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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Application of LANDSAT satellite imagery for iron ore prospecting in the Western Desert of Egypt

Prospecting for iron ore occurrences was conducted by the Remote Sensing Center in Bahariya Oasis-El Faiyum area covering some 100,000 km squared in the Western Desert of Egypt. LANDSAT-1 satellite images were utilized as the main tool in the regional prospecting of the iron ores. The delineation of the geological units and geological structure through the interpretation of the images corroborated by field observations and structural analysis led to the discovery of new iron ore occurrences in the area of investigation.

Elshazly, E. M.↗
NASA NTRS1981

Spatial evolution of ocean wave spectra

The spatially evolving deep water synthetic aperture radar (SAR) directional spectra of a mixed ocean wave system are compared with a comprehensive set of surface and aircraft measurements. The evolution of the SAR spectra, at least for ocean wavelengths greater than 80 m, is seen as generally consistent with the auxiliary data set in both time and space. From the spatial evolution of the angular component of the spectra, it is possible to project back to an apparent remote storm source that is also consistent with the storm location via GOES satellite imagery. The data provide compelling evidence that the spatial evolution of SAR ocean wave spectra can be a useful tool in global ocean wave monitoring and forecasting.

Beal, R. C.↗
NASA NTRS1980

Land use in the Paraiba Valley through remotely sensed data

A methodology for land use survey was developed and land use modification rates were determined using LANDSAT imagery of the Paraiba Valley (state of Sao Paulo). Both visual and automatic interpretation methods were employed to analyze seven land use classes: urban area, industrial area, bare soil, cultivated area, pastureland, reforestation and natural vegetation. By means of visual interpretation, little spectral differences are observed among those classes. The automatic classification of LANDSAT MSS data using maximum likelihood algorithm shows a 39% average error of omission and a 3.4% error of inclusion for the seven classes. The complexity of land uses in the study area, the large spectral variations of analyzed classes, and the low resolution of LANDSAT data influenced the classification results.

Dejesusparada, N.↗
NASA NTRS1982

Use of remote sensing for land use policy formulation

Research projects described include: (1) identifying coniferous forest types in Michigan using LANDSAT imagery; (2) investigating synoptic temperature patterns in Michigan as determined via GOES and HCMM thermal imagery; (3) land surface change detection using satellite data and a geographic data base; (4) determining soil map unit composition by electronic scanning densitometry; and (5) delimiting areas of virus infection in vineyards and blueberry fields in southwestern and western Michigan. Contractual activities involve important farmlands inventory, changes in aquatic vegetation in Saginaw Bay, digitized soil association map of Michigan, and aerial photography for hybrid-poplar research. On-going projects are also being conducted in Jamaica, Honduras, the Dominican Republic and Kenya.

Source record↗
NASA NTRS1988

Selecting the spatial resolution of satellite sensors required for global monitoring of land transformations

The paper provides preliminary evidence for the spatial resolutions required to monitor land transformations at broad scales. This is obtained from simulations of imagery at various spatial resolutions between 125 and 4000 m derived from Landsat MSS imagery. Consideration is given to the various types of spatial images detectable by remotely-sensed systems, as well as to the difficulties associated in disentangling permanent land transformations from shorter term changes such as phenological and interannual changes.

Townshend, J. R. G.↗
NASA NTRS1988

Satellite remote sensing and cloud modeling of St. Anthony, Minnesota storm clouds and dew point depression

Rawinsonde data and geosynchronous satellite imagery were used to investigate the life cycles of St. Anthony, Minnesota's severe convective storms. It is found that the fully developed storm clouds, with overshooting cloud tops penetrating above the tropopause, collapsed about three minutes before the touchdown of the tornadoes. Results indicate that the probability of producing an outbreak of tornadoes causing greater damage increases when there are higher values of potential energy storage per unit area for overshooting cloud tops penetrating the tropopause. It is also found that there is less chance for clouds with a lower moisture content to be outgrown as a storm cloud than clouds with a higher moisture content.

