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At least 793 records · Page 44

Remote sensing and human health: new sensors and new opportunities

Since the launch of Landsat-1 28 years ago, remotely sensed data have been used to map features on the earth's surface. An increasing number of health studies have used remotely sensed data for monitoring, surveillance, or risk mapping, particularly of vector-borne diseases. Nearly all studies used data from Landsat, the French Systeme Pour l'Observation de la Terre, and the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer. New sensor systems are in orbit, or soon to be launched, whose data may prove useful for characterizing and monitoring the spatial and temporal patterns of infectious diseases. Increased computing power and spatial modeling capabilities of geographic information systems could extend the use of remote sensing beyond the research community into operational disease surveillance and control. This article illustrates how remotely sensed data have been used in health applications and assesses earth-observing satellites that could detect and map environmental variables related to the distribution of vector-borne and other diseases.

Satellite Communications↗

The mosaic structure of plasma bulk flows in the Earth's magnetotail

Moments of plasma distributions observed in the magnetotail vary with different time scales. In this paper we attempt to explain the observed variability on intermediate timescales of approximately 10-20 min that result from the simultaneous energization and spatial structuring of solar wind plasma in the distant magnetotail. These processes stimulate the formation of a system of spatially disjointed. highly accelerated filaments (beamlets) in the tail. We use the results from large-scale kinetic modeling of magnetotail formation from a plasma mantle source to calculate moments of ion distribution functions throughout the tail. Statistical restrictions related to the limited number of particles in our system naturally reduce the spatial resolution of our results, but we show that our model is valid on intermediate spatial scales Delta(x) x Delta(z) equal to approximately 1 R(sub E) x 1000 km. For these spatial scales the resulting pattern, which resembles a mosaic, appears to be quite variable. The complexity of the pattern is related to the spatial interference between beamlets accelerated at various locations within the distant tail which mirror in the strong near-Earth magnetic field. Global motion of the magnetotail results in the displacement of spacecraft with respect to this mosaic pattern and can produce variations in all of the moments (especially the x-component of the bulk velocity) on intermediate timescales. The results obtained enable us to view the magnetotail plasma as consisting of two different populations: a tailward-Earthward system of highly accelerated beamlets interfering with each other, and an energized quasithermal population which gradually builds as the Earth is approached. In the near-Earth tail, these populations merge into a hot quasi-isotropic ion population typical of the near-Earth plasma sheet. The transformation of plasma sheet boundary layer (PSBL) beam energy into central plasma sheet (CPS) quasi-thermal energy occurs in the absence of collisions or noise. This paper also clarifies the relationship between the global scale where an MHD description might be appropriate and the lower intermediate scales where MHD fails and large-scale kinetic theory should be used.

Ashour-Abdalla, M.↗

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. Overall, we focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.

97 MATHEMATICS AND COMPUTING↗

Pattern recogition using reduced information content filters

Pattern recognition by optical spatial filtering procedures is discussed using general considerations with the objective of reducing the information content in the spatial filter. The achievement of this objective is very useful toward the wide application of spatial light modulators and also for facilitating distortion invariant recognition. The proposed novel approach is demonstrated by an example employing bipolar spatial filters for rotation invariant pattern recognition.

Shamir, Joseph↗

The connection between forest degradation and urban energy demand in sub-Saharan Africa: a characterization based on high-resolution remote sensing data

Charcoal is a key energy source for urban households in sub-Saharan Africa and charcoal production is the main cause of forest degradation across the region. We used multitemporal high-resolution remote sensing optical imagery to quantify the extent and intensity of forest degradation associated with charcoal production and its impact on forest carbon stocks for the main supplying area of an African capital. This analysis documents the advance of forest degradation and quantifies its aboveground biomass removals over a seven-year period, registering that, between 2013 and 2016, the average annual area under charcoal production was 103 sq.km and the annual aboveground biomass removals reached 1081 000 (SD = 2461) Mg. Kiln densities in the study area rose to 2 kilns-ha, with an average of 90.7 Mg ha^(−1) of extracted aboveground biomass. Charcoal production was responsible for the degradation of 55.5% of the mopane woodlands in the study area between 2013 and 2019. We estimated post-disturbance recovery times using an ecosystem model calibrated for the study area. The simulations showed that recovery times could require up to 150 years for current aboveground biomass extraction rates. The results of the remote sensing analysis and the simulations of the ecosystem model corroborate the unsustainability of the present patterns of charcoal production. The detailed characterization of the spatial and temporal patterns of charcoal production was combined with household survey information to quantify the impact of the urban energy demand of the Maputo urban area on forest carbon stocks. The analysis shows that Maputo charcoal demand was responsible for the annual degradation of up to 175.3 sq.km and that the contribution of the study area to this demand fluctuated between 75% and 33% over the study period. The extent, advance pace and distance from urban centers documented in this study support the idea that forest degradation from charcoal production cannot merely be considered a peri-urban process. The intensity of the aboveground biomass (AGB) removals and its contribution to forest carbon stocks changes is significant at the national and regional levels.

