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Silicon–Germanium: The Legacy Lives On
Alloy systems comprised of silicon with germanium, lead with tellurium, and bismuth with antimony have constituted a majority of thermoelectric applications during the last half-century. These legacy materials are primarily covalently bonded with a maximum ZT near one. Silicon–germanium alloys have provided the thermal to electrical conversion for many of NASA’s radioisotope thermoelectric generator (RTG) configurations and for nearly all of its deep space and outer planetary flights, such as Pioneer I and II, Voyager I and 11, Ulysses, Galileo, and Cassini. The remarkable success of these materials and their respective devices is evidenced by the fact that there has never been a failure of the RTG systems even after over 1 billion cumulative mission-hours. The history of this alloy system as a thermoelectric conversion material spans over six decades and research to further improve its performance continues to this day. Si-Ge alloys have long been a mainstay of thermoelectric research because of a fortuitous combination of a sufficiently high melting temperature, reasonable energy band gap, high solubility for both n- and p-type dopants, and the fact that this alloy system exhibits complete miscibility in the solid state, which enable tuning of both electrical and thermal properties. This article reviews the history of silicon–germanium as a thermoelectric material and its use in NASA’s RTG programs. Since the device technology is also a critical operational consideration, a brief review of some of the unique challenges imposed by the use in an RTG is also discussed.
Journal Article
استعادة التوازن : استراتيجية للشرق الأوسط برسم الرئيس الجديد
لا شك في أن الرئيس الرابع والأربعين للولايات المتحدة الأميركية سيجد في انتظاره سلسلة من التحديات الحاسمة، المعقدة والمتشابكة في الشرق الأوسط، التي تتطلب إيلاءها اهتماما عاجلا. ذلك أن النموذج الذي اعتمده جورج بدليو بوش القائم على تغيير أنظمة الحكم ونشر الديمقراطية بالقوة لم يعد يتلاءم والظروف المتغيرة التي ستواجه الإدارة الجديدة على الأرجح الحاجة ماسة إذن لأفكار جديدة، وتحليلات غير حزبية، وتوصيات حصيفة. والكتاب الذي بين أيديكم يفي بتلك الحاجة على أفضل وجه. في اتسعادة التوازن، تتضافر جهود الخبراء والمختصين بشؤون الشرق الأوسط من مجلس العلاقات الخارجية ومركز صبان لسياسة الشرق الأوسط بمعهد بروكنغز، لتطرح استراتيجية أميركية جديدة لمنظمة حيوية لكن متفجرة كالشرق الأوسط فبناء على أبحاث ميدانية معنقة، قام هؤلاء الخبراء ببلورة مجموعة من التوصيات السياسية برسم الرئيس الأميركي الجديد وقد قامت بفحصها وتمحيصها ونقدها هيئة من المختصين من كلا الحزبين يتمتعون بخبرة سياسية واسعة ومعرفة غنية بالمنطقة، هذا التمرين في تخطيط السياسات الذي استغرق سنة كاملة هو الأول من نوعه على الإطلاق، الذي يوحد جهود وقدرات العاملين في هاتين المؤسستين المحترمتين بالسياسة الخارجية لتنصب على درس وتحليل واحدة من أخطر وأهم مناطق العالم. وكل فصل من هذا الكتاب يستضيف اثنين أو أكثر من الباحثين في مجلس العلاقات الخارجية ومعهد بروكنغز لمعاينة واستعراض التحديات التي ستواجه الرئيس المقبل.
Book
Efficient Algorithms for Bayesian Nearest Neighbor Gaussian Processes
We consider alternate formulations of recently proposed hierarchical nearest neighbor Gaussian process (NNGP) models for improved convergence, faster computing time, and more robust and reproducible Bayesian inference. Algorithms are defined that improve CPU memory management and exploit existing high-performance numerical linear algebra libraries. Computational and inferential benefits are assessed for alternate NNGP specifications using simulated datasets and remotely sensed light detection and ranging data collected over the U.S. Forest Service Tanana Inventory Unit (TIU) in a remote portion of Interior Alaska. The resulting data product is the first statistically robust map of forest canopy for the TIU. Supplemental materials for this article are available online.
