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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Retrieval of ice thickness from polarimetric SAR data

We describe a potential procedure for retrieving ice thickness from multi-frequency polarimetric SAR data for thin ice. This procedure includes first masking out the thicker ice types with a simple classifier and then deriving the thickness of the remaining pixels using a model-inversion technique. The technique used to derive ice thickness from polarimetric observations is provided by a numerical estimator or neural network. A three-layer perceptron implemented with the backpropagation algorithm is used in this investigation with several improved aspects for a faster convergence rate and a better accuracy of the neural network. These improvements include weight initialization, normalization of the output range, the selection of offset constant, and a heuristic learning algorithm. The performance of the neural network is demonstrated by using training data generated by a theoretical scattering model for sea ice matched to the database of interest. The training data are comprised of the polarimetric backscattering coefficients of thin ice and the corresponding input ice parameters to the scattering model. The retrieved ice thickness from the theoretical backscattering coefficients is compare with the input ice thickness to the scattering model to illustrate the accuracy of the inversion method. Results indicate that the network convergence rate and accuracy are higher when multi-frequency training sets are presented. In addition, the dominant backscattering coefficients in retrieving ice thickness are found by comparing the behavior of the network trained backscattering data at various incidence angels. After the neural network is trained with the theoretical backscattering data at various incidence anges, the interconnection weights between nodes are saved and applied to the experimental data to be investigated. In this paper, we illustrate the effectiveness of this technique using polarimetric SAR data collected by the JPL DC-8 radar over a sea ice scene.

Kwok, R.↗
DOE OSTI2025

A comparative analysis of YOLOv8 and U-Net image segmentation approaches for transmission electron micrographs of polycrystalline thin films

Metallic thin films offer a platform to experimentally study the dynamics of microstructural evolution, but the required transmission electron microscopy (TEM)-based imaging generates complex images that are challenging to segment and quantify. This work provides a comparative analysis of a new YOLOv8 model and an established U-Net model for bright-field TEM images of polycrystals, employing a framework leveraging physical observables to evaluate performance against two hand-traced benchmark datasets. This methodology obviates the comparison of large, diversely structured, and manually labeled datasets that are required to assess performance on a per-image/per-pixel basis. It is found that the YOLOv8 model, adapted for real-time instance segmentation, has up to 43× faster inferencing (NVIDIA GeForce RTX 4090) compared to U-Net and reconstructs hand-traced grain size distributions (GSDs) with excellent fidelity, finding mean diameter within 3% for grains near an optimal magnification; for grains that deviate from the optimal pixel-diameter, the size of small- (large)-diameter grains is systematically over- (under)-estimated. This is partially mitigated by including scale-aware augmentations during training. Moreover, when the bias is corrected post-inference by a rigid shift in distribution, the YOLOv8 model reproduces ground truth GSDs with exceptional fidelity, with statistical tests indicating <5% probability that the distributions are distinct. Based on ground truth data, calibration curves pertaining to this shift can be constructed for a given model. This issue is not present in the U-Net model’s results, indicating that for quantitative measurements where the true size of objects is of interest, special procedures must be implemented for YOLO-based models.

36 MATERIALS SCIENCE↗
NASA NTRS1995

The CCD/Transit Instrument (CTI) data-analysis system

The automated software system for archiving, analyzing, and interrogating data from the CCD/Transit Instrument (CTI) is described. The CTI collects up to 450 Mbytes of image-data each clear night in the form of a narrow strip of sky observed in two colors. The large data-volumes and the scientific aims of the project make it imperative that the data are analyzed within the 24-hour period following the observations. To this end a fully automatic and self evaluating software system has been developed. The data are collected from the telescope in real-time and then transported to Tucson for analysis. Verification is performed by visual inspection of random subsets of the data and obvious cosmic rays are detected and removed before permanent archival is made to the optical disc. The analysis phase is performed by a pair of linked algorithms, one operating on the absolute pixel-values and the other on the spatial derivative of the data. In this way both isolated and merged images are reliably detected in a single pass. In order to isolate the latter algorithm from the effects of noise spikes a 3x3 Hanning filter is applied to the raw data before the analysis is run. The algorithms reduce the input pixel-data to a database of measured parameters for each image which has been found. A contrast filter is applied in order to assign a detection-probability to each image and then x-y calibration and intensity calibration are performed using known reference stars in the strip. These are added to as necessary by secondary standards boot-strapped from the CTI data itself. The final stages involve merging the new data into the CTI Master-list and History-list and the automatic comparison of each new detection with a set of pre-defined templates in parameter-space to find interesting objects such as supernovae, quasars and variable stars. Each stage of the processing from verification to interesting image selection is performed under a data-logging system which both controls the pipe-lining of data through the system and records key performance monitor parameters which are built into the software. Furthermore, the data from each stage are stored in databases to facilitate evaluation, and all stages offer the facility to enter keyword-indexed free-format text into the data-logging system. In this way a large measure of certification is built into the system to provide the necessary confidence in the end results.

