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O'Brien, Joseph J., E. Louise Loudermilk, Benjamin Hornsby, Andrew T. Hudak, Benjamin C. Bright, Matthew B. Dickinson, J. Kevin Hiers, Casey Teske, and Roger D. Ottmar. "High-resolution infrared thermography for capturing wildland fire behaviour: RxCADRE 2012." International Journal of Wildland Fire 25, no. 1 (2016): 62. http://dx.doi.org/10.1071/wf14165.
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Wildland fire radiant energy emission is one of the only measurements of combustion that can be made at wide spatial extents and high temporal and spatial resolutions. Furthermore, spatially and temporally explicit measurements are critical for making inferences about fire effects and useful for examining patterns of fire spread. In this study we describe our methods for capturing and analysing spatially and temporally explicit long-wave infrared (LWIR) imagery from the RxCADRE (Prescribed Fire Combustion and Atmospheric Dynamics Research Experiment) project and examine the usefulness of these data in investigating fire behaviour and effects. We compare LWIR imagery captured at fine and moderate spatial and temporal resolutions (from 1 cm2 to 1 m2; and from 0.12 to 1 Hz) using both nadir and oblique measurements. We analyse fine-scale spatial heterogeneity of fire radiant power and energy released in several experimental burns. There was concurrence between the measurements, although the oblique view estimates of fire radiative power were consistently higher than the nadir view estimates. The nadir measurements illustrate the significance of fuel characteristics, particularly type and connectivity, in driving spatial variability at fine scales. The nadir and oblique measurements illustrate the usefulness of the data for describing the location and movement of the fire front at discrete moments in time at these fine and moderate resolutions. Spatially and temporally resolved data from these techniques show promise to effectively link the combustion environment with post-fire processes, remote sensing at larger scales and wildland fire modelling efforts.
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Grim, Joseph A., and James O. Pinto. "Estimating Continuous-Coverage Instantaneous Precipitation Rates Using Remotely Sensed and Ground-Based Measurements." Journal of Applied Meteorology and Climatology 50, no. 10 (October 2011): 2073–91. http://dx.doi.org/10.1175/jamc-d-11-033.1.
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AbstractThis study demonstrates a method of temporally and spatially scaling precipitation rates at low probability of precipitation-rate exceedance levels (e.g., 0.1%) from coarser-resolution global datasets to near-instantaneous localized rain gauge precipitation rates. In particular, the 8-km-, 1-h-resolution Climate Prediction Center Morphing (CMORPH) dataset was scaled to 1-min localized rates using the Automated Surface Observing Station (ASOS) rain gauge data. Maps of these scaled precipitation rates show overall patterns and magnitudes that are nearly identical to the lower-spatial-resolution rain gauge maps yet retain the much higher resolution of the original remotely sensed global dataset, which is particularly important over regions of complex geography and sparse surface observing stations. To scale the CMORPH data, temporal and spatial conversion factor arrays were calculated by comparing precipitation rates at different temporal (ASOS 1-min and 1-h) and spatial (ASOS 1-h and CMORPH 1-h) resolutions. These temporal and spatial conversion factors were found to vary by probability level, season, and climatological region. Meteorological implications of these variations are discussed.
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Kim, Joohwan S., and Martin S. Banks. "65.1: Effective Spatial Resolution of Temporally and Spatially Interlaced Stereo 3D Televisions." SID Symposium Digest of Technical Papers 43, no. 1 (June 2012): 879–82. http://dx.doi.org/10.1002/j.2168-0159.2012.tb05927.x.
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Decking, Ulrich K. M., Vinay M. Pai, Eric Bennett, Joni L. Taylor, Christian D. Fingas, Klaus Zanger, Han Wen, and Robert S. Balaban. "High-resolution imaging reveals a limit in spatial resolution of blood flow measurements by microspheres." American Journal of Physiology-Heart and Circulatory Physiology 287, no. 3 (September 2004): H1132—H1140. http://dx.doi.org/10.1152/ajpheart.00119.2004.
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Density of 15-μm microspheres after left atrial application is the standard measure of regional perfusion. In the heart, substantial differences in microsphere density are seen at spatial resolutions <5 ml, implying perfusion heterogeneity. Microsphere deposition imaging permits a superior evaluation of the distribution pattern. Therefore, fluorescent microspheres (FMS) were applied, FMS deposition in the canine heart was imaged by epifluorescence microscopy in vitro, and the patterns were observed compared with MR images of iron oxide microspheres (IMS) obtained in vivo and in vitro. FMS deposition in myocardial slices revealed the following: 1) a nonrandom distribution, with sequentially applied FMS of different color stacked within the same vessel, 2) general FMS clustering, and 3) rather large areas devoid of FMS ( n = 3). This pattern was also seen in reconstructed three-dimensional images (<1 nl resolution) of FMS distribution ( n = 4). Surprisingly, the deposition pattern of sequentially applied FMS remained virtually identical over 3 days. Augmenting flow by intracoronary adenosine (>2 μM) enhanced local microsphere density, but did not alter the deposition pattern ( n = 3). The nonrandom, temporally stable pattern was quantitatively confirmed by a three-dimensional intermicrosphere distance analysis of sequentially applied FMS. T2-weighted short-axis MR images (2-μl resolution) of IMS revealed similar patterns in vivo and in vitro ( n = 6), as seen with FMS. The observed temporally stable microsphere patterns are not consistent with the notion that microsphere deposition is solely governed by blood flow. We propose that at high spatial resolution (<2 μl) structural aspects of the vascular network dominate microsphere distribution, resulting in the organized patterns observed.
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Eggert, B., P. Berg, J. O. Haerter, D. Jacob, and C. Moseley. "Temporal and spatial scaling impacts on extreme precipitation." Atmospheric Chemistry and Physics Discussions 15, no. 2 (January 23, 2015): 2157–96. http://dx.doi.org/10.5194/acpd-15-2157-2015.
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Abstract. Both in the current climate and in the light of climate change, understanding of the causes and risk of precipitation extremes is essential for protection of human life and adequate design of infrastructure. Precipitation extreme events depend qualitatively on the temporal and spatial scales at which they are measured, in part due to the distinct types of rain formation processes that dominate extremes at different scales. To capture these differences, we first filter large datasets of high-resolution radar measurements over Germany (5 min temporally and 1 km spatially) using synoptic cloud observations, to distinguish convective and stratiform rain events. In a second step, for each precipitation type, the observed data are aggregated over a sequence of time intervals and spatial areas. The resulting matrix allows a detailed investigation of the resolutions at which convective or stratiform events are expected to contribute most to the extremes. We analyze where the statistics of the two types differ and discuss at which resolutions transitions occur between dominance of either of the two precipitation types. We characterize the scales at which the convective or stratiform events will dominate the statistics. For both types, we further develop a mapping between pairs of spatially and temporally aggregated statistics. The resulting curve is relevant when deciding on data resolutions where statistical information in space and time is balanced. Our study may hence also serve as a practical guide for modelers, and for planning the space–time layout of measurement campaigns. We also describe a mapping between different pairs of resolutions, possibly relevant when working with mismatched model and observational resolutions, such as in statistical bias correction.
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Pan, Xiaoduo, Xin Li, Kun Yang, Jie He, Yanlin Zhang, and Xujun Han. "Comparison of Downscaled Precipitation Data over a Mountainous Watershed: A Case Study in the Heihe River Basin." Journal of Hydrometeorology 15, no. 4 (July 30, 2014): 1560–74. http://dx.doi.org/10.1175/jhm-d-13-0202.1.
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Abstract Development of an accurate precipitation dataset is of primary importance for regional hydrological process studies and water resources management. Here, four regional precipitation products are evaluated for the Heihe River basin (HRB): 1) a spatially and temporally disaggregated Climate Prediction Center Merged Analysis of Precipitation (CMAP) at 0.25° spatial resolution (DCMAP); 2) a fusion product obtained by merging China Meteorological Administration station data and Tropical Rainfall Measuring Mission precipitation data at 0.1° spatial resolution supported by the Institute of Tibetan Plateau Research (ITP), Chinese Academy of Sciences (ITP-F); 3) a disaggregated CMAP downscaled by a statistical meteorological model tool at 1-km spatial resolution (DCMAP–MicroMet); and 4) a Weather Research and Forecasting (WRF) Model simulation with 5-km resolution (WRF-P). The validation metrics include spatial pattern, temporal pattern, error analysis with respect to observation data, and precipitation event verification indicators. The results indicate that 1) precipitation from the DCMAP product may not be suitable for water cycle studies at the watershed scale because of its coarser spatial resolution and 2) ITP-F, WRF-P, and DCMAP–MicroMet precipitation products generally show similar spatial–temporal patterns in HRB but have varying performances between different subbasins.
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Jin, Huaan, Weixing Xu, Ainong Li, Xinyao Xie, Zhengjian Zhang, and Haoming Xia. "Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data." Remote Sensing 11, no. 21 (October 28, 2019): 2517. http://dx.doi.org/10.3390/rs11212517.
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As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas.
