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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

The Land Analysis System (LAS)

The Land Analysis System (LAS) is an interactive software system, available in the public domain, for the analysis, display, and management of multispectral and other digital image data. The system was developed to support earth sciences research and development activities. LAS provides over 240 applications functions and utilities, a flexible user interface, complete on-line and hardcopy documentation, extensive image data file management, reformatting, and conversion utilities, and high level device independent access to image display hardware. The capabilities are summarized of the latest release of the system (version 5). Emphasis is given to the system portability and the isolation of hardware and software dependencies in this release.

Lu, Yun-Chi↗
NASA NTRS1995

Systolic Processor Array For Recognition Of Spectra

Spectral signatures of materials detected and identified quickly. Spectral Analysis Systolic Processor Array (SPA2) relatively inexpensive and satisfies need to analyze large, complex volume of multispectral data generated by imaging spectrometers to extract desired information: computational performance needed to do this in real time exceeds that of current supercomputers. Locates highly similar segments or contiguous subsegments in two different spectra at time. Compares sampled spectra from instruments with data base of spectral signatures of known materials. Computes and reports scores that express degrees of similarity between sampled and data-base spectra.

Chow, Edward T.↗
NASA NTRS1998

Auroral Observations from the POLAR Ultraviolet Imager (UVI)

Because of the importance of the auroral regions as a remote diagnostic of near-Earth plasma processes and magnetospheric structure, spacebased instrumentation for imaging the auroral regions have been designed and operated for the last twenty-five years. The latest generation of imagers, including those flown on the POLAR satellite, extends this quest for multispectral resolution by providing three separate imagers for the visible, ultraviolet, and X ray images of the aurora. The ability to observe extended regions allows imaging missions to significantly extend the observations available from in situ or groundbased instrumentation. The complementary nature of imaging and other observations is illustrated below using results from tile GGS Ultraviolet Imager (UVI). Details of the requisite energy and intensity analysis are also presented.

Germany, G. A.↗
NASA NTRS2004

Mapping Rock and Soil Units in the MPF IMP SuperPan Using a Kohonen Self Organizing Map

The 1997 Mars Pathfinder mission provided information on a site in the Ares Vallis floodplain. Initial analysis of multispectral data from the Imager for Mars Pathfinder (IMP) indicated the presence of only a single rock type, the 'gray rock' spectral class and various coated variants thereof (e.g., 'maroon rock'). Continued analysis of the IMP 'SuperPan' mosaic has confirmed multiple examples of a second 'black rock' spectral class existing as small cobbles in the near field and as boulders in the far field. These results are consistent with recent analysis of MGS Thermal Emission Spectrometer (TES) data which indicates that there is likely a mix of both 'Surface Type 1' (ST1) and 'Surface Type 2' (ST2) spectral classes at the MPF landing site. Nominally, the black rock spectral class would correspond to ST1 (basalts) and 'gray rock' would correspond to ST2 (andesites). Orbital remote sensing has also revealed the pervasive presence of layering on Mars. Recently it was suggested that there are extensive outcrops of the black rock spectral class in the SuperPan far field on the flanks of the Twin Peaks and on the rim of Big Crater. These authors suggested that these exposures represented outcrops of black rock from beneath a surficial, flood deposited layer. In this work, we have reexamined the MPF IMP SuperPan mosaic using an artificial neural network self organizing map (SOM) processing architecture in order to classify the distribution of spectral classes within the SuperPan. In this paper, we present initial results from that work and draw specific attention to a subset of the identified spectral classes in order to address questions relating to whether there are extensive exposures of black rock in the IMP far field, what other materials might be exposed in the far field, and what evidence there is for subsurface layering at the MPF landing site.