Cawson, M. G. M.↗
NASA NTRS2012

Computational Ghost Imaging for Remote Sensing

This work relates to the generic problem of remote active imaging; that is, a source illuminates a target of interest and a receiver collects the scattered light off the target to obtain an image. Conventional imaging systems consist of an imaging lens and a high-resolution detector array [e.g., a CCD (charge coupled device) array] to register the image. However, conventional imaging systems for remote sensing require high-quality optics and need to support large detector arrays and associated electronics. This results in suboptimal size, weight, and power consumption. Computational ghost imaging (CGI) is a computational alternative to this traditional imaging concept that has a very simple receiver structure. In CGI, the transmitter illuminates the target with a modulated light source. A single-pixel (bucket) detector collects the scattered light. Then, via computation (i.e., postprocessing), the receiver can reconstruct the image using the knowledge of the modulation that was projected onto the target by the transmitter. This way, one can construct a very simple receiver that, in principle, requires no lens to image a target. Ghost imaging is a transverse imaging modality that has been receiving much attention owing to a rich interconnection of novel physical characteristics and novel signal processing algorithms suitable for active computational imaging. The original ghost imaging experiments consisted of two correlated optical beams traversing distinct paths and impinging on two spatially-separated photodetectors: one beam interacts with the target and then illuminates on a single-pixel (bucket) detector that provides no spatial resolution, whereas the other beam traverses an independent path and impinges on a high-resolution camera without any interaction with the target. The term ghost imaging was coined soon after the initial experiments were reported, to emphasize the fact that by cross-correlating two photocurrents, one generates an image of the target. In CGI, the measurement obtained from the reference arm (with the high-resolution detector) is replaced by a computational derivation of the measurement-plane intensity profile of the reference-arm beam. The algorithms applied to computational ghost imaging have diversified beyond simple correlation measurements, and now include modern reconstruction algorithms based on compressive sensing.

Erkmen, Baris I.↗
NASA NTRS1999

Swift: A Widefield Imaging Fabry Perot for Sofia

Contract was to pursue feasibility studies of the SOFIA Widefield Imaging Fabry-Perot (SWIFT). SWIFT was proposed as a two color 18 to 40 microns imaging Fabry-Perot that utilized two Rockwell/Boeing 256 x 256 pixel Si:Sb BIBs as detective devices. The colors were to be split between 26 and 30 microns using a MgO dichroic. The resolution achieving devices were to be a pair of cryogenic fully tunable scanning Fabry-Perot interferometers (FPIs), two in each band. For high resolving powers, a third, fixed FPI is inserted into the beam. The FPI mirrors were to be made of free standing metal mesh. We also proposed to look into a long wavelength (40 to 210 microns) band during the feasibility study period. We produced a proposal to USRA, submitted in July 1997 that substantially refined our ideas. We decided the long wavelength science was compelling, so the baseline wavelength coverage for SWIFT was widened to 17 to 205 microns. Under typical operations, we proposed to simultaneously image in two bands: 22 to 38 microns, and 50 to 205 microns. The bands were to be split by a cold CaF2 dichroic. The short wavelength (SW) band was to employ a 256 x 256 pixel Boeing/Rockwell Si:Sb BIB array, and the long wavelength (LW) band was to employ a Goddard 6 x 32 (upgradable to 32 x 32) element array of monolithic silicon "pop-up" bolometers as detective devices. The two color capability doubled the data taking efficiency, and ensured "perfect" registration between the images obtained in each band. For the SW band, the beam was to be fully sampled (0.7" pixels, 1.4 in. beam) at 17 microns, and over sampled at longer wavelengths. Even so, SWIFT has a very large (3 ft x 3 ft) field of view. To match the SW and LW fields of view (initially in one dimension only, but in 2-dimensions with 32 x 32 upgrade), SWIFT was to under sample at 63 microns (5.6 in pixels, 5.2 in beam) resulting in a 0.56 x 3 in (upgrade to 3 in x 3 in) field of view. Each band has both Lo-Res (R triple bond lambda/Delta(lambda) approx. 30 to 200) and Hi-Res (R approx. 1000 up to 10(exp 5)) modes, that can be changed in less than a minute. Spectral resolution is obtained with four identical cryogenic, fully tunable, scanning FPIS, two in each band, plus a set of fixed order FPI that can be rotated into the beam for good spectral purity at the highest resolving powers. SWIFT was proposed to explore three primary topics of scientific interest: 1. Protostars, Young Stellar Objects, and Protoplanetary Disks. 2. The Galactic Center. 3. External Galaxies.