Journal Article
NASA Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) Airborne Imager
The combination of LiDAR and optical remotely sensed data provides unique information about ecosystem structure and function. Here, we describe the development, validation and application of a new airborne system that integrates commercial off the shelf LiDAR hyperspectral and thermal components in a compact, lightweight and portable system. Goddard’s LiDAR, Hyperspectral and Thermal (G-LiHT) airborne imager is a unique system that permits simultaneous measurements of vegetation structure, foliar spectra and surface temperatures at very high spatial resolution (~1 m) on a wide range of airborne platforms. The complementary nature of LiDAR, optical and thermal data provide an analytical framework for the development of new algorithms to map plant species composition, plant functional types, biodiversity, biomass and carbon stocks, and plant growth. In addition, G-LiHT data enhance our ability to validate data from existing satellite missions and support NASA Earth Science research. G-LiHT’s data processing and distribution system is designed to give scientists open access to both low- and high-level data products (http://gliht.gsfc.nasa.gov), which will stimulate the community development of synergistic data fusion algorithms. G-LiHT has been used to collect more than 6,500 km2 of data for NASA-sponsored studies across a broad range of ecoregions in the USA and Mexico. In this paper, we document G-LiHT design considerations, physical specifications, instrument performance and calibration and acquisition parameters. In addition, we describe the data processing system and higher-level data products that are freely distributed under NASA’s Data and Information policy.
Journal Article
Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion
The vast extent and inaccessibility of boreal forest ecosystems are barriers to routine monitoring of forest structure and composition. In this research, we bridge the scale gap between intensive but sparse plot measurements and extensive remote sensing studies by collecting forest inventory variables at the plot scale using an unmanned aerial vehicle (UAV) and a structure from motion (SfM) approach. At 20 Forest Inventory and Analysis (FIA) subplots in interior Alaska, we acquired overlapping imagery and generated dense, 3D, RGB (red, green, blue) point clouds. We used these data to model forest type at the individual crown scale as well as subplot-scale tree density (TD), basal area (BA), and aboveground biomass (AGB). We achieved 85% cross-validation accuracy for five species at the crown level. Classification accuracy was maximized using three variables representing crown height, form, and color. Consistent with previous UAV-based studies, SfM point cloud data generated robust models of TD (r(sup 2) = 0.91), BA (r(sup 2) = 0.79), and AGB (r(sup 2) = 0.92), using a mix of plot- and crown-scale information. Precise estimation of TD required either segment counts or species information to differentiate black spruce from mixed white spruce plots. The accuracy of species-specific estimates of TD, BA, and AGB at the plot scale was somewhat variable, ranging from accurate estimates of black spruce TD (+/−1%) and aspen BA (−2%) to misallocation of aspen AGB (+118%) and white spruce AGB (−50%). These results convey the potential utility of SfM data for forest type discrimination in FIA plots and the remaining challenges to develop classification approaches for species-specific estimates at the plot scale that are more robust to segmentation error.
Journal Article
Storm Surge and Ponding Explain Mangrove Dieback in Southwest Florida Following Hurricane Irma
Mangroves buffer inland ecosystems from hurricane winds and storm surge. However, their ability to withstand harsh cyclone conditions depends on plant resilience traits and geomorphology. Using airborne lidar and satellite imagery collected before and after Hurricane Irma, we estimated that 62% of mangroves in southwest Florida suffered canopy damage, with largest impacts in tall forests (>10 m). Mangroves on well-drained sites (83%) resprouted new leaves within one year after the storm. By contrast, in poorly-drained inland sites, we detected one of the largest mangrove diebacks on record (10,760 ha), triggered by Irma. We found evidence that the combination of low elevation (median = 9.4 cm asl), storm surge water levels (>1.4 m above the ground surface), and hydrologic isolation drove coastal forest vulnerability and were independent of tree height or wind exposure. Our results indicated that storm surge and ponding caused dieback, not wind. Tidal restoration and hydrologic management in these vulnerable, low-lying coastal areas can reduce mangrove mortality and improve resilience to future cyclones.
