Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “remote imagery”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

The absolute radiometric calibration of the advanced very high resolution radiometer

An increasing number of remote sensing investigations require radiometrically calibrated imagery from NOAA Advanced Very High Resolution Radiation (AVHRR) sensors. Although a prelaunch calibration is done for these sensors, there is no capability for monitoring any changes in the in-flight absolute calibration for the visible and near infrared spectral channels. Hence, the possibility of using the reflectance-based method developed at White Sands for in-orbit calibration of LANDSAT Thematic Mapper (TM) and SPOT Haute Resolution Visible (HVR) data to calibrate the AVHRR sensor was investigated. Three diffrent approaches were considered: Method 1 - ground and atmospheric measurements and reference to another calibrated satellite sensor; Method 2 - ground and atmospheric measurements with no reference to another sensor; and Method 3 - no ground and atmospheric measurements but reference to another satellite sensor. The purpose is to describe an investigation on the use of Method 2 to calibrate NOAA-9 AVHRR channels 1 and 2 with the help of ground and atmospheric measurements at Rogers (dry) Lake, Edwards Air Force Base (EAFB) in the Mojave desert of California.

Slater, P. N.↗
NASA NTRS2009

A Landsat-Based Assessment of Mobile Bay Land Use and Land Cover Change from 1974 to 2008

The Mobile Bay region has experienced noteworthy land use and land cover (LULC) change in the latter half of the 20th century. Accompanying this change has been urban expansion and a reduction of rural land uses. Much of this LULC change has reportedly occurred since the landfall of Hurricane Frederic in 1979. The Mobile Bay region provides great economic and ecologic benefits to the Nation, including important coastal habitat for a broad diversity of fisheries and wildlife. Regional urbanization threatens the estuary s water quality and aquatic-habitat dependent biota, including commercial fisheries and avian wildlife. Coastal conservation and urban land use planners require additional information on historical LULC change to support coastal habitat restoration and resiliency management efforts. This presentation discusses results of a Gulf of Mexico Application Pilot project that was conducted in 2008 to quantify and assess LULC change from 1974 to 2008. This project was led by NASA Stennis Space Center and involved multiple Gulf of Mexico Alliance (GOMA) partners, including the Mobile Bay National Estuary Program (NEP), the U.S. Army Corps of Engineers, the National Oceanic and Atmospheric Administration s (NOAA s) National Coastal Data Development Center (NCDDC), and the NOAA Coastal Services Center. Nine Landsat images were employed to compute LULC products because of their availability and suitability for the application. The project also used Landsat-based national LULC products, including coastal LULC products from NOAA s Coastal Change & Analysis Program (C-CAP), available at 5-year intervals since 1995. Our study was initiated in part because C-CAP LULC products were not available to assess the region s urbanization prior to 1995 and subsequent to post Hurricane Katrina in 2006. This project assessed LULC change across the 34-year time frame and at decadal and middecadal scales. The study area included the majority of Mobile and Baldwin counties that encompass Mobile Bay. In doing so, each date of Landsat data was classified using an end-user defined modified Anderson level 1 classification scheme. LULC classifications were refined using a decision rule approach in conjunction with available C-CAP products. Individual dates of LULC classifications were validated by image interpretation of stratified random locations on raw Landsat color composite imagery in combination with higher resolution remote sensing and in-situ reference data. The results indicate that during the 34-year study period, urban areas increased from 96,688 to 150,227 acres, representing a 55.37% increase, or 1.63% per annum. Most of the identified urban expansion results from conversion of rural forest and agriculture to urban cover types. Final LULC mapping and metadata products were produced for the entire study area as well as watersheds of concern within the study area. Final project products, including LULC trend information, were incorporated into the Mobile Bay NEP State of the Bay report. Products and metadata were transferred to NOAA NCDDC to allow free online accessibility and use by GOMA partners and by the public.

