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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
NASA NTRS1974

Remote sensing by ERTS satellite of vegetational resources believed to be under possible threat of environmental stress

The distribution of natural vegetation types on North Merritt Island, Florida, was studied by analysis of ERTS multispectral scanner data on the image-100 computer system. The boundaries of six distinct plant associations were located on photos made on the image analyzer, with an insignificant mean error of -24.38 meters. The six plant associations are described; each had a characteristic spectral signature. The difference in average reflectance grey level between the lowest of the four spectral scanning bands and the highest spectral scanning band for the six vegetation types was determined. The decreasing trend of the differences is strongly negatively correlated with height of land.

Poonai, P.↗
NASA NTRS1975

Remote servicing of free-flying spacecraft

A ground simulation to demonstrate system compatibility by enacting satellite ground installation, orbit delivery, space servicing, and contingency retrieval with the space shuttle orbiter was conducted. The basic configuration approach adopted for the spacecraft groups the standardized subsystems in the aft portion of the spacecraft and leaves the volume forward of the transition structure available for the mission peculiar equipment and sensors. The version used for this simulation is configured as an earth observatory with multispectral mapping and high resolution imaging sensors. All modules including the propulsion package and the sensor modules are replaceable on orbit in the event of depletion, wearout, malfunction, or the availability of an improved version.

Cepollina, F. J.↗
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 NTRS1983

Developments with multispectral thermal-IR and active microwave systems - TIMS, SIR-A, SIR-B, and radarsat

An update of current and future systems for spectral scanning of geological features in the thermal-IR, and active microwave bands is presented. The design characteristics of four individual systems are described, including the NASA Thermal Infrared Multispectral Scanner (TIMS); the Shuttle Imaging Radars A and B (SIRA-A and SIRA-B); and the Canadian RADARSAT satellite for geological mapping and ice monitoring. The applications of spectral data from the Side Looking Airborne Radar (SLAR), the GEMS 100 mapping system, and the Landsat RBV instrument to the mapping of large geological structures are also described.

Harrison, P. G.↗
NASA NTRS1991

Approaches to vegetation mapping and ecophysiological hypothesis testing using combined information from TIMS, AVIRIS, and AIRSAR

The Tropical Rainforest Ecology Experiment (TREE) had two primary objectives: (1) to design a method for mapping vegetation in tropical regions using remote sensing and determine whether the result improves on available vegetation maps; and (2) to test a specific hypothesis on plant/water relations. Both objectives were thought achievable with the combined information from the Thermal Infrared Multispectral Scanner (TIMS), Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and Airborne Synthetic Aperture Radar (AIRSAR). Implicitly, two additional objectives were: (1) to ascertain that the range within each variable potentially measurable with the three instruments is large enough in the site, relative to the sensitivity of the instruments, so that differences between ecological groups may be detectable; and (2) to determine the ability of the three systems to quantify different variables and sensitivities. We found that the ranges in values of foliar nitrogen concentration, water availability, stand structure and species composition, and plant/water relations were large, even within the upland broadleaf vegetation type. The range was larger when other vegetation types were considered. Unfortunately, cloud cover and navigation errors compromised the utility of the TIMS and AVIRIS data. Nevertheless, the AIRSAR data alone appear to have improved on the available vegetation map for the study area. An example from an area converted to a farm is given to demonstrate how the combined information from AIRSAR, TIMS, and AVIRIS can uniquely identify distinct classes of land use. The example alludes to the potential utility of the three instruments for identifying vegetation at an ecological scale finer than vegetation types.