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Fecht, Daniela, Samantha Cockings, Susan Hodgson, Frédéric B. Piel, David Martin, and Lance A. Waller. "Advances in mapping population and demographic characteristics at small-area levels." International Journal of Epidemiology 49, Supplement_1 (April 1, 2020): i15—i25. http://dx.doi.org/10.1093/ije/dyz179.
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Abstract Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health. In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys. We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.
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Turner, Alexander J., Daniel J. Jacob, Joshua Benmergui, Jeremy Brandman, Laurent White, and Cynthia A. Randles. "Assessing the capability of different satellite observing configurations to resolve the distribution of methane emissions at kilometer scales." Atmospheric Chemistry and Physics 18, no. 11 (June 13, 2018): 8265–78. http://dx.doi.org/10.5194/acp-18-8265-2018.
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Abstract. Anthropogenic methane emissions originate from a large number of fine-scale and often transient point sources. Satellite observations of atmospheric methane columns are an attractive approach for monitoring these emissions but have limitations from instrument precision, pixel resolution, and measurement frequency. Dense observations will soon be available in both low-Earth and geostationary orbits, but the extent to which they can provide fine-scale information on methane sources has yet to be explored. Here we present an observation system simulation experiment (OSSE) to assess the capabilities of different satellite observing system configurations. We conduct a 1-week WRF-STILT simulation to generate methane column footprints at 1.3 × 1.3 km2 spatial resolution and hourly temporal resolution over a 290 × 235 km2 domain in the Barnett Shale, a major oil and gas field in Texas with a large number of point sources. We sub-sample these footprints to match the observing characteristics of the recently launched TROPOMI instrument (7 × 7 km2 pixels, 11 ppb precision, daily frequency), the planned GeoCARB instrument (2.7 × 3.0 km2 pixels, 4 ppb precision, nominal twice-daily frequency), and other proposed observing configurations. The information content of the various observing systems is evaluated using the Fisher information matrix and its eigenvalues. We find that a week of TROPOMI observations should provide information on temporally invariant emissions at ∼ 30 km spatial resolution. GeoCARB should provide information available on temporally invariant emissions ∼ 2–7 km spatial resolution depending on sampling frequency (hourly to daily). Improvements to the instrument precision yield greater increases in information content than improved sampling frequency. A precision better than 6 ppb is critical for GeoCARB to achieve fine resolution of emissions. Transient emissions would be missed with either TROPOMI or GeoCARB. An aspirational high-resolution geostationary instrument with 1.3 × 1.3 km2 pixel resolution, hourly return time, and 1 ppb precision would effectively constrain the temporally invariant emissions in the Barnett Shale at the kilometer scale and provide some information on hourly variability of sources.
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Li, Xiuxia, Shunlin Liang, and Huaan Jin. "An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products." Remote Sensing 13, no. 4 (February 16, 2021): 719. http://dx.doi.org/10.3390/rs13040719.
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Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.
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Webb, Thompson. "Constructing the past from late-Quaternary pollen data: Temporal resolution and a zoom lens space-time perspective." Short Courses in Paleontology 6 (1993): 79–101. http://dx.doi.org/10.1017/s2475263000001069.
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Pollen analysis is an exercise in seeing. The ultimate goal is to see into the past, to send down a periscope and view what went on. The metaphor of the periscope is too limited, however; a video recorder from high in space with resolution in places up to 10 m is more encompassing of what is possible. The images that are retrieved can be of high or low resolution temporally, spatially, taxonomically, and numerically, and they can illustrate local to global changes in plant populations, vegetation, climate, human activity, fire frequency, and plant diseases over decades to millennia. Because each of these entities or phenomena varies spatially and temporally, records of data covering a breadth of scales in space and time are needed. To obtain the highest quality images about a specific phenomenon requires an understanding of the sensing system that accumulated the data. How does the periscope or video recorder work and what are the scaling characteristics of the images that it registers? These characteristics include breadth of coverage, sampling resolution, and sampling density in time, space, and taxonomy. Actualistic and taphonomic studies of Quaternary data covering a variety of temporal and spatial scales have helped provide this understanding, and temporal resolution is just one concern in these studies.
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Fonseca, A. R., and J. A. Santos. "High-Resolution Temperature Datasets in Portugal from a Geostatistical Approach: Variability and Extremes." Journal of Applied Meteorology and Climatology 57, no. 3 (March 2018): 627–44. http://dx.doi.org/10.1175/jamc-d-17-0215.1.
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AbstractClimate research in Portugal is often constrained by the lack of homogeneous, temporally and spatially consistent, and long-term climatic series. To overcome this limitation, the authors developed new high-resolution gridded datasets (~1 km) of daily mean, minimum, and maximum air temperatures over Portugal (1950–2015, 66 yr), based on gridded daily temperatures (E-OBS) at ~25-km spatial resolution. A two-step approach was followed, under the assumption that daily temperature variability in Portugal is mainly controlled by atmospheric large-scale forcing, while local processes are mostly expressed as strong spatial gradients. First, monthly baseline (1971–2000) patterns were estimated at 1-km grid resolution by applying multivariate linear regressions (exploratory variables: elevation, latitude, and distance to coastline). A kriging of residuals from baseline normals of 36 weather stations was applied for bias corrections. Second, bilinearly interpolated daily temperature anomalies were then added to the daily baseline patterns to obtain the final datasets. The method performance was evaluated using fivefold cross-validations. The datasets were also validated using daily temperatures from 23 stations not incorporated in E-OBS. A climatological analysis based on these datasets was carried out, highlighting spatial heterogeneities, seasonality, long-term trends, interannual variability, and extremes. The spatial and temporal variability is generally coherent with previous studies at coarser resolutions. An overall warming trend is apparent for all variables and indices, but showing different strengths and spatial variability. These datasets show important advantages over preexisting data, including more detailed and accurate information on trends and interannual variability of precipitation extremes, and can thus be applied to several areas of research in Portugal, such as hydrology, ecology, agriculture, and forestry.
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Benjankar, Rohan, Ravin Kafle, Shanti Satyal, and Nirajan Adhikari. "Analyses of Spatial and Temporal Variations of Salt Concentration in Waterbodies Based on High Resolution Measurements Using Sensors." Hydrology 8, no. 2 (April 8, 2021): 64. http://dx.doi.org/10.3390/hydrology8020064.
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Studies have shown that salt concentrations are increasing in waterbodies such as lakes, rivers, wetlands, and streams in areas where deicers are commonly applied for winter road maintenance, resulting in degraded water quality. As the salt concentration varies spatially and temporally based on environmental and hydrological characteristics, we monitored high resolution (15 min) salt concentrations for a relatively long period (winter and spring season) at different sites (i.e., stream, urban-stream, roadside drain, and parking-lot drain) using multiple electric conductivity-based sensors. The salt concentrations were significantly different from each other considering individual sensors and different sites in both winter and spring seasons, which support past research results that concentration varies spatially. Parking-lot (1136 ± 674 ppm) and Roadside (701 ± 263 ppm) drain measured significantly higher concentration than for Stream (260 ± 60 ppm) and Urban-stream (562 ± 266 ppm) in the winter season. Similar trends were observed for the spring season, however, the mean concentrations were lower in the spring. Furthermore, salt concentrations were significantly higher during the winter (242 ± 47 ppm to 1695 ± 629 ppm) than for the spring (140 ± 23 ppm to 863 ± 440 ppm) season considering different sites, which have been attributed to the winter snow maintenance practice using deicers in past studies. All sites exceed the United States Environmental Protection Agency (USEPA) threshold (salt concentration higher than 230 mg/L) for chronic exposure level for 59% to 94% and 10% to 83% of days in winter and spring seasons, respectively. The study has highlighted the usefulness and advantages of high resolution (spatially and temporally) salt concentration measurement using sensor technology. Furthermore, the salt concentration in waterbodies can vary spatially and temporally within a small spatial scale, which may be important information for managing water quality locally. The high resolution measurements (i.e., 15 min) were helpful to capture the highest potential salt concentrations in the waterbody. Therefore, the sensor technology can help to measure high resolution salt concentrations, which can be used to quantify impacts of high salt concentrations, e.g., application of deicer for winter road maintenance on aquatic systems based on the criteria developed by USEPA.
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Atencia, A., L. Mediero, M. C. Llasat, and L. Garrote. "Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model." Hydrology and Earth System Sciences 15, no. 12 (December 21, 2011): 3809–27. http://dx.doi.org/10.5194/hess-15-3809-2011.
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Abstract. The performance of a hydrologic model depends on the rainfall input data, both spatially and temporally. As the spatial distribution of rainfall exerts a great influence on both runoff volumes and peak flows, the use of a distributed hydrologic model can improve the results in the case of convective rainfall in a basin where the storm area is smaller than the basin area. The aim of this study was to perform a sensitivity analysis of the rainfall time resolution on the results of a distributed hydrologic model in a flash-flood prone basin. Within such a catchment, floods are produced by heavy rainfall events with a large convective component. A second objective of the current paper is the proposal of a methodology that improves the radar rainfall estimation at a higher spatial and temporal resolution. Composite radar data from a network of three C-band radars with 6-min temporal and 2 × 2 km2 spatial resolution were used to feed the RIBS distributed hydrological model. A modification of the Window Probability Matching Method (gauge-adjustment method) was applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation by computing new Z/R relationships for both convective and stratiform reflectivities. An advection correction technique based on the cross-correlation between two consecutive images was introduced to obtain several time resolutions from 1 min to 30 min. The RIBS hydrologic model was calibrated using a probabilistic approach based on a multiobjective methodology for each time resolution. A sensitivity analysis of rainfall time resolution was conducted to find the resolution that best represents the hydrological basin behaviour.