Farrand, W.↗
NASA NTRS2008

Processing LiDAR Data to Predict Natural Hazards

ELF-Base and ELF-Hazards (wherein 'ELF' signifies 'Extract LiDAR Features' and 'LiDAR' signifies 'light detection and ranging') are developmental software modules for processing remote-sensing LiDAR data to identify past natural hazards (principally, landslides) and predict future ones. ELF-Base processes raw LiDAR data, including LiDAR intensity data that are often ignored in other software, to create digital terrain models (DTMs) and digital feature models (DFMs) with sub-meter accuracy. ELF-Hazards fuses raw LiDAR data, data from multispectral and hyperspectral optical images, and DTMs and DFMs generated by ELF-Base to generate hazard risk maps. Advanced algorithms in these software modules include line-enhancement and edge-detection algorithms, surface-characterization algorithms, and algorithms that implement innovative data-fusion techniques. The line-extraction and edge-detection algorithms enable users to locate such features as faults and landslide headwall scarps. Also implemented in this software are improved methodologies for identification and mapping of past landslide events by use of (1) accurate, ELF-derived surface characterizations and (2) three LiDAR/optical-data-fusion techniques: post-classification data fusion, maximum-likelihood estimation modeling, and hierarchical within-class discrimination. This software is expected to enable faster, more accurate forecasting of natural hazards than has previously been possible.

Fairweather, Ian↗
NASA NTRS2009

Lunar Geologic Mapping: A Preliminary Map of a Portion of the LQ-10 ("Marius") Quadrangle

Since the first lunar mapping program ended in the 1970s, new topographical, multispectral, elemental and albedo imaging datasets have become available (e.g., Clementine, Lunar Prospector, Galileo). Lunar science has also advanced within the intervening time period. A new systematic lunar geologic mapping effort endeavors to build on the success of earlier mapping programs by fully integrating the many disparate datasets using GIS software and bringing to bear the most current understanding of lunar geologic history. As part of this program, we report on a 1:2,500,000-scale preliminary map of a subset of Lunar Quadrangle 10 ("LQ-10" or the "Marius Quadrangle," see Figures 1 and 2), and discuss the first-order science results. By generating a geologic map of this region, we can constrain the stratigraphic and geologic relationships between features, revealing information about the Moon s chemical and thermal evolution.

Gregg, T. K. P.↗
NASA NTRS2010

Special Opportunities for Highly Sampled Areas

This slide presentation reviews the coverage areas for the two instruments that are scheduled to be on board the HyspIRI mission, (i.e.,visible to short wave infrared (VSWIR) and a multispectral thermal infrared (TIR) imager.)

Knox, Robert G.↗
NASA NTRS2010

An Overview of the Landsat Data Continuity Mission

The advent of the Landsat Data Continuity Mission (LDCM), currently with a launch readiness date of December, 2012, will see evolutionary changes in the Landsat data products available from the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The USGS initiated a revolution in 2009 when EROS began distributing Landsat data products at no cost to requestors in contrast to the past practice of charging the cost of fulfilling a request; that is, charging $600 per Landsat scene. To implement this drastic change, EROS terminated data processing options for requestors and began to produce all data products using a consistent processing recipe. EROS plans to continue this practice for the LDCM and will required new algorithms to process data from the LDCM sensors. All previous Landsat satellites flew multispectral scanners to collect image data of the global land surface. Additionally, Landsats 4, 5, and 7 flew sensors that acquired imagery for both reflective spectral bands and a single thermal band. In contrast, the LDCM will carry two pushbroom sensors; the Operational Land Imager (OLI) for reflective spectral bands and the Thermal InfraRed Sensor (TIRS) for two thermal bands. EROS is developing the ground data processing system that will both calibrate and correct the data from the thousands of detectors employed by the pushbroom sensors and that will also combine the data from the two sensors to create a single data product with registered data for all of the OLI and TIRS bands.