Stacey, Gordon J.↗
DOE OSTI2024

Automated Assembly Progress Monitoring in Modular Construction Factories Using Computer Vision-Based Instance Segmentation

Modular construction has recently gained interest as a transformative construction method. In this method, a large portion of the construction is performed inside factories, where processes are fast-paced and interdependent; therefore, any deviation from the schedule can delay the production. Such deviations are frequent in modular factories due to the labor-intensive nature of the tasks. This propagation of delays can be mitigated by continuously monitoring each process; however, current manual monitoring methods are laborious, and recently proposed contact sensor-based methods are intrusive to the work. In addition, recent computer vision-based monitoring methods inside factories are limited to detection algorithms that fail to provide the pixel-level accuracy required for assembly progress monitoring in highly occluded factory scenes, and they require a large number of manual annotations. Therefore, this paper proposes a method to monitor the installation of subassemblies in modular construction factories using mask R-CNN instance segmentation and improves the data efficiency of the model using a copy-paste augmentation method. This method was validated on the CCTV videos captured from a modular construction factory in the US, resulting in a 9% mAP improvement in segmentation.

computer vision↗
NASA NTRS2019

SPARROW: Steam Propelled Autonomous Retrieval Robot for Ocean Worlds

The Steam Propelled Autonomous Retrieval Robot (SPARROW) for Ocean Worlds was a Phase I mission concept study funded under the NASA NIAC program. This report represents the findings of that study and recommendations for future work. SPARROW, envisioned as a soccer ball-sized payload to a primary lander mission, is a propulsively hopping robot for the exploration of Europa's rugged, icy surface. A multi-thruster, passively gimballed robot within a protective, spherical shell, SPARROW is able to freely rotate, self-right, and tumble over chaotic terrains. Europa's abundant surface ice would be harvested as an in situ propellant source. The principal objective of SPARROW is to increase the science return of a Europa landed asset by enabling access to distal, spatially distributed geologic units. The design of mobility systems for Europa is challenging, due in part to its almost entirely unconstrained surface topography and strength. Images returned by Voyager and Galileo yielded resolutions on the order of hundreds of meters per pixel, with localized regions reaching 6 meters per pixel—still far larger than a typical rover. A key benefit of SPARROW's hopping, impact-tolerant design, is that it eliminates the need for a priori information regarding terrain topography and surface strength; no surface reaction forces are required for motion. In this context, SPARROW is believed to be entirely terrain agnostic. In this report we detail the results of three study objectives: i) to quantify the energy required to collect surface ice, change its phase, and maintain propellant temperature, ii) to identify control and estimation strategies that enable SPARROW to successfully reach, and return from, regions of scientific interest, and iii) to characterize the impact of SPARROW's range on likely science return. Five water-based propellant architectures are presented alongside their mass, power, and volume requirements. Monte Carlo simulations of SPARROW hopping and tumbling over 1 km of glacial ice are summarized, characterizing SPARROW's sensitivity to uncertainty in: initial pose, thrust profile, and vehicle-terrain interaction. A science traceability matrix is presented, which details the effect of sortie range on three science goals: constraining Europa's evolutionary morphology, assessing sub-surface ocean habitability, and searching for life and/or biosignatures.

