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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Remotely Estimating Total Suspended Solids Concentration in Clear to Extremely Turbid Waters Using a Novel Semi-Analytical Method

Total suspended solids (TSS) concentration is an important biogeochemical parameter for water quality management and sediment-transport studies. In this study, we propose a novel semi-analytical method for estimating TSS in clear to extremely turbid waters from remote-sensing reflectance (Rrs). The proposed method includes three sub-algorithms used sequentially. First, the remotely sensed waters are classified into clear (Type I), moderately turbid (Type II), highly turbid (Type III), and extremely turbid (Type IV) water types by comparing the values of Rrs at 490, 560, 620, and 754 nm. Second, semi-analytical models specific to each water type are used to determine the particulate backscattering coefficients (bbp) at a corresponding single wavelength (i.e., 560 nm for Type I, 665 nm for Type II, 754 nm for Type III, and 865 nm for Type IV). Third, a specific relationship between TSS and bbp at the corresponding wavelength is used in each water type. Unlike other existing approaches, this method is strictly semi-analytical and its sub-algorithms were developed using synthetic datasets only. The performance of the proposed method was compared to that of three other state-of-the-art methods using simulated (N = 1000, TSS ranging from 0.01 to 1100 g/m3) and in situ measured (N = 3421, TSS ranging from 0.09 to 2627 g/m3) pairs of Rrs and TSS. Results showed a significant improvement with a Median Absolute Percentage Error (MAPE) of 16.0% versus 30.2–90.3% for simulated data and 39.7% versus 45.9–58.1% for in situ data, respectively. The new method was subsequently applied to 175 MEdium Resolution Imaging Spectrometer (MERIS) and 498 Ocean and Land Colour Instrument (OLCI) images acquired in the 2003–2020 timeframe to produce long-term TSS time-series for Lake Suwa and Lake Kasumigaura, Japan. Performance assessments using MERIS and OLCI matchups showed good agreements with in situ TSS measurements.

Mulit-Wavelength↗
NASA NTRS2022

Bryce Canyon Water Resources: Monitoring Vegetation Health and Water Availability in Bryce Canyon National Park for Drought Stress Mitigation Planning

Bryce Canyon National Park is home to groundwater-dependent ecosystems (GDEs) that are threatened by a multidecadal drought and increased groundwater extraction due to a spike in tourism. These ecosystems contain unique species that are only found in areas where near-surface groundwater is present, such as aspen groves and fens. These species contribute to the high biodiversity found in Bryce Canyon, which boosts an ecosystem’s productivity and the services it provides to the park. Unfortunately, many of these GDEs are too small to identify with traditional Earth observation platforms and are difficult to physically reach for monitoring purposes. This project partnered with the National Park Service to identify springs and seeps as a proxy for GDEs within Bryce Canyon from 2013–2022. Furthermore, this project tested the feasibility of various methods to detect and monitor springs and seeps and therefore facilitate the partner’s efforts to conserve these ecologically valuable GDEs in Bryce Canyon. The team mapped groundwater discharge with high resolution National Agriculture Imagery Program (NAIP) and assessed park vegetation trends with Landsat 8 Operational Land Imager (OLI) and PlanetScope imagery. In-situ precipitation data and the Western Land Data Assimilation System (WLDAS) were used to produce time series of climatic variables. Seeps and spring locations were predicted using random forest classification and maximum entropy machine learning models.

Groundwater dependent ecosystems↗
NASA NTRS2018

Supervised Learning Applied to Air Traffic Trajectory Classification

Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

Bosson, Christabelle S.↗
NASA NTRS2018

Supervised Learning Applied to Air Traffic Trajectory Classification

Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

Bosson, Christabelle↗
NASA NTRS1992

Classification with spatio-temporal interpixel class dependency contexts

A contextual classifier which can utilize both spatial and temporal interpixel dependency contexts is investigated. After spatial and temporal neighbors are defined, a general form of maximum a posterior spatiotemporal contextual classifier is derived. This contextual classifier is simplified under several assumptions. Joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Gibbs random field. The classification is performed in a recursive manner to allow a computationally efficient contextual classification. Experimental results with bitemporal TM data show significant improvement of classification accuracy over noncontextual pixelwise classifiers. This spatiotemporal contextual classifier should find use in many applications of remote sensing, especially when the classification accuracy is important.