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Huie, Carmen W., and Edward S. Yeung. "Spatial Mapping of Transient Atomic Concentrations Using Acousto-Optic Deflection." Applied Spectroscopy 40, no. 6 (August 1986): 863–68. http://dx.doi.org/10.1366/0003702864508278.
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We report a new imaging system for obtaining spatially and temporally resolved atomic absorption profiles for transient events. This is based on an aconsto-optic beam deflector that scans the probe laser beam in one dimension repeatedly across the spatial region of interest. Scan rates of 10 µs durations essentially freeze the absorbing species in time to allow a spatial resolution of 0.06 cm over a 1.2 cm length. With the use of 2K of buffer memory and a digitization interval of 200 ns (12 bits), the time evolution can be followed up to a total of 400 µs. The capabilities are demonstrated in the study of atom formation in a laser-generated plume from a sodium tungstate surface.
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Soldovieri, F., R. Bernini, I. Catapano, and L. Crocco. "The integration of novel diagnostics techniques for multi-scale monitoring of large civil infrastructures." Advances in Geosciences 19 (November 14, 2008): 67–74. http://dx.doi.org/10.5194/adgeo-19-67-2008.
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Abstract. In the recent years, structural monitoring of large infrastructures (buildings, dams, bridges) or more generally man-made structures has raised an increased attention due to the growing interest about safety and security issues and risk assessment through early detection. In this framework, aim of the paper is to introduce a new integrated approach which combines two sensing techniques acting on different spatial and temporal scales. The first one is a distributed optic fiber sensor based on the Brillouin scattering phenomenon, which allows a spatially and temporally continuous monitoring of the structure with a "low" spatial resolution (meter). The second technique is based on the use of Ground Penetrating Radar (GPR), which can provide detailed images of the inner status of the structure (with a spatial resolution less then tens centimetres), but does not allow a temporal continuous monitoring. The paper describes the features of these two techniques and provides experimental results concerning preliminary test cases.
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Vicente-Serrano, S. M., S. Beguería, J. I. López-Moreno, M. Angulo, and A. El Kenawy. "A New Global 0.5° Gridded Dataset (1901–2006) of a Multiscalar Drought Index: Comparison with Current Drought Index Datasets Based on the Palmer Drought Severity Index." Journal of Hydrometeorology 11, no. 4 (August 1, 2010): 1033–43. http://dx.doi.org/10.1175/2010jhm1224.1.
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Abstract A monthly global dataset of a multiscalar drought index is presented and compared in terms of spatial and temporal variability with the existing continental and global drought datasets based on the Palmer drought severity index (PDSI). The presented dataset is based on the standardized precipitation evapotranspiration index (SPEI). The index was obtained using the Climatic Research Unit (CRU) TS3.0 dataset at a spatial resolution of 0.5°. The advantages of the new dataset are that (i) it improves the spatial resolution of the unique global drought dataset at a global scale; (ii) it is spatially and temporally comparable to other datasets, given the probabilistic nature of the SPEI; and, in particular, (iii) it enables the identification of various drought types, given the multiscalar character of the SPEI. The dataset is freely available on the Web page of the Spanish National Research Council (CSIC) in three different formats [network Common Data Form (netCDF), binary raster, and plain text].
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van der Plas, Emiel, Maurice Schmeits, Nicolien Hooijman, and Kees Kok. "A Comparative Verification of High-Resolution Precipitation Forecasts Using Model Output Statistics." Monthly Weather Review 145, no. 10 (October 2017): 4037–54. http://dx.doi.org/10.1175/mwr-d-16-0256.1.
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Verification of localized events such as precipitation has become even more challenging with the advent of high-resolution mesoscale numerical weather prediction (NWP). The realism of a forecast suggests that it should compare well against precipitation radar imagery with similar resolution, both spatially and temporally. Spatial verification methods solve some of the representativity issues that point verification gives rise to. In this paper, a verification strategy based on model output statistics (MOS) is applied that aims to address both double-penalty and resolution effects that are inherent to comparisons of NWP models with different resolutions. Using predictors based on spatial precipitation patterns around a set of stations, an extended logistic regression (ELR) equation is deduced, leading to a probability forecast distribution of precipitation for each NWP model, analysis, and lead time. The ELR equations are derived for predictands based on areal-calibrated radar precipitation and SYNOP observations. The aim is to extract maximum information from a series of precipitation forecasts, like a trained forecaster would. The method is applied to the nonhydrostatic model Harmonie-AROME (2.5-km resolution), HIRLAM (11-km resolution), and the ECMWF model (16-km resolution), overall yielding similar Brier skill scores for the three postprocessed models, but somewhat larger differences for individual lead times. In addition, the fractions skill score is computed using the three deterministic forecasts, showing slightly higher skill for the Harmonie-AROME model. In other words, despite the realism of Harmonie-AROME precipitation forecasts, they only perform similarly or somewhat better than precipitation forecasts from the two lower-resolution models, at least in the Netherlands.
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Htitiou, A., A. Boudhar, Y. Lebrini, and T. Benabdelouahab. "DEEP LEARNING-BASED RECONSTRUCTION OF SPATIOTEMPORALLY FUSED SATELLITE IMAGES FOR SMART AGRICULTURE APPLICATIONS IN A HETEROGENEOUS AGRICULTURAL REGION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W3-2020 (November 23, 2020): 249–54. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w3-2020-249-2020.
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Abstract. Remote sensing offers spatially explicit and temporally continuous observational data of various land surface parameters such as vegetation index, land surface temperature, soil moisture, leaf area index, and evapotranspiration, which can be widely leveraged for various applications at different scales and contexts. One of the main applications is agricultural monitoring, where a smart system based on precision agriculture requires a set of satellite images with a high resolution, both in time and space to capture the phenological stages and fine spatial details, especially in landscapes with various spatial heterogeneity and temporal variation. These requirements sometimes cannot be provided by a single sensor due to the trade-off required between spatial and temporal resolutions and/or the influence of cloud cover. The data availability of new generation multispectral sensors of Landsat-8 (L8) and Sentinel-2 (S2) satellites offers unprecedented options for such applications. Given this, the current study aims to display how the synergistic use of these optical sensors can efficiently support such an application. Herein, this study proposes a deep learning spatiotemporal data fusion method to fill the need for predicting a dense time series of vegetation index with fine spatial resolution. The results show that the developed method creates more accurate fused NDVI time-series data that were able to derive phenological stages and characteristics in single-crop fields, while keeps more spatial details in such a heterogeneous landscape.
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Montazeri, M., MSK Kiany, and SA Masoodian. "Evaluation of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA v7) in drought monitoring over southwest Iran." Climate Research 82 (November 5, 2020): 55–73. http://dx.doi.org/10.3354/cr01622.
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Characterizing the errors in satellite-based precipitation estimations for drought monitoring is of great importance, as these estimations provide both spatially and temporally complete records. The aim of this study was to evaluate satellite-based quantitative precipitation estimates to monitor meteorological drought in southwestern Iran. The reliability of the Tropical Rainfall Measuring Mission Version 7 products (3B42 and 3B43) in estimating the standardized precipitation index (SPI) was evaluated against a ground-based gridded precipitation dataset at 0.25° spatial resolution for 1998-2016. The analysis conducted for the SPI at various time scales revealed that both products (3B42 and 3B43) are capable of capturing the spatial and temporal behavior of drought events over the study region, with the best performance at SPI6. 3B43 is also more efficient in the identification of shorter severe drought events compared to 3B42. The findings suggest that both satellite products, particularly 3B43, are suitable to be used directly for SPI computation in the region for drought monitoring and early warning in terms of the accuracy and the spatial and temporal resolutions they provide.
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Tai, Chang-Kou. "The Temporal Aliasing Formulas for the Tandem Mission of Jason-1 and TOPEX/Poseidon." Journal of Atmospheric and Oceanic Technology 26, no. 2 (February 1, 2009): 352–67. http://dx.doi.org/10.1175/2008jtecho610.1.
