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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Coniferous Canopy BRF Simulation Based on 3-D Realistic Scene

It is difficulties for the computer simulation method to study radiation regime at large-scale. Simplified coniferous model was investigate d in the present study. It makes the computer simulation methods such as L-systems and radiosity-graphics combined method (RGM) more powerf ul in remote sensing of heterogeneous coniferous forests over a large -scale region. L-systems is applied to render 3-D coniferous forest scenarios: and RGM model was used to calculate BRF (bidirectional refle ctance factor) in visible and near-infrared regions. Results in this study show that in most cases both agreed well. Meanwhiie at a tree and forest level. the results are also good.

Wang, Xin-yun↗
NASA NTRS1991

Radar modeling of a boreal forest

Microwave modeling, ground truth, and SAR data are used to investigate the characteristics of forest stands. A mixed coniferous forest stand has been modeled at P, L, and C bands. Extensive measurements of ground truth and canopy geometry parameters were performed in a 200-m-square hemlock-dominated forest plot. About 10 percent of the trees were sampled to determine a distribution of diameter at breast height (DBH). Hemlock trees in the forest are modeled by characterizing tree trunks, branches, and needles as randomly oriented lossy dielectric cylinders whose area and orientation distributions are prescribed. The distorted Born approximation is used to compute the backscatter at P, L, and C bands. The theoretical results are found to be lower than the calibrated ground-truth data. The experiment and model results agree quite closely, however, when the ratios of VV to HH and HV to HH are compared.

Chauhan, Narinder S.↗
NASA NTRS2010

Complex Spatial Structure in a Population of Didymopanax pittieri, A Tree of Wind-Exposed Lower Montane Rain Forest

Didymopanax pittieri is a common shade-intolerant tree colonizing treefall gaps in the elfin forests on windswept ridgecrests in the lower montane rain forests of the Cordillera de Tilarain, Costa Rica. All D. pittieri taller than > 0.5 m in a 5.2-ha elfin forested portion of a gridded study watershed in the Monteverde Cloud Forest Preserve were located, mapped, and measured. This local population of D. pittieri is spatially inhomogeneous, in that density increases with increasing wind exposure; D. pittieri are more abundant near ridge crests than lower on windward slopes. The important and ubiquitous phenomenon of spatial inhomogeneity in population density is addressed and corrected for in spatial analyses by the application of the inhomogeneous version of Ripley's K. The spatial patterns of four size classes of D. pittieri (<5 cm dbh, 5-10 cm dbh, 10-20 cm dbh, and> 20 cm dbh) were investigated. Within the large-scale trend in density driven by wind exposure, D. pittieri saplings are clumped at the scale of treefall gaps and at the scale of patches of aggregated gaps. D. pittieri 5-10 cm dbh are randomly distributed, apparently due to competitive thinning of sapling clumps during the early stages of gap-phase regeneration. D. pittieri larger than 10 cm dbh are overdispersed at a scale larger than that of patches of gaps. Natural disturbance can influence the distribution of shade intolerant tree populations at several different spatial scales, and can have discordant effects at different life history stages.

Lawton, Robert M.↗
NASA NTRS1990

Applications of artificial intelligence to mission planning

The following subject areas are covered: object-oriented programming task; rule-based programming task; algorithms for resource allocation; connecting a Symbolics to a VAX; FORTRAN from Lisp; trees and forest task; software data structure conversion; software functionality modifications and enhancements; portability of resource allocation to a TI MicroExplorer; frontier of feasibility software system; and conclusions.

