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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data, scripts, and figures associated with a manuscript studying impact of climate and topography on post-fire vegetation recovery.

This data package is associated with the publication “Impact of Topography and Climate on Post-fire Vegetation Recovery Across Different Burn Severity and Land Cover Types through Machine Learning” submitted to Remote Sensing of Environment (Zahura et al. 2023). In this research, a machine learning algorithm, random forest (RF), was utilized to examine the impact of climate and topography on post-fire vegetation recovery. We used enhanced vegetation index (EVI) to examine varying burn severity and land cover types. The data package includes the input files for RF model training, outputs from model predictions and analysis, and python scripts to run the model, analyze the results to understand model performance and interpretability, and plot manuscript figures. This data package contains three folders (Data, Scripts, and Figures), a file-level metadata (FLMD) csv, and a data dictionary (dd) csv. Please see Postfire_recovery_flmd.csv for a list of all files contained in this data package and descriptions for each. The data dictionary (Postfire_recovery_dd.csv) describes the csv column headers. The “Data” folder provides all the inputs and outputs to train the RF model, evaluate performance, and interpret predictions. The “Scripts” folder contains python scripts and jupyter notebooks for model training and result analysis. The “Figures” folder includes the figures used in the manuscript in “.png” and “.jpg” format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Onslow Beach Washover Fan Morphology from 2012 to 2015

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.These grid files were created from data collected using a Riegl 3-D laser scanner. Millions of x, y, and z points from the F2 Washover Fan on Onslow Beach were processed using the MARS software package to define a bare-earth model. Grid files were imported into Golden Software Surfer version 19 for editing and exported from that application as .grd files. Files can be opened in Surfer or ArcMap, but the coordinate system information will be lost if opened in ArcMap. These data were collected to quantify the morphological changes that occur on a washover fan through time.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2025

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed'

This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRS2016

The Characterization of a DIRSIG Simulation Environment to Support the Inter-Calibration of Spaceborne Sensors

Climate change studies require long-term, continuous records that extend beyond the lifetime, and the temporal resolution, of a single remote sensing satellite sensor. The inter-calibration of spaceborne sensors is therefore desired to provide spatially, spectrally, and temporally homogeneous datasets. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) tool is a first principle-based synthetic image generation model that has the potential to characterize the parameters that impact the accuracy of the inter-calibration of spaceborne sensors. To demonstrate the potential utility of the model, we compare the radiance observed in real image data to the radiance observed in simulated image from DIRSIG. In the present work, a synthetic landscape of the Algodones Sand Dunes System is created. The terrain is facetized using a 2-meter digital elevation model generated from NASA Goddard's LiDAR, Hyperspectral, and Thermal (G-LiHT) imager. The material spectra are assigned using hyperspectral measurements of sand collected from the Algodones Sand Dunes System. Lastly, the bidirectional reflectance distribution function (BRDF) properties are assigned to the modeled terrain using the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF product in conjunction with DIRSIG's Ross-Li capability. The results of this work indicate that DIRSIG is in good agreement with real image data. The potential sources of residual error are identified and the possibilities for future work are discussed..

Ambeau, Brittany L.↗
DOE OSTI2022

Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”

The package contains the data layers used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”. Spatial data layers include: topography, wetland vegetation cover, time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. The study aims to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Data-Driven Surface Traversability Analysis for Mars 2020 Landing Site Selection

The objective of this paper is three-fold: 1) to describe the engineering challenges in the surface mobility of the Mars 2020 Rover mission that are considered in the landing site selection processs, 2) to introduce new automated traversability analysis capabilities, and 3) to present the preliminary analysis results for top candidate landing sites. The analysis capabilities presented in this paper include automated terrain classification, automated rock detection, digital elevation model (DEM) generation, and multi-ROI (region of interest) route planning. These analysis capabilities enable to fully utilize the vast volume of high-resolution orbiter imagery, quantitatively evaluate surface mobility requirements for each candidate site, and reject subjectivity in the comparison between sites in terms of engineering considerations. The analysis results supported the discussion in the Second Landing Site Workshop held in August 2015, which resulted in selecting eight candidate sites that will be considered in the third workshop.

Ono, Masahiro↗
NASA NTRS2015

Small UAV Automatic Ground Collision Avoidance System Design Considerations and Flight Test Results

The National Aeronautics and Space Administration (NASA) Armstrong Flight Research Center Small Unmanned Aerial Vehicle (SUAV) Automatic Ground Collision Avoidance System (Auto GCAS) project demonstrated several important collision avoidance technologies. First, the SUAV Auto GCAS design included capabilities to take advantage of terrain avoidance maneuvers flying turns to either side as well as straight over terrain. Second, the design also included innovative digital elevation model (DEM) scanning methods. The combination of multi-trajectory options and new scanning methods demonstrated the ability to reduce the nuisance potential of the SUAV while maintaining robust terrain avoidance. Third, the Auto GCAS algorithms were hosted on the processor inside a smartphone, providing a lightweight hardware configuration for use in either the ground control station or on board the test aircraft. Finally, compression of DEM data for the entire Earth and successful hosting of that data on the smartphone was demonstrated. The SUAV Auto GCAS project demonstrated that together these methods and technologies have the potential to dramatically reduce the number of controlled flight into terrain mishaps across a wide range of aviation platforms with similar capabilities including UAVs, general aviation aircraft, helicopters, and model aircraft.