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Abstract The temporal aliasing formulas are derived for the Tandem Mission of Jason-1 and the Ocean Topography Experiment (TOPEX)/Poseidon. Previously, aliasing formulas were derived for a single satellite or a constellation of coordinated satellites, wherein the coordination is such that the collective samplings appear as if they were carried out by a single satellite. In this vein, Jason-1 and TOPEX/Poseidon are coordinated spatially but not temporally. The problem is treated as a general problem about the temporal phasing between any two satellites that are coordinated spatially so that the Tandem Mission is just one special case, whereas the temporally coordinated case is another special case. The present results do agree with the formulas for a constellation of coordinated satellites when the temporal phasing yields temporal coordination, as they should. The benefit of temporal coordination shows itself as having a higher spatial resolution for temporally aliased features. The physical implication is twofold. First, a high-frequency and low-wavenumber feature (e.g., the barotropic Rossby waves) has a better chance of being aliased as a low-frequency and higher-wavenumber feature in a perfectly coordinated mission than it has in the Tandem Mission. Second, in a perfectly coordinated mission, a high-frequency and high-wavenumber feature could be aliased as a low-frequency and high-wavenumber feature rather than as a low-frequency and low-wavenumber feature in the Tandem Mission. Despite the extreme mathematical complexity, the physical case is rather intuitive. Namely, the two satellites need temporal coordination to work as one in fending off temporal aliasing. Without it, the two satellites behave as two independent satellites, thus each reverting to their original (i.e., lower) spatial resolution capability in dealing with temporal aliasing.
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Chaney, Nathaniel W., Justin Sheffield, Gabriele Villarini, and Eric F. Wood. "Development of a High-Resolution Gridded Daily Meteorological Dataset over Sub-Saharan Africa: Spatial Analysis of Trends in Climate Extremes." Journal of Climate 27, no. 15 (July 29, 2014): 5815–35. http://dx.doi.org/10.1175/jcli-d-13-00423.1.
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Abstract Assessing changes in the frequency and intensity of extreme meteorological events and their impact on water resources, agriculture, and infrastructure is necessary to adequately prepare and adapt to future change. This is a challenge in data-sparse regions such as sub-Saharan Africa, where a lack of high-density and temporally consistent long-term in situ measurements complicates the analysis. To address this, a temporally homogenous and high-temporal- and high-spatial-resolution meteorological dataset is developed over sub-Saharan Africa (5°S–25°N), covering the time period between 1979 and 2005. It is developed by spatially downscaling the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis to a 0.1° spatial resolution, detecting and correcting for temporal inhomogeneities, and by removing random errors and biases by assimilating quality-controlled and gap-filled Global Summary of the Day (GSOD) in situ measurements. The dataset is then used to determine the statistical significance and magnitude of changes in climate extremes between 1979 and 2005. The results suggest a shift in the distribution of daily maximum and minimum temperatures toward a warmer mean with a faster increase in warm than cold events. Changes in the mean annual precipitation and heavy rainfall events are significant only in regions affected by the Sahel droughts of the 1970s and 1980s.
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Su, Jianbin, Haishen Lü, Wade T. Crow, Yonghua Zhu, and Yifan Cui. "The Effect of Spatiotemporal Resolution Degradation on the Accuracy of IMERG Products over the Huai River Basin." Journal of Hydrometeorology 21, no. 5 (May 2020): 1073–88. http://dx.doi.org/10.1175/jhm-d-19-0158.1.
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AbstractThe rapid development of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) precipitation product provides new opportunities for a wide range of Earth system and natural hazard applications. Spatiotemporal averaging is a common method for IMERG users to acquire suitable resolutions specific to their research or application purpose and has a direct impact on the overall quality of IMERG precipitation estimates. Here, three different IMERG, version 06 (V06), latency run products (i.e., early, late, and final) are assessed against a ground-based benchmark along a continuous series of spatiotemporal resolutions over the Huai River basin (HuaiRB) between June 2014 and May 2017. In general, IMERG products better capture the spatial pattern of precipitation, and demonstrate better reliability, in the southern portion of the HuaiRB relative to its northern region. Furthermore, the degradation of spatiotemporal resolution is associated with better rain/no-rain determination and the consistent improvement of rainfall product performance metrics. This improvement is more pronounced for IMERG products at fine spatiotemporal resolution. However, due to the presence of autocorrelated errors, the performance improvement associated with the degradation of spatiotemporal resolution is less than theoretical expectations assuming purely uncorrelated errors. Component analysis indicates that while both temporal and spatial aggregation do not mitigate temporally autocorrelated errors, temporal averaging can remove spatially autocorrelated error. Hence, temporal averaging is found to be more effective than spatial averaging for improving the quality of IMERG products. These results will inform users of the reliability of IMERG products at different spatiotemporal scales and assist in unifying former disparate validation assessments applied at different scales within the literature.
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Yu, Karen, Christoph A. Keller, Daniel J. Jacob, Andrea M. Molod, Sebastian D. Eastham, and Michael S. Long. "Errors and improvements in the use of archived meteorological data for chemical transport modeling: an analysis using GEOS-Chem v11-01 driven by GEOS-5 meteorology." Geoscientific Model Development 11, no. 1 (January 23, 2018): 305–19. http://dx.doi.org/10.5194/gmd-11-305-2018.
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Abstract. Global simulations of atmospheric chemistry are commonly conducted with off-line chemical transport models (CTMs) driven by archived meteorological data from general circulation models (GCMs). The off-line approach has the advantages of simplicity and expediency, but it incurs errors due to temporal averaging in the meteorological archive and the inability to reproduce the GCM transport algorithms exactly. The CTM simulation is also often conducted at coarser grid resolution than the parent GCM. Here we investigate this cascade of CTM errors by using 222Rn–210Pb–7Be chemical tracer simulations off-line in the GEOS-Chem CTM at rectilinear 0.25∘ × 0.3125∘ (≈ 25 km) and 2∘ × 2.5∘ (≈ 200 km) resolutions and online in the parent GEOS-5 GCM at cubed-sphere c360 (≈ 25 km) and c48 (≈ 200 km) horizontal resolutions. The c360 GEOS-5 GCM meteorological archive, updated every 3 h and remapped to 0.25∘ × 0.3125∘, is the standard operational product generated by the NASA Global Modeling and Assimilation Office (GMAO) and used as input by GEOS-Chem. We find that the GEOS-Chem 222Rn simulation at native 0.25∘ × 0.3125∘ resolution is affected by vertical transport errors of up to 20 % relative to the GEOS-5 c360 online simulation, in part due to loss of transient organized vertical motions in the GCM (resolved convection) that are temporally averaged out in the 3 h meteorological archive. There is also significant error caused by operational remapping of the meteorological archive from a cubed-sphere to a rectilinear grid. Decreasing the GEOS-Chem resolution from 0.25∘ × 0.3125∘ to 2∘ × 2.5∘ induces further weakening of vertical transport as transient vertical motions are averaged out spatially and temporally. The resulting 222Rn concentrations simulated by the coarse-resolution GEOS-Chem are overestimated by up to 40 % in surface air relative to the online c360 simulations and underestimated by up to 40 % in the upper troposphere, while the tropospheric lifetimes of 210Pb and 7Be against aerosol deposition are affected by 5–10 %. The lost vertical transport in the coarse-resolution GEOS-Chem simulation can be partly restored by recomputing the convective mass fluxes at the appropriate resolution to replace the archived convective mass fluxes and by correcting for bias in the spatial averaging of boundary layer mixing depths.
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Denes, Gyorgy, and Rafał K. Mantiuk. "Predicting visible flicker in temporally changing images." Electronic Imaging 2020, no. 11 (January 26, 2020): 233–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.11.hvei-233.
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Novel display algorithms such as low-persistence displays, black frame insertion, and temporal resolution multiplexing introduce temporal change into images at 40-180 Hz, on the boundary of the temporal integration of the visual system. This can lead to flicker, a highly-objectionable artifact known to induce viewer discomfort. The critical flicker frequency (CFF) alone does not model this phenomenon well, as flicker sensitivity varies with contrast, and spatial frequency; a content-aware model is required. In this paper, we introduce a visual model for predicting flicker visibility in temporally changing images. The model performs a multi-scale analysis on the difference between consecutive frames, normalizing values with the spatio-temporal contrast sensitivity function as approximated by the pyramid of visibility. The output of the model is a 2D detection probability map. We ran a subjective flicker marking experiment to fit the model parameters, then analyze the difference between two display algorithms, black frame insertion and temporal resolution multiplexing, to demonstrate the application of our model.
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Sinha, Tushar, A. Sankarasubramanian, and Amirhossein Mazrooei. "Decomposition of Sources of Errors in Monthly to Seasonal Streamflow Forecasts in a Rainfall–Runoff Regime." Journal of Hydrometeorology 15, no. 6 (December 1, 2014): 2470–83. http://dx.doi.org/10.1175/jhm-d-13-0155.1.