F Sedano↗

Binary pseudo-random array standards for calibration of 3D optical surface profilers used for metrology with aspheric x-ray optics

High-accuracy surface metrology is vitally important in manufacturing ultra-high-quality free-form mirrors designed to manipulate x-ray light with nanometer-scale wavelengths. The current and potential capabilities of x˗ray mirror manufacturing are limited by inherent imperfections of the integrated metrology tools. Metrology tools are currently calibrated with super-polished flat test-standard/reference mirrors. This is acceptable for fabrication of slightly curved x-ray optics. However, for even moderately curved aspherical x-ray mirrors the flat-reference calibration is not sufficiently accurate. For micro-stitching interferometry developed for surface measurements with curved x-ray mirrors, the tool aberration errors are known to be transferred into the optical surface topography of x-ray mirrors. Our approach to improving metrology is to thoroughly calibrate the measuring tool and apply the results of the calibration to deconvolution of the measured data. Here we explore the application of a recently developed technique for calibrating the instrument transfer function (ITF) of 3D optical surface profilers to metrology with significantly curved x-ray optics. The technique, based on test standards patterned with two-dimensional (2D) binary pseudo-random arrays (BPRAs), employs the unique properties of the BPRA patterns in the spatial frequency domain. The inherent 2D power spectral density of the pattern has a deterministic white-noise-like character that allows direct determination of the ITF with uniform sensitivity over the entire spatial frequency range and field of view of an instrument. The high efficacy of the technique has been previously demonstrated in application to metrology with flat and slightly curved optics. Here, we concentrate on development of an efficient fabrication process for production of highly randomized (HR) BPRA test standards on flat and 500-mm spherical optical substrates. We also compare and discuss the results of the ITF calibration of an interferometric microscope when using the HR BPRA standards on flat and curved substrates.

Munechika, Keiko↗

Visualization of Multifractal Superconductivity in a Two-Dimensional Transition Metal Dichalcogenide in the Weak-Disorder Regime

Eigenstate multifractality is a distinctive feature of noninteracting disordered metals close to a metal–insulator transition, whose properties are expected to extend to superconductivity. While multifractality in three dimensions (3D) only develops near the critical point for specific strong-disorder strengths, multifractality in 2D systems is expected to be observable even for weak disorder. Here we provide evidence for multifractal features in the superconducting state of an intrinsic, weakly disordered single-layer NbSe 2 by means of low-temperature scanning tunneling microscopy/spectroscopy. The superconducting gap, characterized by its width, depth, and coherence peaks’ amplitude, shows a characteristic spatial modulation coincident with the periodicity of the quasiparticle interference pattern. The strong spatial inhomogeneity of the superconducting gap width, proportional to the local order parameter in the weak-disorder regime, follows a log-normal statistical distribution as well as a power-law decay of the two-point correlation function, in agreement with our theoretical model. Furthermore, the experimental singularity spectrum f(α) shows anomalous scaling behavior typical from 2D weakly disordered systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Simulated measurement of power flow in structures near to simple sources and simple boundaries

Advances in electronics technology along with the advent of low cost multichannel Fast Fourier analyzers have made it practical to use higher order central difference formulas to measure power flow in 1- and 2-D structures. The method discussed uses five point differencing for the spatial derivatives in 1-D and a thirteen point difference pattern for the spatial derivatives in 2-D plates and shells. It is assumed that the measuring transducers are accelerometers. An analytical study of the higher order differencing method and the conventional two accelerometer method was performed as a preliminary to the application of these methods to actual aircraft structures. Some classical problems were analyzed in order to simulate and compare the performance of the two methods under near field measurement conditions. These near field conditions include examples of power flows near simple sources and simple boundaries. The estimates produced by the two methods were compared to the exact solution in each example. Presented are the theory and selected results of the study, which indicate that the bias errors of the two accelerometer method under near field measurement conditions may be much larger than previous studies have suggested.