Ford, Donnie R.↗
NASA NTRS1999

Intercomparison of Satellite-Derived Snow-Cover Maps

In anticipation of the launch of the Earth Observing System (EOS) Terra, and the PM-1 spacecraft in 1999 and 2000, respectively, efforts are ongoing to determine errors of satellite-derived snow-cover maps. EOS Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer-E (AMSR-E) snow-cover products will be produced. For this study we compare snow maps covering the same study area acquired from different sensors using different snow- mapping algorithms. Four locations are studied: 1) southern Saskatchewan; 2) a part of New England (New Hampshire, Vermont and Massachusetts) and eastern New York; 3) central Idaho and western Montana; and 4) parts of North and South Dakota. Snow maps were produced using a prototype MODIS snow-mapping algorithm used on Landsat Thematic Mapper (TM) scenes of each study area at 30-m and when the TM data were degraded to 1 -km resolution. National Operational Hydrologic Remote Sensing Center (NOHRSC) 1 -km resolution snow maps were also used, as were snow maps derived from 1/2 deg. x 1/2 deg. resolution Special Sensor Microwave Imager (SSM/1) data. A land-cover map derived from the International Geosphere-Biosphere Program (IGBP) land-cover map of North America was also registered to the scenes. The TM, NOHRSC and SSM/I snow maps, and land-cover maps were compared digitally. In most cases, TM-derived maps show less snow cover than the NOHRSC and SSM/I maps because areas of incomplete snow cover in forests (e.g., tree canopies, branches and trunks) are seen in the TM data, but not in the coarser-resolution maps. The snow maps generally agree with respect to the spatial variability of the snow cover. The 30-m resolution TM data provide the most accurate snow maps, and are thus used as the baseline for comparison with the other maps. Comparisons show that the percent change in amount of snow cover relative to the 3 0-m resolution TM maps is lowest using the TM I -km resolution maps, ranging from 0 to 40%. The highest percent change (less than 100%) is found in the New England study area, probably due to the presence of patchy snow cover. A scene with patchy snow cover is more difficult to map accurately than is a scene with a well-defined snowline such as is found on the North and South Dakota scene where the percent change ranged from 0 to 40%. There are also some important differences in the amount of snow mapped using the two different SSM/I algorithms because they utilize different channels.

Hall, Dorothy K.↗
NASA NTRS2016

Machine-z: Rapid Machine-Learned Redshift Indicator for Swift Gamma-Ray Bursts

Studies of high-redshift gamma-ray bursts (GRBs) provide important information about the early Universe such as the rates of stellar collapsars and mergers, the metallicity content, constraints on the re-ionization period, and probes of the Hubble expansion. Rapid selection of high-z candidates from GRB samples reported in real time by dedicated space missions such as Swift is the key to identifying the most distant bursts before the optical afterglow becomes too dim to warrant a good spectrum. Here, we introduce 'machine-z', a redshift prediction algorithm and a 'high-z' classifier for Swift GRBs based on machine learning. Our method relies exclusively on canonical data commonly available within the first few hours after the GRB trigger. Using a sample of 284 bursts with measured redshifts, we trained a randomized ensemble of decision trees (random forest) to perform both regression and classification. Cross-validated performance studies show that the correlation coefficient between machine-z predictions and the true redshift is nearly 0.6. At the same time, our high-z classifier can achieve 80 per cent recall of true high-redshift bursts, while incurring a false positive rate of 20 per cent. With 40 per cent false positive rate the classifier can achieve approximately 100 per cent recall. The most reliable selection of high-redshift GRBs is obtained by combining predictions from both the high-z classifier and the machine-z regressor.

gamma-ray burst: general↗
NASA NTRS2019

Evaluation of Classifier Complexity for Delay Tolerant Network Routing

The growing popularity of small cost effective satellites (SmallSats, CubeSats, etc.) creates the potential for a variety of new science applications involving multiple nodes functioning together or independently to achieve a task, such as swarms and constellations. As this technology develops and is deployed for missions in Low Earth Orbit and beyond, the use of delay tolerant networking (DTN) techniques may improve communication capabilities within the network. In this paper, a network hierarchy is developed from heterogeneous networks of SmallSats, surface vehicles, relay satellites and ground stations which form an integrated network. There is a tradeoff between complexity, flexibility, and scalability of user defined schedules versus autonomous routing as the number of nodes in the network increases. To address these issues, this work proposes a machine learning classifier based on DTN routing metrics. A framework is developed which will allow for the use of several categories of machine learning algorithms (decision tree, random forest and deep learning) to be applied to a dataset of historical network statistics, which allows for the evaluation of algorithm complexity versus performance to be explored. We develop the emulation of a hierarchical network, consisting of tens of nodes which form a cognitive network architecture. CORE (Common Open Research Emulator) is used to emulate the network using bundle protocol and DTN IP neighbor discovery.