Journal Article
Discrete Anisotropic Radiative Transfer (DART 5) for Modeling Airborne and Satellite Spectroradiometer and LIDAR Acquisitions of Natural and Urban Landscapes
Satellite and airborne optical sensors are increasingly used by scientists, and policy makers, and managers for studying and managing forests, agriculture crops, and urban areas. Their data acquired with given instrumental specifications (spectral resolution, viewing direction, sensor field-of-view, etc.) and for a specific experimental configuration (surface and atmosphere conditions, sun direction, etc.) are commonly translated into qualitative and quantitative Earth surface parameters. However, atmosphere properties and Earth surface 3D architecture often confound their interpretation. Radiative transfer models capable of simulating the Earth and atmosphere complexity are, therefore, ideal tools for linking remotely sensed data to the surface parameters. Still, many existing models are oversimplifying the Earth-atmosphere system interactions and their parameterization of sensor specifications is often neglected or poorly considered. The Discrete Anisotropic Radiative Transfer (DART) model is one of the most comprehensive physically based 3D models simulating the Earth-atmosphere radiation interaction from visible to thermal infrared wavelengths. It has been developed since 1992. It models optical signals at the entrance of imaging radiometers and laser scanners on board of satellites and airplanes, as well as the 3D radiative budget, of urban and natural landscapes for any experimental configuration and instrumental specification. It is freely distributed for research and teaching activities. This paper presents DART physical bases and its latest functionality for simulating imaging spectroscopy of natural and urban landscapes with atmosphere, including the perspective projection of airborne acquisitions and LIght Detection And Ranging (LIDAR) waveform and photon counting signals.
Journal Article
Amazon forests maintain consistent canopy structure and greenness during the dry season
Lidar and optical satellite observations of Amazon forests indicate consistent canopy structure and reflectance during the dry season, challenging the paradigm of light-limited tropical forest productivity.
'Greener' Amazon was a trick of the light
Recent remote-sensing data from the Amazon suggested that there is a 'green up' of vegetation during dry seasons, implying that light rather than water is the major limiting factor for forest productivity. Douglas Morton and colleagues have now reanalysed the evidence and show that the green up is in fact an optical artefact of the observation method, the result of changes in the relative azimuth angle of satellite observations between the June solstice and September equinox. Correcting for this removes the green-up phenomenon, adding support to other studies that indicate that water availability, rather than light, is the main driver of plant productivity in Amazon forests.
The seasonality of sunlight and rainfall regulates net primary production in tropical forests
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. Previous studies have suggested that light is more limiting than water for tropical forest productivity
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, consistent with greening of Amazon forests during the dry season in satellite data
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. We evaluated four potential mechanisms for the seasonal green-up phenomenon, including increases in leaf area
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or leaf reflectance
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, using a sophisticated radiative transfer model
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and independent satellite observations from lidar and optical sensors. Here we show that the apparent green up of Amazon forests in optical remote sensing data resulted from seasonal changes in near-infrared reflectance, an artefact of variations in sun-sensor geometry. Correcting this bidirectional reflectance effect eliminated seasonal changes in surface reflectance, consistent with independent lidar observations and model simulations with unchanging canopy properties. The stability of Amazon forest structure and reflectance over seasonal timescales challenges the paradigm of light-limited net primary production in Amazon forests and enhanced forest growth during drought conditions. Correcting optical remote sensing data for artefacts of sun-sensor geometry is essential to isolate the response of global vegetation to seasonal and interannual climate variability.
Journal Article
Integrating Solar Induced Fluorescence and the Photochemical Reflectance Index for Estimating Gross Primary Production in a Cornfield
The utilization of remotely sensed observations for light use efficiency (LUE) and tower-based gross primary production (GPP) estimates was studied in a USDA cornfield. Nadir hyperspectral reflectance measurements were acquired at canopy level during a collaborative field campaign conducted in four growing seasons. The Photochemical Reflectance Index (PRI) and solar induced chlorophyll fluorescence (SIF), were derived. SIF retrievals were accomplished in the two telluric atmospheric oxygen absorption features centered at 688 nm (O2-B) and 760 nm (O2-A). The PRI and SIF were examined in conjunction with GPP and LUE determined by flux tower-based measurements. All of these fluxes, environmental variables, and the PRI and SIF exhibited diurnal as well as day-to-day dynamics across the four growing seasons. Consistent with previous studies, the PRI was shown to be related to LUE (r^2 = 0.54 with a logarithm fit), but the relationship varied each year. By combining the PRI and SIF in a linear regression model, stronger performances for GPP estimation were obtained. The strongest relationship (r^2 = 0.80, RMSE = 0.186 mg CO2/m^2/s) was achieved when using the PRI and SIF retrievals at 688 nm. Cross-validation approaches were utilized to demonstrate the robustness and consistency of the performance. This study highlights a GPP retrieval method based entirely on hyperspectral remote sensing observations.
Journal Article