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Predictive ecological models are being studied for the management and conservation of the walrus, and the bowhead whale in the Bering Sea. The influence of sea ice on the distribution, and carrying capacity of the area for these two mammals is to be investigated with the primary target species being the walrus. Remote sensing and radio tracking is considered a requirement for assessing the walrus ecosystem.
In order to study the spectral atmospheric effect on remote sensing, measurements of the spectral signature of surface cover were conducted during hazy conditions. Simultaneous measurements of the aerosol optical thickness and its vertical distribution were carried out. The upward radiance in high flight were also simulated by radiative transfer computations, and the results were compared with the measurements. It is suggested that the radiances over dark areas can be used to derive the aerosol optical thickness. Combined with climatological information, the derived optical thickness can be used to perform corrections of the atmospheric effect.
To gain a better understanding of northern/boreal forest dynamics over a range of spatial and temporal scales, an approach to integrate models of forest growth, soil processes and radiative transfer with remote sensing observations was developed. The integrated model and remote sensing can be used to examine descriptors of ecosystem dynamics. To examine the scaling of vegetation pattern from the local to the regional domain, distributions of area and perimeters of vegetation community associations were determined from satellite and aircraft images. Relationships between computed fractal dimensions (1.5 to 1.8) and succession history for managed and unmanaged areas are being explored.
Accurate determination of spatial soil moisture distribution and monitoring its temporal variation have a significant impact on the outcomes of hydrologic, ecologic, and climatic models. Development of a successful remote sensing instrument for soil moisture relies on the accurate knowledge of the soil dielectric constant (epsilon(sub soil)) to its moisture content. Two existing methods for measurement of dielectric constant of soil at low and high frequencies are, respectively, the time domain reflectometry and the reflection coefficient measurement using an open-ended coaxial probe. The major shortcoming of these methods is the lack of accurate determination of the imaginary part of epsilon(sub soil). In this paper a microstrip ring resonator is proposed for the accurate measurement of soil dielectric constant. In this technique the microstrip ring resonator is placed in contact with soil medium and the real and imaginary parts of epsilon(sub soil) are determined from the changes in the resonant frequency and the quality factor of the resonator respectively. The solution of the electromagnetic problem is obtained using a hybrid approach based on the method of moments solution of the quasi-static formulation in conjunction with experimental data obtained from reference dielectric samples. Also a simple inversion algorithm for epsilon(sub soil) = epsilon'(sub r) + j(epsilon"(sub r)) based on regression analysis is obtained. It is shown that the wide dynamic range of the measured quantities provides excellent accuracy in the dielectric constant measurement. A prototype microstrip ring resonator at L-band is designed and measurements of soil with different moisture contents are presented and compared with other approaches.
The Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model is used to simulate sulfate, dust, carbonaceous, and sea salt aerosols. The simulations are conducted using the present and future emission scenarios based on the IPCC projection. Distributions of individual aerosol components for the present day are compared with observations from field experiments, ground-based sun photometer networks, and remote sensing. Aerosol radiative forcing for the present day and the projected future will also be discussed.
Since 1995 we have had one or more optical lightning sensors in low earth orbit (LEO) providing near continuous lightning observations of the earth. The resulting data sets have enabled scientists to study global lighting distributions and their variable. Diurnal, seasonal and interannual variabilities are clearly revealed. In addition, because of the exceptionally high detection efficiency of the optical sensing technique and the high spatial resolution, it is possible to study individual clouds and cloud system despite a viewing time that is often as short as 80 seconds. These case studies have demonstrated the importance of total lightning measurements in the study of severe weather. Results from these space-based lightning measurements will be presented as well as the next logical concept - optical observations from geostationary orbit (GEO). With a Lightning Mapper Sensor (LMS) in GEO, it will be possible to monitor severe weather on a continuous basis and to disseminate the data in less than 60 seconds.
High concentrations of suspended particulate matter in coastal waters directly effect or govern numerous water column and benthic processes. The concentration of suspended sediments derived from bottom sediment resuspension or discharge of sediment-laden rivers is highly variable over a wide range of time and space scales. Although there has been considerable effort to use remotely sensed images to provide synoptic maps of suspended particulate matter, there are limited routine applications of this technology due in-part to the low spatial resolution, long revisit period, or cost of most remotely sensed data. In contrast, near daily coverage of medium-resolution data is available from the MODIS Terra instrument without charge from several data distribution gateways. Equally important, several display and processing programs are available that operate on low cost computers.
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.
