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NASA NTRSDate not supplied

Advanced Weather Hazard Perception Capabilities for Remotely Piloted Aircraft in Wildland Fire Operations

Weather, specifically winds, are the most variable and least predictable of the weather-related elements that affect wildfire behavior.1 This leads to aviation operations which must deal with increased risks due to dynamic weather factors. Remotely piloted aircraft provide the opportunity to remove human loss of life from these risk consequences and mitigate the overall risks for air operations for fire suppression and dropping of retardant. UAS also allows the opportunity to continue fire suppression with nighttime operations. However, with the use of new technologies in aviation operations comes additional risk factors and the need for remote sensing capabilities to identify and permit navigation around extreme weather conditions, with turbulence being the most unforeseeable. This paper presents advanced concepts for weather hazard perception capabilities for remote pilot operators to include data and display techniques intended for dynamic environments and low altitude operations. This work can support the potential of weather sensing capabilities to create hazard maps to track high-risk weather patterns in mountainous regions. Advanced sensing may provide opportunity to locate extreme turbulence hazards for uninhabited aerial systems (UAS) and decrease the likelihood of flying into unknown weather conditions to enable safe operations to assist in wildfire air support.

Tyler L Willhite↗
NASA NTRS2014

Recent Advances in Registration, Integration and Fusion of Remotely Sensed Data: Redundant Representations and Frames

In recent years, sophisticated mathematical techniques have been successfully applied to the field of remote sensing to produce significant advances in applications such as registration, integration and fusion of remotely sensed data. Registration, integration and fusion of multiple source imagery are the most important issues when dealing with Earth Science remote sensing data where information from multiple sensors, exhibiting various resolutions, must be integrated. Issues ranging from different sensor geometries, different spectral responses, differing illumination conditions, different seasons, and various amounts of noise need to be dealt with when designing an image registration, integration or fusion method. This tutorial will first define the problems and challenges associated with these applications and then will review some mathematical techniques that have been successfully utilized to solve them. In particular, we will cover topics on geometric multiscale representations, redundant representations and fusion frames, graph operators, diffusion wavelets, as well as spatial-spectral and operator-based data fusion. All the algorithms will be illustrated using remotely sensed data, with an emphasis on current and operational instruments.

Fusion↗
NASA NTRS1998

Characterization of an Ozone DIAL Receiver for Operation on an Unpiloted Atmospheric Vehicle

Laser remote sensing from aircraft has become a very important technique for observing ozone in the environment. NASA Langley has an active aircraft based research program which presently uses Nd:YAG-pumped dye lasers that are then doubled into the UV to probe both the stratosphere and troposphere for ozone using the differential absorption lidar (DIAL) technique. This large system can only fly on large (NASA DC-8, Electra) aircraft and has been deployed on many missions throughout the world. In the future it will be desirable to fly autonomous, lightweight, compact ozone DIAL instruments on unpiloted atmospheric vehicles (UAV) aircraft. Such aircraft could fly at high altitudes for extended times collecting science data without risk to the operator. Cost for such missions may be substantially reduced over present large aircraft based missions. Presently there are no ozone DIAL systems capable of flying on an UAV aircraft. In order to facilitate UAV missions, small more efficient laser transmitters need to be developed that emit approximately 25mJ near 300nm for each of the DIAL 'on' and 'off' line pulses. Also lightweight, compact DIAL receiver systems need to be built and demonstrated. Such receiver systems may incorporate fiber optic coupled telescopes for maximum light gathering capability per unit area, high quantum efficiency gated photomultiplier tubes with reasonable gain and very narrow-band filters for background light rejection with high light throughput. A compact high-performance 16-bit digitizer and a data storage system are also required. A conceptional design of such a UAV DIAL instrument is presented. Here a pulsed UV laser emits pulses into the atmosphere where elastic scattering occurs which results in light being scattered into the receiver telescope. The subject of this paper is the design, construction and testing of a robust, compact ozone DIAL receiver system that would be a prototype for eventual use in a UAV aircraft.

Goldschmidt, Soenke↗
NASA NTRS1994

Gulf offshore satellite applications project (GOSAP) (ERS-1 Pilot Project PP-USA-1)

Project GOSAP is a multi organizational effort to determine how best to use remote sensing technology, and ERS-1 data in particular, to address offshore problems and operations faced by the exploration and marine engineering industries, in the Gulf of Mexico. Remotely sensed data integrated with sea truth are used to quantify meteorologic and oceanographic events, to detect and track ocean currents and gyres, to image the sea floor, map subsurface geology, or detect oil seeps from orbital altitudes. Participants are evaluating the potential for satellite based offshore exploration, ocean engineering, and environmental applications using combined satellite and airborne measurements constrained by real time sea truth.