Oren, R.↗
NASA NTRS1992

Sea surface velocities from visible and infrared multispectral atmospheric mapping sensor imagery

High resolution (100 m), sequential Multispectral Atmospheric Mapping Sensor (MAMS) images were used in a study to calculate advective surface velocities using the Maximum Cross Correlation (MCC) technique. Radiance and brightness temperature gradient magnitude images were formed from visible (0.48 microns) and infrared (11.12 microns) image pairs, respectively, of Chandeleur Sound, which is a shallow body of water northeast of the Mississippi delta, at 145546 GMT and 170701 GMT on 30 Mar. 1989. The gradient magnitude images enhanced the surface water feature boundaries, and a lower cutoff on the gradient magnitudes calculated allowed the undesirable sunglare and backscatter gradients in the visible images, and the water vapor absorption gradients in the infrared images, to be reduced in strength. Requiring high (greater than 0.4) maximum cross correlation coefficients and spatial coherence of the vector field aided in the selection of an optimal template size of 10 x 10 pixels (first image) and search limit of 20 pixels (second image) to use in the MCC technique. Use of these optimum input parameters to the MCC algorithm, and high correlation and spatial coherence filtering of the resulting velocity field from the MCC calculation yielded a clustered velocity distribution over the visible and infrared gradient images. The velocity field calculated from the visible gradient image pair agreed well with a subjective analysis of the motion, but the velocity field from the infrared gradient image pair did not. This was attributed to the changing shapes of the gradient features, their nonuniqueness, and large displacements relative to the mean distance between them. These problems implied a lower repeat time for the imagery was needed in order to improve the velocity field derived from gradient imagery. Suggestions are given for optimizing the repeat time of sequential imagery when using the MCC method for motion studies. Applying the MCC method to the infrared brightness temperature imagery yielded a velocity field which did agree with the subjective analysis of the motion and that derived from the visible gradient imagery. Differences between the visible and infrared derived velocities were 14.9 cm/s in speed and 56.7 degrees in direction. Both of these velocity fields also agreed well with the motion expected from considerations of the ocean bottom topography and wind and tidal forcing in the study area during the 2.175 hour time interval.

Pope, P. A.↗
NASA NTRS1993

Science & Technology Laboratory Applications Software (ELAS)

Digital image analysis system processes Landsat multispectral data, aircraft-acquired scanner data, digitized topographical data, and such ancillary data as soil types and rainfall information. Over 230 modules aid user in performing wide range of land cover analyses and manipulations.

Junkin, B. G.↗
NASA NTRS1999

Tropical Cirrus Physical and Radiation Characteristics from the NASA Aircraft Observations in Toga/Coare

During the Toga/Coare experiment in the tropical west Pacific, a cloud radiation experiment involving measurement from the NASA ER-2 high altitude aircraft and DC-8 airborne laboratory was performed. Observations include multispectral visible/IR and microwave radiometric imaging, active lidar profiling and radiation flux measurements. On a number of missions coordinated flights were made for simultaneous cloud observations with the ER-2 remote sensing and DC-8 below cloud and in cloud measurements. Since the field experiment there has been systematic analysis of the observations. Intercomparison of retrieved measurements will be presented. These include the relation between visible bi-directional reflectivity and infrared emissivity of tropical cirrus. Upper troposphere microwave water vapor retrievals and cirrus optical parameters have been correlated. The relation observed short wave flux divergence to retrieved cloud thickness and type has been studied. Calculations and statistics for the short and long wave heating profiles of observed clouds have been derived. A summary of key results will be presented.

Spinhirne, James D.↗
NASA NTRS2004

Communications: Mosquito Habitats, Land Use, and Malaria Risk in Belize from Satellite Imagery

Satellite imagery of northern Belize is used to examine the distribution of land use and breeding habitats of the malaria vector the Anopheles mosquito. A land cover classification based on multispectral SPOT and multitemporal Radarsat images identified eleven land cover classes, including agricultural, forest, and marsh types. Two of the land cover types, Typha domingensis marsh and flooded forest, are Anopheles vestitipennis larval habitats, and one, Eleocharis spp. marsh, is the larval habitat for Anopheles albimanus. Geographic Information Systems (GIS) analyses of land cover demonstrate that the amount of Typha domingensis in a marsh is positively correlated with the amount of agricultural land in the adjacent upland, and negatively correlated with the amount of adjacent forest. This finding is consistent with the hypothesis that nutrient (phosphorus) runoff from agricultural lands is causing an expansion of Typha domingensis in northern Belize. Thus, land use induced expansion of Anopheles vestitipennis larval habitat is potentially increasing malaria risk in Belize, and in other regions where Anopheles vestitipennis is a major malaria vector.

