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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Needs Assessment for the Use of NASA Remote Sensing Data in the Development and Implementation of Estuarine and Coastal Water Quality Standards

The goals of the project are to provide information from satellite remote sensing to support numeric nutrient criteria development and to determine data processing methods and data quality requirements to support nutrient criteria development and implementation. The approach is to identify water quality indicators that are used by decision makers to assess water quality and that are related to optical properties of the water; to develop remotely sensed data products based on algorithms relating remote sensing imagery to field-based observations of indicator values; to develop methods to assess estuarine water quality, including trends, spatial and temporal variability, and seasonality; and to develop tools to assist in the development and implementation of estuarine and coastal nutrient criteria. Additional slides present process, criteria development, typical data sources and analyses for criteria process, the power of remote sensing data for the process, examples from Pensacola Bay, spatial and temporal variability, pixel matchups, remote sensing validation, remote sensing in coastal waters, requirements for remotely sensed data products, and needs assessment. An additional presentation examines group engagement and information collection. Topics include needs assessment purpose and objectives, understanding water quality decision making, determining information requirements, and next steps.

Spiering, Bruce↗
NASA NTRS1978

Remote sensing as a tool in assessing soil moisture

The effects of soil moisture as it relates to agriculture is briefly discussed. The use of remote sensing to predict scheduling of irrigation, runoff and soil erosion which contributes to the prediction of crop yield is also discussed.

Carlson, C. W.↗
NASA NTRS1981

Application of remote sensing for California irrigated lands assessment

Recognizing the need for land use data as an input to State water planning, the California Department of Water Resources (DWR) and its predecessor agencies have conducted land use surveys since the late 1940's. In 1957 the California Water Plan was established. A statewide assessment is compiled every four to five years to review overall water resource conditions. Satellite remote sensing imagery, such as available from Landsat, has been recognized by DWR as a potential source of land use data. Analysis of Landsat imagery in connection with 'calibrating' data from low altitude photography and/or field checks can provide an economical means for providing a large scale, single year base inventory of general land use. The purpose of the Applications Pilot Test is to develop and document the use of Landsat for such an inventory. Towards the overall goal of developing an operational methodology that DWR could cost effectively implement four tasks have been specified.

Tinney, L.↗
NASA NTRS2014

Integration of Earth Remote Sensing into the NOAA/NWS Damage Assessment Toolkit

Following the occurrence of severe weather, NOAA/NWS meteorologists are tasked with performing a storm damage survey to assess the type and severity of the weather event, primarily focused with the confirmation and assessment of tornadoes. This labor‐intensive process requires meteorologists to venture into the affected area, acquire damage indicators through photos, eyewitness accounts, and other documentation, then aggregation of data in order to make a final determination of the tornado path length, width, maximum intensity, and other characteristics. Earth remote sensing from operational, polar‐orbiting satellites can support the damage assessment process by helping to identify portions of damage tracks that are difficult to access due to road limitations or time constraints by applying change detection techniques. In addition, higher resolution commercial imagery can corroborate ground‐based surveys by examining higher‐resolution commercial imagery. As part of an ongoing collaboration, NASA and NOAA are working to integrate near real‐time Earth remote sensing observations into the NOAA/NWS Damage Assessment Toolkit, a handheld application used by meteorologists in the survey process. The team has recently developed a more streamlined approach for delivering data via a web mapping service and menu interface, allowing for caching of imagery before field deployment. Near real‐time products have been developed using MODIS and VIIRS imagery and change detection for preliminary track identification, along with conduits for higher‐resolution Landsat, ASTER, and commercial imagery as they become available. In addition to tornado damage assessments, the team is also investigating the use of near real‐time imagery for identifying hail damage to vegetation, which also results in large swaths of damage, particularly in the central United States during the peak growing season months of June, July, and August. This presentation will present an overview of recent activities, challenges and successes, best practices, and opportunities for future work and collaboration.