Jeon, Byeungwoo↗
NASA NTRS1981

Sensitivity of geographic information system outputs to errors in remotely sensed data

The sensitivity of the outputs of a geographic information system (GIS) to errors in inputs derived from remotely sensed data (RSD) is investigated using a suitability model with per-cell decisions and a gridded geographic data base whose cells are larger than the RSD pixels. The process of preparing RSD as input to a GIS is analyzed, and the errors associated with classification and registration are examined. In the case of the model considered, it is found that the errors caused during classification and registration are partially compensated by the aggregation of pixels. The compensation is quantified by means of an analytical model, a Monte Carlo simulation, and experiments with Landsat data. The results show that error reductions of the order of 50% occur because of aggregation when 25 pixels of RSD are used per cell in the geographic data base.

Ramapriyan, H. K.↗
NASA NTRS1992

Providing an empirical basis for optimizing the verification and testing phases of software development

Applying equal testing and verification effort to all parts of a software system is not very efficient, especially when resources are limited and scheduling is tight. Therefore, one needs to be able to differentiate low/high fault density components so that the testing/verification effort can be concentrated where needed. Such a strategy is expected to detect more faults and thus improve the resulting reliability of the overall system. This paper presents an alternative approach for constructing such models that is intended to fulfill specific software engineering needs (i.e. dealing with partial/incomplete information and creating models that are easy to interpret). Our approach to classification is as follows: (1) to measure the software system to be considered; and (2) to build multivariate stochastic models for prediction. We present experimental results obtained by classifying FORTRAN components developed at the NASA/GSFC into two fault density classes: low and high. Also we evaluate the accuracy of the model and the insights it provides into the software process.

Briand, Lionel C.↗
NASA NTRS2022

Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery

In mountainous environments, topography strongly affects the reflectance due to illumination effects and cast shadows, which introduce errors in land cover classifications. However, topographic correction is not routinely implemented in standard data pre-processing chains (e.g., Landsat Analysis Ready Data), and there is a lack of consensus whether topographic correction is necessary, and if so, how to conduct it. Furthermore, methods that correct simultaneously for atmospheric and topographic effects are becoming available, but they have not been compared directly. Our objects were to investigate (1) the effectiveness of two topographic correction approaches that integrate atmospheric and topographic correction, (2) improvements in classification accuracy when analyzing topographically corrected single-date imagery (14 July 2016 and 2 October 2016), versus a full Landsat time series from 2014 to 2016, and 3) improvements in classification accuracy when including additional terrain information (i.e., topographic slope, elevation, and aspect). We developed a physical based model and compared it with an enhanced C-correction, both of which integrate atmospheric and topographic correction. We compared classification accuracies with and without topographic correction using combinations of single-date imagery, image composites and spectral-temporal metrics generated from the full Landsat time series, and additional terrain information in the Caucasus Mountains. We found that both the enhanced C-correction and the physical model performed very well and largely eliminated the correlation (Pearson’s correlation coefficient r ranges from 0.06 to 0.24) between surface reflectance and illumination condition, but the physical model performed best (r ranges from 0.05 to 0.11). Both image composites, and spectral-temporal metrics generated from corrected imagery, resulted in significantly (p ≤ 0.05) higher classification accuracies and better forest classifications, especially for the mixed forests. Adding terrain information reduced classification error significantly, but not as much as topographic correction. In summary, topographic correction remains necessary, even when analyzing a full Landsat time series and including a digital elevation model in the classification. We recommend that topographic correction should be applied when analyzing Landsat satellite imagery in mountainous region for forest cover classification.