Autonomous↗
DOE OSTI2024

Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels

Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Housedon-Hill - A ChemCam/RMI mega mosaic to investigate distant features

The ChemCam Remote Microscopic Imager (RMI) onboard Curiosity was originally designed to document the tiny areas analyzed by ChemCam’s laser-induced breakdown spectroscopy (LIBS) technique on rocks at few meters distance around the rover. The RMI produces 1024 x 1024 pixel black and white images directly from ChemCam’s telescope focal plane. Early in the mission, it was recognized that, thanks to its powerful optics, RMI could also play a role as a long-distance reconnaissance tool, complementing other cameras on the remote sensing mast, and taking advantage of its very long 700 mm focal length. Imaging areas several kilometers from the rover provides pictures that are very complementary to orbital observations and gives a more human-like, ground-based perspective. Between sols 2878 and 2921, Curiosity stayed parked at the same place to perform various rock sampling analyses. This opportunity was used to target very distant areas of interest, daily building an RMI mosaic of 216 overlapping images covering an area ranging from the bottom layers of Mount Sharp to the edge of Vera Rubin Ridge. This product is so far the largest RMI mosaic acquired during the entire mission.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2008

Object Recognition using Feature- and Color-Based Methods

An improved adaptive method of processing image data in an artificial neural network has been developed to enable automated, real-time recognition of possibly moving objects under changing (including suddenly changing) conditions of illumination and perspective. The method involves a combination of two prior object-recognition methods one based on adaptive detection of shape features and one based on adaptive color segmentation to enable recognition in situations in which either prior method by itself may be inadequate. The chosen prior feature-based method is known as adaptive principal-component analysis (APCA); the chosen prior color-based method is known as adaptive color segmentation (ACOSE). These methods are made to interact with each other in a closed-loop system to obtain an optimal solution of the object-recognition problem in a dynamic environment. One of the results of the interaction is to increase, beyond what would otherwise be possible, the accuracy of the determination of a region of interest (containing an object that one seeks to recognize) within an image. Another result is to provide a minimized adaptive step that can be used to update the results obtained by the two component methods when changes of color and apparent shape occur. The net effect is to enable the neural network to update its recognition output and improve its recognition capability via an adaptive learning sequence. In principle, the improved method could readily be implemented in integrated circuitry to make a compact, low-power, real-time object-recognition system. It has been proposed to demonstrate the feasibility of such a system by integrating a 256-by-256 active-pixel sensor with APCA, ACOSE, and neural processing circuitry on a single chip. It has been estimated that such a system on a chip would have a volume no larger than a few cubic centimeters, could operate at a rate as high as 1,000 frames per second, and would consume in the order of milliwatts of power.

Duong, Tuan↗
DOE OSTI2021

Surface albedo spatial variability in North America: Gridded data vs. local measurements

Considering the current booming interest for the large-scale deployment of bifacial photovoltaic modules, the solar industry now requires accurate estimates of broadband surface albedo at high spatial resolution. In this context, the present study evaluates the adequacy and performance over North America of two Moderate Resolution Imaging Spectroradiometer (MODIS) white-sky albedo products (at 500-m and 1-km resolution) and the National Solar Radiation Database (NSRDB) product at 4-km resolution. Two variations of the 500-m MODIS product are also considered: black-sky albedo and all-sky albedo. Albedo observations from 36 radiometric stations during 2011–2015 are analyzed while considering the apparent homogeneity of the surface characteristics over the 4x4 km NSRDB pixel in which they are located. Even at sites where the albedo around the station has been found “homogeneous” in the literature, marked differences are found between the daily observations and the gridded estimates at any spatial resolution. Furthermore, differences in seasonal behavior and between the three different albedo types also impact the accuracy of the albedo estimates, with overtones caused by local specificities and inhomogeneities. All this precludes the desirable evaluation of the local albedo at a specific site of relatively small size compared to its corresponding 4x4-km pixel if only the mean albedo over that pixel is known. Significant discrepancies are also found at snow-impacted sites, most importantly in the case of the NSRDB albedo estimates, which are typically much too high.