Biegert, E. K.↗
NASA NTRS2008

Famine Early Warning Systems and Remote Sensing Data

This book describes the interdisciplinary work of USAID's Famine Early Warning System Network (FEWS NET) and its influence on how food security crises are identified, documented and the kind of responses that result. The book describes FEWS NET's systems and methods for using satellite remote sensing to identify and describe how biophysical hazards impact the lives and livelihoods of the population where they occur. It presents several illustrative case studies that will demonstrate the integration of both physical and social science disciplines in its work. FEWS NET s operational needs have driven science in biophysical remote sensing applications through its collaboration with the US Geological Survey, the National Aeronautics and Space Administration, National Oceanographic and Atmospheric Administration, and US Department of Agriculture, as well as methodologies in the social science domain through its support of the US Agency for International Development, UNWorld Food Program and numerous international non-governmental organizations such as Save the Children, Oxfam and others. Because FEWS NET is an organization that must provide a global picture of food insecurity to decision makers, the information it relies on are by necessity observable and able to be documented. Thus many aspects of traditional livelihood analysis, for example, cannot be used by FEWS NET as they rely upon relationships, and ways of expressing power and knowledge at the local scale that cannot be easily scaled up to express variations in access to food at a community level. The book focuses on the ways that remote sensing information is transformed into an understanding of the actions that must be taken in order to ensure that lives and livelihoods are protected, including describing the remote sensing observations and models needed to identify hazards and the information gathering requirements and analytical frameworks needed to understand their impact. Its focus is primarily analysis conducted in Africa, but also touches upon FEWS NET s work in Central America, Haiti and Afghanistan. As an organization that seeks to integrate social and physical science methodologies and strategies into its work on a daily basis, it is a fascinating and rich example of interdisciplinary knowledge generation and innovation.

Brown, Molly E.↗
NASA NTRS1974

Preparation of remotely-sensed image data for land use planning

Preliminary processing operations required before computer compatible tapes (CCT) can be utilized routinely to realize the full potential of ERTS imagery for rapid mensuration, quantitative estimation and for detecting and monitoring changes over time are described. Geographic referencing, geometric manipulations, classification, and association overlays (as of political boundaries) are discussed.

Bond, A. D.↗
NASA NTRSDate not supplied

Science Target Prioritization Framework for Remote Sensing

Behind the scenes of a remote sensing mission there are complex decision making and planning operations. Streamlining these operations, with a quantitative scientific value framework, aids efficient and optimized science data collection.While there have been previous efforts to quantify the science value for specific science scenarios, our work aims to develop a general framework which can be applied across different scenarios. We describe a pipeline of processes which combines model forecast and observation data, in computational forms, as dictated by the mission objectives set forth by subject matter experts. The framework is described with use cases involving the monitoring of nitrogen dioxide (NO2) concentrations over the Gulf of Mexico and methane concentrations over interior Alaska.

Remote Sensing↗
NASA NTRS1986

System architecture for the telerobotic work system

The functional and performance requirements and design concepts for a telerobotic work system (TWS) being considered as an adjunct to the Shuttle Remote Manipulator System and other applications are described. The multiple-armed free-flyer would be controlled by an operator in the Orbiter cabin and would perform remote sensing and manipulative tasks that EVA crew otherwise carry out. The TWS could also perform hazardous tasks, construction operations, and be an asset to the Space Station. Alternative system architectures are described, including the man-machine interface, the types of manipulators and their stowage, and the controls and backup systems. Potential terrestrial applications of the TWS concept, augmented with AI capabilities, are discussed.

Jenkins, Lyle M.↗
NASA NTRS2017

New Earth Observation Capabilities For The Commercial Sector

Earth observation data collected from orbital remote sensing systems are becoming increasingly critical to the short- and long-term operations of many commercial industries including agriculture, energy exploration, environmental management, transportation, and urban planning and operations. In this panel, I will present an overview of current and planned NASA remote sensing systems for Earth observation with relevance to commercial and industrial applications. Special emphasis will be given to the International Space Station (ISS) as a platform for both commercial technology demonstration/development and operational data collection through the ISS National Laboratory.