Spruce, Joseph↗
NASA NTRS2010

Reducing Tick-Borne Disease in Alabama: Linking Health Risk Perception with Spatial Analysis Using the NASA Earth Observing System

Lyme disease (LD) accounts for most vector-borne disease reports in the U.S., and although its existence in Alabama remains controversial, other tick-borne illnesses (TBI) such as Southern Tick-Associated Rash Illness (STARI) pose a health concern in the state. Phase One of the Marshall Space Flight Center-UAB DEVELOP study of TBI identified the presence of the chain of infection for LD (Ixodes scapularis ticks carrying Borrelia burgdorferi bacteria) and STARI (Amblyomma americanum ticks and an as-yet-unconfirmed agent) in Alabama. Both LD and STARI are associated with the development of erythema migrans rashes around an infected tick bite, and while treatable with oral antibiotics, a review of educational resources available to state residents revealed low levels of prevention information. To improve prevention, recognition, and treatment of TBI in Alabama, Phase Two builds a health communication campaign based on vector habitat mapping and risk perception assessment. NASA Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery identified likely tick habitats using remotely sensed measurements of vegetation vigor (Normalized Difference Vegetation Index) and soil moisture. Likely tick habitats, identified as those containing both high vegetation density and soil moisture, included Oak Mountain State Park, Bankhead National Forest, and Talladega National Forest. To target a high-risk group -- outdoor recreation program participants at Alabama universities -- the study developed a behavior survey instrument based on existing studies of LD risk factors and theoretical constructs from the Social Ecological Model and Health Belief Model. The survey instrument was amended to include geographic variables in the assessment of TBI knowledge, attitudes, and prevention behaviors, and the vector habitat model will be expanded to incorporate additional environmental variables and in situ data. Remotely sensed environmental data combined with risk perception assessments inform an ongoing outreach campaign consisting of stakeholder meetings and educational seminars.

Hemmings, S.↗
NASA NTRSDate not supplied

Modeling and Simulation for DAVINCI Entry, Descent, and Landing

The Deep Atmosphere Venus Investigation of Noble gases, Chemistry, and Imaging (DAVINCI) mission will launch in June 2029 and explore Venus via two flybys and a probe landing currently scheduled for June 2031. DAVINCI is the 16th selection of the NASA Discovery Class program and its objectives include quantifying the chemical composition of the Venusian atmosphere, taking infrared descent imagery of the surface, and conducting remote observations of the dynamic atmosphere and cloud-deck. The goals are to study the origin, evolution, and current state of Venus, to understand if it was habitable at a point in the past, and to create an analog to hot terrestrial exo-planets similar to Venus.

Soumyo Dutta↗
NASA NTRSDate not supplied

Using AI/ML to Address Satellite Cloud Remote Sensing Challenges

Various AI/ML tools, employed within the Clouds and the Earth's Radiant Energy System (CERES) Satellite Cloud and Radiation Property retrieval System (SatCORPS) project, are being used to mitigate satellite radiance artifacts and thereby yield more accurate cloud and radiation data products. Neural network and K-nearest neighbor approaches have been developed that enable us to better address common passive satellite remote sensing challenges, such as corrupted imagery, day/night cloud property discontinuities, solar terminator artifacts, inadequate knowledge of the land surface emission temperature (i.e., skin temperature), and poor assumptions about vertical cloud structure, that have otherwise proven difficult to solve using more conventional methods. Fixing these problems promotes a more consistent Earth radiation budget record. These efforts demonstrate effective use of AI/ML architecture to exploit complex, multivariate predictor relationships and produce usable output at satellite spatial and temporal resolutions that would otherwise be ignored or have large biases.

