Engineering topics

Dungan, Jennifer

Publications and source records attributed to Dungan, Jennifer.

12 records

NASA NTRS2019

Surface Biology & Geology Pathfinder Data Analysis Pipeline

NASA's future global orbital mission, currently in development as the Surface Biology and Geology (SBG) Designated Observable study, will acquire relatively high resolution solar-reflected spectroscopy and thermal infrared observations. Innovative processes must be utilized for handling the high volume of data anticipated to be collected, which is anticipated to exceed 100 terabytes/day, greater than NASA's total extant airborne hyperspectral data collection. Collecting, processing/re-processing, disseminating, and exploiting this volume of data presents new challenges. To begin addressing them, NASA is drawing upon the expertise developed from its astrophysics programs to address Earth science and applications. Specifically, NASA is adapting the science processing operations technology developed for the Kepler and TESS planet-hunting missions for imaging spectroscopy data processing. This technology development has been the foundation for the remarkable scientific successes of Kepler and TESS. The Kepler/TESS data processing technology provides a scalable architecture for robust, repeatable, and replicable science and application products while enabling the Earth science community to develop, test, and implement new algorithms. Our effort to leverage this existing capability has begun by ingesting data and applying workflows from the EO-1/Hyperion 17-year mission archive that provides globally sampled visible through shortwave infrared spectra that are representative of SBG data types and volumes. This pathfinding data processing system will help define the solutions to processing SBG data volumes and will enable the scientific community to interact with the data and processing pipeline to create new science products.

Jenkins, Jon↗

NASA NTRS2018

Harmonized Landsat/Sentinel-2 Products for Land Monitoring

The Harmonized Landsat-8 and Sentinel-2 (HLS) project is a NASA initiative aiming to produce a seamless, harmonized surface reflectance record from the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard Landsat-8 and Sentinel-2 remote sensing satellites, respectively. The HLS products are based on a set of algorithms to obtain seamless products from both sensors (OLI and MSI): atmospheric correction, cloud and cloud-shadow masking, geographic co-registration and common gridding, bidirectional reflectance distribution function normalization and bandpass adjustment. As of version 1.3, the HLS v1.3 data set covers 9.12 million km2 and spans from first Landsat-8 data (2013) to present. HLS products provide near-daily surface reflectance information with a common geometric framework, and are suitable for a variety of agricultural and vegetation monitoring tasks, including analysis of crop type, condition, and phenology.

Masek, Jeffrey↗

NASA NTRS2016

Harmonizing Landsat and Sentinel-2 Reflectances for Better Land Monitoring

When combined, Landsat and ESA Sentinel-2 observations can provide 2-4 day coverage for the global land area. A collaboration among NASA GSFC (Goddard Space Flight Center), University of Maryland, and NASA Ames has developed a processing chain to create seamless, "harmonized" reflectance products using standardized atmospheric correction, BRDF (Bidirectional Reflectance Distribution Function) adjustment, spectral bandpass adjustment, and gridding algorithms. These products point the way to a "30-m MODIS (Moderate Resolution Imaging Spectroradiometer)" capability for agricultural and ecosystem monitoring by leveraging international sensors.

GEO-GLAM↗

NASA NTRS2007

Displaying Properties of PDFs

PDFVis is a computer program that assists in visualization of uncertainty as represented by a probability density function (PDF) located at each grid cell in a spatial domain. The functions that PDFV performs are listed.

Kao, David↗

NASA NTRS2005

Coordinated Science Campaign Scheduling for Sensor Webs

Future Earth observing missions will study different aspects and interacting pieces of the Earth's eco-system. Scientists are designing increasingly complex, interdisciplinary campaigns to exploit the diverse capabilities of multiple Earth sensing assets. In addition, spacecraft platforms are being configured into clusters, trains, or other distributed organizations in order to improve either the quality or the coverage of observations. These simultaneous advances in the design of science campaigns and in the missions that will provide the sensing resources to support them offer new challenges in the coordination of data and operations that are not addressed by current practice. For example, the scheduling of scientific observations for satellites in low Earth orbit is currently conducted independently by each mission operations center. An absence of an information infrastructure to enable the scheduling of coordinated observations involving multiple sensors makes it difficult to execute campaigns involving multiple assets. This paper proposes a software architecture and describes a prototype system called DESOPS (Distributed Earth Science Observation Planning and Scheduling) that will address this deficiency.