Mcgary, Michael C.↗

Hiatus of wetland methane emissions associated with recent La Niña episodes in the Asian monsoon region

The variability of methane emissions from wetlands in the tropics and northern temperate regions can explain more than 70% of the interannual variation in global wetland methane emissions, which are largely driven by climate variability. We use climate reanalysis, remote sensing wetland area dataset and simulations from 11 land models contributing to Global Methane Budget to investigate the interannual variation and anomalies of wetland methane emissions in the Asian Monsoon region. Methane emissions in this region steadily increased over 2000–2012. However, abnormally low methane emissions were found in equatorial fully humid (Af), warm temperate winter dry (Cw), and warm temperate fully humid (Cf) Asian Monsoon climate sub-regions in 2008, 2009 and 2011, respectively. These spatially-shifting low emissions occurred simultaneously with observed wetland area shrinkage due to abnormally low precipitation. Interannual variability of wetland methane emissions in Asian Monsoon region are primarily driven by South Asian Monsoon system. However, the abnormally low emissions are related to strong La Niña events, and its accompanying effect of weakened East Asian Monsoon system and eastward Western Pacific subtropical high, which drives the shifting pattern of rainfall, and thus the spatial pattern of methane emission anomalies.

wetlands↗

Spatial Microbial Respiration Variations in the Hyporheic Zones Within the Columbia River Basin

Abstract While the hyporheic zone (HZ) accounts for a significant portion of whole stream CO 2 concentrations, HZ respiration modeling studies are lacking in quantifying their contributions to the total CO 2 at large watershed/basin scales. Quantifying the contribution of anaerobic respiration is also underappreciated. This study used a carbon‐nitrogen‐coupled river corridor model to quantify HZ aerobic and anaerobic respiration and determined the key factors controlling their spatial variability within the Columbia River Basin (CRB). The modeled respiration patterns showed high spatial variability. Among the nine sub‐basins composing the CRB, the Lower Columbia and the Willamette, which receive higher precipitation, had higher respiration. Medium‐sized rivers (fourth to sixth orders) produced the highest aerobic and anaerobic respiration among reaches of different sizes. At the basin scale, aerobic respiration is dominant, representing approximately 98.7% of the total respiration across the CRB. While most of the reaches were dominant with aerobic respiration, reaches in agricultural land showed a relatively higher anaerobic respiration (18%) ratio. A variable importance analysis showed that hyporheic exchange flux controlled most of the spatial variability of HZ respiration, dominating over other physical variables such as residence time, stream dissolved organic carbon (DOC), nitrate, and dissolved oxygen (DO). The influence of substrate concentration (DOC and DO) is larger in modeling anaerobic respiration than aerobic respiration. Future efforts will focus on improving the estimation of the HZ exchange flux and the implementation of spatially explicit parameterizations for the reactions of interest to reduce model uncertainty.

54 ENVIRONMENTAL SCIENCES↗

Ecosystem Impacts of Woody Encroachment In Texas: A Spatial Analysis Using AVIRIS

Woody encroachment, the increase of woody plant density relative to herbaceous vegetation, has been documented in drylands of Texas as well as worldwide (Archer 1994, Harrington and Harman 1995, Moleele et al. 2002). Over-grazing, fire suppression and climate change are implicated in the shift from open grasslands to ecosystems now populated by trees and shrubs (Scholes and Archer 1997, Archer et al. 2001), such as Prosopis glandulosa var. glandulosa (honey mesquite) in north Texas (Teague et al. 1997, Ansley et al. 2001, Asner et al. 2003a). Several studies have examined changes in ecosystem properties accompanying woody vegetation encroachment in the Southwest U.S., with research focused on increases in plant and soil carbon (C) and nitrogen (N) stores (Hoffman and Jackson 2000, Asner et al. 2003a), isotopic shifts in these pools (Boutton 1999, Archer et al. 2001), and increases in N cycling rates (Rundel et al. 1982, Hibbard et al. 2001). However, little is known regarding the impact of woody encroachment on N trace gas emissions from dryland regions such as Texas. NOx is produced in the soil during the processes of nitrification and denitrification (Firestone and Davidson 1989). The total N efflux from soils is most directly influenced by the internal cycling of N, which at a regionalscale, is controlled by the inputs and availability of N from vegetation via litterfall and subsequent decomposition (Robertson et al. 1989). Although plot-scale studies are critical to understanding controls over N oxide emissions, regionalization of the measurements is impeded by spatial variation in the factors contributing most to N cycling processes: soil properties (affecting soil moisture regimes and N stocks) and vegetation cover (affecting litter inputs and N uptake). While broad patterns in ecosystem structure and vegetation composition co-vary with general patterns of trace gas emissions (Matson 1997), there is no easily measured index of N availability that can be applied for regional-scale studies of N oxide fluxes. Remote sensing is arguably the only approach available to develop a spatially-explicit understanding of ecosystem processes. More specifically, remotely detectable spatial patterns in the distal controls over soil N properties, such as vegetation cover, land use and soil type (Robertson et al. 1989), should be exploited for regional studies of N oxide emissions. The woody encroachment phenomenon provides an opportunity to test the strength of the relationship between N oxide emissions and those factors controlling the fluxes that can be remotely measured. If such linkages can be firmly established, and if the spatial pattern of distal controls is relevant, then the combination of field measurements and remote sensing offers to improve regional-scale N oxide estimates. The paper presents the utility of linking field based sampling of soil NOx emissions with very high resolution remote sensing estimates of woody vegetation cover from the NASA AVIRIS, Airborne Visible-Infrared Imaging Spectrometer (Green et al. 1998, Asner and Green 2001) and automated spectral mixture analysis (Asner and Lobell 2000, Asner and Heidebrecht 2002) that provide a means to spatially extrapolate soil NOx emissions to the regional scale.