Atmospheric correction↗
NASA NTRS1988

Summary of along-track data from the earth radiation budget satellite for several representative ocean regions

For several days in January and August 1985, the Earth Radiation Budget Satellite, a component of the Earth Radiation Budget Experiment (ERBE), was operated in an along-track scanning mode. A survey of radiance measurements taken in this mode is given for five ocean regions: the north and south Atlantic, the Arabian Sea, the western Pacific north of the Equator, and part of the Intertropical Convergence Zone. Each overflight contains information about the clear scene and three cloud categories: partly cloudy, mostly cloudy, and overcast. The data presented include the variation of longwave and shortwave radiance in each scene classification as a function of viewing zenity angle during each overflight of one of the five target regions. Several features of interest in the development of anisotropic models are evident, including the azimuthal dependence of shortwave radiance that is an essential feature of shortwave bidirectional models. The data also demonstrate that the scene classification algorithm employed by the ERBE results in scene classifications that are a function of viewing geometry.

Brooks, David R.↗
NASA NTRS1981

A gradient model of vegetation and climate utilizing NOAA satellite imagery. Phase 1: Texas transect

A climatological model/variable termed the sponge (a measure of moisture availability based on daily temperature maxima and minima, and precipitation) was tested for potential biogeograhic, ecological, and agro-climatological applications. Results, depicted in tabular and graphic form, suggest that, as generalized climatic index, sponge is particularly appropriate for large-area and global vegetation monitoring. The feasibility of utilizing NOAA/AVHRR data for vegetation classification was investigated and a vegetation gradient model that utilizes sponge and AVHRR data was initiated. Along an east-west Texas gradient, vegetation, sponge, and AVHRR pixel data (channels 1 and 2) were obtained for 12 locations. The normalized difference values for the AVHRR data when plotted against vegetation characteristics (biomass, net productivity, leaf area) and sponge values along the Texas gradient suggest that a multivariate gradient model incorporating AVHRR and sponge data may indeed be useful in global vegetation stratification and monitoring.

Greegor, D.↗
NASA NTRS1981

A gradient model of vegetation and climate utilizing NOAA satellite imagery. Phase 1: Texas transect

A new experimental climatological model/variable termed the sponge, a measure of moisture availability based on daily temperature maxima and minima and precipitation, is tested for potential biogeographic, ecological, and agro-climatological applications. Results, depicted in tabular and graphic from, suggest that, as a generalized climatic index, sponge's simplicity and sensitivity make particularly appropriate for trans-regional biogeographic studies (e.g., large-area and global vegetation monitoring). The feasibility of utilizing NOAA/AVHRR data for vegetation classification was investigated and a vegetation gradient model that utilizes sponge, and AVHRR pixel data (channels 1 and 2) were obtained for 12 locations. The normalized difference values for the AVHRR data when plotted against vegetation characteristics (biomass, net productivity, leaf area) and sponge values suggest that a multivariate gradient model incorporating AVHRR and sponge data may indeed be useful in global vegetation stratification and monitoring.

Greegor, D. H.↗
NASA NTRS1976

The use of spatial characteristics for the improvement of multispectral classification of remotely sensed data

Two techniques for the classification of multispectral remotely sensed data - the image processing technique of texture features, which is modeled after the human visual system, and the ECHO (Extraction and Classification of Homogeneous Objects) numerical technique - are examined. These two spatial analysis techniques are compared using Landsat imagery of an area of Indiana as an example, and it is found that the numerical approach is superior in classification accuracy and more efficient computationally.

Wiersma, D. J.↗
NASA NTRSDate not supplied

Understanding and Verifying Neural Networks

Deep Neural Networks (DNNs) have gained immense popularity in recent times and have widespread use in applications such as image classification, sentiment analysis, speech recognition and also in safety-critical applications such as autonomous driving. However, they suffer limitations such as lack of explainability and robustness which raise safety and security concerns in their usage. Further, the complex structure and large input spaces of DNNs act as an impediment to thorough verification and testing. The SafeDNN project at the Robust Software Engineering (RSE) group at NASA aims at exploring techniques to ensure that systems that use deep neural networks are safe, robust and interpretable. In this talk, I will be presenting our technique Prophecy that automatically infers formal properties of deep neural network models. The tool extracts patterns based on neuron activations as preconditions that imply certain desirable output properties of the model. I would be highlighting case studies that use Prophecy in obtaining explanations for network decisions, understanding correct and incorrect behavior, providing formal guarantees wrt safety and robustness, and debugging neural network models. We have applied the tool on image classification networks, neural network controllers providing turn advisories in unmanned aircrafts, regression models used for autonomous center-line tracking in aircrafts and neural network object detectors