Edgington, Will↗

NASA NTRS2004

Visualizing Distributions from Multi-Return Lidar Data to Understand Forest Structure

Spatially distributed probability density functions (pdfs) are becoming relevant to the Earth scientists and ecologists because of stochastic models and new sensors that provide numerous realizations or data points per unit area. One source of these data is from multi-return airborne lidar, a type of laser that records multiple returns for each pulse of light sent towards the ground. Data from multi-return lidar is a vital tool in helping us understand the structure of forest canopies over large extents. This paper presents several new visualization tools that allow scientists to rapidly explore, interpret and discover characteristic distributions within the entire spatial field. The major contribution from-this work is a paradigm shift which allows ecologists to think of and analyze their data in terms of the distribution. This provides a way to reveal information on the modality and shape of the distribution previously not possible. The tools allow the scientists to depart from traditional parametric statistical analyses and to associate multimodal distribution characteristics to forest structures. Examples are given using data from High Island, southeast Alaska.

Kao, David L.↗

NASA NTRS2003

Interleaved Observation Execution and Rescheduling on Earth Observing Systems

Observation scheduling for Earth orbiting satellites solves the following problem: given a set of requests for images of the Earth, a set of instruments for acquiring those images distributed on a collecting of orbiting satellites, and a set of temporal and resource constraints, generate a set of assignments of instruments and viewing times to those requests that satisfy those constraints. Observation scheduling is often construed as a constrained optimization problem with the objective of maximizing the overall utility of the science data acquired. The utility of an image is typically based on the intrinsic importance of acquiring it (for example, its importance in meeting a mission or science campaign objective) as well as the expected value of the data given current viewing conditions (for example, if the image is occluded by clouds, its value is usually diminished). Currently, science observation scheduling for Earth Observing Systems is done on the ground, for periods covering a day or more. Schedules are uplinked to the satellites and are executed rigorously. An alternative to this scenario is to do some of the decision-making about what images are to be acquired on-board. The principal argument for this capability is that the desirability of making an observation can change dynamically, because of changes in meteorological conditions (e.g. cloud cover), unforeseen events such as fires, floods, or volcanic eruptions, or un-expected changes in satellite or ground station capability. Furthermore, since satellites can only communicate with the ground between 5% to 10% of the time, it may be infeasible to make the desired changes to the schedule on the ground, and uplink the revisions in time for the on-board system to execute them. Examples of scenarios that motivate an on-board capability for revising schedules include the following. First, if a desired visual scene is completely obscured by clouds, then there is little point in taking it. In this case, satellite resources, such as power and storage space can be better utilized taking another image that is higher quality. Second, if an unexpected but important event occurs (such as a fire, flood, or volcanic eruption), there may be good reason to take images of it, instead of expending satellite resources on some of the lower priority scheduled observations. Finally, if there is unexpected loss of capability, it may be impossible to carry out the schedule of planned observations. For example, if a ground station goes down temporarily, a satellite may not be able to free up enough storage space to continue with the remaining schedule of observations. This paper describes an approach for interleaving execution of observation schedules with dynamic schedule revision based on changes to the expected utility of the acquired images. We describe the problem in detail, formulate an algorithm for interleaving schedule revision and execution, and discuss refinements to the algorithm based on the need for search efficiency. We summarize with a brief discussion of the tests performed on the system.

Khatib, Lina↗

NASA NTRS2002

Visualizing 2D Probability Distributions from Satellite Image-Derived Data

Creating maps of biophysical and geophysical variables using Earth Observing System (EOS) satellite image data is an important component of Earth science. These 2D maps have a single value at every location and standard techniques are used to visualize them. Current tools fall short, however, when it is necessary to describe a distribution of values at each location. Distributions may represent a frequency of occurrence over time, frequency of occurrence from multiple runs of an ensemble forecast or possible values from an uncertainty model. 'Distribution data sets' are described, then a case study is presented to visualize such 2D distributions. Distribution data sets are different from multivariate data sets in the sense that the values are for a single variable instead of multiple variables. Our case study data consists of multiple realizations of percent forest cover, generated using a geostatistical technique that combines ground measurements and satellite imagery to model uncertainty about forest cover. We present several approaches for analyzing and visualizing such data sets. The first is a pixel-wise analysis of the probability density functions for the 2D image while the second is an analysis of features identified within the image. Such pixel-wise and feature-wise views will give Earth scientists a more complete understanding of distribution data sets.