Sorokowski, Paul↗
NASA NTRS1980

Stratification of forest vegetation for timber inventory using Landsat and collateral data

An automated forest stratification procedure based on Landsat and digital terrain data is reviewed. The system uses Landsat multispectral brightness values, a spatial texture channel, and digital terrain data information to divide Klamath National Forest vegetation into timber volume-homogeneous strata. The stratification process involves analyst-supervised class pooling editing, and the modeling of the regional type in a spatial manner using the digital terrain data. It is noted that for a given size and density class, the timber volume will change when the regional type changes, reflecting differences in growth potential.

Woodcock, C. E.↗
NASA NTRS2024

GCAS Visualization Codebase Augmentation Migration to Modern Standards and Feature Enhancements

The Glenn Research Center Communication Analysis Suite (GCAS) includes many analysis tools that can be used to support a wide range of scenarios. It includes a visualization tool that implements the three.js graphics library to display a three-dimensional (3D) representation of its results. This software will enable researchers, engineers, and mission planners to interact intuitively with and understand the results of their analyses, which might not be apparent from raw data. With NASA’s efforts to return humans to the Moon as a part of the Artemis missions, the GCAS has been used extensively for lunar terrain and landing system development analysis, which is vital to ensuring mission achievability and safety. This has created the need to add several significant features to the visualization tool, such as the ability to display the terrain of the lunar surface accurately and to provide information demonstrating how a given region might impact mission objectives. Many of the changes made to the visualization tool can be separated into one of three general advancements: code restructuring to adhere to modern coding standards and practices, new user camera controls for first-person and third-person perspective views, and a terrain generation feature to enable rendering highly accurate terrains based on any celestial body’s digital elevation model (DEM) in GeoTIFF format. These improvements notably elevate the visualization tool's functionality, accuracy, and user interaction while providing a robust foundation for future development.

Visualization↗
NASA NTRS2008

Synthetic Vision for Lunar and Planetary Landing Vehicles

The Crew Vehicle Interface (CVI) group of the Integrated Intelligent Flight Deck Technologies (IIFDT) has done extensive research in the area of Synthetic Vision (SV), and has shown that SV technology can substantially enhance flight crew situation awareness, reduce pilot workload, promote flight path control precision and improve aviation safety. SV technology is being extended to evaluate its utility for lunar and planetary exploration vehicles. SV may hold significant potential for many lunar and planetary missions since the SV presentation provides a computer-generated view of the terrain and other significant environment characteristics independent of the outside visibility conditions, window locations, or vehicle attributes. SV allows unconstrained control of the computer-generated scene lighting, terrain coloring, and virtual camera angles which may provide invaluable visual cues to pilots/astronauts and in addition, important vehicle state information may be conformally displayed on the view such as forward and down velocities, altitude, and fuel remaining to enhance trajectory control and vehicle system status. This paper discusses preliminary SV concepts for tactical and strategic displays for a lunar landing vehicle. The technical challenges and potential solutions to SV applications for the lunar landing mission are explored, including the requirements for high resolution terrain lunar maps and an accurate position and orientation of the vehicle that is essential in providing lunar Synthetic Vision System (SVS) cockpit displays. The paper also discusses the technical challenge of creating an accurate synthetic terrain portrayal using an ellipsoid lunar digital elevation model which eliminates projection errors and can be efficiently rendered in real-time.

Williams, Steven P.↗
NASA NTRS2015

High-Resolution Forest Canopy Height Estimation in an African Blue Carbon Ecosystem

Mangrove forests are one of the most productive and carbon dense ecosystems that are only found at tidally inundated coastal areas. Forest canopy height is an important measure for modeling carbon and biomass dynamics, as well as land cover change. By taking advantage of the flat terrain and dense canopy cover, the present study derived digital surface models (DSMs) using stereophotogrammetric techniques on high-resolution spaceborne imagery (HRSI) for southern Mozambique. A mean-weighted ground surface elevation factor was subtracted from the HRSI DSM to accurately estimate the canopy height in mangrove forests in southern Mozambique. The mean and H100 tree height measured in both the field and with the digital canopy model provided the most accurate results with a vertical error of 1.18-1.84 m, respectively. Distinct patterns were identified in the HRSI canopy height map that could not be discerned from coarse shuttle radar topography mission canopy maps even though the mode and distribution of canopy heights were similar over the same area. Through further investigation, HRSI DSMs have the potential of providing a new type of three-dimensional dataset that could serve as calibration/validation data for other DSMs generated from spaceborne datasets with much larger global coverage. HSRI DSMs could be used in lieu of Lidar acquisitions for canopy height and forest biomass estimation, and be combined with passive optical data to improve land cover classifications.