Benjamin Scarino↗
NASA NTRS2023

WET Water Resources: A Google Earth Engine Python API Tool to Automate Wetland Extent Mapping Using Radar Satellite Sensors for Wetland Management and Monitoring

Wetland ecosystems are annually or seasonally wet transition zones between land and water. They provide a range of ecosystem services such as water filtration, flood mitigation, and carbon sequestration, as well as hosting biodiversity hotspots. Although they fulfill fundamental physical and natural processes, wetland extent and health are threatened by anthropogenic influences related to urbanization, population increase, pollution, and climate change. Recognizing the need to quantitatively monitor changes in these recently threatened ecosystems in a timely and cost-effective way, we developed a Google Earth Engine (GEE) Python API tool for automated wetland extent mapping using optical and radar satellite sensors that can be applied globally. The tool will significantly improve wetland change analysis and monitoring as SAR data provides high resolution (5-10 m) imagery, unaffected by cloud cover and light availability (day vs. night), common limitations for other remotely sensed sensors. The tool utilizes Copernicus Sentinel-1 C-band and NISAR L-band (once operational and available on the GEE repository) synthetic aperture radar (SAR) imagery. During image preprocessing, we applied a Terra Moderate Resolution Imaging Spectroradiometer (MODIS) snow product to determine regional snow coverage, which affects land classification sensitivity. Calibration and validation were conducted through a historical change and sensitivity analysis of the Sudd wetland located in central Sudan. The tool was the first of its kind, as it enables NISAR data processing through an open-source GEE repository, further expanding and improving the utility of NASA Earth observations and contributing to NASA Open Science initiatives. We anticipate the tool will be used by researchers and practitioners interested in wetland monitoring and management.

Inundation↗
NASA NTRS2018

Landslide Mapping Along the Karnali Highway, Nepal using High-Resolution Imagery

The Karnali highway (Figure 1) is the only major transportation link that connects the remote Karnali region to the provincial capital in Province 6 of Nepal. This area becomes inaccessible by roads during every rainy season due to landslides. Despite the known landslide frequency, there have been no systematic landslide inventories conducted along this highway to date. Recent advancements in remote-sensing technologies have significantly increased our ability to map landslides of various sizes rapidly with less in situ surveys or human interaction. Landslide susceptibility, hazard and risk studies require a complete landslide inventory, which might only be possible from very high-resolution (VHR) and high-resolution (HR) imagery. Recent launch of Sentinel-2 in 2015 has provided free access to HR imagery enabling landslide detection at finer scales then what was possible with previous open source satellite imagery obtained from Landsat and ASTER. Satellites providing VHR imagery are commercially owned, expensive and not freely available expect for when disasters charter is activated. NextView licensing agreement, a partnership between the US government and US commercial vendors provides access to VHR imagery to federal agencies in support of scientific research [1]. This partnership provides access to VHR imagery obtained from the DigitalGlobe (DG) constellation which enables mapping of small landslides (< 100 m2). In this study, VHR imagery from DG and HR imagery from Sentinel-2 will be used to map landslides along the Karnali highway using a semi automatic method based on object-oriented analysis (OOA) to create most recent and up-to-date landslide inventory. The effectiveness of this remote sensing based landslide inventory to produce a susceptibility map and its predictive capacity will be tested.

landslide↗
NASA NTRS2012

Current Usage and Future Prospects of Multispectral (RGB) Satellite Imagery in Support of NWS Forecast Offices and National Centers

What is an RGB Composite Image? (1) Current and future satellite instruments provide remote sensing at a variety of wavelengths. (2) RGB composite imagery assign individual wavelengths or channel differences to the intensities of the red, green, and blue components of a pixel color. (3) Each red, green, and blue color intensity is related to physical properties within the final composite image. (4) Final color assignments are therefore related to the characteristics of image pixels. (5) Products may simplify the interpretation of data from multiple bands by displaying information in a single image. Current Products and Usage: Collaborations between SPoRT, CIRA, and NRL have facilitated the use and evaluation of RGB products at a variety of NWS forecast offices and National Centers. These products are listed in table.