Molthan, Andrew↗
NASA NTRS1995

Brazil Fire Characterization and Burn Area Estimation Using the Airborne Infrared Disaster Assessment (AIRDAS) System

Remotely sensed estimations of regional and global emissions from biomass combustion have been used to characterize fire behavior, determine fire intensity, and estimate burn area. Highly temporal, low resolution satellite data have been used to calculate estimates of fire numbers and area burned. These estimates of fire activity and burned area have differed dramatically, resulting in a wide range of predictions on the ecological and environmental impacts of fires. As part of the Brazil/United States Fire Initiative, an aircraft campaign was initiated in 1992 and continued in 1994. This multi-aircraft campaign was designed to assist in the characterization of fire activity, document fire intensity and determine area burned over prescribed, agricultural and wildland fires in the savanna and forests of central Brazil. Using a unique, multispectral scanner (AIRDAS), designed specifically for fire characterization, a variety of fires and burned areas were flown with a high spatial and high thermal resolution scanner. The system was used to measure flame front size, rate of spread, ratio of smoldering to flaming fronts and fire intensity. In addition, long transects were flown to determine the size of burned areas within the cerrado and transitional ecosystems. The authors anticipate that the fire activity and burned area estimates reported here will lead to enhanced information for precise regional trace gas prediction.

Brass, J. A.↗
NASA NTRS2012

Remote Sensing Contributions to Prediction and Risk Assessment of Natural Disasters Caused by Large Scale Rift Valley Fever Outbreaks

Remotely sensed vegetation measurements for the last 30 years combined with other climate data sets such as rainfall and sea surface temperatures have come to play an important role in the study of the ecology of arthropod-borne diseases. We show that epidemics and epizootics of previously unpredictable Rift Valley fever are directly influenced by large scale flooding associated with the El Ni o/Southern Oscillation. This flooding affects the ecology of disease transmitting arthropod vectors through vegetation development and other bioclimatic factors. This information is now utilized to monitor, model, and map areas of potential Rift Valley fever outbreaks and is used as an early warning system for risk reduction of outbreaks to human and animal health, trade, and associated economic impacts. The continuation of such satellite measurements is critical to anticipating, preventing, and managing disease epidemics and epizootics and other climate-related disasters.

Anyamba, Assaf↗
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 NTRS1991

Thematic Conference on Geologic Remote Sensing, 8th, Denver, CO, Apr. 29-May 2, 1991, Proceedings. Vols. 1 & 2

The proceedings contain papers discussing the state-of-the-art exploration, engineering, and environmental applications of geologic remote sensing, along with the research and development activities aimed at increasing the future capabilities of this technology. The following topics are addressed: spectral geology, U.S. and international hydrocarbon exporation, radar and thermal infrared remote sensing, engineering geology and hydrogeology, mineral exploration, remote sensing for marine and environmental applications, image processing and analysis, geobotanical remote sensing, and data integration and geographic information systems. Particular attention is given to spectral alteration mapping with imaging spectrometers, mapping the coastal plain of the Congo with airborne digital radar, applications of remote sensing techniques to the assessment of dam safety, remote sensing of ferric iron minerals as guides for gold exploration, principal component analysis for alteration mappping, and the application of remote sensing techniques for gold prospecting in the north Fujian province.

Source record↗
NASA NTRS1978

Role of remote sensing in documenting living resources

Specific cases of known or potentially useful applications of remote sensing in assessing biological resources are discussed. It is concluded that the more usable remote sensing techniques relate to the measurement of population fluctuations in aquatic systems. Sensing of the flora and the fauna of the Bay is considered with emphasis on direct sensing of aquatic plant populations and of water quality. Recommendations for remote sensing projects are given.

Wagner, P. E.↗
NASA NTRS1981

Prime agricultural land monitoring and assessment component of the California Integrated Remote Sensing System

The use of digital LANDSAT techniques for monitoring agricultural land use conversions was studied. Two study areas were investigated: one in Ventura County and the other in Fresno County (California). Ventura test site investigations included the use of three dates of LANDSAT data to improve classification performance beyond that previously obtained using single data techniques. The 9% improvement is considered highly significant. Also developed and demonstrated using Ventura County data is an automated cluster labeling procedure, considered a useful example of vertical data integration. Fresno County results for a single data LANDSAT classification paralleled those found in Ventura, demonstrating that the urban/rural fringe zone of most interest is a difficult environment to classify using LANDSAT data. A general raster to vector conversion program was developed to allow LANDSAT classification products to be transferred to an operational county level geographic information system in Fresno.

