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NASA NTRS1981

Creation of learning kits

A learning kit is an essential part of any remote sensing workshop, course, or in-house training program to provide the "hands-on" experience of working with remotely sensed imagery. This is the objective of laboratory and field exercises as well as the reason behind the production of imagery/map kits. The way in which these learning kits (containing conventional remotely sensed and collateral data products) are put together is described and some concerns that influence the creation of learning kits are discussed. These include budgetary constraints, number of imagery types, and number of collateral data types.

Stow, D. A.↗

NASA NTRS2006

Edge Response and NIIRS Estimates for Commercial Remote Sensing Satellites

Spatial resolution of panchromatic imagery from commercial remote sensing satellites was characterized based on edge response measurements using edge targets and the tilted-edge technique. Relative Edge Response (RER) was estimated as a geometric mean of normalized edge response differences measured in two directions of image pixels at points distanced from the edge by -0.5 and 0.5 of ground sample distance. RER is one of the engineering parameters used in the General Image Quality Equation to provide predictions of imaging system performance expressed in terms of the National Imagery Interpretability Rating Scale (NIIRS). By assuming a plausible range of signal-to-noise ratio and assessing the effects of Modulation Transfer Function compensation, the NIIRS estimates were made and then compared with vendor-provided values and evaluations conducted by the National Geospatial-Intelligence Agency.

Blonski, Slawomir↗

NASA NTRS2013

International Space Station Instmments Collect Imagery of Natural Disasters

A new focus for utilization of the International Space Station (ISS) is conducting basic and applied research that directly benefits Earth's citizenry. In the Earth Sciences, one such activity is collecting remotely sensed imagery of disaster areas and making those data immediately available through the USGS Hazards Data Distribution System, especially in response to activations of the International Charter for Space and Major Disasters (known informally as the "International Disaster Charter", or IDC). The ISS, together with other NASA orbital sensor assets, responds to IDC activations following notification by the USGS. Most of the activations are due to natural hazard events, including large floods, impacts of tropical systems, major fires, and volcanic eruptions and earthquakes. Through the ISS Program Science Office, we coordinate with ISS instrument teams for image acquisition using several imaging systems. As of 1 August 2013, we have successfully contributed imagery data in support of 14 Disaster Charter Activations, including regions in both Haiti and the east coast of the US impacted by Hurricane Sandy; flooding events in Russia, Mozambique, India, Germany and western Africa; and forest fires in Algeria and Ecuador. ISS‐based sensors contributing data include the Hyperspectral Imager for the Coastal Ocean (HICO), the ISERV (ISS SERVIR Environmental Research and Visualization System) Pathfinder camera mounted in the US Window Observational Research Facility (WORF), the ISS Agricultural Camera (ISSAC), formerly operating from the WORF, and high resolution handheld camera photography collected by crew members (Crew Earth Observations). When orbital parameters and operations support data collection, ISS‐based imagery adds to the resources available to disaster response teams and contributes to the publicdomain record of these events for later analyses.

Evans, C. A.↗

NASA NTRS1983

Current trends and research challenges in land remote sensing

The use of space technology to conduct repetitive global surveys of terrestrial surface conditions is reviewed. The state-of-the-art is determined by the measurement capabilities of orbital sensor systems, the maneuvering and data transmission capabilities of orbital platforms, and the sophistication of the user community. Introduction of the Landsat Thematic Mapper and SPOT sensor systems and operational use of the Space Shuttle are discussed. Research challenges of the 1980's are related to the removal of atmospheric and topographic effects in remotely sensed imagery, the extraction of information from high dimensionality data sets, and the ability to conduct meaningful ground truth surveys to support the analysis and interpretation of remotely sensed data.

Settle, M.↗

NASA NTRS2016

Near Real-Time Flood Monitoring and Impact Assessment Systems

Floods are the costliest natural disaster, causing approximately 6.8 million deaths in the twentieth century alone. Worldwide economic flood damage estimates in 2012 exceed $19 Billion USD. Extended duration floods also pose longer term threats to food security, water, sanitation, hygiene, and community livelihoods, particularly in developing countries. Projections by the Intergovernmental Panel on Climate Change (IPCC) suggest that precipitation extremes, rainfall intensity, storm intensity, and variability are increasing due to climate change. Increasing hydrologic uncertainty will likely lead to unprecedented extreme flood events. As such, there is a vital need to enhance and further develop traditional techniques used to rapidly assess flooding and extend analytical methods to estimate impacted population and infrastructure. Measuring flood extent in situ is generally impractical, time consuming, and can be inaccurate. Remotely sensed imagery acquired from space-borne and airborne sensors provides a viable platform for consistent and rapid wall-to-wall monitoring of large flood events through time. Terabytes of freely available satellite imagery are made available online each day by NASA, ESA, and other international space research institutions. Advances in cloud computing and data storage technologies allow researchers to leverage these satellite data and apply analytical methods at scale. Repeat-survey earth observations help provide insight about how natural phenomena change through time, including the progression and recession of floodwaters. In recent years, cloud-penetrating radar remote sensing techniques (e.g., Synthetic Aperture Radar) and high temporal resolution imagery platforms (e.g., MODIS and its 1-day return period), along with high performance computing infrastructure, have enabled significant advances in software systems that provide flood warning, assessments, and hazard reduction potential. By incorporating social and economic data, researchers can develop systems that automatically quantify the socioeconomic impacts resulting from flood disaster events.