HRSI↗
NASA NTRS1985

Comparison of manually produced and automated cross country movement maps using digital image processing techniques

The Image-Based Information System (IBIS) was used to automate the cross country movement (CCM) mapping model developed by the Defense Mapping Agency (DMA). Existing terrain factor overlays and a CCM map, produced by DMA for the Fort Lewis, Washington area, were digitized and reformatted into geometrically registered images. Terrain factor data from Slope, Soils, and Vegetation overlays were entered into IBIS, and were then combined utilizing IBIS-programmed equations to implement the DMA CCM model. The resulting IBIS-generated CCM map was then compared with the digitized manually produced map to test similarity. The numbers of pixels comprising each CCM region were compared between the two map images, and percent agreement between each two regional counts was computed. The mean percent agreement equalled 86.21%, with an areally weighted standard deviation of 11.11%. Calculation of Pearson's correlation coefficient yielded +9.997. In some cases, the IBIS-calculated map code differed from the DMA codes: analysis revealed that IBIS had calculated the codes correctly. These highly positive results demonstrate the power and accuracy of IBIS in automating models which synthesize a variety of thematic geographic data.

Wynn, L. K.↗
NASA NTRS2011

Orthographic Stereo Correlator on the Terrain Model for Apollo Metric Images

A stereo correlation method on the object domain is proposed to generate the accurate and dense Digital Elevation Models (DEMs) from lunar orbital imagery. The NASA Ames Intelligent Robotics Group (IRG) aims to produce high-quality terrain reconstructions of the Moon from Apollo Metric Camera (AMC) data. In particular, IRG makes use of a stereo vision process, the Ames Stereo Pipeline (ASP), to automatically generate DEMs from consecutive AMC image pairs. Given camera parameters of an image pair from bundle adjustment in ASP, a correlation window is defined on the terrain with the predefined surface normal of a post rather than image domain. The squared error of back-projected images on the local terrain is minimized with respect to the post elevation. This single dimensional optimization is solved efficiently and improves the accuracy of the elevation estimate.

Terrain Model↗
NASA NTRS1984

Comparative assessment of LANDSAT-D MSS and TM data quality for mapping applications in the Southeast

Rectifications of multispectral scanner and thematic mapper data sets for full and subscene areas, analyses of planimetric errors, assessments of the number and distribution of ground control points required to minimize errors, and factors contributing to error residual are examined. Other investigations include the generation of three dimensional terrain models and the effects of spatial resolution on digital classification accuracies.

Source record↗
NASA NTRS1981

Application of digital terrain data to quantify and reduce the topographic effect on Landsat data

Landsat multispectral scanner (MSS) data are integrated with 30 m US Geological Survey digital terrain data to quantify and reduce topographic effects on imagery of a forested mountain ridge in central Pennsylvania. Spectral band ratioing slightly decreased variation of Landsat data for 50% of the transects and could not eliminate direct-light topographic effects. A Lambertian model increased topographic effect, and a modified Lambertian model decreased variation produced by the original model, but gave higher variances than found in the raw Landsat data. A non-Lambertian model, however, decreased variation by 86%, reducing topographic effects. The study demonstrated that high quality digital terrain data can be used to develop and test improved radiative transfer models, and enhance the utility of MSS data.

Justice, C. O.↗
NASA NTRS1981

A model for simulation and processing of radar images

A model for recording, processing, presentation, and analysis of radar images in digital form is presented. The observed image is represented as having two random components, one which models the variation due to the coherent addition of electromagnetic energy scattered from different objects in the illuminated areas. This component is referred to as fading. The other component is a representation of the terrain variation which can be described as the actual signal which the radar is attempting to measure. The combination of these two components provides a description of radar images as being the output of a linear space-variant filter operating on the product of the fading and terrain random processes. In addition, the model is applied to a digital image processing problem using the design and implementation of enhancement scene. Finally, parallel approaches are being employed as possible means of solving other processing problems such as SAR image map-matching, data compression, and pattern recognition.

Stiles, J. A.↗
NASA NTRS1987

Aspects of Voyager photogrammetry

In January 1986, Voyager 2 took a series of pictures of Uranus and its satellites with the Imaging Science System (ISS) on board the spacecraft. Based on six stereo images from the ISS narrow-angle camera, a topographic map was compiled of the Southern Hemisphere of Miranda, one of Uranus' moons. Assuming a spherical figure, a 20-km surface relief is shown on the map. With three additional images from the ISS wide-angle camera, a control network of Miranda's Southern Hemisphere was established by analytical photogrammetry, producing 88 ground points for the control of multiple-model compilation on the AS-11AM analytical stereoplotter. Digital terrain data from the topographic map of Miranda have also been produced. By combining these data and the image data from the Voyager 2 mission, perspective views or even a movie of the mapped area can be made. The application of these newly developed techniques to Voyager 1 imagery, which includes a few overlapping pictures of Io and Ganymede, permits the compilation of contour maps or topographic profiles of these bodies on the analytical stereoplotters.

Wu, Sherman S. C.↗