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Abstract Despite considerable progress in developing real-time climate forecasts, most studies have evaluated the potential in seasonal streamflow forecasting based on ensemble streamflow prediction (ESP) methods, utilizing only climatological forcings while ignoring general circulation model (GCM)-based climate forecasts. The primary limitation in using GCM forecasts is their coarse resolution, which requires spatiotemporal downscaling to implement land surface models. Consequently, multiple sources of errors are introduced in developing real-time streamflow forecasts utilizing GCM forecasts. A set of error decomposition metrics is provided to address the following questions: 1) How are errors in monthly streamflow forecasts attributed to various sources such as temporal disaggregation, spatial downscaling, imprecise initial hydrologic conditions (IHCs), climatological forcings, and imprecise forecasts? and 2) How do these errors propagate with lead time over different seasons? A calibrated Variable Infiltration Capacity model is used over the Apalachicola River at Chattahoochee in the southeastern United States. The model is forced with a combination of daily precipitation forcings (temporally disaggregated observed precipitation, spatially downscaled and temporally disaggregated observed precipitation, ESP, ECHAM4.5 forecasts, and observed) and IHCs [simulated and climatological ensemble reverse ESP (RESP)] but with observed air temperature and wind speed at ⅛° resolution. Then, errors in forecasting monthly streamflow at up to a 3-month lead time are decomposed by comparing the forecasted streamflow to simulated streamflow under observed forcings. Results indicate that the errors due to temporal disaggregation are much higher than the spatial downscaling errors. During winter and early spring, the increasing order of errors at a 1-month lead time is spatial downscaling, model, temporal disaggregation, RESP, large-scale precipitation forecasts, and ESP.
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Nejadmalayeri, Alireza, Alexei Vezolainen, Giuliano De Stefano, and Oleg V. Vasilyev. "Fully adaptive turbulence simulations based on Lagrangian spatio-temporally varying wavelet thresholding." Journal of Fluid Mechanics 749 (May 22, 2014): 794–817. http://dx.doi.org/10.1017/jfm.2014.241.
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AbstractA new framework for spatio-temporally adaptive turbulence simulations is proposed. The method is based on a variable-fidelity representation that tightly integrates numerics and modelling of subgrid-scale turbulence and aims to capture the flow physics on a near-optimal adaptive mesh. The integration is achieved by combining hierarchical wavelet-based computational modelling with spatially and temporally varying wavelet threshold filtering. The proposed approach provides automatic smooth transition from directly resolving all flow physics to capturing only the energetic/coherent structures, which leads to a dynamically adaptive variable-fidelity approach. The self-regulating continuous switch between different fidelity regimes is accomplished through a two-way feedback mechanism between the modelled dissipation and the local grid resolution, which is based on spatio-temporal variation of the wavelet filtering threshold. The proposed methodology systematically accounts for and exploits the spatial and temporal intermittency of turbulence. Thus, it overcomes the major limitation of all existing wavelet-based multi-resolution techniques, namely, the use of a global thresholding criterion. The procedure consists of tracking the wavelet thresholding factor within a Lagrangian frame by exploiting a Lagrangian path-line diffusive averaging approach, based on either interpolation along characteristics or direct solution of the corresponding evolution equation. This new methodology is tested for linearly forced homogeneous turbulence at different Reynolds numbers and provides very promising results on a benchmark with time-varying prescribed level of turbulence resolution.
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Privé, N. C., and R. M. Errico. "Temporal and Spatial Interpolation Errors of High-Resolution Modeled Atmospheric Fields." Journal of Atmospheric and Oceanic Technology 33, no. 2 (February 2016): 303–11. http://dx.doi.org/10.1175/jtech-d-15-0132.1.
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AbstractGeneral circulation models can now be run at very high spatial resolutions to capture finescale features, but saving the full-spatial-resolution output at every model time step is usually not practical because of storage limitations. To reduce storage requirements, the model output may be produced at reduced temporal and/or spatial resolutions. When this reduced-resolution output is then used in situations where spatiotemporal interpolation is required, such as the generation of synthetic observations for observing system simulation experiments, interpolation errors can significantly affect the quality and usefulness of the reduced-resolution model output. Although it is common in practice to record model output at the highest possible spatial resolution with relatively infrequent temporal output, this may not be the best option to minimize interpolation errors. In this study, two examples using a high-resolution global run of the Goddard Earth Observing System Model, version 5 (GEOS-5), are presented to illustrate cases in which the optimal output dataset configurations for interpolation have high temporal frequency but reduced spatial resolutions. Interpolation errors of tropospheric temperature, specific humidity, and wind fields are investigated. The relationship between spatial and temporal output resolutions and interpolation errors is also characterized for the example model.
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Lin, S., J. Li, and Q. Liu. "ESTIMATING GROSS PRIMARY PRODUCTION IN CROPLAND WITH HIGH SPATIAL AND TEMPORAL SCALE REMOTE SENSING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1009–14. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1009-2018.
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Satellite remote sensing data provide spatially continuous and temporally repetitive observations of land surfaces, and they have become increasingly important for monitoring large region of vegetation photosynthetic dynamic. But remote sensing data have their limitation on spatial and temporal scale, for example, higher spatial resolution data as Landsat data have 30-m spatial resolution but 16&thinsp;days revisit period, while high temporal scale data such as geostationary data have 30-minute imaging period, which has lower spatial resolution (&gt;&thinsp;1&thinsp;km). The objective of this study is to investigate whether combining high spatial and temporal resolution remote sensing data can improve the gross primary production (GPP) estimation accuracy in cropland. For this analysis we used three years (from 2010 to 2012) Landsat based NDVI data, MOD13 vegetation index product and Geostationary Operational Environmental Satellite (GOES) geostationary data as input parameters to estimate GPP in a small region cropland of Nebraska, US. Then we validated the remote sensing based GPP with the in-situ measurement carbon flux data. Results showed that: 1) the overall correlation between GOES visible band and in-situ measurement photosynthesis active radiation (PAR) is about 50&thinsp;% (R<sup>2</sup>&thinsp;=&thinsp;0.52) and the European Center for Medium-Range Weather Forecasts ERA-Interim reanalysis data can explain 64&thinsp;% of PAR variance (R<sup>2</sup>&thinsp;=&thinsp;0.64); 2) estimating GPP with Landsat 30-m spatial resolution data and ERA daily meteorology data has the highest accuracy(R<sup>2</sup>&thinsp;=&thinsp;0.85, RMSE &lt;&thinsp;3&thinsp;gC/m<sup>2</sup>/day), which has better performance than using MODIS 1-km NDVI/EVI product import; 3) using daily meteorology data as input for GPP estimation in high spatial resolution data would have higher relevance than 8-day and 16-day input. Generally speaking, using the high spatial resolution and high frequency satellite based remote sensing data can improve GPP estimation accuracy in cropland.
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Zhang, Guangyuan, Xiaoping Rui, Stefan Poslad, Xianfeng Song, Yonglei Fan, and Zixiang Ma. "Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users’ Distributions Based upon a Convolution Long Short-Term Model." Sensors 19, no. 9 (May 9, 2019): 2156. http://dx.doi.org/10.3390/s19092156.
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Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. We propose a novel method, firstly based upon using a kernel density estimation (KDE) to estimate dynamic mobile phone users’ distributions at a two-hourly scale temporal resolution. Secondly, a convolutional long short-term memory (ConvLSTM) model was used in our study to predict mobile phone users’ spatial and temporal distributions for the first time at such a fine-grained temporal resolution. The evaluation results show that the predicted people’s mobility derived from the mobile phone users’ density correlates much better with the actual density, both temporally and spatially, as compared to traditional methods such as time-series prediction, autoregressive moving average model (ARMA), and LSTM.
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Yeshurun, Yaffa, and Liat Levy. "Transient Spatial Attention Degrades Temporal Resolution." Psychological Science 14, no. 3 (May 2003): 225–31. http://dx.doi.org/10.1111/1467-9280.02436.
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To better understand the interplay between the temporal and spatial components of visual perception, we studied the effects of transient spatial attention on temporal resolution. Given that spatial attention sharpens spatial resolution, can it also affect temporal resolution? To assess temporal resolution, we measured the two-flash fusion threshold. When two flashes of light are presented successively to the same location, the two-flash fusion threshold is the minimal interval between the flashes at which they are still perceived as two flashes, rather than a single flash. This assessment of temporal resolution was combined with peripheral precuing—a direct manipulation of transient spatial attention. This allowed us to demonstrate, for the first time, that spatial attention can indeed affect temporal resolution. However, in contrast to its effect on spatial resolution, spatial attention degrades temporal resolution. Two attentional mechanisms that could account for both attentional effects—enhanced spatial resolution and reduced temporal resolution—are discussed.
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Boushaki, Farid Ishak, Kuo-Lin Hsu, Soroosh Sorooshian, Gi-Hyeon Park, Shayesteh Mahani, and Wei Shi. "Bias Adjustment of Satellite Precipitation Estimation Using Ground-Based Measurement: A Case Study Evaluation over the Southwestern United States." Journal of Hydrometeorology 10, no. 5 (October 1, 2009): 1231–42. http://dx.doi.org/10.1175/2009jhm1099.1.
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Abstract Reliable precipitation measurement is a crucial component in hydrologic studies. Although satellite-based observation is able to provide spatial and temporal distribution of precipitation, the measurements tend to show systematic bias. This paper introduces a grid-based precipitation merging procedure in which satellite estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN–CCS) are adjusted based on the Climate Prediction Center (CPC) daily rain gauge analysis. To remove the bias, the hourly CCS estimates were spatially and temporally accumulated to the daily 1° × 1° scale, the resolution of CPC rain gauge analysis. The daily CCS bias was then downscaled to the hourly temporal scale to correct hourly CCS estimates. The bias corrected CCS estimates are called the adjusted CCS (CCSA) product. With the adjustment from the gauge measurement, CCSA data have been generated to provide more reliable high temporal/spatial-resolution precipitation estimates. In the case study, the CCSA precipitation estimates from the proposed approach are compared against ground-based measurements in high-density gauge networks located in the southwestern United States.
