22 research outputs found

    The Flora Mission for Ecosystem Composition, Disturbance and Productivity

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    Global land use and climate variability alter ecosystem conditions - including structure, function, and biological diversity - at a pace that requires unambiguous observations from satellite vantage points. Current global measurements are limited to general land cover, some disturbances, vegetation leaf area index, and canopy energy absorption. Flora is a pathfinding mission that provides new measurements of ecosystem structure, function, and diversity to understand the spatial and temporal dynamics of human and natural disturbances, and the biogeochemical and physiological responses of ecosystems to disturbance. The mission relies upon high-fidelity imaging spectroscopy to deliver full optical spectrum measurements (400-2500 nm) of the global land surface on a monthly time step at 45 meter spatial resolution for three years. The Flora measurement objectives are: (i) fractional cover of biological materials, (ii) canopy water content, (iii) vegetation pigments and light-use efficiency, (iv) plant functional types, (v) fire fuel load and fuel moisture content, and (vi) disturbance occurrence, type and intensity. These measurements are made using a multi-parameter, spectroscopic analysis approach afforded by observation of the full optical spectrum. Combining these measurements, along with additional observations from multispectral sensors, Flora will far advance global studies and models of ecosystem dynamics and change

    EO-1/Hyperion: Nearing Twelve Years of Successful Mission Science Operation and Future Plans

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    The Earth Observing One (EO-1) satellite is a technology demonstration mission that was launched in November 2000, and by July 2012 will have successfully completed almost 12 years of high spatial resolution (30 m) imaging operations from a low Earth orbit. EO-1 has two unique instruments, the Hyperion and the Advanced Land Imager (ALI). Both instruments have served as prototypes for NASA's newer satellite missions, including the forthcoming (in early 2013) Landsat-8 and the future Hyperspectral Infrared Imager (HyspIRI). As well, EO-1 is a heritage platform for the upcoming German satellite, EnMAP (2015). Here, we provide an overview of the mission, and highlight the capabilities of the Hyperion for support of science investigations, and present prototype products developed with Hyperion imagery for the HyspIRI and other space-borne spectrometers

    EO-1 Data Quality and Sensor Stability with Changing Orbital Precession at the End of a 16 Year Mission

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    The Earth Observing One (EO-1) satellite has completed 16 years of Earth observations in early 2017. What started as a technology mission to test various new advancements turned into a science and application mission that extended many years beyond the satellites planned life expectancy. EO-1s primary instruments are spectral imagers: Hyperion, the only civilian full spectrum spectrometer (430-2400 nm) in orbit; and the Advanced Land Imager (ALI), the prototype for Landsat-8s pushbroom imaging technology. Both Hyperion and ALI instruments have continued to perform well, but in February 2011 the satellite ran out of the fuel necessary to maintain orbit, which initiated a change in precession rate that led to increasingly earlier equatorial crossing times during its last five years. The change from EO-1s original orbit, when it was formation flying with Landsat-7 at a 10:01am equatorial overpass time, to earlier overpass times results in image acquisitions with increasing solar zenith angles (SZAs). In this study, we take several approaches to characterize data quality as SZAs increased. Our results show that for both EO-1 sensors, atmospherically corrected reflectance products are within 5 to 10 of mean pre-drift products. No marked trend in decreasing quality in ALI or Hyperion is apparent through 2016, and these data remain a high quality resource through the end of the mission

    A Second-Generation Device for Automated Training and Quantitative Behavior Analyses of Molecularly-Tractable Model Organisms

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    A deep understanding of cognitive processes requires functional, quantitative analyses of the steps leading from genetics and the development of nervous system structure to behavior. Molecularly-tractable model systems such as Xenopus laevis and planaria offer an unprecedented opportunity to dissect the mechanisms determining the complex structure of the brain and CNS. A standardized platform that facilitated quantitative analysis of behavior would make a significant impact on evolutionary ethology, neuropharmacology, and cognitive science. While some animal tracking systems exist, the available systems do not allow automated training (feedback to individual subjects in real time, which is necessary for operant conditioning assays). The lack of standardization in the field, and the numerous technical challenges that face the development of a versatile system with the necessary capabilities, comprise a significant barrier keeping molecular developmental biology labs from integrating behavior analysis endpoints into their pharmacological and genetic perturbations. Here we report the development of a second-generation system that is a highly flexible, powerful machine vision and environmental control platform. In order to enable multidisciplinary studies aimed at understanding the roles of genes in brain function and behavior, and aid other laboratories that do not have the facilities to undergo complex engineering development, we describe the device and the problems that it overcomes. We also present sample data using frog tadpoles and flatworms to illustrate its use. Having solved significant engineering challenges in its construction, the resulting design is a relatively inexpensive instrument of wide relevance for several fields, and will accelerate interdisciplinary discovery in pharmacology, neurobiology, regenerative medicine, and cognitive science

    Monitoring Earth Albedo from LANDSAT

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    A technique has been developed for extracting ground albedo from LANDSAT data. Initial application to Central Kansas indicates a trend of increasing ground albedo superimposed on seasonal variations. The measured albedo apparently is associated with drought. The results suggest that LANDSAT data can be used as an input to global climatic prediction models which depend on ground albedo information. The technique for obtaining the albedos uses a doubling radiative transfer model with scaling approximation. Reflectivities are extrapolated beyond the LANDSAT MSS spectral region based on categorization of the scene into ground classes using the original LANDSAT data. The extrapolation factors have been derived from empirical signatures. The proportions of ground classes are determined from supervised classification when more than one class is present. A cloud detection algorithm is used to remove the cloud contaminations from LANDSAT measurements. The shortwave radiation measurements with low spatial resolution from weather satellites such as Nimbus III and Nimbus 6 had been used to calculate earth albedo in synoptic scale. Because of LANDSAT\u27S much higher spatial resolution, its measurements are particularly useful in calculating the total area of each climatic system, monitoring natural and anthropogenic impact on the earth\u27s surface, and preparing a detailed albedo atlas

    Using EO-1 Hyperion Images to Prototype Environmental Products for Hyspiri

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    In November 2010, the Earth Observing One (EO-1) Satellite Mission will successfully complete a decade of Earth imaging by its two unique instruments, the Hyperion and the Advanced Land Imager (ALI). Both instruments are serving as prototypes for new orbital sensors, and the EO-1 is a heritage platform for the upcoming German mission, EnMAP. We provide an overview of the mission's lifetime. We briefly describe calibration & validation activities and overview the technical and scientific accomplishments of this mission. Some examples of the Mission Science Office (MSO) products are provided, as is an example of a image collected for disaster monitoring
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