14 SOLAR ENERGY↗
NASA NTRS2007

Improved Underwater Excitation-Emission Matrix Fluorometer

A compact, high-resolution, two-dimensional excitation-emission matrix fluorometer (EEMF) has been designed and built specifically for use in identifying and measuring the concentrations of organic compounds, including polluting hydrocarbons, in natural underwater settings. Heretofore, most EEMFs have been designed and built for installation in laboratories, where they are used to analyze the contents of samples collected in the field and brought to the laboratories. Because the present EEMF can be operated in the field, it is better suited to measurement of spatially and temporally varying concentrations of substances of interest. In excitation-emission matrix (EEM) fluorometry, fluorescence is excited by irradiating a sample at one or more wavelengths, and the fluorescent emission from the sample is measured at multiple wavelengths. When excitation is provided at only one wavelength, the technique is termed one-dimensional (1D) EEM fluorometry because the resulting matrix of fluorescence emission data (the EEM) contains only one row or column. When excitation is provided at multiple wavelengths, the technique is termed two-dimensional (2D) EEM fluorometry because the resulting EEM contains multiple rows and columns. EEM fluorometry - especially the 2D variety - is well established as a means of simultaneously detecting numerous dissolved and particulate compounds in water. Each compound or pool of compounds has a unique spectral fluorescence signature, and each EEM is rich in information content, in that it can contain multiple fluorescence signatures. By use of deconvolution and/or other mixture-analyses techniques, it is often possible to isolate the spectral signature of compounds of interest, even when their fluorescence spectra overlap. What distinguishes the present 2D EEMF over prior laboratory-type 2D EEMFs are several improvements in packaging (including a sealed housing) and other aspects of design that render it suitable for use in natural underwater settings. In addition, the design of the present 2D EEMF incorporates improvements over the one prior commercial underwater 2D EEMF, developed in 1994 by the same company that developed the present one. Notable advanced features of the present EEMF include the following: 1) High sensitivity and spectral resolution are achieved by use of an off-the-shelf grating spectrometer equipped with a sensor in the form of a commercial astronomical- grade 256 532-pixel charge-coupled-device (CCD) array. 2) All of the power supply, timing, control, and readout circuits for the illumination source and the CCD, ancillary environmental monitoring sensors, and circuitry for controlling a shutter or filter motor are custom-designed and mounted compactly on three circuit boards below a fourth circuit board that holds the CCD (see figure). 3) The compactness of the grating spectrometer, CCD, and circuit assembly makes it possible to fit the entire instrument into a compact package that is intended to be maneuverable underwater by one person. 4) In mass production, the cost of the complete instrument would be relatively low - estimated at approximately $30,000 at 2005 prices.

Moore, Casey↗
NASA NTRS2011

Hypersonic Boundary Layer Transition Measurements Using NO2 approaches NO Photo-dissociation Tagging Velocimetry

Measurements of instantaneous and mean streamwise velocity profiles in a hypersonic laminar boundary layer as well as a boundary layer undergoing laminar-to-turbulent transition were obtained over a 10-degree half-angle wedge model. A molecular tagging velocimetry technique consisting of a NO2 approaches?NO photo-dissociation reaction and two subsequent excitations of NO was used. The measurement of the transitional boundary layer velocity profiles was made downstream of a 1-mm tall, 4-mm diameter cylindrical trip along several lines lying within a streamwise measurement plane normal to the model surface and offset 6-mm from the model centerline. For laminar and transitional boundary layer measurements, the magnitudes of streamwise velocity fluctuations are compared. In the transitional boundary layer the fluctuations were, in general, 2-4 times larger than those in the laminar boundary layer. Of particular interest were fluctuations corresponding to a height of approximately 50% of the laminar boundary layer thickness having a magnitude of nearly 30% of the mean measured velocity. For comparison, the measured fluctuations in the laminar boundary layer were approximately 5% of the mean measured velocity at the same location. For the highest 10% signal-to-noise ratio data, average single-shot uncertainties using a 1 ?Es and 50 ?Es interframe delay were ~115 m/s and ~3 m/s, respectively. By averaging single-shot measurements of the transitional boundary layer, uncertainties in mean velocity as low as 39 m/s were obtained in the wind tunnel. The wall-normal and streamwise spatial resolutions were 0.14-mm (2 pixel) and 0.82-mm (~11 pixels), respectively. These measurements were performed in the 31-inch Mach 10 Air Wind Tunnel at the NASA Langley Research Center.