Pope, Kevin↗
NASA NTRS2011

Control Program and Optical Improvements of Fresnel Microspectrometer

A microspectrometer has a circular geometry, and is designed with the Fresnel diffraction equation. This enables a dramatic miniaturization of the optical parts of a spectrometer over 100 times by volume. Therefore, it enables the construction of spectrometer arrays such as 100X100 microspectrometers for tunable multispectral or hyper-spectral imaging. It can be used for a massive, simultaneous spectral scan from multiple optical sources such as 10,000 optical fibers.

Park, Yeonjoon↗
NASA NTRS2019

Empirical Absolute Calibration Model for Multiple Pseudo-Invariant Calibration Sites

This work extends an empirical absolute calibration model initially developed for the Libya 4 Pseudo-Invariant Calibration Site (PICS) to five additional Saharan Desert PICS (Egypt 1, Libya 1, Niger 1, Niger 2, and Sudan 1), and demonstrates the efficacy of the resulting models at predicting sensor top-of-atmosphere (TOA) reflectance. It attempts to generate absolute calibration models for these PICS that have an accuracy and precision comparable to or better than the current Libya 4 model, with the intent of providing additional opportunities for sensor calibration. In addition, this work attempts to validate the general applicability of the model to other sites. The method uses Terra Moderate Resolution Imaging Spectroradiometer (MODIS) as the reference radiometer and Earth Observing-1 (EO-1) Hyperion image data to provide a representative hyperspectral reflectance profile of the PICS. Data from a region of interest (ROI) in an “optimal region” of 3% temporal, spatial, and spectral stability within the PICS are used for developing the model. The developed models were used to simulate observations of the Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), Landsat 8 (L8) Operational Land Imager (OLI), Sentinel 2A (S2A) MultiSpectral Instrument (MSI) and Sentinel 2B (S2B) MultiSpectral Instrument (MSI) from their respective launch date through 2018. The models developed for the Egypt 1, Libya 1 and Sudan 1 PICS have an estimated accuracy of approximately 3% and precision of approximately 2% for the sensors used in the study, comparable to the current Libya 4 model. The models developed for the Niger 1 and Niger 2 sites are significantly less accurate with similar precision.

Raut, Bipin↗
NASA NTRS2015

Reductions in Productivity Due to Land Degradation in the Drylands of the Southwestern United States

Dryland degradation has long been recognized at regional, national, and global scales, yet there are no objective assessments of its location and severity. An assessment of reductions in net primary production (NPP) due to dryland degradation in the southwestern United States is reported. The local NPP scaling (LNS) approach was applied to map the extent and magnitude of degradation. LNS seeks to identify reference sites in which there is no degradation that can be used as a standard for comparison with other sites that share the same environment, except for degradation. Twelve years were analyzed (2000–2011), using Normalized Difference Vegetation Index (NDVI) data (250 m) from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite‐borne multispectral sensor. The results indicated that the total NPP reductions in the study area were about 35.9 ± 4.7 Tg C/yr, which equates to 0.31 ± 0.04 Mg C/ha·y. The NPP reductions in grassland‐savanna and livestock grazing areas were large and mostly consistent between years in spite of large variations in overall NPP caused by differences in land‐use, interannual variations in rainfall, and other aspects of weather. In comparison with other cover types, forested land generally had higher NPP reduction per unit area. The maps also enable attribution of degradation from the finest management units to entire agencies, such as the Bureau of Land Management, which had 50% less production per unit area than the U.S. Forest Service. The degradation within Native American land was low with total NPP reduction of about 2.41 ± 0.24 Tg C/yr and unit area reduction of productivity of just 0.21 ± 0.02 Mg C/ha·yr, yet the percent reduction from potential was in equivalence with other land management agencies.