Molthan, Andrew↗
NASA NTRS1995

Looking at Earth from Space: Teacher's Guide with Activities for Earth and Space Science

The Maryland Pilot Earth Science and Technology Education Network (MAPS-NET) project was sponsored by the National Aeronautics and Space Administration (NASA) to enrich teacher preparation and classroom learning in the area of Earth system science. This publication includes a teacher's guide that replicates material taught during a graduate-level course of the project and activities developed by the teachers. The publication was developed to provide teachers with a comprehensive approach to using satellite imagery to enhance science education. The teacher's guide is divided into topical chapters and enables teachers to expand their knowledge of the atmosphere, common weather patterns, and remote sensing. Topics include: weather systems and satellite imagery including mid-latitude weather systems; wave motion and the general circulation; cyclonic disturbances and baroclinic instability; clouds; additional common weather patterns; satellite images and the internet; environmental satellites; orbits; and ground station set-up. Activities are listed by suggested grade level and include the following topics: using weather symbols; forecasting the weather; cloud families and identification; classification of cloud types through infrared Automatic Picture Transmission (APT) imagery; comparison of visible and infrared imagery; cold fronts; to ski or not to ski (imagery as a decision making tool), infrared and visible satellite images; thunderstorms; looping satellite images; hurricanes; intertropical convergence zone; and using weather satellite images to enhance a study of the Chesapeake Bay. A list of resources is also included.

Steele, Colleen↗
DOE OSTI2022

Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths

We report the extent and duration of snow cover, a critical component of the hydrologic cycle and the global climate system, is expected to shift dramatically under climate change. Therefore, developing high-resolution assessments of snow cover change is crucial for estimating the impact of changing snow cover on watershed and ecosystems processes in cold regions. Remote sensing tools provide a powerful method for mapping snow-covered area (SCA) across a landscape. The most common method for estimating SCA utilizes the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI can effectively estimate catchment- to regional-scale SCA, but it cannot be used to assess fine-scale SCA because of current limitations on the spatial resolution of satellite-derived SWIR measurements. Here, we map SCA using a threshold of blue wavelengths and high-resolution satellite imagery. The thresholding method, which we call the Blue Snow Threshold algorithm (BST), has previously been used with digital camera imagery. We refine and automate the algorithm for use with cloud-free high-resolution satellite imagery and find that the BST can be used to assess fine-scale SCA. For validation, we compared BST-derived estimates of SCA to a) airborne lidar surveys, b) Landsat fractional SCA, and c) snow disappearance dates from Snow Telemetry (SNOTEL) stations. When compared to airborne lidar surveys of SCA, the BST predicted SCA had a range of F-scores between 0.81 and 0.94 in four study areas in California and Colorado. We also found general agreement between SCA and snow disappearance at multiple SNOTEL sites across the western United States. Given the relatively recent availability of high-resolution satellite imagery with spectral measurements in the visible wavelengths but lacking in SWIR, the BST offers a reliable and easy-to-apply tool for examining fine-scale snow-related processes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

Contribution of space platforms to a ground and airborne remote-sensing programme over active Italian volcanoes

ERTS-1 imagery of the volcanic areas of southern Italy was used primarily for the evaluation of space platform capabilties in the domains of regional geology, soil and rock-type classification and, more generally, to study the environment of active volcanoes. The test sites were selected and equipped primarily to monitor thermal emission, but ground truth data was also collected in other domains (reflectance of rocks, soils and vegetation). The test areas were overflown with a two channel thermal scanner, while a thermo camera was used on the ground to monitor the hot spots. The primary goal of this survey was to plot the changes in thermal emission with time in the framework of a research program for the surveillance of active volcanoes. However, another task was an evaluation of emissivity changes by comparing the outputs of the two thermal channels. These results were compared with the reflectance changes observed on multispectral ERTS-1 imagery.

Cassinis, R.↗
NASA NTRS2015

Assessing Wetland Hydroperiod and Soil Moisture with Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

Assist with the evaluation and measuring of wetlands hydroperiod at the Plum Brook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: (1) Show the relative length of hydroperiod using available remote sensing datasets, (2) Date linked table of wetlands extent over time for all feasible non-forested wetlands, (3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables (4), A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment; and (5) A MTRI style report summarizing year 2 results.