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Guallpa, Mario, Johanna Orellana-Alvear, and Jörg Bendix. "Tropical Andes Radar Precipitation Estimates Need High Temporal and Moderate Spatial Resolution." Water 11, no. 5 (May 18, 2019): 1038. http://dx.doi.org/10.3390/w11051038.
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Weather radar networks are an excellent tool for quantitative precipitation estimation (QPE), due to their high resolution in space and time, particularly in remote mountain areas such as the Tropical Andes. Nevertheless, reduction of the temporal and spatial resolution might severely reduce the quality of QPE. Thus, the main objective of this study was to analyze the impact of spatial and temporal resolutions of radar data on the cumulative QPE. For this, data from the world’s highest X-band weather radar (4450 m a.s.l.), located in the Andes of Ecuador (Paute River basin), and from a rain gauge network were used. Different time resolutions (1, 5, 10, 15, 20, 30, and 60 min) and spatial resolutions (0.5, 0.25, and 0.1 km) were evaluated. An optical flow method was validated for 11 rainfall events (with different features) and applied to enhance the temporal resolution of radar data to 1-min intervals. The results show that 1-min temporal resolution images are able to capture rain event features in detail. The radar–rain gauge correlation decreases considerably when the time resolution increases (r from 0.69 to 0.31, time resolution from 1 to 60 min). No significant difference was found in the rain total volume (3%) calculated with the three spatial resolution data. A spatial resolution of 0.5 km on radar imagery is suitable to quantify rainfall in the Andes Mountains. This study improves knowledge on rainfall spatial distribution in the Ecuadorian Andes, and it will be the basis for future hydrometeorological studies.
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Liao, Yi, Yiqiang Sheng, and Jinlin Wang. "A Temporally Hierarchical Deployment Architecture for an Enhanced Name Resolution System." Applied Sciences 9, no. 14 (July 19, 2019): 2891. http://dx.doi.org/10.3390/app9142891.
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The challenging requirements of the fifth generation (5G) cellular networks motivate the need to explore the feasibility of delivering services over new network architectures. Information-Centric Networking (ICN) is an emerging and promising network to satisfy 5G requirements. The name resolution is at the heart of ICN. We focus on satisfying the delay-sensitive requirement of the Name Resolution Service (NRS) in a 5G-ICN integrated network. We aim to design a local NRS that provides a deterministic low latency name resolution service. In this paper, we propose a temporally hierarchical deployment architecture for an Enhanced Name Resolution System (ENRS) to realize deterministic latency. The ENRS quantifiably organizes the nodes into hierarchical and nested domains by latency constraints. We design demand-aware name registration and resolution schemes to achieve constant forwarding hops in order to realize local resolution and forwarding locality. We introduce a tolerable latency-based peer resolver forwarding algorithm to improve the query hit ratio. We present a proactive name binding replicas distribution approach based on temporal–spatial features to reduce the resolution latency and query traffic. The video streaming monitoring service in Smart Home is used as a typical use case to show the continuity of service guaranteed by ENRS. Analysis demonstrates that ENRS can achieve deterministic latency. Evaluation results show that the average query hit ratio of ENRS outperforms the K-NearestNeighbor-Distributed Name Resolution System (KNN-DNRS) and Random Name Resolution System (Random-NRS) with 23.2% and 18.1%, respectively. The query traffic overhead of ENRS is up to 33.3 times smaller than KNN-DNRS. ENRS can process up to 21 GB/s name resolution traffic when the user nodes are in the magnitude order of 106.
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Skliutas, Edvinas, Migle Lebedevaite, Elmina Kabouraki, Tommaso Baldacchini, Jolita Ostrauskaite, Maria Vamvakaki, Maria Farsari, Saulius Juodkazis, and Mangirdas Malinauskas. "Polymerization mechanisms initiated by spatio-temporally confined light." Nanophotonics 10, no. 4 (January 1, 2021): 1211–42. http://dx.doi.org/10.1515/nanoph-2020-0551.
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Abstract Ultrafast laser 3D lithography based on non-linear light–matter interactions, widely known as multi-photon lithography (MPL), offers unrivaled precision rapid prototyping and flexible additive manufacturing options. 3D printing equipment based on MPL is already commercially available, yet there is still no comprehensive understanding of factors determining spatial resolution, accuracy, fabrication throughput, repeatability, and standardized metrology methods for the accurate characterization of the produced 3D objects and their functionalities. The photoexcitation mechanisms, spatial-control or photo-modified volumes, and the variety of processable materials are topics actively investigated. The complexity of the research field is underlined by a limited understanding and fragmented knowledge of light-excitation and material response. Research to date has only provided case-specific findings on photoexcitation, chemical modification, and material characterization of the experimental data. In this review, we aim to provide a consistent and comprehensive summary of the existing literature on photopolymerization mechanisms under highly confined spatial and temporal conditions, where, besides the excitation and cross-linking, parameters such as diffusion, temperature accumulation, and the finite amount of monomer molecules start to become of critical importance. Key parameters such as photoexcitation, polymerization kinetics, and the properties of the additively manufactured materials at the nanoscale in 3D are examined, whereas, the perspectives for future research and as well as emerging applications are outlined.
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Liu, Ke, Zhu Liang Yu, Wei Wu, Zhenghui Gu, and Yuanqing Li. "STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging." International Journal of Neural Systems 25, no. 04 (May 25, 2015): 1550016. http://dx.doi.org/10.1142/s0129065715500161.
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For M/EEG-based distributed source imaging, it has been established that the L2-norm-based methods are effective in imaging spatially extended sources, whereas the L1-norm-based methods are more suited for estimating focal and sparse sources. However, when the spatial extents of the sources are unknown a priori, the rationale for using either type of methods is not adequately supported. Bayesian inference by exploiting the spatio-temporal information of the patch sources holds great promise as a tool for adaptive source imaging, but both computational and methodological limitations remain to be overcome. In this paper, based on state-space modeling of the M/EEG data, we propose a fully data-driven and scalable algorithm, termed STRAPS, for M/EEG patch source imaging on high-resolution cortices. Unlike the existing algorithms, the recursive penalized least squares (RPLS) procedure is employed to efficiently estimate the source activities as opposed to the computationally demanding Kalman filtering/smoothing. Furthermore, the coefficients of the multivariate autoregressive (MVAR) model characterizing the spatial-temporal dynamics of the patch sources are estimated in a principled manner via empirical Bayes. Extensive numerical experiments demonstrate STRAPS's excellent performance in the estimation of locations, spatial extents and amplitudes of the patch sources with varying spatial extents.
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Haldorsen, Jakob B. U. "Spatial aliasing and 3C seismic sensors." GEOPHYSICS 86, no. 4 (June 1, 2021): V255—V267. http://dx.doi.org/10.1190/geo2020-0172.1.
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Temporal aliasing occurs when a waveform is sampled with less than two points per time period for a signal at a given frequency. This insufficiently sampled frequency will be incorrectly mapped into a lower (aliased) frequency. Analogous to this, spatial aliasing is said to occur when a propagating waveform is measured at spatial intervals larger than half the wavelength of any given signal in that waveform. Temporally aliased frequencies cannot be recovered with standard methods. On the other hand, we argue that “spatial aliasing” can be viewed as an expression of a nonuniqueness for estimating the direction of the propagation for signal at a given frequency, and that spatial aliasing may be overcome when 3C seismic sensors are used. Realizing this allows for using higher frequencies, and it therefore enables the generation of higher resolution images from the data. This is particularly useful for borehole-seismic data, which tend to contain higher frequencies than surface-seismic data, but does require that an array of 3C sensors is used or that an array of less expensive 1C sensors is supplemented by 3C sensors.
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De Silva, Varuna, Jamie Roche, and Ahmet Kondoz. "Robust Fusion of LiDAR and Wide-Angle Camera Data for Autonomous Mobile Robots." Sensors 18, no. 8 (August 20, 2018): 2730. http://dx.doi.org/10.3390/s18082730.
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Autonomous robots that assist humans in day to day living tasks are becoming increasingly popular. Autonomous mobile robots operate by sensing and perceiving their surrounding environment to make accurate driving decisions. A combination of several different sensors such as LiDAR, radar, ultrasound sensors and cameras are utilized to sense the surrounding environment of autonomous vehicles. These heterogeneous sensors simultaneously capture various physical attributes of the environment. Such multimodality and redundancy of sensing need to be positively utilized for reliable and consistent perception of the environment through sensor data fusion. However, these multimodal sensor data streams are different from each other in many ways, such as temporal and spatial resolution, data format, and geometric alignment. For the subsequent perception algorithms to utilize the diversity offered by multimodal sensing, the data streams need to be spatially, geometrically and temporally aligned with each other. In this paper, we address the problem of fusing the outputs of a Light Detection and Ranging (LiDAR) scanner and a wide-angle monocular image sensor for free space detection. The outputs of LiDAR scanner and the image sensor are of different spatial resolutions and need to be aligned with each other. A geometrical model is used to spatially align the two sensor outputs, followed by a Gaussian Process (GP) regression-based resolution matching algorithm to interpolate the missing data with quantifiable uncertainty. The results indicate that the proposed sensor data fusion framework significantly aids the subsequent perception steps, as illustrated by the performance improvement of a uncertainty aware free space detection algorithm.