Stefanov, William L.↗
NASA NTRS1995

Airborne Visible/Infrared Imaging Spectrometer (AVIRIS): Sensor Improvements for 1994 and 1995

AVIRIS is a NASA-sponsored Earth-remote-sensing imaging spectrometer designed, built, and operated by JPL. In the time that AVIRIS has been operational since 1989, major improvements have been completed in most of the subsystems of the sensor during the winter maintenance cycles. As a consequence of these efforts, the capabilities of AVIRIS to reliably acquire and deliver consistently high quality, calibrated imaging spectrometer data continue to improve annually, significantly over those in 1989. Improvements to AVIRIS prior to 1994 have been described elsewhere. This paper details recent and planned improvements to AVIRIS in the sensor task.

remote↗
NASA NTRSDate not supplied

Supporting Aquaculture in the Chesapeake Bay Using Artificial Intelligence to Detect Poor Water Quality with Remote Sensing

Reliable information on water quality is not currently available at the space and time scales that are required for aquaculture and other resource management needs. For example, shellfish growing areas maybe impacted by harmful algal blooms or runoff from land that increases turbidity, lowers salinity, or introduces contaminants. Shellfish resource managers in the Chesapeake Bay are especially concerned with sources of bacteria from land such as failing onsite waste systems, failing wastewater infrastructure, and concentrated animal feeding operations. There is an urgent need for remote sensing of water quality indicators beyond chlorophyll-a and suspended sediments to augment field sampling programs. Artificial Intelligence trained with simultaneous in situ and satellite observations is explored in preparation for future hyperspectral satellite missions, which offer potential to detect additional water quality indicators not previously possible. This first step identifies and develops a method to harmonize disparate, unlinked aquatic datasets to derive information about where water quality is likely degraded.

Stephanie Schollaert Uz↗
NASA NTRS1985

Methods of satellite oceanography

The theoretical basis for remote sensing measurements of climate and ocean dynamics is examined. Consideration is given to: the absorption of electromagnetic radiation in the atmosphere; scattering in the atmosphere; and satellite observations using visible light. Consideration is also given to: the theory of radio scatter from the sea; scatter of centimeter waves from the sea; and the theory of operation of synthetic aperture radars. Additional topics include: the coordinate systems of satellite orbits for oceanographic remote sensing applications; the operating features of the major U.S. satellite systems for viewing the ocean; and satellite altimetry.

Stewart, R. H.↗
NASA NTRS1980

A transportable executive system for use with remote sensing applications software

This report describes a transportable software executive system under development. This executive will facilitate user access to data and programs used in remote sensing applications and will provide the necessary system control, bookkeeping, operating system interface, man-machine communications and other services needed to make the computer an effective and economical tool for the remote sensing investigator.

Van Wie, P.↗
NASA NTRS1987

Gaussian maximum likelihood and contextual classification algorithms for multicrop classification

The paper reviews some of the ways in which context has been handled in the remote-sensing literature, and additional possibilities are introduced. The problem of computing exhaustive and normalized class-membership probabilities from the likelihoods provided by the Gaussian maximum likelihood classifier (to be used as initial probability estimates to start relaxation) is discussed. An efficient implementation of probabilistic relaxation is proposed, suiting the needs of actual remote-sensing applications. A modified fuzzy-relaxation algorithm using generalized operations between fuzzy sets is presented. Combined use of the two relaxation algorithms is proposed to exploit context in multispectral classification of remotely sensed data. Results on both one artificially created image and one MSS data set are reported.

Di Zenzo, Silvano↗
NASA NTRS2000

Applications of Fractal Analytical Techniques in the Estimation of Operational Scale