Bathel, Brett F.↗
NASA NTRS2013

MODIS 3 Km Aerosol Product: Applications over Land in an Urban/suburban Region

MODerate resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites have provided a rich dataset of aerosol information at a 10 km spatial scale. Although originally intended for climate applications, the air quality community quickly became interested in using the MODIS aerosol data. However, 10 km resolution is not sufficient to resolve local scale aerosol features. With this in mind, MODIS Collection 6 is including a global aerosol product with a 3 km resolution. Here, we evaluate the 3 km product over the Baltimore/Washington D.C., USA, corridor during the summer of 2011, by comparing with spatially dense data collected as part of the DISCOVER-AQ campaign these data were measured by the NASA Langley Research Center airborne High Spectral Resolution Lidar (HSRL) and a network of 44 sun photometers (SP) spaced approximately 10 km apart. The HSRL instrument shows that AOD can vary by up to 0.2 within a single 10 km MODIS pixel, meaning that higher resolution satellite retrievals may help to characterize aerosol spatial distributions in this region. Different techniques for validating a high-resolution aerosol product against SP measurements are considered. Although the 10 km product is more statistically reliable than the 3 km product, the 3 km product still performs acceptably, with more than two-thirds of MODIS/SP collocations falling within the expected error envelope with high correlation (R > 0.90). The 3 km product can better resolve aerosol gradients and retrieve closer to clouds and shorelines than the 10 km product, but tends to show more significant noise especially in urban areas. This urban degradation is quantified using ancillary land cover data. Overall, we show that the MODIS 3 km product adds new information to the existing set of satellite derived aerosol products and validates well over the region, but due to noise and problems in urban areas, should be treated with some degree of caution.

MODIS↗
DOE OSTI2023

Remote Sensing Low Signal-to-Noise-Ratio Target Detection Enhancement

In real-time remote sensing application, frames of data are continuously flowing into the processing system. The capability of detecting objects of interest and tracking them as they move is crucial to many critical surveillance and monitoring missions. Detecting small objects using remote sensors is an ongoing, challenging problem. Since object(s) are located far away from the sensor, the target’s Signal-to-Noise-Ratio (SNR) is low. The Limit of Detection (LOD) for remote sensors is bounded by what is observable on each image frame. In this paper, we present a new method, a “Multi-frame Moving Object Detection System (MMODS)”, to detect small, low SNR objects that are beyond what a human can observe in a single video frame. This is demonstrated by using simulated data where our technology-detected objects are as small as one pixel with a targeted SNR, close to 1:1. We also demonstrate a similar improvement using live data collected with a remote camera. The MMODS technology fills a major technology gap in remote sensing surveillance applications for small target detection. Our method does not require prior knowledge about the environment, pre-labeled targets, or training data to effectively detect and track slow- and fast-moving targets, regardless of the size or the distance.

47 OTHER INSTRUMENTATION↗
NASA NTRS2023

Front Range Wildland Fires: Evaluating the Efficacy of Remote Sensing Imagery in Monitoring Forest Fuels Treatment Methods

Over the last several decades, wildfire frequency and severity in forested areas along Colorado’s Front Range have increased due to a buildup of fuels. This has led to an increase in forest treatments, as well as an increased need to evaluate the success of these treatments. Remote sensing products offer an efficient and cost-effective way to monitor forest treatments; however, not all remote sensing products and analysis techniques have been explored by Coloradan land managers. Specifically, project partners at the Colorado State Forest Service (CSFS) and the Colorado Forest Restoration Institute (CFRI) were interested in using an effective and streamlined method of mapping canopy cover to better monitor forest treatment success. To support their needs, the NASA DEVELOP Front Range Wildland Fires team explored National Agricultural Imagery Program (NAIP) imagery at different spatial resolutions and numbers of training points with NASA’s Shuttle Radar Topography Mission (SRTM) Data Elevation Model (DEM) as a predictor in addition to NAIP imagery spectral predictors. From this analysis, we created classified canopy cover rasters, and compared accuracy metrics across model iterations. We also determined that the best performing model, with an overall accuracy of 0.900 uses 2021 NAIP imagery at 2-meter resolution, 800 training points, 200 testing points, does not use topographic predictors, and reclassifies shadow pixels via a pre-selected NDVI threshold.