Estes, J. E.↗
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 NTRS2016

How Universal is the Relationship Between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment

Leaf Area Index (LAI) is a key variable that bridges remote sensing observations to the quantification of agroecosystem processes. In this study, we assessed the universality of the relationships between crop LAI and remotely sensed Vegetation Indices (VIs). We first compiled a global dataset of 1459 in situ quality-controlled crop LAI measurements and collected Landsat satellite images to derive five different VIs including Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), two versions of the Enhanced Vegetation Index (EVI and EVI2), and Green Chlorophyll Index (CI(sub Green)). Based on this dataset, we developed global LAI-VI relationships for each crop type and VI using symbolic regression and Theil-Sen (TS) robust estimator. Results suggest that the global LAI-VI relationships are statistically significant, crop-specific, and mostly non-linear. These relationships explain more than half of the total variance in ground LAI observations (R2 greater than 0.5), and provide LAI estimates with RMSE below 1.2 m2/m2. Among the five VIs, EVI/EVI2 are the most effective, and the crop-specific LAI-EVI and LAI-EVI2 relationships constructed by TS, are robust when tested by three independent validation datasets of varied spatial scales. While the heterogeneity of agricultural landscapes leads to a diverse set of local LAI-VI relationships, the relationships provided here represent global universality on an average basis, allowing the generation of large-scale spatial-explicit LAI maps. This study contributes to the operationalization of large-area crop modeling and, by extension, has relevance to both fundamental and applied agroecosystem research.

Vegetation Index↗
NASA NTRS2018

Seasonal and Interannual Assessment of Cloud Cover and Atmospheric Constituents Across the Amazon (2000 - 2015): Insights for Remote Sensing and Climate Analysis

The quantitative assessment of cloud cover and atmospheric constituents improves our ability to exploit the climate feedback into the Amazon basin. In the 21st century, three droughts have already occurred in the Amazonia (e.g. 2005, 2010, 2015), inducing regional changes in the seasonal patterns of atmospheric constituents. In addition to climate, the atmospheric dynamic and attenuation properties are long-term challenges for satellite-based remote sensing of this ecosystem: high cloudiness, abundant water vapor content and biomass burning season. Therefore, while climatology analysis supports the understanding of atmospheric variability and trends, it also offers valuable insights for remote sensing applications. In this study, we evaluate the seasonal and interannual variability of cloud cover and atmospheric constituents (aerosol loading, water vapor and ozone content) over the Amazon basin, with focus on both climate analysis and remote sensing implications. We take the advantage of new atmosphere daily products at 1 km resolution derived from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data. An intercomparison of Aerosol Robotic Network (AERONET) and MAIAC aerosol optical depth (AOD) and columnar water vapor (CWV) showed quantitative information with a correlation coefficient higher than 0.81. Our results show distinct regional patterns of cloud cover across the Amazon basin: northwestern region presets a persistent cloud cover (> 80%) throughout the year, while low cloud cover (0-20%) occurs in the southern Amazon during the dry season. The cloud-free period in the southern Amazon is followed by an increase in the atmospheric burden due to fire emissions. Our results reveal that AOD records are changing in terms of area and intensity. During the 2005 and 2010 droughts, the positive AOD anomalies (δ > 0.1) occurred over 39.03% (240.3 million ha) and 27.14% (165.99 million ha) of total basin in the SON season, respectively. In contrast, the recent 2015 drought occurred towards the end of year (October through December) and these anomalies were observed over 23.72% (145 million ha) affecting areas in the central and eastern Amazon unlike previous droughts. The water vapor presents high concentration values (4.0-5.0 g/sq.cm) in the wet season (DJF), while we observed a strong spatial gradient from northwestern to southeastern of the basin during the dry season. In addition, we also found a positive trend of water vapor content ( ∼0.3 g/sq.cm) between 2000 and 2015. The total ozone typically varies between 220 and 270 DU, and it has a seasonal change of ∼25-35 DU from wet season to dry season caused by large emissions of ozone precursors and long-range transport. Finally, while this study contributes to climatological analysis of atmospheric constituents, the remote sensing users can also understand the regional constraints caused by atmospheric attenuation, such as high aerosol loading and cloud obstacles for surface observations.

ozone↗
NASA NTRS1986

An assessment of OH airglow interference on the remote sensing of stratospheric HCl via limb sounding in the near-I.R.

Chemiluminescent emission from the formation of vibrationally excited OH at an altitude of approximately 85 km presents a barrier to earth-limb measurements of NIR emissions originating in the stratosphere. High-resolution measured spectra of OH airglow have not been reported, and thus it is difficult to assess its impact on moderately-high-resolution earth-limb spectroscopy capable of looking between strong OH lines. Low-resolution rocketborne spectrometer earth-limb measurements of OH airglow are mathematically inverted to recover vibrational-state populations that are used to simulate a higher resolution spectrum in the neighbourhood of 2843/cm. Implications for remote sensing HCl are presented.

Mergenthaler, J. L.↗