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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Remote assessment of ocean color for interpretation of satellite visible imagery: A review

An assessment is presented of the state-of-the-art of remote, (satellite-based) Coastal Zone Color (CZCS) Scanning of color variations in the ocean due to phytoplankton. Attention is given to physical problems associated with ocean color remote sensing, in-water algorithms for the correction of atmospheric effects, constituent retrieval algorithms and application of the algorithms to CZCS imagery. The applicability of CZCS to both near-coast and mid-ocean waters is considered, and it is concluded that while differences between the two environments are complex, universal algorithms can be used for the case of mid-ocean waters, and site-specific algorithms are adequate for CZCS imaging of the near-coast oceanic environment. A short description of CVCS and some sample photographs are provided in an appendix.

Gordon, H. R.↗
NASA NTRS1992

Remote Assessment of Lunar Resource Potential

Assessing the resource potential of the lunar surface requires a well-planned program to determine the chemical and mineralogical composition of the Moon's surface at a range of scales. The exploration program must include remote sensing measurements (from both Earth's surface and lunar orbit), robotic in situ analysis of specific places, and eventually, human field work by trained geologists. Remote sensing data is discussed. Resource assessment requires some idea of what resources will be needed. Studies thus far have concentrated on oxygen and hydrogen production for propellant and life support, He-3 for export as fuel for nuclear fusion reactors, and use of bulk regolith for shielding and construction materials. The measurement requirements for assessing these resources are given and discussed briefly.

Taylor, G. Jeffrey↗
NASA NTRS2000

Advances in Fluorescence Sensing Systems for the Remote Assessment of Nitrogen Supply in Field Corn

The studies described herein were conducted to better define changes in fluorescence properties of leaves from field grown corn (Zea mays L.) as they relate to varying levels of nitrogen (N) fertilization. This research was directed toward: 1) providing a remote non-destructive sensing technique to aid in the determination of optimal rates of N fertilization in corn crops and, 2) defining parameters for further development of fluorescence instrumentation to be operated remotely at field canopy levels. Fluorescence imaging bands centered in the blue (450 nm), green (525 nm), red (680 nm), and far-red (740 nm) and ratios of these bands were compared with the following plant parameters: rates of photosynthesis, N:C ratio, pigment concentrations, and grain yields. Both the fluorescence and physiological measures exhibited similar curvilinear responses to N fertilization level while significant linear correlations were obtained among fluorescence bands and band ratios to certain physiological measures of plant productivity. The red / blue, red / green, far-red / blue, far-red /green fluorescence ratios are well suited for remote observation and provided high correlations to grain yield, LAI, N:C, and chlorophyll contents. The results from this investigation indicate that fluorescence technology could aid in the determination of N fertilization requirements for corn. This discussion will also address design concepts and preliminary field trials of a mobile field-based Laser Induced Fluorescence Imaging System (LIFIS) capable of simultaneously acquiring images of four fluorescence emission bands from areas of plant canopies equaling 1 sq m and greater without interference of ambient solar radiation.

Corp, L. A.↗
NASA NTRS1991

Remote sensing and landslide hazard assessment

Remotely acquired multispectral data are used to improve landslide hazard assessments at all scales of investigation. A vegetation map produced from automated interpretation of TM data is used in a GIS context to explore the effect of vegetation type on debris flow occurrence in preparation for inclusion in debris flow hazard modeling. Spectral vegetation indices map spatial patterns of grass senescence which are found to be correlated with soil thickness variations on hillslopes. Grassland senescence is delayed over deeper, wetter soils that are likely debris flow source areas. Prediction of actual soil depths using vegetation indices may be possible up to some limiting depth greater than the grass rooting zone. On forested earthflows, the slow slide movement disrupts the overhead timber canopy, exposes understory vegetation and soils, and alters site spectral characteristics. Both spectral and textural measures from broad band multispectral data are successful at detecting an earthflow within an undisturbed old-growth forest.

Mckean, J.↗
NASA NTRS1975

Results from the crop identification technology assessment for remote sensing /CITARS/ project

The CITARS (Crop Identification Technology Assessment for Remote Sensing) task design, objectives, and results are reviewed along with relevant conclusions and recommendations. The principal assessment concern crop identification performance for corn and soybeans in six sites in Illinois and Indiana. Use of quantitative measures of classification performance and statistical evaluations of the results have been important parts of the technology assessment. Relation of crop and sensor characteristics is discussed. Factors affecting crop identification performance are identified as crop maturity and site characteristics, type of single-date automatic data processing procedure used for local recognition, nonlocal recognition with and without processing for extension of recognition signatures, and use of multidate or multitemporal data. In particular, the probability of correct classification of field center pixels is not well correlated and thus is not a reliable indicator of proportion estimation performance.