Irons, James R.↗
NASA NTRS2010

Phenological Parameters Estimation Tool

The Phenological Parameters Estimation Tool (PPET) is a set of algorithms implemented in MATLAB that estimates key vegetative phenological parameters. For a given year, the PPET software package takes in temporally processed vegetation index data (3D spatio-temporal arrays) generated by the time series product tool (TSPT) and outputs spatial grids (2D arrays) of vegetation phenological parameters. As a precursor to PPET, the TSPT uses quality information for each pixel of each date to remove bad or suspect data, and then interpolates and digitally fills data voids in the time series to produce a continuous, smoothed vegetation index product. During processing, the TSPT displays NDVI (Normalized Difference Vegetation Index) time series plots and images from the temporally processed pixels. Both the TSPT and PPET currently use moderate resolution imaging spectroradiometer (MODIS) satellite multispectral data as a default, but each software package is modifiable and could be used with any high-temporal-rate remote sensing data collection system that is capable of producing vegetation indices. Raw MODIS data from the Aqua and Terra satellites is processed using the TSPT to generate a filtered time series data product. The PPET then uses the TSPT output to generate phenological parameters for desired locations. PPET output data tiles are mosaicked into a Conterminous United States (CONUS) data layer using ERDAS IMAGINE, or equivalent software package. Mosaics of the vegetation phenology data products are then reprojected to the desired map projection using ERDAS IMAGINE

McKellip, Rodney D.↗
NASA NTRS2015

Ocean Color Measurements with the Operational Land Imager on Landsat-8: Implementation and Evaluation in SeaDAS

The Operational Land Imager (OLI) is a multispectral radiometer hosted on the recently launched Landsat8 satellite. OLI includes a suite of relatively narrow spectral bands at 30 m spatial resolution in the visible to shortwave infrared, which makes it a potential tool for ocean color radiometry: measurement of the reflected spectral radiance upwelling from beneath the ocean surface that carries information on the biogeochemical constituents of the upper ocean euphotic zone. To evaluate the potential of OLI to measure ocean color, processing support was implemented in Sea-viewing Wide Field-of-View Sensor (SeaWiFS) Data Analysis System (SeaDAS), which is an open-source software package distributed by NASA for processing, analysis, and display of ocean remote sensing measurements from a variety of spaceborne multispectral radiometers. Here we describe the implementation of OLI processing capabilities within SeaDAS, including support for various methods of atmospheric correction to remove the effects of atmospheric scattering and absorption and retrieve the spectral remote sensing reflectance (Rrs; sr−1). The quality of the retrieved Rrs imagery will be assessed, as will the derived water column constituents, such as the concentration of the phytoplankton pigment chlorophyll a.

Measurements↗
NASA NTRS2021

Coastal California Water Resources: Assessing Estuarine Ecosystems in California for Improved Wetland Monitoring and Management

Estuaries are vital ecosystems that serve important ecological functions. The Marine Life Protection Act aims to protect these ecosystems by establishing a network of marine protected areas (MPAs), in part by requiring regulatory agencies to monitor estuary extent and health. However, California has 23 estuarine MPAs (EMPAs) and approximately 440,000 total acres of estuarine habitat and, therefore, ground-based data collection can be time and resource intensive. This project used remotely sensed data to examine the health of California EMPAs in an effort to supplement ground-based field measurements. Specifically using Landsat 8 Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), this project assessed mouth state, inundation extent, turbidity, Chlorophyll-a, and colored dissolved organic matter (CDOM) for estuaries observable with these sensors. The Normalized Water Difference Index (NDWI) from Sentinel-2 MSI was capable of capturing estuary mouth state and inundation extent. Meanwhile, Landsat 8 OLI and Sentinel-2 MSI indicated a capacity to capture differences in water quality metrics coinciding with changes to estuary mouth state using algorithms applied in Google Earth Engine (GEE). The GEE California Estuary Assessment (CEA) tools will allow project partners to better monitor and understand estuarine dynamics and health.