Knowledge on geographical location and distribution of crops at global, national and regional scales is an extremely valuable source of information applications. Traditional approaches to crop mapping using remote sensing data rely heavily on reference or ground truth data in order to train/calibrate classification models. As a rule, such models are only applicable to a single vegetation season and should be recalibrated to be applicable for other seasons. This paper addresses the problem of early season large-area winter crop mapping using Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) time-series and growing degree days (GDD) information derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product. The model is based on the assumption that winter crops have developed biomass during early spring while other crops (spring and summer) have no biomass. As winter crop development is temporally and spatially non-uniform due to the presence of different agro-climatic zones, we use GDD to account for such discrepancies. A Gaussian mixture model (GMM) is applied to discriminate winter crops from other crops (spring and summer). The proposed method has the following advantages: low input data requirements, robustness, applicability to global scale application and can provide winter crop maps 1.5-2 months before harvest. The model is applied to two study regions, the State of Kansas in the US and Ukraine, and for multiple seasons (2001-2014). Validation using the US Department of Agriculture (USDA) Crop Data Layer (CDL) for Kansas and ground measurements for Ukraine shows that accuracies of greater than 90% can be achieved in mapping winter crops 1.5-2 months before harvest. Results also show good correspondence to official statistics with average coefficients of determination R(exp. 2) greater than 0.85.
Impact-generated spherule layers provide information pertinent to the environmental consequences of very large impacts on Earth. The spherules are condensed from high velocity impact ejecta ballistically distributed worldwide. These ejecta comprise material from both the impacting body and the target. Much of this material was vaporized or atomized (in the sense of small droplets of fluid, although doubtless some of the vapor species were atomic) in the impact event, cooled and condensed, and then was re-melted or partially evaporated again on re-entry into the atmosphere far from the crater. The energy deposited in the atmosphere by the re-entering ejecta heat the stratosphere where the particles stop to the temperature of hot lava, and thermal radiation from the superheated stratosphere heats the lower atmosphere, any land surfaces, and the evaporate the surface of the ocean; how hot the atmosphere gets and how much water gets evaporated depends on the scale of the impact. The molten or solid raindrops and hailstones eventually fell out of the atmosphere and onto land or into the ocean over the course of hours and days to pile up as spherule beds, and later the finer dust falls out over months and years.
Systematic and concurrent observation of column-integrated, surface, and vertically-resolved distributions of aerosols and trace gases relevant to air quality as they evolve throughout the day. Three major observational components: - NASA King Air (Remote sensing) Continuous mapping of aerosols with HSRL and trace gas columns with ACAM - NASA P-3B (in situ meas.) In situ profiling of aerosols and trace gases over surface measurement sites - Ground sites In situ trace gases and aerosols Remote sensing of trace gas and aerosol columns (Pandora and Aeronet) Ozonesondes Lidar observations of aerosol (and ozone).
Limb sounders measure atmospheric radiation that is dependent on atmospheric temperature and constituents that have a radial and angular distribution in Earth-centered coordinates.
Severe weather phenomena represent the extreme upper end of the spectrum of convection and precipitation and tend to be highly localized and relatively rare compared to the rest of the distribution, but they can cause damage and loss disproportionate to their scale and frequency. Fortunately, severe convection exhibits distinct signatures in spaceborne remote-sensing datasets (e.g. overshooting cloud tops in visible/IR, or brightness temperature depressions in passive-microwave imagery). Leveraging these signatures individually has become a long-established practice to detect, analyze and establish climatologies of severe thunderstorms, especially in instances where traditional ground-based data may be unavailable. Spaceborne visible/IR and passive-microwave approaches are not without their pitfalls, however: passive-microwave channels have large footprints and exhibit non-uniform beam filling. Visible/IR instruments have fine horizontal resolution but are limited by their insensitivity to processes occurring below cloud top. To address this, we investigate the nearly simultaneous and colocated MODIS (visible/IR) and AMSR-E (passive-microwave) onboard the Aqua satellite to leverage both datasets together and assess the extent to which these datasets can be combined to improve severe thunderstorm detection. We pair AMSR-E and MODIS signatures of severe convection with ground-based weather radar, severe weather reports, and environmental parameters defined by the MERRA-2 reanalysis in six different geographical regimes throughout the Aqua domain. We present a census of potentially severe convective storms and their environments as seen by multiple instruments simultaneously, investigating how MODIS and AMSR-E signatures may be used together to diagnose storm properties and processes, and how the interrelationships between the signatures varies seasonally and geographically. Using statistical machine learning analysis, we aim to quantify the optimal MODIS and AMSR-E parameter sets for discriminating severe from non-severe storm cells and assess what improvement (if any) in detection results from combining the IR, visible, and microwave datasets.