Martin, Roberta E.↗

Sampling characteristics of satellite orbits

The irregular space-time sampling of any finite region by an orbiting satellite raises difficult questions as to which frequencies and wavenumbers can be determined and which will alias into others. Conventional sampling theorems must be extended to account for both irregular data distributions and observational noise - the sampling irregularity making the system much more susceptible to noise than in regularly sampled cases. The problem is formulated here in terms of least-squares and applied to spacecraft in 10-day and 17-day repeating orbits. The 'diamond-pattern' laid down spatially in such repeating orbits means that either repeat period adequately samples the spatial variables, but the slow overall temporal coverage in the 17-day pattern leads to much greater uncertainty than in the shorter repeat cycle. The result is not definitive and it is not concluded that a 10-day orbit repeat is the most appropriate one. A major conclusion, however, is that different orbital choices have potentially quite different sampling characteristics which need to be analyzed in terms of the spectral characteristics of the moving sea surface.

Wunsch, Carl↗

Statistical Modeling of Landscape Pattern and the Effects of Coarse Spatial Resolution

Analysis of classified satellite imagery was conducted to characterize errors in estimates of area based on coarse resolution satellite imagery which are due to distortions in sizes of small fragments, and to explore the feasibility of correcting for these errors using a statistical modeling approach. Sizes of bodies of open water on European Remote-Sensing (ERS-1) synthetic aperture radar (SAR) and fire scars on Landsat Multispectral Scanner (MSS) imagery were measured using geographic information system (GIS) software. Statistical analysis of the smaller scars and ponds as observed with this imagery of relatively fine resolution demonstrated that the distribution of the sizes could be modeled by either of two types of statistical distributions-a power distribution related to fractal processes or a simple exponential distribution. A comparison of the size distribution of small burn scars as observed with Landsat with the distribution observed with the much coarser Advanced Very High Resolution Radiometer (AVHRR) imagery revealed effects of coarse resolution, each of which were associated with a particular size range, and whose net effect accounted for the bias in total burn area due to coarse spatial resolution. Size dependent area correction procedures are therefore feasible. A statistical modeling approach to area estimation, based models of the underlying and observed size distributions of fragments in the scene, may lead to a procedure which requires little or no ancillary data.

Hlavka, C. A.↗

Operation Program for the Spatially Phase-Shifted Digital Speckle Pattern Interferometer - SPS-DSPI

SPS-DSPI software has been revised so that Goddard optical engineers can operate the instrument, instead of data programmers. The user interface has been improved to view the data collected by the SPS-DSPI, with a real-time mode and a play-back mode. The SPS-DSPI has been developed by NASA/GSFC to measure the temperature distortions of the primary-mirror backplane structure for the James Webb Space Telescope. It requires a team of computer specialists to run successfully, because, at the time of this reporting, it just finished the prototype stage. This software improvement will transition the instrument to become available for use by many programs that measure distortion

Blake, Peter N.↗

Investigating Vegetation Responses to Underground Nuclear Explosions Through Integrated Analyses