Bizzell, R. M.↗
NASA NTRS1975

Crop identification technology assessment for remote sensing (CITARS). Volume 6: Data processing at the laboratory for applications of remote sensing

The results of classifications and experiments for the crop identification technology assessment for remote sensing are summarized. Using two analysis procedures, 15 data sets were classified. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. Additionally, 20 data sets were classified using training statistics from another segment or date. The classification and proportion estimation results of the local and nonlocal classifications are reported. Data also describe several other experiments to provide additional understanding of the results of the crop identification technology assessment for remote sensing. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, spectral discriminability of corn, soybeans, and other, and analyses of aircraft multispectral data.

Bauer, M. E.↗
NASA NTRS1975

Crop Identification Technology Assessment for Remote Sensing (CITARS)

The results of classifications and experiments performed for the Crop Identification Technology Assessment for Remote Sensing (CITARS) project are summarized. Fifteen data sets were classified using two analysis procedures. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. In addition, 20 data sets were classified using training statistics from another segment or date. The results of both the local and non-local classifications in terms of classification and proportion estimation are presented. Several additional experiments are described which were performed to provide additional understanding of the CITARS results. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, the spectral discriminability of corn, soybeans, and other, and analysis of aircraft multispectral data.

Bauer, M. E.↗
NASA NTRS1993

Extraction of auxiliary data from AVIRIS distribution tape for spectral, radiometric, and geometric quality assessment

Remotely sensed data are affected by system (sensor and platform), and scene related effects. For quantitative investigations the spectral, radiometric characteristics of the system and scene have to be known. The relevant effects and their possible influence on an image have to be specifically determined for every remote sensing system and adequate description parameters need to be updated and reported on a regular basis as they are carried out, e.g., for the AVIRIS system. It is evident that the strength of the influence of similar effects in very dependent on the accessibility of auxiliary information about such sensor systems. Degradation in a spaceborne system can normally be just reported and cannot be corrected. In contrast, an airborne sensor can be evaluated, maintained and improved periodically. Such maintenance efforts are particularly important because airborne systems are exposed to extreme and changing environments. These include tens of takeoffs and landing each year as well as extreme changes in temperature and humidity on the tarmac and in flight. For the AVIRIS system there are environmental stresses such as changes in temperature, air pressure, humidity, vibration of the platform or scene-related reasons like atmospheric conditions, and topography. The information contained in the auxiliary files included with the AVIRIS data can be used to assess these effects and compensate for them. In addition the spectral, radiometer and geometric calibration data contained in the auxiliary file are required for quantitative analysis of the data. The paper describes tools to access the auxiliary information that characterizes the AVIRIS system. These tools allow the examination of parameters that may impact the quality of the measured AVIRIS image. An example of the use of this auxiliary data was carried out with regard to a parametric geocoding approach. Emphasis is placed on the reported auxiliary information that describes the geometric character of the AVIRIS data in 1991. Results are presented using data from the AVIRIS flight #910705, run 6 and 7 of the NASA MAC Europe 1991 campaign in a test site in Central Switzerland.

Meyer, Peter↗
NASA NTRS1975

Crop Identification Technolgy Assessment for Remote Sensing (CITARS). Volume 1: Task design plan

A plan for quantifying the crop identification performances resulting from the remote identification of corn, soybeans, and wheat is described. Steps for the conversion of multispectral data tapes to classification results are specified. The crop identification performances resulting from the use of several basic types of automatic data processing techniques are compared and examined for significant differences. The techniques are evaluated also for changes in geographic location, time of the year, management practices, and other physical factors. The results of the Crop Identification Technology Assessment for Remote Sensing task will be applied extensively in the Large Area Crop Inventory Experiment.

Hall, F. G.↗
NASA NTRS2003

Rapid Assessment of In Situ Wheat Straw Residue Via Remote Sensing Platforms

Crop residues influence near surface soil organic carbon content (SOC), impact our ability to remotely assess soil properties, and play a role in global carbon budgets. Methods that measure crop residues are laborious, and largely inappropriate for regional estimates. The objective of this study was to evaluate remote sensing (RS) data for rapid quantification of residue cover. In March 2000 and April 2001, residue plots (15 m x 15 m) were established in the Coastal Plain and Appalachian Plateau physiographic regions of Alabama. Treatments consisted of five wheat (Triticum aestivum L.) straw cover rates (0, 10, 20, 50, and 80%) replicated 3 times. Soil water content and residue decomposition were monitored. Spectral measurements were acquired via spectroradiometer (350 - 1050 nm), Airborne Terrestrial Applications Sensor (ATLAS) (400 - 12,500 nm), airborne color photography (400 - 600 nm), and IKONOS satellite (450 - 900 nm). Spectroradiometer data were acquired monthly, aircraft images yearly, and satellite per availability. Results showed all platforms successfully estimated residue cover variability using red, near infrared (NIR) and thermal infrared (TIR) regions of the spectrum. Airborne ATLAS imagery was best explaining as much as 98% of the variability in wheat straw cover. Spectroradiometer, color infrared photography, and IKONOS imagery accounted for 84, 56, and 24% of the variability, respectively.