Deep Neural Networks↗
NASA NTRS2008

Multiscale Modeling of Damage Processes in fcc Aluminum: From Atoms to Grains

Molecular dynamics (MD) methods are opening new opportunities for simulating the fundamental processes of material behavior at the atomistic level. However, current analysis is limited to small domains and increasing the size of the MD domain quickly presents intractable computational demands. A preferred approach to surmount this computational limitation has been to combine continuum mechanics-based modeling procedures, such as the finite element method (FEM), with MD analyses thereby reducing the region of atomic scale refinement. Such multiscale modeling strategies can be divided into two broad classifications: concurrent multiscale methods that directly incorporate an atomistic domain within a continuum domain and sequential multiscale methods that extract an averaged response from the atomistic simulation for later use as a constitutive model in a continuum analysis.

Glaessgen, E. H.↗
NASA NTRS1991

Object links in the repository

Some of the architectural ramifications of extending the Eichmann/Atkins lattice-based classification scheme to encompass the assets of the full life-cycle of software development are explored. In particular, we wish to consider a model which provides explicit links between objects in addition to the edges connecting classification vertices in the standard lattice. The model we consider uses object-oriented terminology. Thus, the lattice is viewed as a data structure which contains class objects which exhibit inheritance. A description of the types of objects in the repository is presented, followed by a discussion of how they interrelate. We discuss features of the object-oriented model which support these objects and their links, and consider behavior which an implementation of the model should exhibit. Finally, we indicate some thoughts on implementing a prototype of this repository architecture.

Beck, Jon↗
NASA NTRS1996

What Drives Spacecraft Cost: A Look Into How Material Characteristics Relate to the Material Complexity Multipliers

Today a variety of engineered materials are used to build the space vehicles and satellites that NASA, DOD and the aerospace community will use in future projects. These materials can be a significant part of the cost when designing and building these systems. Current cost models such as NASCOM, SEER-H and PRICE allow the cost analysis to select materials requirements during the development of the cost model. It should be noted however that some of these models do not always give the most detailed information with respect to material specifications for the given cost model. Instead the materials are defined within broad classification, giving questionable data with regard to specific material cost. It is the objective of this paper to present a summary of basic information on materials to assist the cost analyst in the development of their models. Specifically, this paper will compare materials and their complexity multipliers to some specific material properties.

Rosmait, Russell L.↗
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 NTRS1990

Remote sensing of Earth terrain

Remote sensing of earth terrain is examined. The layered random medium model is used to investigate the fully polarimetric scattering of electromagnetic waves from vegetation. The model is used to interpret the measured data for vegetation fields such as rice, wheat, or soybean over water or soil. Accurate calibration of polarimetric radar systems is essential for the polarimetric remote sensing of earth terrain. A polarimetric calibration algorithm using three arbitrary in-scene reflectors is developed. In the interpretation of active and passive microwave remote sensing data from the earth terrain, the random medium model was shown to be quite successful. A multivariate K-distribution is proposed to model the statistics of fully polarimetric radar returns from earth terrain. In the terrain cover classification using the synthetic aperture radar (SAR) images, the applications of the K-distribution model will provide better performance than the conventional Gaussian classifiers. The layered random medium model is used to study the polarimetric response of sea ice. Supervised and unsupervised classification procedures are also developed and applied to synthetic aperture radar polarimetric images in order to identify their various earth terrain components for more than two classes. These classification procedures were applied to San Francisco Bay and Traverse City SAR images.

Kong, Jin AU↗