Vegetation has the potential to respond to underground nuclear explosions, yet these links have not been fully explored. Given the lack of previously described signatures, the changes in vegetation are possibly subtle. The integration of multiple different data streams is potentially a useful approach to improve signal detection. Here, we investigate whether semi-arid vegetation growth patterns responded to eight legacy underground nuclear tests at the Nevada National Security Site in southern Nevada, USA. We tested for spatial and temporal changes in vegetation cover, tree growth patterns, and tree leaf spectral properties using ground-based measurements, including those from tree-rings and hyperspectral surface vegetation reflectance, as well as space-based measurements of Normalized Difference Vegetation Index (NDVI) from Landsat. Multiple data streams suggest a localized (<1.2 km) spatial pattern whereby tree growth is enhanced closer to the source of the underground test relative to sites further away. We also observed a more regional (>1.2–9 km) pattern whereby tree growth is suppressed coincident with a drought beginning 1 year before the 1989 tests, but continuing in the 5 years following the tests, which is anomalous relative to what is expected based on the response of tree growth to previous droughts. furthermore, quantification of the relative effects of the tests on vegetation remains a challenge due to the coincident drought and the potential for other disturbances to have impacted tree growth at this time, but the integration of these data reveals a more nuanced growth response than any other one data set indicates alone.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

How MJO Teleconnections and ENSO Interference Impacts U.S. Precipitation

A composite analysis reveals how the Madden–Julian oscillation (MJO) impacts North American rainfall through perturbations in both the upper-tropospheric flow and regional low-level moisture availability. Upper-level divergence associated with the MJO tropical convection drives a quasi-stationary Rossby wave response to the midlatitudes. This forces a midlatitude upper-level dipolar geopotential height anomaly that is accompanied by a westward retraction of the jet stream and reduced rainfall over the central-eastern North Pacific. A reverse effect is found as the MJO propagates eastward across the Maritime Continent. These large differences in the extratropical upper-level flow, combined with anomalies in the regional supply of water vapor, have a profound impact on southeastern U.S. rainfall. The low-frequency variability, including that associated with ENSO, can modify the seasonal background flow (e.g., El Niño and La Niña basic states) affecting the distribution, strength, and propagation of the intraseasonal oscillation and the extratropical teleconnection patterns. The combined effects of the ENSO and the MJO signals result in both spatial and temporal patterns of interference and modulation of North American rainfall. The results from this study show that during a particular phase of an active MJO, the extratropical response can considerably enhance or mask the interannual ENSO signal in the United States, potentially resulting in anomalies of the opposite sign than that expected during a specific ENSO phase. Analyses of specific MJO events during an El Niño or La Niña episode reveal significant contributions to extreme events via constructive and destructive interference of the MJO and ENSO signals.

54 ENVIRONMENTAL SCIENCES↗

Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model

Digital soil maps are commonly data-driven as the development of physically-based models for soil mapping is difficult due to the complexity of soils. However, physically-based hydrologic models have been successful in simulating water dynamics. Since water movement is a major driver of pedogenesis, the physical rules that govern water movement might help explain and predict the spatial variation of soil properties. Here, we demonstrate the novel use of a physically-based, distributed hydrologic model to inform the spatial distribution of soil properties. The Distributed Hydrology Soil Vegetation Model (DHSVM) was utilized to simulate soil moisture content (SM) and water table depth (WTD) in two hillslope catchments under pasture and forest management wherein hydrologic model outputs were then compared with soil properties measured in situ. SM sensors and wells were installed in both catchments to validate simulations of soil water movement via Nash-Sutcliffe Efficiency (E). In-situ observations were made at 87 sites within both catchments to study the connection between simulated water movement (SM and WTD) and observed soil properties, namely the depth and thickness of the argillic (Bt), fragic (Btx), and C horizons, and the depth of redoximorphic features. The simulated time series of SM and WTD were also clustered per season using Dynamic Time Warping (DTW), which identified similarity among time series at varying timescales. Model validation suggested that simulations of surficial SM (0–20 cm) were reasonable (E = 0.45), however, simulated subsurface SM (45–60 cm) and WTD were not sufficiently accurate. The thickness of Btx horizons were spatially grouped into different populations by SM clusters from every season except spring. For the other properties, only SM dynamics of specific seasons grouped into significantly different populations, suggesting that the explanatory power of simulated water movement varies seasonally and was greater during winter. Here, we show clusters of simulated SM separated soil properties into statistically different populations, showing that hydrologic models could inform areas that followed different water dynamics related to pedogenic trajectories and related biogeochemical processes not necessarily simulated by the model. As such, physically-based modeling of water dynamics can, therefore, inform and advance digital soil mapping by linking water movement patterns stemming from hydrologic model outputs to spatial patterns of soil properties and pedogenesis.

54 ENVIRONMENTAL SCIENCES↗