Karina Alvarez↗
NASA NTRS2021

Powder River Basin Water Resources: Mapping Russian Olive in the Powder River Basin to Inform Invasive Species Management

Since its introduction in the late 1800s, Elaeagnus augustifolia (Russian olive) has become a widespread invasive shrub that poses a threat to native riparian species in the United States by competing with native riparian plants for space and resources. To date, limited information on the distribution of Russian olive in the Powder River Basin of Montana and Wyoming have hampered management efforts and decision making. Here, we detect and model the distribution of Russian Olive using field surveys, ocular sampling, and variables from Landsat 8 Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and Shuttle Radar Topography Mission (SRTM) using the Random Forest algorithm. We derived topographic, spectral, and hydrological variables from Landsat 8 OLI, Sentinel-2 MSI, and SRTM to utilize as model inputs. The team was able to successfully create a spectral Russian olive detection map for the Powder River Basin (RMSE =15.44%, R2 = 0.6482). The team also examined change in stream channel geomorphology from 1984-2020 in a time-series analysis using Landsat visible imagery and the RivMap MATLAB package and found little change. Our results will help our partners at the Powder River County Weed Board, Gay Ranch, United States Geological Survey, and University of Northern Colorado to locate and prioritize areas for riparian habitat restoration and to understand the region’s hydrology and geomorphology.

Catherine Buczek↗
NASA NTRS2024

Big Cypress Water Resources: Using Earth Observations to Assess Water Quality in Big Cypress Reservation, FL

Upstream agricultural development and runoff are driving harmful algal blooms in the Big Cypress Reservation. The Seminole Tribe of Florida Environmental Resource Management Department monitors water quality in the Big Cypress Reservation, located on the north end of Big Cypress National Preserve. The Environmental Resource Management Department aims to incorporate Earth observations into its investigative approach, which consists of Geographic Information Systems and in situ water sampling. We assessed the feasibility of using Earth observations to measure harmful algal blooms over time and identify vulnerable areas. We analyzed data from multiple remote sensing platforms and sensors, including Landsat 8 Thermal Infrared Spectrometer(TIRS), Landsat 9 TIRS-2, Landsat 9 Operational Land Imager 2, Sentinel-2 MultiSpectral Instrument, and Sentinel-3 Ocean and Land Color Instrument. We determined that it was challenging to use Sentinel-3, Landsat 8, and Landsat 9 imagery for monitoring the canals within the Reservation, and that Sentinel-2 was the most capable platform for the study area. Using Sentinel-2, we created spectral indices for the detection of algal blooms. We used these indices to measure algal blooms at 8 stations across the Big Cypress Reservation. We then created time series and seasonal decompositions of in situ water sample data and indices to visualize relationships between datasets, along with correlation matrices. Our correlations were not conclusive. However, we found that the spectral indices have the potential to detect algal blooms. Lastly, we evaluated Maxar WorldView-3 imagery and found that using data from sensors with higher spatial resolutions would improve results.

Remote sensing↗
NASA NTRS2024

South Africa Ecological Conservation: Identifying and Mapping Riparian Areas in South Africa with Earth Observations

Riparian zones are thin strips of wetland that occur along the edges of rivers, lakes, and other water bodies. They provide many ecosystem and societal benefits such as stream bank stabilization, flood control, and habitat stability, making these zones very important areas to preserve and protect. Recognizing the need to accurately map these ecosystems in a cost-effective way, we partnered with the South African National Biodiversity Institute (SANBI) and the Biodiversity Survey of the Cape(BioSCape) to develop a GI methodology using Esri's ArcGIS Pro and data from Landsat 9 Operational Land Imager-2, Sentinel-2 Multispectral Instrument, and the Shuttle Radar Topography Mission to identify potential riparian zones (PRZ) and observed riparian vegetation(ORV) in the Southern Cape and the North West Province of South Africa to estimate the actual riparian vegetation (ARV) within those areas. Potential riparian ecosystems were calculated by utilizing a Topographic Wetness Index (TWI) and a water feature layer, while observed riparian vegetation were calculated by overlaying a Normalized Difference Vegetation Index (NDVI) and TWI. Actual Riparian Vegetation is determined by the overlap of ORV with PRZ and is done with conditional statements; ORV within PRZ is ARV. This method significantly improved a current riparian land cover classification since it combined high resolution optical imagery and topographic data. We anticipate such maps will be used by conservationists and practitioners interested in riparian monitoring and management.