Dudukovich, Rachel↗
NASA NTRSDate not supplied

Comparison Study of Machine Learning Techniques to Predict Flight Energy Consumption for Advanced Air Mobility

This paper addresses the need to predict the flight energy consumption of aerial vehicles in the presence of wind using machine learning techniques. The presented work is critical to achieving sustainable and efficient operations for Advanced Air Mobility (AAM) and to evaluating the readiness of the ground-supporting energy infrastructure, e.g., electric grid and AAM portals. The flight energy consumption is described using the "energy per meter" (EPM) metric. We present a comparison study of influential machine learning techniques in predicting EPM using real-world flight test data. We presented new results of using the Decision Tree, Random Forest, and linear regression techniques, along with our previous results using the Recurrent Neural Network and Feed Forward Neural Network techniques. The comparison results show that the Linear Regression method outperforms other methods on the basis of the Mean Squared Error and error variance.

Machine Learning↗
NASA NTRS2020

Climate change reshapes the drivers of false spring risk across European trees

(1) Temperate forests are shaped by late spring freezes after budburst—false springs—which may shift with climate change. Research to date has generated conflicting results, potentially because few studies focus on the multiple underlying drivers of false spring risk. (2) Here, we assessed the effects of mean spring temperature, distance from the coast, elevation and the North Atlantic Oscillation (NAO) using PEP725 leafout data for six tree species across 11648 sites in Europe, to determine which were the strongest predictors of false spring risk and how these predictors shifted with climate change. (3) All predictors influenced false spring risk before recent warming, but their effects have shifted in both magnitude and direction with warming. These shifts have potentially magnified the variation in false spring risk among species with an increase in risk for early‐leafout species (i.e., Aesculus hippocastanum , Alnus glutinosa , Betula pendula ) versus a decline or no change in risk among late‐leafout species (i.e., Fagus sylvatica , Fraxinus excelsior , Quercus robur ). (4) Our results show how climate change has reshaped the drivers of false spring risk, complicating forecasts of future false springs, and potentially reshaping plant community dynamics given uneven shifts in risk across species.

false spring↗
NASA NTRS2020

Lambayeque Water Resources: Assessing Hydrologic Patterns Using NASA Earth Observations to Address Tree Mortality in Peru’s Coastal Mesquite Forests

The mesquite (Prosopi ssp.) forests in Northwestern Peru have had a significant increase in tree mortality in the past fifty years. Within this time frame, 17% of the forest extent was lost and the forest saw an average annual declination rate of 0.33% (Ektvedt, Vetaas, & Lundberg, 2012). These habitats support the region's rich biodiversity and play an important role in local community economies. Several hydrologic causes for mesquite mortality have been hypothesized, but local researchers lack spatially comprehensive techniques to address the problem. While in situ research is currently being utilized in an attempt to explain this recent anomaly, landscape-level visualizations through remote sensing have not been produced to find connections between hydrologic trends and the health of Northwestern Peru’s mesquite forests. Climate Hazards Group InfraRed Precipitation with Station dataand Global Land Data Assimilation Systemdata were analyzed to assess hydrologic patterns pertaining to precipitation and soil moisture in the Lambayeque region of Peru. These data were then paired with vegetation indices derived from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) to display how changing hydrologic patterns relate to the health of mesquite trees. These data were then compiled on a monthly basis over the 30-year study period to create a time series product, which was later referenced with background research to find the likely causes of recent forest decline. The results will assist in the Lambayeque Regional Government’s understanding of recent biological declination and will help them design strategies to mitigate forest decline in Lambayeque, Peru and surrounding regions.