Noojipady, Praveen↗
NASA NTRS2020

DEVELOP Poster: Medicine Bow Disasters

The Medicine Bow National Forest (MBNF) consists of approximately 1,383,790 acres of forested land, grassland, and sagebrush steppe in southeastern Wyoming. Cheatgrass (Bromus tectorum), an invasive plant species in the Western US, occurs in the grasslands throughout MBNF. Cheatgrass is known to rapidly colonize disturbed sites and dramatically alter historic fire regimes and nutrient/water dynamics as well as outcompete native plant species that are important forage for mule deer (Odocoileus hemionus) and elk (Cervus canadensis). In 2012, the Squirrel Creek Fire burned approximately 10,587 acres of land within MBNF, exacerbating the spread of cheatgrass. In 2015, the Wyoming Ecological Forecasting DEVELOP team identified areas of high cheatgrass abundance within the fire boundary in order to guide US Forest Service (USFS) herbicide spraying efforts to reduce cheatgrass in 2016. This research used Landsat 8 Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data to create a 2019 probabilistic cheatgrass occurrence map. This map allowed an analysis of the effectiveness of aerial spraying to inform future land management techniques for the USFS. Based on the results of the Generalized Linear Model, we found that treated areas decreased in cheatgrass cover by 36% while untreated areas increased in cheatgrass cover by 6%, suggesting that herbicide treatment has been effective.

Disasters↗
NASA NTRS2021

Highland Lakes Water Resources: Using NASA Earth Observations to Improve Detection Systems for Harmful Algal Events in the Highland Lakes in Central Texas

Beginning in 2019, harmful algal events in Austin, Texas, caused canine deaths in the Lady Bird Lake and Lake Travis reservoirs. These reservoirs are part of the larger Highland Lakes chain, managed by the Lower Colorado River Authority (LCRA) and the City of Austin Department of Watershed Protection (CoA DWP), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable algal event conditions in central Texas, the LCRA and CoA DWP partnered with NASA DEVELOP to improve algal event early-warning systems through the application of remote sensing and machine learning. An Earth observation-based algal monitoring system will assist the responsible agencies in predicting algal conditions and communicating hazards to the public. The NASA DEVELOP team utilized Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data to produce products including chlorophyll-a concentrations, cyanobacteria detections, turbidity, and water surface temperature. Chlorophyll-a concentrations were retrieved with a pre-trained machine learning model (mixture density network) and spectral indices, while the other products were derived from spectral indices. In situ field data were used to validate and quantify uncertainties for each product. The validations show strong correlations for chlorophyll-a and water surface temperature. Time series analyses of chlorophyll-a concentrations show peaks in the severe drought years (2015 and 2016). This project's resulting products enable monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain and will ultimately support water management, decision making, and risk communication.

Kaitlynn Hietpas↗
NASA NTRS2021

Assateague Island National Seashore Ecological Forecasting: Characterizing Nearshore Suspended Sediments and Landcover Change Relative to Sediment Bypassing and Catastrophic Events

Assateague Island is located off the coast of Maryland and Virginia and serves as a home to sensitive species and habitats. However, infrastructure development disrupted the natural sediment transport processes of the barrier island, which accelerated erosion of the island’s shoreline. To counteract this, the United States Army Corps of Engineers (USACE) initiated semiannual sediment bypassing operations in 2004. Over time, financial constraints limited the amount of sediment deposition possible, leading to concerns over navigational issues in nearby channels and the possibility of the operations not providing their intended benefits. To address these issues, NASA DEVELOP partnered with the National Park Service NPS and USACE. The team performed time series analyses of nearshore suspended sediment from 2004-2020 and landcover change from 2006-2018 with satellite imagery from Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), and Sentinel-2 MultiSpectral Instrument (MSI). The sediment transport analyses showed that suspended sediment levels are seasonally dependent. Meanwhile, historical land cover trends included a net increase in unconsolidated shore and a net decrease in open water. Land cover change was then forecast to 2021, 2031, and 2046 using the IDRISI TerrSet Land Change Modeler. The model predicted the most drastic land cover changes in the southern portion and the least on the eastern foreshore of the island. As a result, this project allows our partners to understand the impact of sediment bypassing operations more fully and make better-informed decisions regarding the island’s management.