The observational scale and the resolution of remotely sensed imagery are essential considerations in the interpretation process. Many atmospheric, hydrologic, and other natural and human-influenced spatial phenomena are inherently scale dependent and are governed by different physical processes at different spatial domains. This spatial and operational heterogeneity constrains the ability to compare interpretations of phenomena and processes observed in higher spatial resolution imagery to similar interpretations obtained from lower resolution imagery. This is a particularly acute problem, since longterm global change investigations will require high spatial resolution Earth Observing System (EOS), Landsat 7, or commercial satellite data to be combined with lower resolution imagery from older sensors such as Landsat TM and MSS. Fractal analysis is a useful technique for identifying the effects of scale changes on remotely sensed imagery. The fractal dimension of an image is a non-integer value between two and three which indicates the degree of complexity in the texture and shapes depicted in the image. A true fractal surface exhibits self-similarity, a property of curves or surfaces where each part is indistinguishable from the whole, or where the form of the curve or surface is invariant with respect to scale. Theoretically, if the digital numbers of a remotely sensed image resemble an ideal fractal surface, then due to the self-similarity property, the fractal dimension of the image will not vary with scale and resolution, and the slope of the fractal dimension-resolution relationship would be zero. Most geographical phenomena, however, are not self-similar at all scales, but they can be modeled by a stochastic fractal in which the scaling properties of the image exhibit patterns that can be described by statistics such as area-perimeter ratios and autocovariances. Stochastic fractal sets relax the self-similarity assumption and measure many scales and resolutions to represent the varying form of a phenomenon as the pixel size is increased in a convolution process. We have observed that for images of homogeneous land covers, the fractal dimension varies linearly with changes in resolution or pixel size over the range of past, current, and planned space-borne sensors. This relationship differs significantly in images of agricultural, urban, and forest land covers, with urban areas retaining the same level of complexity, forested areas growing smoother, and agricultural areas growing more complex as small pixels are aggregated into larger, mixed pixels. Images of scenes having a mixture of land covers have fractal dimensions that exhibit a non-linear, complex relationship to pixel size. Measuring the fractal dimension of a difference image derived from two images of the same area obtained on different dates showed that the fractal dimension increased steadily, then exhibited a sharp decrease at increasing levels of pixel aggregation. This breakpoint of the fractal dimension/resolution plot is related to the spatial domain or operational scale of the phenomenon exhibiting the predominant visible difference between the two images (in this case, mountain snow cover). The degree to which an image departs from a theoretical ideal fractal surface provides clues as to how much information is altered or lost in the processes of rescaling and rectification. The measured fractal dimension of complex, composite land covers such as urban areas also provides a useful textural index that can assist image classification of complex scenes.

Emerson, Charles W.↗
NASA NTRS1974

The application of remote sensing techniques: Technical and methodological issues

Capabilities and limitations of modern imaging electromagnetic sensor systems are outlined, and the products of such systems are compared with those of the traditional aerial photographic system. Focus is given to the interface between the rapidly developing remote sensing technology and the information needs of operational agencies, and communication gaps are shown to retard early adoption of the technology by these agencies. An assessment is made of the current status of imaging remote sensors and their potential for the future. Public sources of remote sensor data and several cost comparisons are included.

Polcyn, F. C.↗
NASA NTRS2018

Using Remote Sensing Mapping and Growth Response to Environmental Variability to Aide Aquatic Invasive Plant Management

Management of aquatic weeds in complex watersheds and river systems present many challenges to assessment, planning and implementation of management practices for floating and submerged aquatic invasive plants. The Delta Region Areawide Aquatic Weed Project (DRAAWP), a USDA sponsored area-wide project, is working to enhance planning, decision-making and operational efficiency in the California Sacramento-San Joaquin Delta. Satellite and airborne remote sensing are used map (area coverage and biomass density), direct operations, and assess management impacts on plant communities. Archived satellite records enable review of results following previous climate and management events and aide in developing long-term strategies. Examples of remote sensing aiding effectiveness of aquatic weed management will be discussed as well as areas for potential technological improvement. Modeling at local and watershed scales using the SWAT modeling tool provides insight into land-use effects on water quality (described by Zhang in same Symposium). Controlled environment growth studies have been conducted to quantify the growth response of invasive aquatic plants to water quality and other environmental factors. Environmental variability occurs across a range of time scales from long-term climate and seasonal trends to short-term water flow mediated variations. Response time for invasive species response are examined at time scales of weeks, day, and hours using a combination of study duration and growth assessment techniques to assess water quality, temperature (air and water), nitrogen, phosphorus, and light effects. These provide response parameters for plant growth models in response to the variation and interact with management and economic models associated with aquatic weed management. Plant growth models are to be informed by remote sensing and applied spatially across the Delta to balance location and type of aquatic plant, growth response to altered environments and phenology. Initial utilization of remote sensing tools developed for mapping of aquatic invasive plants improved operational efficiency in management practices. These assessment methods provide a comprehensive and quantitative view of aquatic invasive plants communities in the California Delta.

Remote Sensing↗
NASA NTRS2017

The Radio Frequency Environment at 240-270 MHz with Application to Signal-of-Opportunity Remote Sensing

Low frequency observations are desired for soil moisture and biomass remote sensing. Long wavelengths are needed to penetrate vegetation and Earths land surface. In addition to the technical challenges of developing Earth observing spaceflight instruments operating at low frequencies, the radio frequency spectrum allocated to remote sensing is limited. Signal-of-opportunity remote sensing offers the chance to use existing signals exploiting their allocated spectrum to make Earth science measurements. We have made observations of the radio frequency environment around 240-270 MHz and discuss properties of desired and undesired signals.

Soil Moisture↗