Natural disasters - including such events as tropical storms, earthquakes, floods, volcanic eruptions, and wildfires -effect hundreds of millions of people worldwide, and also cause billions of dollars (USD) in damage to the global economy. Remotely sensed data acquired by orbital sensor systems has emerged as a vital tool to identify the extent of damage resulting from a natural disaster, as well as providing near-real time mapping support to response efforts on the ground and humanitarian aid efforts. The International Space Station (ISS) is a unique terrestrial remote sensing platform for acquiring disaster response imagery. Unlike automated remote-sensing platforms it has a human crew; is equipped with both internal and externally-mounted remote sensing instruments; and has an inclined, low-Earth orbit that provides variable views and lighting (day and night) over 95 percent of the inhabited surface of the Earth. As such, it provides a useful complement to free-flyer based, sun-synchronous sensor systems in higher altitude polar orbits. While several nations have well-developed terrestrial remote sensing programs and assets for data collection, many developing nations do not have ready access to such resources. The International Charter, Space and Major Disasters (also known as the "International Disaster Charter", or IDC; http://www.disasterscharter.org/home) addresses this disparity. It is an agreement between agencies of several countries to provide - on a best-effort basis - remotely sensed data of natural disasters to requesting countries in support of disaster response. The lead US agency for interaction with the IDC is the United States Geological Survey (USGS); when an IDC request or "activation" is received, the USGS notifies the science teams for NASA instruments with targeting information for data collection. In the case of the ISS, the Earth Sciences and Remote Sensing (ESRS) Unit, part of the Astromaterials Research and Exploration Science Directorate and supporting the ISS Program Science Office at NASA's Johnson Space Center, receives notification from the USGS and coordinates targeting and data collection with the NASA ISS sensor teams. If data is collected, it is passed back to the USGS for posting on their Hazards Data Distribution System and made available for download. The ISS International Partners (CSA, ESA, JAXA, Roscosmos/Energia) have their own procedures for independently supporting IDC activations using their assets on ISS, and there is currently no joint coordination with NASA ISS sensor teams. Following completion of ISS assembly, NASA remote sensing assets began collecting IDC response data in May 2012. The initial NASA ISS sensor systems available to respond to IDC activations included the ISS Agricultural Camera (ISSAC), an internal multispectral visible-near infrared wavelength system mounted in the Window Observational Research Facility, or WORF; the Crew Earth Observations (CEO) Facility, where the crew collects imagery through Station windows using off-the-shelf handheld digital visible-wavelength cameras; and the Hyperspectral Imager for the Coastal Oceans (HICO), a visible to near-infrared system mounted externally on the Japan Experiment Module Exposed Facility. The ISSAC completed its primary mission and was removed from the WORF in January 2013. It was replaced by the very high resolution ISS SERVIR Environmental Research and Visualization System (ISERV) Pathfinder, a visible-wavelength digital camera, telescope, and pointing system. Since the start of IDC response by NASA sensors on the ISS in May 2012 and as of this report, there have been eighty IDC activations; NASA sensor systems have collected data for twenty-three of these events. Of the twenty-three successful data collections, five involved 2 or more ISS sensor systems responding to the same event. Data has also been collected by International Partners in response to natural disasters, most notably JAXA and Roscosmos/Energia through the Urugan program. Data collected in response to IDC activations is delivered by the ISS sensor teams to the ESRS for quality review and transfer to the USGS, where it is ingested into the Hazards Data Distribution System, or HDDS (https://hdds.usgs.gov/hdds2/; figure 1). This system allows the local agencies that issued the IDC activation request to review and download data. The data is then used to develop secondary products useful for humanitarian response such as flood maps. As of this report, approximately 1000 images collected by NASA ISS sensor systems have been downloaded from the HDDS, indicating that the ISS has assumed a valuable role in disaster response efforts. The ISS is also a unique platform in that it will have multiple users over its lifetime, and that no single remote sensing system has a permanent internal or external berth. This scheduled turnover provides for development of new remote sensing capabilities relevant to disaster response -as well as both research and applied science-and represents a significant contribution to continuance and enhancement of the NASA mission to investigate changes on our home planet.