Hung, R. J.↗
NASA NTRS1994

Remote sensing for hurricane Andrew impact assessment

Stennis Space Center personnel flew a Learjet equipped with instrumentation designed to acquire imagery in many spectral bands into areas most damaged by Hurricane Andrew. The calibrated airborne multispectral scanner (CAMS), a NASA-developed sensor, and a Zeiss camera acquired images of these areas. The information derived from the imagery was used to assist Florida officials in assessing the devastation caused by the hurricane. The imagery provided the relief teams with an assessment of the debris covering roads and highways so cleanup plans could be prioritized. The imagery also mapped the level of damage in residential and commercial areas of southern Florida and provided maps of beaches and land cover for determination of beach loss and vegetation damage, particularly the mangrove population. Stennis Space Center personnel demonstrated the ability to respond quickly and the value of such response in an emergency situation. The digital imagery from the CAMS can be processed, analyzed, and developed into products for field crews faster than conventional photography. The resulting information is versatile and allows for rapid updating and editing. Stennis Space Center and state officials worked diligently to compile information to complete analyses of the hurricane's impact.

Davis, Bruce A.↗
NASA NTRS2018

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

satellite data↗
NASA NTRS2018

NASA NeMO-Net

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low- resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

NASA↗
NASA NTRS2004

Anthropogenic Land-use Change and the Dynamics of Amazon Forest Biomass

This project was focused on assessing the effects of prevailing land uses, such as habitat fragmentation, selective logging, and fire, on biomass and carbon storage in Amazonian forests, and on the dynamics of carbon sequestration in regenerating forests. Ancillary goals included developing GIs models to help predict the future condition of Amazonian forests, and assessing the effects of anthropogenic climate change and ENS0 droughts on intact and fragmented forests. Ground-based studies using networks of permanent plots were linked with remote-sensing data (including Landsat TM and AVHRR) at regional scales, and higher-resolution techniques (IKONOS imagery, videography, LIDAR, aerial photographs) at landscape and local scales. The project s specific goals were quite eclectic and included: Determining the effects of habitat fragmentation on forest dynamics, floristic composition, and the various components of above- and below-ground biomass. Assessing historical and physical factors that affect trajectories of forest regeneration and carbon sequestration on abandoned lands. Extrapolating results from local studies of biomass dynamics in fragmented and regenerating forests to landscape and regional scales in Amazonia, using remote sensing and GIS. Testing the hypothesis that intact Amazonian forests are functioning as a significant carbon sink. Examining destructive synergisms between forest fragmentation and fire. Assessing the short-term impacts of selective logging on aboveground biomass. Developing GIS models that integrate current spatial data on forest cover, deforestation, logging, mining, highway and roads, navigable rivers, vulnerability to wild fires, protected areas, and existing and planned infrastructure projects, in an effort to predict the future condition of Brazilian Amazonian forests over the next 20-25 years. Devising predictive spatial models to assess the influence of varied biophysical and anthropogenic predictors on Amazonian deforestation.

Laurance, William F.↗
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 NTRS1998

A New Image Processing and GIS Package

The image processing and GIS package ELAS was developed during the 1980's by NASA. It proved to be a popular, influential and powerful in the manipulation of digital imagery. Before the advent of PC's it was used by hundreds of institutions, mostly schools. It is the unquestioned, direct progenitor or two commercial GIS remote sensing packages, ERDAS and MapX and influenced others, such as PCI. Its power was demonstrated by its use for work far beyond its original purpose, having worked several different types of medical imagery, photomicrographs of rock, images of turtle flippers and numerous other esoteric imagery. Although development largely stopped in the early 1990's the package still offers as much or more power and flexibility than any other roughly comparable package, public or commercial. It is a huge body or code, representing more than a decade of work by full time, professional programmers. The current versions all have several deficiencies compared to current software standards and usage, notably its strictly command line interface. In order to support their research needs the authors are in the process of fundamentally changing ELAS, and in the process greatly increasing its power, utility, and ease of use. The new software is called ELAS II. This paper discusses the design of ELAS II.