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Li, Xing, and Jingfeng Xiao. "A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data." Remote Sensing 11, no. 5 (March 4, 2019): 517. http://dx.doi.org/10.3390/rs11050517.
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Solar-induced chlorophyll fluorescence (SIF) brings major advancements in measuring terrestrial photosynthesis. Several recent studies have evaluated the potential of SIF retrievals from the Orbiting Carbon Observatory-2 (OCO-2) in estimating gross primary productivity (GPP) based on GPP data from eddy covariance (EC) flux towers. However, the spatially and temporally sparse nature of OCO-2 data makes it challenging to use these data for many applications from the ecosystem to the global scale. Here, we developed a new global ‘OCO-2’ SIF data set (GOSIF) with high spatial and temporal resolutions (i.e., 0.05°, 8-day) over the period 2000–2017 based on a data-driven approach. The predictive SIF model was developed based on discrete OCO-2 SIF soundings, remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological reanalysis data. Our model performed well in estimating SIF (R2 = 0.79, root mean squared error (RMSE) = 0.07 W m−2 μm−1 sr−1). The model was then used to estimate SIF for each 0.05° × 0.05° grid cell and each 8-day interval for the study period. The resulting GOSIF product has reasonable seasonal cycles, and captures the similar seasonality as both the coarse-resolution OCO-2 SIF (1°), directly aggregated from the discrete OCO-2 soundings, and tower-based GPP. Our SIF estimates are highly correlated with GPP from 91 EC flux sites (R2 = 0.73, p < 0.001). They capture the expected spatial and temporal patterns and also have remarkable ability to highlight the crop areas with the highest daily productivity across the globe. Our product also allows us to examine the long-term trends in SIF globally. Compared with the coarse-resolution SIF that was directly aggregated from OCO-2 soundings, GOSIF has finer spatial resolution, globally continuous coverage, and a much longer record. Our GOSIF product is valuable for assessing terrestrial photosynthesis and ecosystem function, and benchmarking terrestrial biosphere and Earth system models.
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Brook, A., M. Polinova, D. Kopel, D. Malkinson, L. Wittenberg, D. Roberts, and N. Shtober-Zisu. "REMOTE SENSING TECHNIQUES TO ASSESS POST-FIRE EFFECTS AT THE HILLSLOPE AND SUB-BASIN SCALES VIA MULTI-SCALE MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 135–41. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-135-2017.
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Post-fire environmental footprint is expected at varying scales in space and in time and demands development of multi-scale monitoring approaches. In this paper, a spatially and temporally explicit multi-scale model that reveals the physical and morphological indicators affecting hillslope susceptibility at varying scales, is explained and demonstrated. The qualitative and quantitative suitability classification procedures are adapted to translate the large-scale space-borne data supplied by satellite systems (Landsat OLS8 and Sentinel 2 and 3) to local scale produced by a regional airborne survey performed by unmanned aerial vehicle (UAV). At the smallest spatial and temporal resolution, a daily airborne imagery collection by UAV is linked to micro-topography model, using statistical and mathematical approaches.
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Chapman, Henry N., and Rod Balhorn. "Coherent soft x-ray single unit-cell diffraction." Proceedings, annual meeting, Electron Microscopy Society of America 51 (August 1, 1993): 646–47. http://dx.doi.org/10.1017/s0424820100149064.
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Following the basic ideas of x-ray structure analysis of single unit-cell specimens by Sayre it should be possible to determine the structure of weakly-scattering non-crystalline material from the radiation scattered from it, at a maximum resolution equal to half the wavelength of the radiation. Therefore, in principle, it should be possible to determine structures to 1 nm resolution by soft x-ray diffraction at 2 nm wavelength. An imaging method based solely on diffraction should be an extremely powerful x-ray microscopy tool.In order to determine the structure of the diffracting object however, the incident beam must be completely temporally and spatially coherent and the complex wave-field must be sampled across some surface at spatial frequency intervals of 1/(2 * Δ), where Δ is the resolution. Thus, a three-dimensional data set (a two-dimensional complex field) must be recorded to obtain the three-dimensional structure of the scatterer. In most cases, an area detector is used to determine the intensity of the wave-field, losing the phase information.
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Tang, Wei, Mingsheng Liao, Lu Zhang, Wei Li, and Weimin Yu. "High-spatial-resolution mapping of precipitable water vapour using SAR interferograms, GPS observations and ERA-Interim reanalysis." Atmospheric Measurement Techniques 9, no. 9 (September 12, 2016): 4487–501. http://dx.doi.org/10.5194/amt-9-4487-2016.
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Abstract. A high spatial and temporal resolution of the precipitable water vapour (PWV) in the atmosphere is a key requirement for the short-scale weather forecasting and climate research. The aim of this work is to derive temporally differenced maps of the spatial distribution of PWV by analysing the tropospheric delay "noise" in interferometric synthetic aperture radar (InSAR). Time series maps of differential PWV were obtained by processing a set of ENVISAT ASAR (Advanced Synthetic Aperture Radar) images covering the area of southern California, USA from 6 October 2007 to 29 November 2008. To get a more accurate PWV, the component of hydrostatic delay was calculated and subtracted by using ERA-Interim reanalysis products. In addition, the ERA-Interim was used to compute the conversion factors required to convert the zenith wet delay to water vapour. The InSAR-derived differential PWV maps were calibrated by means of the GPS PWV measurements over the study area. We validated our results against the measurements of PWV derived from the Medium Resolution Imaging Spectrometer (MERIS) which was located together with the ASAR sensor on board the ENVISAT satellite. Our comparative results show strong spatial correlations between the two data sets. The difference maps have Gaussian distributions with mean values close to zero and standard deviations below 2 mm. The advantage of the InSAR technique is that it provides water vapour distribution with a spatial resolution as fine as 20 m and an accuracy of ∼ 2 mm. Such high-spatial-resolution maps of PWV could lead to much greater accuracy in meteorological understanding and quantitative precipitation forecasts. With the launch of Sentinel-1A and Sentinel-1B satellites, every few days (6 days) new SAR images can be acquired with a wide swath up to 250 km, enabling a unique operational service for InSAR-based water vapour maps with unprecedented spatial and temporal resolution.
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Wilson, Kerrie A., Megan C. Evans, Moreno Di Marco, David C. Green, Luigi Boitani, Hugh P. Possingham, Federica Chiozza, and Carlo Rondinini. "Prioritizing conservation investments for mammal species globally." Philosophical Transactions of the Royal Society B: Biological Sciences 366, no. 1578 (September 27, 2011): 2670–80. http://dx.doi.org/10.1098/rstb.2011.0108.
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We need to set priorities for conservation because we cannot do everything, everywhere, at the same time. We determined priority areas for investment in threat abatement actions, in both a cost-effective and spatially and temporally explicit way, for the threatened mammals of the world. Our analysis presents the first fine-resolution prioritization analysis for mammals at a global scale that accounts for the risk of habitat loss, the actions required to abate this risk, the costs of these actions and the likelihood of investment success. We evaluated the likelihood of success of investments using information on the past frequency and duration of legislative effectiveness at a country scale. The establishment of new protected areas was the action receiving the greatest investment, while restoration was never chosen. The resolution of the analysis and the incorporation of likelihood of success made little difference to this result, but affected the spatial location of these investments.
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Czarnetzki, U., D. Luggenhölscher, V. A. Kadetov, and H. F. Döbele. "Plasma diagnostics by laser spectroscopic electric field measurement." Pure and Applied Chemistry 77, no. 2 (January 1, 2005): 345–58. http://dx.doi.org/10.1351/pac200577020345.
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Laser spectroscopic electric field measurements have the potential to become a versatile tool for the diagnostics of low-temperature plasmas. From the spatially and temporally resolved field distribution in the sheath close to electrodes or surfaces in general, a broad range of important plasma parameters can be inferred directly: electron temperature; ion density distribution; displacement-, ion-, electron-diffusion current density; and the sheath potential. Indirectly, the electron and ion energy distribution functions and information on the ion dynamics in the sheath can also be obtained. Finally, measurements in the quasi-neutral bulk can also reveal even the plasma density distribution with high spatial and temporal resolution. The basic concepts for analysis of the field data are introduced and demonstrated by examples in hydrogen discharges.
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Blume, T., E. Zehe, and A. Bronstert. "Use of soil moisture dynamics and patterns at different spatio-temporal scales for the investigation of subsurface flow processes." Hydrology and Earth System Sciences 13, no. 7 (July 17, 2009): 1215–33. http://dx.doi.org/10.5194/hess-13-1215-2009.