Water Resources↗
NASA NTRS2020

Lambayeque Water Resources: Assessing Hydrologic Patterns Using NASA Earth Observations to Address Tree Mortality in Peru’s Coastal Mesquite Forests

The mesquite (Prosopis sp.) forests in Northwestern Peru have had a significant increase in tree mortality in the past fifty years. Within this time frame, 17% of the forest extent was lost and the forest saw an average annual declination rate of 0.33% (Ektvedt, Vetaas, & Lundberg, 2012). These habitats support the region's rich biodiversity and play an important role in local community economies. Several hydrologic causes for mesquite mortality have been hypothesized, but local researchers lack spatially comprehensive techniques to address the problem. While in situ research is currently being utilized in an attempt to explain this recent anomaly, landscape-level visualizations through remote sensing have not been produced to find connections between hydrologic trends and the health of Northwestern Peru’s mesquite forests. Climate Hazards Group InfraRed Precipitation with Station data and Global Land Data Assimilation System data were analyzed to assess hydrologic patterns pertaining to precipitation and soil moisture in the Lambayeque region of Peru. These data were then paired with vegetation indices derived from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) to display how changing hydrologic patterns relate to the health of mesquite trees. These data were then compiled on a monthly basis over the 30-year study period to create a time series product, which was later referenced with background research to find the likely causes of recent forest decline. The results will assist in the Lambayeque Regional Government’s understanding of recent biological declination and will help them design strategies to mitigate forest decline in Lambayeque, Peru and surrounding regions.

Water Resources↗
NASA NTRS2016

Calibration and Validation of Landsat Tree Cover in the Taiga-Tundra Ecotone

Monitoring current forest characteristics in the taiga-tundra ecotone (TTE) at multiple scales is critical for understanding its vulnerability to structural changes. A 30 m spatial resolution Landsat-based tree canopy cover map has been calibrated and validated in the TTE with reference tree cover data from airborne LiDAR and high resolution spaceborne images across the full range of boreal forest tree cover. This domain-specific calibration model used estimates of forest height to determine reference forest cover that best matched Landsat estimates. The model removed the systematic under-estimation of tree canopy cover greater than 80% and indicated that Landsat estimates of tree canopy cover more closely matched canopies at least 2 m in height rather than 5 m. The validation improved estimates of uncertainty in tree canopy cover in discontinuous TTE forests for three temporal epochs (2000, 2005, and 2010) by reducing systematic errors, leading to increases in tree canopy cover uncertainty. Average pixel-level uncertainties in tree canopy cover were 29.0%, 27.1% and 31.1% for the 2000, 2005 and 2010 epochs, respectively. Maps from these calibrated data improve the uncertainty associated with Landsat tree canopy cover estimates in the discontinuous forests of the circumpolar TTE.

Montesano, Paul Mannix↗
NASA NTRS2016

Climate-Induced Mortality of Siberian Pine and Fir in the Lake Baikal Watershed, Siberia

Siberian pine (Pinus sibirica) and fir (Abies sibirica) (so called ''dark needle conifers", DNC) showed decreased radial growth increment within the Lake Baikal watershed since the 1980s with increasing mortality recorded since the year 2000. Tree ring width was strongly correlated with vapor pressure deficit, aridity and root zone moisture. Water stress from droughts made trees more susceptible to insect attacks causing mortality in about 10% of DNC stands within the Lake Baikal watershed. Within Siberia DNC mortality increased in the southern part of the DNC range. Biogeographically, tree mortality was located within the DNC - forest-steppes transition. Tree mortality was significantly correlated with drought and soil moisture anomalies. Within the interior of the DNC range mortality occurred within relief features with high water stress risk (i.e., steep convex south facing slopes with shallow well-drained soils). In general, DNC mortality in Siberia was induced by increased aridity and severe drought (inciting factors) in synergy with biotic attacks (contributing factor). In future climate scenarios with predicted increase in aridity DNC could be eliminated from the southern part of its current range and will be replaced by drought-resistant conifers and broadleaf species (e.g., Larix sibirica, Pinus silvestris, and Betula pubescence).