Remote Sensing↗
DOE OSTI2021

NACHOS, a CubeSat-based high-resolution UV-Visible hyperspectral imager for remote sensing of trace gases: system overview and science objectives

Here, the Nano-satellite Atmospheric Chemistry Hyperspectral Observation System (NACHOS) is a high-throughput (f/2.9), high spectral resolution (~1.3 nm optical resolution, 0.6 nm sampling) Offner-design hyperspectral imager operating in the 300-500 nm spectral region. The 1.5U instrument payload (1U optical system, 0.5U electronics module) is hosted by a 1.5U LANL-designed CubeSat bus to comprise a 3U complete satellite. Spectroscopically similar to NASA’s Ozone Monitoring Instrument (OMI), which provides wide-field global mapping of ozone and other gases at coarse spatial resolution, NACHOS fills the complementary niche of targeted measurements at much higher spatial resolution. With 350 across-track spatial pixels and a 15-degree across-track field of view, NACHOS will provide spectral imaging at roughly 0.4 km per pixel from 500 km altitude. NACHOS incorporates highly streamlined gas-retrieval algorithms for rapid onboard processing, alleviating the need to routinely downlink massive hyperspectral data cubes. We will discuss the instrument design, challenges in achieving mechanical robustness to launch vibration in such a compact instrument, the onboard calibration system, and gas-retrieval data downlink strategy. We will also discuss potential science missions, including monitoring of NO 2 as an easily detected proxy for anthropogenic fossil-fuel greenhouse gases, monitoring lowlevel SO 2 degassing at pre-eruptive volcanoes, H 2 CO from wildfires, and characterization of aerosols. The long-term vision is for a many-satellite constellation that could provide both high spatial resolution and frequent revisits for selected targets of interest. As an initial technology demonstration of this vision, the NACHOS project is currently slated to launch two CubeSats in early 2022.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1999

Remote Sensing May Provide Unprecedented Hydrological Data

Basic hydrological research and water resources management may reap tremendous benefits from remote sensing technology, studies are showing. Satellite coverage may allow unprecedented accuracy in the quantification of the global hydrological cycle, for example. Yet despite such benefits, few hydrologists currently use such data. This is partly because the needed tools and algorithms are not fully developed. Such development requires field experiments that combine remotely sensed data with detailed in situ observations. AGU's Remote Sensing in Hydrology Committee has constructed a Web site (http://Iand.gsfc.nasa.gov/RSHC.html) that gives an overview of many such experiments. Included on the site is information on each experiment's overall goal, the types of in situ and remotely sensed measurements taken, relevant climate and vegetation conditions, and so forth. Links to additional relevant Web sites are included. The site is designed to be a suitable starting point for those interested in learning more about remote sensing in hydrology. It lists members of the committee who can be contacted for further information. Hydrologists have recognized the potential of remote sensing technology since the 1970s. It offers a way to avoid the logistical and economic difficulties associated with obtaining continuous in situ measurements of various hydrological variables, difficulties that are particularly pronounced in remote regions. Microwave instruments in particular can potentially provide all-weather, areally averaged estimates of certain variables (such as precipitation, soil moisture, and snow water content) that have been essentially unattainable in the past. In remote sensing, the conversion of emitted and reflected radiances into useful hydrological data is a complex problem. The measured radiances, for example, reflect the integrated character of a pixel area, a scale inconsistent with the point measurements of traditional hydrology. To develop the needed algorithms, field experiments must be designed that combine relevant satellite measurements with traditional in situ measurements in regions that are al- ready well understood hydrologically. Such field experiments can lead to the development of hydrological models that are driven with remotely sensed data. Once the performance of these models is deemed acceptable in the heavily monitored basins, they can be "transported" for use in regions having little or no in situ measurement system. Author,5: Randal D. Koster, Paul R. Houser and Edwin T. Engman, Hydrological Sciences Branch, Laboratory for Hydrospheric Process, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA; William P. Kustas, Hydrology Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, Maryland, USA.

Koster, R.↗