective electrode, packaged into a hydrodynamic, lightweight housing. The WavepHOx has been deployed on a stand-up paddleboard and a Liquid Robotics Wave Glider in multiple near-shore settings in the Southern California Bight. Integration of the WavepHOx into these mobile platforms has enabled high spatiotemporal resolution pH and dissolved oxygen data collection. It is a particularly valuable tool for mapping shallow, fragile, or densely vegetated ecosystems which cannot be easily accessed by other platforms. Results from three surveys in San Diego, California, are reported. We show pH and dissolved oxygen variability >0.3 and >50% saturation, respectively, over tens to hundreds of meters to highlight the degree of natural spatial variability in these vegetated ecosystems. When deployed during an extensive discrete sampling program, the WavepHOx pH had a root mean squared error of 0.028 relative to pH calculated from fifty six measurements of total alkalinity and dissolved inorganic carbon, confirming its capacity for accurate, high spatiotemporal resolution data collection.

PECULIAR STATIONARY EUV WAVE FRONTS IN THE ERUPTION ON 2011 MAY 11

ABSTRACT We present and interpret the observations of extreme ultraviolet (EUV) waves associated with a filament eruption on 2011 May 11. The filament eruption also produces a small B-class two ribbon flare and a coronal mass ejection. The event is observed by the Solar Dynamic Observatory with high spatio-temporal resolution data recorded by the Atmospheric Imaging Assembly. As the filament erupts, we observe two types of EUV waves (slow and fast) propagating outwards. The faster EUV wave has a propagation velocity of ∼500 km s−1 and the slower EUV wave has an initial velocity of ∼120 km s−1. We report, for the first time, that not only does the slower EUV wave stop at a magnetic separatrix to form bright stationary fronts, but also the faster EUV wave transits a magnetic separatrix, leaving another stationary EUV front behind.

Reconstruction of Daily 30 m Data from HJ CCD, GF-1 WFV, Landsat, and MODIS Data for Crop Monitoring

With the recent launch of new satellites and the developments of spatiotemporal data fusion methods, we are entering an era of high spatiotemporal resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m remote-sensing data for monitoring crop types and phenology in two study areas located in Xinjiang Province, China. First, the Spatial and Temporal Data Fusion Approach (STDFA) was used to reconstruct the time series high spatiotemporal resolution data from the Huanjing satellite charge coupled device (HJ CCD), Gaofen satellite no. 1 wide field-of-view camera (GF-1 WFV), Landsat, and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Then, the reconstructed time series were applied to extract crop phenology using a Hybrid Piecewise Logistic Model (HPLM). In addition, the onset date of greenness increase (OGI) and greenness decrease (OGD) were also calculated using the simulated phenology. Finally, crop types were mapped using the phenology information. The results show that the reconstructed high spatiotemporal data had a high quality with a proportion of good observations (PGQ) higher than 0.95 and the HPLM approach can simulate time series Normalized Different Vegetation Index (NDVI) very well with R2 ranging from 0.635 to 0.952 in Luntai and 0.719 to 0.991 in Bole, respectively. The reconstructed high spatiotemporal data were able to extract crop phenology in single crop fields, which provided a very detailed pattern relative to that from time series MODIS data. Moreover, the crop types can be classified using the reconstructed time series high spatiotemporal data with overall accuracy equal to 0.91 in Luntai and 0.95 in Bole, which is 0.028 and 0.046 higher than those obtained by using multi-temporal Landsat NDVI data.

Classification of C3 and C4 Vegetation Types Using MODIS and ETM+ Blended High Spatio-Temporal Resolution Data

The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their ecological functions and climate dependencies. In this study, we classified C3 and C4 vegetation at a high resolution for spatially heterogeneous landscapes. First, we generated a high spatial and temporal land surface reflectance dataset by blending MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper Plus) data. The blended data exhibited a high correlation (R2 = 0.88) with the satellite derived ETM+ data. The time-series NDVI (Normalized Difference Vegetation Index) data were then generated using the blended high spatio-temporal resolution data to capture the phenological differences between the C3 and C4 vegetation. The time-series NDVI revealed that the C3 vegetation turns green earlier in spring than the C4 vegetation, and senesces later in autumn than the C4 vegetation. C4 vegetation has a higher NDVI value than the C3 vegetation during summer time. Based on the distinguished characteristics, the time-series NDVI was used to extract the C3 and C4 classification features. Five features were selected from the 18 classification features according to the ground investigation data, and subsequently used for the C3 and C4 classification. The overall accuracy of the C3 and C4 vegetation classification was 85.75% with a kappa of 0.725 in our study area.