Sullivan, D. G.↗
NASA NTRSDate not supplied

Assessment of Remote Pilot Maneuver Taskload under Multi-Vehicle Control

Multi-vehicle control schemes where a number of remote pilots (m) supervise a number of uncrewed vehicles (m:N) are desired to enable scalability of operations, such as air cargo delivery, in the face of pilot shortage and other constraints. We use queuing models derived from historical track data to assess the increased task load on a remote pilot due to maneuvering demands as the number of supervised vehicles increase. We quantify metrics such as the probability that the inter-maneuver time and inter-communication time.

Multi-Vehicle Control↗
NASA NTRS1971

An assessment of remote sensor imagery in the determination of housing quality data

Selected census tracts in the metropolitan Washington area were examined using varying scales of aerial photography. Observable characteristics of housing and neighborhoods were assessed to determine feasibility of providing data on housing stock and quality and neighborhood condition from the imagery. Small scale imagery is shown to be of relatively marginal value in providing much of the data in the detail required, but can be useful for general survey purposes.

Mallon, H. J.↗
NASA NTRS2016

FluidCam 1 & 2 - UAV-based Fluid Lensing Instruments for High-Resolution 3D Subaqueous Imaging and Automated Remote Biosphere Assessment of Reef Ecosystems

We present NASA ESTO FluidCam 1 & 2, Visible and NIR Fluid-Lensing-enabled imaging payloads for Unmanned Aerial Vehicles (UAVs). Developed as part of a focused 2014 earth science technology grant, FluidCam 1&2 are Fluid-Lensing-based computational optical imagers designed for automated 3D mapping and remote sensing of underwater coastal targets from airborne platforms. Fluid Lensing has been used to map underwater reefs in 3D in American Samoa and Hamelin Pool, Australia from UAV platforms at sub-cm scale, which has proven a valuable tool in modern marine research for marine biosphere assessment and conservation. We share FluidCam 1&2 instrument validation and testing results as well as preliminary processed data from field campaigns. Petabyte-scale aerial survey efforts using Fluid Lensing to image at-risk reefs demonstrate broad applicability to large-scale automated species identification, morphology studies and reef ecosystem characterization for shallow marine environments and terrestrial biospheres, of crucial importance to improving bathymetry data for physical oceanographic models and understanding climate change’s impact on coastal zones, global oxygen production, carbon sequestration.

Chirayath, Ved↗
NASA NTRS1974

First results from the crop identification technology assessment for remote sensing /CITARS/

The CITARS task design, including objectives, analysis methodology and experimental procedures, is described and first results from this effort are presented. The extensive ground truth data set acquired for the CITARS task is described and discussed in some detail. Results of the accuracy tests for the photo interpretative CITARS ground truth are given. Results of the assessment of the ERTS MSS data for cloud cover and electronic quality are presented. Some results of the geometric correction and registration of the time sequential CITARS ERTS data are given. Finally, the field boundary selection problem is addressed and the results of the use of new technology for boundary selection are presented.

Hall, F. G.↗
NASA NTRS2004

Remote Sensing Assessment of Lunar Resources: We Know Where to Go to Find What We Need

The utilization of space resources is necessary to not only foster the growth of human activities in space, but is essential to the President s vision of a "sustained and affordable human and robotic program to explore the solar system and beyond." The distribution of resources will shape planning permanent settlements by affecting decisions about where to locate a settlement. Mapping the location of such resources, however, is not the limiting factor in selecting a site for a lunar base. It is indecision about which resources to use that leaves the location uncertain. A wealth of remotely sensed data exists that can be used to identify targets for future detailed exploration. Thus, the future of space resource utilization pre-dominantly rests upon developing a strategy for resource exploration and efficient methods of extraction.

Gillis, J. J.↗
NASA NTRS1976

Results from the Crop Identification Technology Assessment for Remote Sensing (CITARS) project

The author has identified the following significant results. It was found that several factors had a significant effect on crop identification performance: (1) crop maturity and site characteristics, (2) which of several different single date automatic data processing procedures was used for local recognition, (3) nonlocal recognition, both with and without preprocessing for the extension of recognition signatures, and (4) use of multidate data. It also was found that classification accuracy for field center pixels was not a reliable indicator of proportion estimation performance for whole areas, that bias was present in proportion estimates, and that training data and procedures strongly influenced crop identification performance.

Bauer, M. E.↗