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Abstract. Spatial patterns as well as temporal dynamics of soil moisture have a major influence on runoff generation. The investigation of these dynamics and patterns can thus yield valuable information on hydrological processes, especially in data scarce or previously ungauged catchments. The combination of spatially scarce but temporally high resolution soil moisture profiles with episodic and thus temporally scarce moisture profiles at additional locations provides information on spatial as well as temporal patterns of soil moisture at the hillslope transect scale. This approach is better suited to difficult terrain (dense forest, steep slopes) than geophysical techniques and at the same time less cost-intensive than a high resolution grid of continuously measuring sensors. Rainfall simulation experiments with dye tracers while continuously monitoring soil moisture response allows for visualization of flow processes in the unsaturated zone at these locations. Data was analyzed at different spacio-temporal scales using various graphical methods, such as space-time colour maps (for the event and plot scale) and binary indicator maps (for the long-term and hillslope scale). Annual dynamics of soil moisture and decimeter-scale variability were also investigated. The proposed approach proved to be successful in the investigation of flow processes in the unsaturated zone and showed the importance of preferential flow in the Malalcahuello Catchment, a data-scarce catchment in the Andes of Southern Chile. Fast response times of stream flow indicate that preferential flow observed at the plot scale might also be of importance at the hillslope or catchment scale. Flow patterns were highly variable in space but persistent in time. The most likely explanation for preferential flow in this catchment is a combination of hydrophobicity, small scale heterogeneity in rainfall due to redistribution in the canopy and strong gradients in unsaturated conductivities leading to self-reinforcing flow paths.
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Blume, T., E. Zehe, and A. Bronstert. "Use of soil moisture dynamics and patterns for the investigation of runoff generation processes with emphasis on preferential flow." Hydrology and Earth System Sciences Discussions 4, no. 4 (August 9, 2007): 2587–624. http://dx.doi.org/10.5194/hessd-4-2587-2007.
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Abstract. Spatial patterns as well as temporal dynamics of soil moisture have a major influence on runoff generation. The investigation of these dynamics and patterns can thus yield valuable information on hydrological processes, especially in data scarce or previously ungauged catchments. The combination of spatially scarce but temporally high resolution soil moisture profiles with episodic and thus temporally scarce moisture profiles at additional locations provides information on spatial as well as temporal patterns of soil moisture at the hillslope transect scale. This approach is better suited to difficult terrain (dense forest, steep slopes) than geophysical techniques and at the same time less cost-intensive than a high resolution grid of continuously measuring sensors. Rainfall simulation experiments with dye tracers while continuously monitoring soil moisture response allows for visualization of flow processes in the unsaturated zone at these locations. Data was analyzed at different spacio-temporal scales using various graphical methods, such as space-time colour maps (for the event and plot scale) and indicator maps (for the long-term and hillslope scale). Annual dynamics of soil moisture and decimeter-scale variability were also investigated. The proposed approach proved to be successful in the investigation of flow processes in the unsaturated zone and showed the importance of preferential flow in the Malalcahuello Catchment, a data-scarce catchment in the Andes of Southern Chile. Fast response times of stream flow indicate that preferential flow observed at the plot scale might also be of importance at the hillslope or catchment scale. Flow patterns were highly variable in space but persistent in time. The most likely explanation for preferential flow in this catchment is a combination of hydrophobicity, small scale heterogeneity in rainfall due to redistribution in the canopy and strong gradients in unsaturated conductivities leading to self-reinforcing flow paths.
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Sarangi, Chandan, Yun Qian, Karl Rittger, Kathryn J. Bormann, Ying Liu, Hailong Wang, Hui Wan, Guangxing Lin, and Thomas H. Painter. "Impact of light-absorbing particles on snow albedo darkening and associated radiative forcing over high-mountain Asia: high-resolution WRF-Chem modeling and new satellite observations." Atmospheric Chemistry and Physics 19, no. 10 (May 27, 2019): 7105–28. http://dx.doi.org/10.5194/acp-19-7105-2019.
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Abstract. Light-absorbing particles (LAPs), mainly dust and black carbon, can significantly impact snowmelt and regional water availability over high-mountain Asia (HMA). In this study, for the first time, online aerosol–snow interactions are enabled and a fully coupled chemistry Weather Research and Forecasting (WRF-Chem) regional model is used to simulate LAP-induced radiative forcing on snow surfaces in HMA at relatively high spatial resolution (12 km, WRF-HR) compared with previous studies. Simulated macro- and microphysical properties of the snowpack and LAP-induced snow darkening are evaluated against new spatially and temporally complete datasets of snow-covered area, grain size, and impurity-induced albedo reduction over HMA. A WRF-Chem quasi-global simulation with the same configuration as WRF-HR but a coarser spatial resolution (1∘, WRF-CR) is also used to illustrate the impact of spatial resolution on simulations of snow properties and aerosol distribution over HMA. Due to a more realistic representation of terrain slopes over HMA, the higher-resolution model (WRF-HR) shows significantly better performance in simulating snow area cover, duration of snow cover, snow albedo and snow grain size over HMA, as well as an evidently better atmospheric aerosol loading and mean LAP concentration in snow. However, the differences in albedo reduction from model and satellite retrievals is large during winter due to associated overestimation in simulated snow fraction. It is noteworthy that Himalayan snow cover has high magnitudes of LAP-induced snow albedo reduction (4 %–8 %) in pre-monsoon seasons (both from WRF-HR and satellite estimates), which induces a snow-mediated radiative forcing of ∼30–50 W m−2. As a result, the Himalayas (specifically the western Himalayas) hold the most vulnerable glaciers and mountain snowpack to the LAP-induced snow darkening effect within HMA. In summary, coarse spatial resolution and absence of snow–aerosol interactions over the Himalayan cryosphere will result in significant underestimation of aerosol effects on snow melting and regional hydroclimate.
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Danuser, G. "Coupling the dynamics of two actin networks – new views on the mechanics of cell protrusion." Biochemical Society Transactions 33, no. 6 (October 26, 2005): 1250–53. http://dx.doi.org/10.1042/bst0331250.
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We study how mechanical forces integrate spatially and temporally with regulatory signals at the leading edge of migrating cells. To probe the dynamics of this system, we developed quantitative fluorescent speckle microscopy, which maps out actin cytoskeleton transport, assembly and disassembly with high spatial resolution. Statistical processing of single speckle properties revealed two kinetically, kinematically and molecularly distinct, yet spatially overlapping, actin arrays at the leading edge of migrating epithelial cells. The first network, referred to as the lamellipodium, polymerizes and depolymerizes 1–2 μm from the edge in an Arp2/3 (actin-related protein 2/3)- and cofilin-dependent fashion. The second network, referred to as the lamella, exhibits Arp2/3-independent polymerization. To elucidate the dynamic relationship between the two networks, we have begun to examine how assembly and flow are temporally modulated with respect to a protrusion event. In control cells we found bursts of protrusion preceding bursts of F-actin assembly. The time lag disappears in cells where Arp2/3-function is impaired. This and other results allowed us to propose a model in which tropomyosin protects lamella filaments from branching and severing, and to conjecture that Arp2/3-mediated lamellipodium assembly is a natural consequence of lamella expansion, but not the initiator of cell protrusion.
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Yang, Xiaohong, Zhong Xie, Feng Ling, Xiaodong Li, Yihang Zhang, and Ming Zhong. "Spatio-Temporal Super-Resolution Land Cover Mapping Based on Fuzzy C-Means Clustering." Remote Sensing 10, no. 8 (August 2, 2018): 1212. http://dx.doi.org/10.3390/rs10081212.
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Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images.
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Kim, Yeseul, Phaedon C. Kyriakidis, and No-Wook Park. "A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions." Remote Sensing 12, no. 10 (May 13, 2020): 1553. http://dx.doi.org/10.3390/rs12101553.
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Dense time-series with coarse spatial resolution (DTCS) and sparse time-series with fine spatial resolution (STFS) data often provide complementary information. To make full use of this complementarity, this paper presents a novel spatiotemporal fusion model, the spatial time-series geostatistical deconvolution/fusion model (STGDFM), to generate synthesized dense time-series with fine spatial resolution (DTFS) data. Attributes from the DTCS and STFS data are decomposed into trend and residual components, and the spatiotemporal distributions of these components are predicted through novel schemes. The novelty of STGDFM lies in its ability to (1) consider temporal trend information using land-cover-specific temporal profiles from an entire DTCS dataset, (2) reflect local details of the STFS data using resolution matrix representation, and (3) use residual correction to account for temporary variations or abrupt changes that cannot be modeled from the trend components. The potential of STGDFM is evaluated by conducting extensive experiments that focus on different environments; spatially degraded datasets and real Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images are employed. The prediction performance of STGDFM is compared with those of a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM). Experimental results indicate that STGDFM delivers the best prediction performance with respect to prediction errors and preservation of spatial structures as it captures temporal change information on the prediction date. The superiority of STGDFM is significant when the difference between pair dates and prediction dates increases. These results indicate that STGDFM can be effectively applied to predict DTFS data that are essential for various environmental monitoring tasks.