Extended range (10–30 days) heavy rain forecasting study based on a nonlinear cross-prediction error model

Extended range (10–30 d) heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper, a nonlinear cross prediction error (NCPE) algorithm that combines nonlinear dynamics and statistical methods is proposed. The method is based on phase space reconstruction of chaotic single-variable time series of precipitable water and is tested in 100 global cases of heavy rain. First, nonlinear relative dynamic error for local attractor pairs is calculated at different stages of the heavy rain process, after which the local change characteristics of the attractors are analyzed. Second, the eigen-peak is defined as a prediction indicator based on an error threshold of about 1.5, and is then used to analyze the forecasting validity period. The results reveal that the prediction indicator features regarded as eigenpeaks for heavy rain extreme weather are all reflected consistently, without failure, based on the NCPE model; the prediction validity periods for 1–2 d, 3–9 d and 10–30 d are 4, 22 and 74 cases, respectively, without false alarm or omission. The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10–30 d and has the potential to be used to explore the mechanisms involved in the development of heavy rain according to a segmentation scale. This novel method provides new insights into extended range forecasting and atmospheric predictability, and also allows the creation of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.

Initial set of oceanographic data from Bay of Bengal using an underwater glider as mobile sensor node

Underwater gliders measure high-resolution spatiotemporal oceanographic data. However, glider operations have not been carried out in the Indian Ocean region so far. In September 2013, the National Institute of Ocean Technology, Chennai introduced a mobile sensor node, the underwater glider ‘Barathi’, for observation in the Bay of Bengal (BoB). Herein we address ballasting procedure of the glider operated in highly variable density waters of the BoB. The temperature and conductivity data collected by us are strongly correlated with commercially available instrument with coefficient of determination R2 > 0.97. This article reports results from a long-duration (127 days) mission in 2014. The variation of temperature, salinity, density, sound velocity, mixed layer depth, sonic layer depth and lower cutoff frequency of surface duct along 13°N lat. and between 80.76°E and 86.28°E long. are also presented. The results show a trace of the East Indian Coastal Current. The glider operations demonstrate a novel in situ observation platform in the BoB.

A fluorescent hormone biosensor reveals the dynamics of jasmonate signalling in plants

Activated forms of jasmonic acid (JA) are central signals coordinating plant responses to stresses, yet tools to analyse their spatial and temporal distribution are lacking. Here we describe a JA perception biosensor termed Jas9-VENUS that allows the quantification of dynamic changes in JA distribution in response to stress with high spatiotemporal sensitivity. We show that Jas9-VENUS abundance is dependent on bioactive JA isoforms, the COI1 co-receptor, a functional Jas motif and proteasome activity. We demonstrate the utility of Jas9-VENUS to analyse responses to JA in planta at a cellular scale, both quantitatively and dynamically. This included using Jas9-VENUS to determine the cotyledon-to-root JA signal velocities on wounding, revealing two distinct phases of JA activity in the root. Our results demonstrate the value of developing quantitative sensors such as Jas9-VENUS to provide high-resolution spatiotemporal data about hormone distribution in response to plant abiotic and biotic stresses.

Route-dependent switch between hierarchical and egalitarian strategies in pigeon flocks.

The mechanisms that underlie fascinating inter-individual interactions among animal groups have attracted increasing attention from biologists, physicists, and system scientists. There are two well-known types of interaction patterns: hierarchical and egalitarian. In the former type, individuals follow their leaders, whereas they follow their neighbors in the latter. Using high-resolution spatiotemporal data derived from the free flights of a flock of pigeons, we show that pigeon flocks actually adopt a mode that switches between the two aforementioned strategies. To determine its flight direction, each pigeon tends to follow the average of its neighbors while moving along a smooth trajectory, whereas it switches to follow its leaders when sudden turns or zigzags occur. By contrast, when deciding how fast to fly, each pigeon synthesizes the average velocity of its neighbors. This switching mechanism is promising for possible industrial applications in multi-robot system coordination, unmanned vehicle formation control, and other areas.

From mobile phone data to the spatial structure of cities.

Pervasive infrastructures, such as cell phone networks, enable to capture large amounts of human behavioral data but also provide information about the structure of cities and their dynamical properties. In this article, we focus on these last aspects by studying phone data recorded during 55 days in 31 Spanish cities. We first define an urban dilatation index which measures how the average distance between individuals evolves during the day, allowing us to highlight different types of city structure. We then focus on hotspots, the most crowded places in the city. We propose a parameter free method to detect them and to test the robustness of our results. The number of these hotspots scales sublinearly with the population size, a result in agreement with previous theoretical arguments and measures on employment datasets. We study the lifetime of these hotspots and show in particular that the hierarchy of permanent ones, which constitute the ‘heart' of the city, is very stable whatever the size of the city. The spatial structure of these hotspots is also of interest and allows us to distinguish different categories of cities, from monocentric and “segregated” where the spatial distribution is very dependent on land use, to polycentric where the spatial mixing between land uses is much more important. These results point towards the possibility of a new, quantitative classification of cities using high resolution spatio-temporal data.