Conifer Mortality↗
NASA NTRS2016

Climate-Induced Mortality of Siberian Pine and Fir in the Lake Baikal Watershed, Siberia

Siberian pine (Pinus sibirica) and fir (Abies sibirica) (so called "dark needle conifers", DNC) showed decreased radial growth increment within the Lake Baikal watershed since the 1980s with increasing mortality recorded since the year 2000. Tree ring width was strongly correlated with vapor pressure deficit, aridity and root zone moisture. Water stress from droughts made trees more susceptible to insect attacks causing mortality in about 10% of DNC stands within the Lake Baikal watershed. Within Siberia DNC mortality increased in the southern part of the DNC range. Biogeographically, tree mortality was located within the DNC - forest-steppes transition. Tree mortality was significantly correlated with drought and soil moisture anomalies. Within the interior of the DNC range mortality occurred within relief features with high water stress risk (i.e., steep convex south facing slopes with shallow well-drained soils). In general, DNC mortality in Siberia was induced by increased aridity and severe drought (inciting factors) in synergy with biotic attacks (contributing factor). In future climate scenarios with predicted increase in aridity DNC could be eliminated from the southern part of its current range and will be replaced by drought-resistant conifers and broadleaf species (e.g., Larix sibirica, Pinus silvestris, and Betula pubescence).

Viacheslav I Kharuk↗
NASA NTRS1984

Interpreting Michigan forest cover types from color infrared aerial photographs

The characteristics of 17 cover types (13 forest types or tree species and 4 nonforest cover types) in Michigan are discussed as well as their interpretation from medium scale color infrared photography. The occurrence of each type is described by region and site requirements. Those attributes of a tree or stand which are helpful when attempting to interpret the type from a vertical perspective are discussed as well as common crown types. The identification of the forest type or tree species by using image characteristics (size, shape, shadow, color, texture, pattern, or association) is discussed. Ground photographs and sketches of individual trees are included. Stereograms of typical stands are available.

Hudson, W. D.↗
NASA NTRS1995

Spaceborne Applications of P Band Imaging Radars for Measuring Forest Biomass

In three sites of boreal and temperate forests, P band HH, HV, and VV polarization data combined estimate total aboveground dry woody biomass within 12 to 27% of the values derived from allometric equations, depending on forest complexity. Biomass estimates derived from HV-polarization data only are 2 to 14% less accurate. When the radar operates at circular polarization, the errors exceed 100% over flooded forests, wet or damaged trees and sparse open tall forests because double-bounce reflections of the radar signals yield radar signatures similar to that of tall and massive forests. Circular polarizations, which minimize the effect of Faraday rotation in spaceborne applications, are therefore of limited use for measuring forest biomass. In the tropical rain forest of Manu, in Peru, where forest biomass ranges from 4 kg/sq m in young forest succession up to 50 kg/sq m in old, undisturbed floodplain stands, the P band horizontal and vertical polarization data combined separate biomass classes in good agreement with forest inventory estimates. The worldwide need for large scale, updated, biomass estimates, achieved with a uniformly applied method, justifies a more in-depth exploration of multi-polarization long wavelength imaging radar applications for tropical forests inventories.

Rignot, Eric J.↗
NASA NTRS1989

Shade images of forested areas obtained from Landsat MSS data

The objective of this report is to generate a shade (shadow) image of forested areas from Landsat MSS data by implementing a linear mixing model, where shadow is considered as one of the primary components in a pixel. The shade images are related to the observed variation in forest structure; i.e., the proportion of inferred shadow in a pixel is related to different forest ages, forest types, and tree crown cover. The constrained least-squares method is used to generate shade images for forest of eucalyptus and vegetation of 'cerrado' over the Itapeva study area in Brazil. The resulted shade images may explain the difference on ages for forest of eucalyptus and the difference on tree crown cover for vegetation of cerrado.

Shimabukuro, Yosio Edemir↗