Formation flying for spacecraft is a rapidly developing field that will enable a new era of space science. For one of its missions, the Terrestrial Planet Finder (TPF) project has selected a formation flying interferometer design to detect earth-like planets orbiting distant stars. In order to advance technology needed for the TPF formation flying interferometer, the TPF project has been developing a distributed real-time testbed to demonstrate end-to-end operation of formation flying with TPF-like functionality and precision. This is the Formation Algorithms and Simulation Testbed (FAST) . This FAST was conceived to bring out issues in timing, data fusion, inter-spacecraft communication, inter-spacecraft sensing and system-wide formation robustness. In this paper we describe the FAST and show results from a two-spacecraft formation scenario. The two-spacecraft simulation is the first time that precision end-to-end formation flying operation has been demonstrated in a distributed real-time simulation environment.
Malaria causes more than one million deaths every year worldwide, with most of the mortality in Sub-Saharan Africa. It is also a significant public health concern in Afghanistan, with approximately 60% of the population, or nearly 14 million people, living in a malaria-endemic area. Malaria transmission has been shown to be dependent on a number of environmental and meteorological variables. For countries in the tropics and the subtropics, rainfall is normally the most important variable, except for regions with high altitude where temperature may also be important. Afghanistan s diverse landscape contributes to the heterogeneous malaria distribution. Understanding the environmental effects on malaria transmission is essential to the effective control of malaria in Afghanistan. Provincial malaria data gathered by Health Posts in 23 provinces during 2004-2007 are used in this study. Remotely sensed geophysical parameters, including precipitation from TRMM, and surface temperature and vegetation index from MODIS are used to derive the empirical relationship between malaria cases and these geophysical parameters. Both neural network methods and regression analyses are used to examine the environmental dependency of malaria transmission. And the trained models are used for predicting future transmission. While neural network methods are intrinsically more adaptive for nonlinear relationship, the regression approach lends itself in providing statistical significance measures. Our results indicate that NDVI is the strongest predictor. This reflects the role of irrigation, instead of precipitation, in Afghanistan for agricultural production. The second strongest prediction is surface temperature. Precipitation is not shown as a significant predictor, contrary to other malarious countries in the tropics or subtropics. With the regression approach, the malaria time series are modelled well, with average R2 of 0.845. For cumulative 6-month prediction of malaria cases, the average provincial accuracy reaches 91%. The developed predictive and early warning capabilities support the Third Strategic Approach of the WHO EMRO Malaria Control and Elimination Plan.
A canopy is modeled as a two-layer medium above a rough interface. The upper layer stands for the forest crown, with the leaves modeled as randomly oriented and distributed disks and needles and the branches modeled as randomly oriented finite dielectric cylinders. The lower layer contains the tree trunks, modeled as randomly positioned vertical cylinders above the rough soil. Radiative-transfer theory is applied to calculate EM scattering from such a canopy, is expressed in terms of the scattering-amplitude tensors (SATs). For leaves, the generalized Rayleigh-Gans approximation is applied, whereas the branch and trunk SATs are obtained by estimating the inner field by fields inside a similar cylinder of infinite length. The Kirchhoff method is used to calculate the soil SAT. For a plane wave exciting the canopy, the radiative-transfer equations are solved by iteration to the first order in albedo of the leaves and the branches. Numerical results are illustrated as a function of the incidence angle.
Retrievals of aerosol optical depth (AOD) and related parameters from satellite measurements typically involve prescribed models of aerosol size and composition, and are therefore dependent on how well these models are able to represent the radiative behaviour of real aerosols, This study uses aerosol volume size distributions retrieved from Sun-photometer measurements at 11 Aerosol Robotic Network (AERONET) island sites, spread throughout the world's oceans, as a basis to define such a model for unpolluted maritime aerosols. Size distributions are observed to be bimodal and approximately lognormal, although the coarse mode is skewed with a long tail on the low-radius end, The relationship of AOD and size distribution parameters to meteorological conditions is also examined, As wind speed increases, so do coarse-mode volume and radius, The AOD and Angstrom exponent (alpha) show linear relationships with wind speed, although there is considerable scatter in all these relationships, limiting their predictive power. Links between aerosol properties and near-surface relative humidity, columnar water vapor, and sea surface temperature are also explored. A recommended bimodal maritime model, which is able to reconstruct the AERONET AOD with accuracy of order 0.01-0.02, is presented for use in aerosol remote sensing applications. This accuracy holds at most sites and for wavelengths between 340 nm and 1020 nm. Calculated lidar ratios are also provided, and differ significantly from those currently used in Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) processing.