Automatic cycle averaging for denoising approximately periodic spatiotemporal signals.

Optical mapping has become a common tool for cardiovascular research, providing high resolution spatiotemporal data of cardiac action potential propagation. However, noise in cardiac optical mapping (COM) data hampers quantitative measurements and analyses. Spatial and temporal filters have been used to increase the signal-to-noise ratio (SNR) of COM data. Although these help reduce noise and increase SNR, they also lower the spatial and temporal resolution of the data, which is not desirable. This paper utilizes the approximate periodicity of COM data to perform denoising by averaging cycles. We consider the entire approximately periodic spatiotemporal (APST) COM data as a concatenation of random samples generated from a deterministic single cycle spatiotemporal signal. The image difference signal (IDS) was defined and calculated to provide "global" periodicity information. Parameters for cycle segmentation, scaling and alignment were estimated based on the IDS. Finally, these parameters were used to segment, align and average cycles from each individual signal, which produces a clean and denoised single cycle spatiotemporal signal. The novel, fully automated pipeline was validated both qualitatively and quantitatively on zebrafish heart optical mapping data.

Alternative stable states and the role of fire–vegetation–soil feedbacks in the temperate wilderness of southwest Tasmania

Two ecological models have been put forward to explain the dynamics of fire-promoting and fire-sensitive vegetation in southwest Tasmania: the alternative stable states model of Jackson (in Proc Ecol Soc Aust 3:9–16, 1968) and the sharpening switch model of Mount (in Search 10:180–186, 1979). Assessing the efficacy of these models requires high resolution spatio-temporal data on whether vegetation patterns are stable or dynamic across landscapes. We analysed ortho-rectified sequences of aerial photography and satellite imagery from 1948, 1988 and 2010 to detect decadal scale changes in forest and non-forest vegetation cover in southwest Tasmania. There was negligible change from forest to non-forest (<0.05%) and only a modest change from non-forest to forest over the study period. Forest cover increased by 4.1% between 1948 and 1988, apparently due to the recovery of forest vegetation following stand-replacing fire prior to 1948. Forest cover increased by 0.8% between 1988 and 2010, reflecting the limited ability of forest to invade treeless areas. The two models include interactions between vegetation, fire and soil, which we investigated by analysing the chemical (phosphorus, nitrogen) and physical properties (clay, silt) of 128 soil samples collected across 34 forest–non-forest boundaries. Phosphorus in the upper horizon was typically lower in non-forest vegetation compared to forest vegetation, which is consistent with proposed fire–vegetation–soil feedbacks. Mineral horizons were dominated by sand, with low levels of clay under all vegetation types. Available field evidence lends support to the Jackson (1968) alternative stable states model as the most suitable model of vegetation dynamics on nutrient poor substrates in southwest Tasmania although modifications of the timeframes for transitions toward rainforest are required.

Protein induced fluorescence enhancement as a single molecule assay with short distance sensitivity

Single-molecule FRET has been widely used for monitoring protein-nucleic acids interactions. Direct visualization of the interactions, however, often requires a site-specific labeling of the protein, which can be circuitous and inefficient. In addition, FRET is insensitive to distance changes in the 0-3-nm range. Here, we report a systematic calibration of a single molecule fluorescence assay termed protein induced fluorescence enhancement. This method circumvents protein labeling and displays a marked distance dependence below the 4-nm distance range. The enhancement of fluorescence is based on the photophysical phenomenon whereby the intensity of a fluorophore increases upon proximal binding of a protein. Our data reveals that the method can resolve as small as a single base pair distance at the extreme vicinity of the fluorophore, where the enhancement is maximized. We demonstrate the general applicability and distance sensitivity using (a) a finely spaced DNA ladder carrying a restriction site for BamHI, (b) RNA translocation by DExH enzyme RIG-I, and (c) filament dynamics of RecA on single-stranded DNA. The high spatio-temporal resolution data and sensitivity to short distances combined with the ability to bypass protein labeling makes this assay an effective alternative or a complement to FRET.

Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling

Land surface and climate modelling requires continuous and consistent Leaf Area Index (LAI). High spatiotemporal resolution and long-time record data are more in demand nowadays and will continue to be in the future. MODIS LAI products meet these requirements to some degree. However, due to the presence of cloud and seasonal snow cover, the instrument problems and the uncertainties of retrieval algorithm, the current MODIS LAI products are spatially and temporally discontinuous and inconsistent, which limits their application in land surface and climate modelling. To improve the MODIS LAI products on a global scale, we considered the characteristics of the MODIS LAI data and made the best use of quality control (QC) information, and developed an integrated two-step method to derive the improved MODIS LAI products effectively and efficiently on a global scale. First, we used the modified temporal spatial filter (mTSF) method taking advantage of background values and QC information at each pixel to do a simple data assimilation for relatively low quality data. Then we applied the post processing-TIMESAT (A software package to analyze time-series of satellite sensor data) Savitzky–Golay (SG) filter to get the final result. We implemented the method to 10 years of the MODIS Collection 5 LAI data. In comparison with the LAI reference maps and the MODIS LAI data, our results showed that the improved MODIS LAI data are closer to the LAI reference maps in magnitude and also more continuous and consistent in both time-series and spatial domains. In addition, simple statistics were used to evaluate the differences between the MODIS LAI and the improved MODIS LAI.

Using a spatiotemporal climate model to assess population-level Douglas-fir growth sensitivity to climate change across large climatic gradients in British Columbia, Canada

Effective adaptation of forest management practices to climate change will require a good understanding of the ecological and climatic factors influencing tree sensitivities and responses to climate. Using tree-ring data collected from 33 stands of mature interior Douglas-fir (Pseudotsuga menziesii var. glauca) spanning a wide climatic range in British Columbia (BC), Canada, we present an approach combining high-resolution spatiotemporal climate data with traditional dendroecological analyses to quantify relationships between population climate–growth sensitivity and provenance (i.e., seed-source origin) climate. Key results showed that Douglas-fir climate–growth sensitivities were strongly linked to provenance climate and varied in coherent patterns across climatic gradients. Climate–growth sensitivities and responses were sometimes opposite between provenances from disparate climates. Perhaps most importantly, our results showed that Douglas-fir productivity across most of its range was sensitive to moisture limitations, and this sensitivity increased strongly with decreasing provenance mean annual precipitation and increasing heat-moisture index. Using geographic information systems, we visualize the link between provenance mean annual precipitation and climatic sensitivity of Douglas-fir across BC to identify “high risk” populations. By understanding the link between biological responses and climate, forest managers may be able to spatially identify sensitive populations using spatiotemporal climate data.

Hierarchical group dynamics in pigeon flocks

Animals that travel together in groups display a variety of fascinating motion patterns thought to be the result of delicate local interactions among group members. Although the most informative way of investigating and interpreting collective movement phenomena would be afforded by the collection of high-resolution spatiotemporal data from moving individuals, such data are scarce and are virtually non-existent for long-distance group motion within a natural setting because of the associated technological difficulties. Here we present results of experiments in which track logs of homing pigeons flying in flocks of up to 10 individuals have been obtained by high-resolution lightweight GPS devices and analysed using a variety of correlation functions inspired by approaches common in statistical physics. We find a well-defined hierarchy among flock members from data concerning leading roles in pairwise interactions, defined on the basis of characteristic delay times between birds' directional choices. The average spatial position of a pigeon within the flock strongly correlates with its place in the hierarchy, and birds respond more quickly to conspecifics perceived primarily through the left eye-both results revealing differential roles for birds that assume different positions with respect to flock-mates. From an evolutionary perspective, our results suggest that hierarchical organization of group flight may be more efficient than an egalitarian one, at least for those flock sizes that permit regular pairwise interactions among group members, during which leader-follower relationships are consistently manifested.

SensorScope

SensorScope is a turnkey solution for environmental monitoring systems, based on a wireless sensor network and resulting from a collaboration between environmental and network researchers. Given the interest in climate change, environmental monitoring is a domain where sensor networks will have great impact by providing high resolution spatio-temporal data for long periods of time. SensorScope is such a system, which has already been successfully deployed multiple times in various environments (e.g., mountainous, urban). Here, we describe the overall hardware and software architectures and especially focus on the sensor network itself. We also describe one of our most prominent deployments, on top of a rock glacier in Switzerland, which resulted in the description of a micro-climate phenomenon leading to cold air release from a rock-covered glacier in a region of high alpine risks. Another focus of this paper is the description of what happened behind the scenes to turn SensorScope from a laboratory experiment into successful outdoor deployments in harsh environments. Illustrated by various examples, we point out many lessons learned while working on the project. We indicate the importance of simple code, well suited to the application, as well as the value of close interaction with end-users in planning and running the network and finally exploiting the data.