Brooks, Colin↗
NASA NTRS2015

Assessing Wetland Hydroperiod and Soil Moisture With Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

Primary Goal: Assist with the evaluation and measuring of wetlands hydroperiod at the PlumBrook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: 1) Show the relative length of hydroperiod using available remote sensing datasets 2) Date linked table of wetlands extent over time for all feasible non-forested wetlands 3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables 4) A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment 5) A MTRI style report summarizing year 2 results. This report serves as a descriptive summary of our completion of these our deliverables. Additionally, two formal meetings were held with Larry Liou and Amanda Sprinzl to provide project updates and receive direction on outputs. These were held on 2/26/15 and 9/17/15 at the Plum Brook Station. Principal Component Analysis (PCA) is a multivariate statistical technique used to identify dominant spatial and temporal backscatter signatures. PCA reduces the information contained in the temporal dataset to the first few new Principal Component (PC) images. Some advantages of PCA include the ability to filter out temporal autocorrelation and reduce speckle to the higher order PC images. A PCA was performed using ERDAS Imagine on a time series of PALSAR dates. Hydroperiod maps were created by separating the PALSAR dates into two date ranges, 2006-2008 and 2010, and performing an unsupervised classification on the PCAs.

Brooks, Colin↗
NASA NTRS1976

Interpretation key for SAR /L-band/ imagery of sea ice

An interpretation key, similar to those previously developed for use with aerial photography and other remotely sensed data, was developed for L-band (25 cm) radar imagery collected over the Arctic Ocean. Data from April, August, and October were considered. The procedure for developing a valid interpretation key for operation use involves substituting time for space. Open water situations (polynyas, leads, flaws), examples of unconsolidated ice (frazil, slush, brash), thin ice (nilas), and annual ice (first year, multi-year ice) situations are examined. It is suggested that the interpretation key will enhance the use of side looking airborne radar data in the qualitative photo interpretation mode.

Bryan, M. L.↗
NASA NTRS2002

Demonstrating Acquisition of Real-Time Thermal Data Over Fires Utilizing UAVs

A disaster mitigation demonstration, designed to integrate remote-piloted aerial platforms, a thermal infrared imaging payload, over-the-horizon (OTH) data telemetry and advanced image geo-rectification technologies was initiated in 2001. Project FiRE incorporates the use of a remotely piloted Uninhabited Aerial Vehicle (UAV), thermal imagery, and over-the-horizon satellite data telemetry to provide geo-corrected data over a controlled burn, to a fire management community in near real-time. The experiment demonstrated the use of a thermal multi-spectral scanner, integrated on a large payload capacity UAV, distributing data over-the-horizon via satellite communication telemetry equipment, and precision geo-rectification of the resultant data on the ground for data distribution to the Internet. The use of the UAV allowed remote-piloted flight (thereby reducing the potential for loss of human life during hazardous missions), and the ability to "finger and stare" over the fire for extended periods of time (beyond the capabilities of human-pilot endurance). Improved bit-rate capacity telemetry capabilities increased the amount, structure, and information content of the image data relayed to the ground. The integration of precision navigation instrumentation allowed improved accuracies in geo-rectification of the resultant imagery, easing data ingestion and overlay in a GIS framework. We focus on these technological advances and demonstrate how these emerging technologies can be readily integrated to support disaster mitigation and monitoring strategies regionally and nationally.

Ambrosia, Vincent G.↗
NASA NTRS1980

Cornell University remote sensing program

High altitude, color infrared aerial photography as well as imagery from Skylab and LANDSAT were used to inventory timber and assess potential sites for industrial development in New York State. The utility of small scale remotely sensed data for monitoring clearcutting in hardwood forests was also investigated. Consultation was provided regarding the Love Canal Landfill as part of environment protection efforts.

Liang, T.↗