Rickman, D.↗
NASA NTRS2007

Proceedings of the 2006 Civil Commercial Imagery Evaluation Workshop

The Joint Agency Commercial Imagery Evaluation (JACIE) team is a collaborative interagency working group formed to leverage different government agencies' capabilities for the characterization of commercial remote sensing products. The team is composed of staff from the National Aeronautics and Space Administration (NASA), the National Geospatial-Intelligence Agency (NGA), and the U.S. Geological Survey (USGS). Each JACIE agency has a vested interest in the purchase and use of commercial imagery to support government research and operational applications. The intent of the 2006 workshop is to exchange information regarding the characterization and application of commercial imagery used by the government. The main focus of previous workshops has been on high-resolution satellite imagery from systems; such as, IKONOS (Space Imaging, Inc.), QuickBird (DigitalGlobe, Inc.), and OrbView-3 (ORBIMAGE). This workshop is being expanded to cover all civil medium- and high-resolution commercial imagery used by the government.

Stanley, Thomas↗
NASA NTRS2000

BOREAS Level-3b Landsat TM Imagery: At-sensor Radiances in BSQ Format

For BOREAS, the level-3b Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI. Although very similar in content to the level-3a Landsat TM products, the level-3b images were created to provide users with a directly usable at-sensor radiance image. Geographically, the level-3b images cover the BOREAS NSA and SSA. Temporally, the images cover the period of 22-Jun-1984 to 09-Jul-1996. The images are available in binary, image format files.

Hall, Forrest G.↗
NASA NTRS1995

Accelerating Commercial Remote Sensing

Through the Visiting Investigator Program (VIP) at Stennis Space Center, Community Coffee was able to use satellites to forecast coffee crops in Guatemala. Using satellite imagery, the company can produce detailed maps that separate coffee cropland from wild vegetation and show information on the health of specific crops. The data can control coffee prices and eventually may be used to optimize application of fertilizers, pesticides and irrigation. This would result in maximal crop yields, minimal pollution and lower production costs. VIP is a mechanism involving NASA funding designed to accelerate the growth of commercial remote sensing by promoting general awareness and basic training in the technology.

Source record↗
NASA NTRS1981

Ten-Ecosystem Study

Remote sensing methodology developed for the Nationwide Forestry Applications Program utilize computer data processing procedures for performing inventories from satellite imagery. The Ten-Ecosystem Study (TES) was developed to test the processing procedures in an intermediate-sized application study. The results of TES indicate that LANDSAT multispectral imagery and associated automatic data processing techniques can be used to distinguish softwood, hardwood, grassland, and water and make inventory of these classes with an accuracy of 70 percent or better. The technical problems encountered during the TES and the solutions and insights to these problems are discussed. The TES experience is useful in planning subsequent inventories utilizing remote sensing technology.

Mazade, A. V.↗
NASA NTRS1979

Overview of the NASA tropospheric environmental quality remote sensing program

This paper will summarize the current NASA Tropospheric Environmental Quality Remote Sensing Program for studying the global and regional troposphere from space, airborne and ground-based platforms. As part of the program to develop remote sensors for utilization from space, NASA has developed a series of passive and active remote sensors which have undergone field test measurements from airborne and ground platforms. Recent measurements with active lidar and passive gas filter correlation and infrared heterodyne techniques will be summarized for measurements of atmospheric aerosols, CO, SO2, O3, and NH3. These measurements provide the data base required to assess the sensitivity of remote sensors for applications to urban and regional field measurement programs. Studies of Earth Observation Satellite Systems are currently being performed by the scientific community to assess the capability of satellite imagery to detect regions of elevated pollution in the troposphere. The status of NASA sponsored research efforts in interpreting satellite imagery for determining aerosol loadings over land and inland bodies of water will be presented, and comments on the potential of these measurements to supplement in situ and airborne remote sensors in detecting regional haze will be made.

Allario, F.↗