M Colin Marvin↗
NASA NTRS2022

Fire Island Water Resources: Assessing Sediment Dynamics and Turbidity Changes Along Fire Island National Seashore Using Satellite Data

Fire Island National Seashore has experienced damaging effects as a result of coastal erosion. Erosion has become an increasingly damaging problem that has led to the destruction of park and community infrastructure, contributed to rising groundwater tables, and posed a serious threat to a globally rare holly maritime forest. Beach nourishment project efforts have been made to mitigate damage, but dredging is expensive and can be thwarted by high rates of erosion. The NASA DEVELOP team partnered with the National Park Service and Fire Island National Seashore and used imagery from Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), and Sentinel-2 MultiSpectral Instrument (MSI) to analyze turbidity and sediment dynamics through surface reflectance data from 2000-2021. Imagery was atmospherically corrected using Atmospheric Correction for OLI Lite (ACOLITE) and visualized in SeaWiFS Data Analysis Systems (SeaDAS). Additionally, shoreline change was analyzed using high-resolution imagery from WorldView-2 acquired from Maxar for pre-Hurricane Sandy on July 25th, 2010 and post-Hurricane Sandy on December 18th, 2019. The results of this analysis showed that turbidity is highest in the winter seasons. The shoreline analysis estimated total shoreline loss of about 62 acres on the ocean side, and 11 acres on the bay side of the island. These results will be used to better inform future partner-designed shoreline management projects in the face of further erosion and sea level rise.

Kelly Young↗
NASA NTRSDate not supplied

Black Hills Wildfires: Mapping Post-fire Conifer Regeneration using Snow-on Imagery

The 2000 Jasper Fire in the Black Hills of South Dakota was the largest wildfire to date in the region, burning over 83,000 acres of ponderosa pine forest. In collaboration with partners from the United States Forest Service (USFS) Black Hills Experimental Forest, USFS Rocky Mountain Research Station, and United States Geological Survey Geosciences and Environmental Change Science Center, we characterized post-fire forest regeneration within high-severity burn patches. We accomplished this by implementing novel conifer detection techniques using a snow index mask to create a winter, snow-on image composite from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data. We utilized 2015 USFS stem maps of field-observed regeneration plots and ocularly sampled additional reforestation sites planted in 2001–2013. In Google Earth Engine (GEE), the field data and imagery were used to train a Random Forest (RF) model. The RF model classified 2021 conifer regeneration density as low, medium, or high across the high-severity burn area with an overall accuracy of 81.3%. Approximately 45.9% of the high-severity burn had low or no regeneration (0-40 trees per acre) 20 years post-fire. Given our partners' desire to find easily accessible low conifer regeneration zones, we identified 4,079 acres of priority planting sites that were within 1,500 feet of roads, had not been planted previously, and were larger than 50 acres. This method supports the use of snow-on imagery as a successful technique to identify conifer regeneration.

Casey Menick​↗
NASA NTRS2022

Black Hills Wildfires: Mapping Post-Fire Conifer Regeneration using Snow-On Imagery

The 2000 Jasper Fire in the Black Hills of South Dakota was the largest wildfire to date in the region, burning over 83,000 acres of ponderosa pine forest. In collaboration with partners from the United States Forest Service (USFS) Black Hills Experimental Forest, USFS Rocky Mountain Research Station, and United States Geological Survey Geosciences and Environmental Change Science Center, we characterized post-fire forest regeneration within high-severity burn patches. We accomplished this by implementing novel conifer detection techniques using a snow index mask to create a winter, snow-on image composite from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data. We utilized 2015 USFS stem maps of field-observed regeneration plots and ocularly sampled additional reforestation sites planted in 2001–2013. In Google Earth Engine (GEE), the field data and imagery were used to train a Random Forest (RF) model. The RF model classified 2021 conifer regeneration density as low, medium, or high across the high-severity burn area with an overall accuracy of 81.3%. Approximately 45.9% of the high-severity burn had low or no regeneration (0-40 trees per acre) 20 years post-fire. Given our partners' desire to find easily accessible low conifer regeneration zones, we identified 4,079 acres of priority planting sites that were within 1,500 feet of roads, had not been planted previously, and were larger than 50 acres. This method supports the use of snow-on imagery as a successful technique to identify conifer regeneration.

Casey Menick↗