Remote Sensing↗
DOE OSTI2023

Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning

In overhead image segmentation tasks, including additional spectral bands beyond the traditional RGB channels can improve model performance. However, it is still unclear how incorporating this additional data impacts model robustness to adversarial attacks and natural perturbations. For adversarial robustness, the additional in-formation could improve the model’s ability to distinguish malicious inputs, or simply provide new attack avenues and vulnerabilities. For natural perturbations, the additional information could better inform model decisions and weaken perturbation effects or have no significant influence at all. In this work, we seek to characterize the performance and robustness of a multispectral (RGB and near infrared) image segmentation model subjected to adversarial attacks and natural perturbations. While existing adversarial and natural robustness research has focused primarily on digital perturbations, we prioritize on creating realistic perturbations designed with physical world conditions in mind. For adversarial robustness, we focus on data poisoning attacks whereas for natural robustness, we focus on extending ImageNet-C common corruptions for fog and snow that coherently and self-consistently perturbs the input data. Overall, we find both RGB and multispectral models are vulnerable to data poisoning attacks regardless of input or fusion architectures and that while physically-realizable natural perturbations still degrade model performance, the impact differs based on fusion architecture and input data.

Deep learning, multispectral images, multimodal fu↗
NASA NTRS2018

Smallholder Crop Area Mapped with Wall-To-Wall WorldView Sub-Meter Panchromatic Image Texture: A Test Case for Tigray, Ethiopia

Global food production in the developing world occurs within sub-hectare fields that are difficult to identify with moderate resolution satellite imagery. Knowledge about the distribution of these fields is critical in food security programs. We developed a semi-automated image segmentation approach using wall-to-wall sub-meter imagery with high-performance computing to map crop area (CA) throughout Tigray, Ethiopia that encompasses over 41,000 km (exp 2). Multiple processing streams were tested to minimize mapping error while applying five unique smoothing kernels to capture differences in land surface texture associated to CA. Typically, very-small fields (mean < 2 ha) have a smooth image roughness compared to natural scrub/shrub woody vegetation at the ~1m scale and these features can be segmented in panchromatic imagery with multi-level histogram thresholding. Multi-temporal very-high resolution (VHR) panchromatic imagery with multi-spectral VHR are sufficient in extracting critical CA information needed in food security programs. A 2011 to 2015 CA map was produced, using over 3000 WorldView-1 panchromatic images wall-to-wall in 1/2 deg mosaics for Tigray, Ethiopia. CA was evaluated with nearly 3000 WorldView-2 2m multispectral 250 X 250 m image subsets by seven expert interpretations, and with in-situ global positioning system photography. CA estimates ranged from 32 to 41% in sub regions of Tigray with median maximum per bin commission and omission errors of 11% and 1% respectively, with most of the error occurring in bins <15%. This empirical, simple, and low direct cost approach via U.S. government license agreement to access commercial VHR data, could be a viable big-data high-performance computing methodology to extract wall-to-wall CA for other regions of the world that have very-small agriculture fields with similar image texture."

Ethiopia↗
NASA NTRS1995

Applications of GOES-8/9 data to hurricane analysis

GOES-8 was successfully launched in 1994, and has performed up to expeditions and in some cases has actually exceeded pre-launch specifications. A similar geostationary satellite, GOES-9, was launched in May of 1995. This new generation of NOAA's geostationary satellites carry a superior design and instrumentation package that allow for greater detection of meteorological features and parameters. The new GOES imager has a 5 band multispectral capability with high spatial resolution, while the sounder contains 18 thermal infrared (IR) bands plus a low resolution visible band. The imager carries a visible channel with 1 10-bit quantization and increased sampling frequency, a short-wave and long-wave window channel, and a water vapor band with a twofold increase in spatial resolution and a factor of 3 improvement in signal-to-noise over that obtained from previous GOES sensors. The advances in observing the earth's atmospheric system anticipated from these improvements are outlined in Menzel and Purdom (1994). The specific impact of this improved remote sensing capability on the analysis of tropical cyclones is discussed here.

Velden, Christopher S.↗