Kao, David↗

NASA NTRS2002

High Resolution Soil Water from Regional Databases and Satellite Images

This viewgraph presentation provides information on the ways in which plant growth can be inferred from satellite data and can then be used to infer soil water. There are several steps in this process, the first of which is the acquisition of data from satellite observations and relevant information databases such as the State Soil Geographic Database (STATSGO). Then probabilistic analysis and inversion with the Bayes' theorem reveals sources of uncertainty. The Markov chain Monte Carlo method is also used.

Morris, Robin D.↗

NASA NTRS2002

Improving Land Cover Product-Based Estimates of the Extent of Fragmented Cover Types

The effects of changing land use/land cover on regional and global climate ecosystems depends on accurate estimates of the extent of critical land cover types such as Arctic wetlands and fire scars in boreal forests. To address this information requirement, land cover products at coarse spatial resolution such as Advanced Very High Resolution Radiometer (AVHRR) -based maps and the MODIS Land Cover Product are being produced. The accuracy of the extent of highly fragmented cover types such as fire scars and ponds is in doubt because much (the numerous scars and ponds smaller than the pixel size) is missed. A promising method for improving areal estimates involves modeling the observed distribution of the fragment sizes as a type of truncated distribution, then estimating the sum of unobserved sizes in the lower, truncated tail and adding it to the sum of observed fragment sizes. The method has been tested with both simulated and actual cover products.

Hlavka, Christine A.↗

NASA NTRS1998

A Modeling Approach to Global Land Surface Monitoring with Low Resolution Satellite Imaging

The effects of changing land use/land cover on global climate and ecosystems due to greenhouse gas emissions and changing energy and nutrient exchange rates are being addressed by federal programs such as NASA's Mission to Planet Earth (MTPE) and by international efforts such as the International Geosphere-Biosphere Program (IGBP). The quantification of these effects depends on accurate estimates of the global extent of critical land cover types such as fire scars in tropical savannas and ponds in Arctic tundra. To address the requirement for accurate areal estimates, methods for producing regional to global maps with satellite imagery are being developed. The only practical way to produce maps over large regions of the globe is with data of coarse spatial resolution, such as Advanced Very High Resolution Radiometer (AVHRR) weather satellite imagery at 1.1 km resolution or European Remote-Sensing Satellite (ERS) radar imagery at 100 m resolution. The accuracy of pixel counts as areal estimates is in doubt, especially for highly fragmented cover types such as fire scars and ponds. Efforts to improve areal estimates from coarse resolution maps have involved regression of apparent area from coarse data versus that from fine resolution in sample areas, but it has proven difficult to acquire sufficient fine scale data to develop the regression. A method for computing accurate estimates from coarse resolution maps using little or no fine data is therefore needed.

Hlavka, Christine A.↗

NASA NTRS1998

Fragmented Land Cover Types and Estimation of Area with Course Spatial Resolution Imagery

Imagery of coarse resolution, such weather satellite imagery with 1 sq km pixels, is increasingly used to monitor dynamic and fragmented types of land surface types, such as scars from recent fires and ponds in wetlands. Accurate estimates of these land cover types at regional to global scales are required to assess the roles of fires and wetlands in global warming, yet difficult to compute when much of the area is accounted for by fragments about the same size as the pixels. In previous research, we found that size distribution of the fragments in several example scenes fit simple two-parameter models and related effects of coarse resolution to errors in area estimates based on pixel counts. We report on progress to develop accurate area estimates based on modelling the size distribution of the fragments, including analysis of size distributions on an expanded set of maps developed from digital imagery and a test of a procedure to correct for effects of coarse spatial resolution.

Hlavka, Chris↗