Ahamed, Aakash↗

NASA NTRS1993

Intercomparisons between passive and active microwave remote sensing, and hydrological modeling for soil moisture

Soil moisture estimations from a distributed hydrological model and two microwave sensors were compared with ground measurements collected during the MAC-HYDRO'90 experiment. The comparison was done with the purpose of evaluating the performance of the hydrological model and examining the limitations of remote sensing techniques used in soil moisture estimation. An image integration technique was used to integrate and analyze rainfall, soil properties, land cover, topography, and remote sensing imagery. Results indicate that the hydrological model and microwave sensors successfully picked up temporal variations of soil moisture and that the spatial soil moisture pattern may be remotely sensed with reasonable accuracy using existing algorithms.

Wood, E. F.↗

NASA NTRS1976

Remote measurement of water color

This paper contains a discussion of the requirements and approaches which can be taken in the development of techniques for the analysis of remote multispectral imagery of natural bodies of water. There are two general approaches which can be used in the analysis of multispectral water color data collected by aircraft or satellite remote sensors. With the theoretical modeling approach, optically important constituents of natural waters are mathematically related to the upwelling radiance spectrum received by the remote sensor. With the empirical approach, the relationships are determined empirically by comparing remote sensing data with surface truth data. There are several levels of mathematical sophistication which can be applied to both approaches. The two approaches are discussed in some detail and it is concluded that the two approaches are closely related and should be pursued simultaneously for maximum utilization of the laboratory and field measurement data which will be needed. A set of minimum surface truth measurement parameters and techniques is suggested.

Mccluney, W. R.↗

NASA NTRS1976

Use of remote sensing for land use policy formulation

Uses of remote sensing imagery were investigated based on exploring and evaluating the capability and reliability of all kinds of imagery for improving decision making on issues of land use at all scales of governmental administration. Emphasis was placed on applications to solving immediate problems confronting public agencies and private organizations. Resulting applications of remote sensing use by public agencies, public organizations, and related private corporations are described.

Boylan, M.↗

NASA NTRS1975

Design criteria for a multiple input land use system

A design is presented that proposes the use of digital image processing techniques to interface existing geocoded data sets and information management systems with thematic maps and remote sensed imagery. The basic premise is that geocoded data sets can be referenced to a raster scan that is equivalent to a grid cell data set, and that images taken of thematic maps or from remote sensing platforms can be converted to a raster scan. A major advantage of the raster format is that x, y coordinates are implicitly recognized by their position in the scan, and z values can be treated as Boolean layers in a three-dimensional data space. Such a system permits the rapid incorporation of data sets, rapid comparison of data sets, and adaptation to variable scales by resampling the raster scans.

Billingsley, F. C.↗

NASA NTRS1997

Digital Processing of Remotely Sensed Imaging

Digital images can be acquired from various devices. Image scanners on personal computers can generate digital images of hard copy material.

process imagery remote sensing deep space missions↗

NASA NTRS1976

The use of ERTS/LANDSAT imagery in relation to airborne remote sensing for terrain analysis in western Queensland, Australia

The author has identified the following significant results. Distinctive spectral signatures were found associated with areas of near surface bedrock with covered ground east of Dugald River and along the Thorntonia River valley west of Lady Annie. Linears identified in the Dugald River area on LANDSAT 2 imagery taken in March and July 1975 over the Cloncurry-Dobbyn area, displayed preferred orientation. A linear group with NE-SW orientation was identified in the Lady Annie area. In this area, the copper mineralization in the Mt. Kelly area occurs along a well marked linear with NNW/SSE direction apparent on images for March, September, and November 1975. Geobotanical anomalies provided surface expression of the copper deposits in Mt. Kelley.

Cole, M. M.↗

NASA NTRS1998

Remote Sensing in Archeology: Classifying Bajos of the Paten, Guatemala

This project focuses on the adaptation of human populations to their environments from prehistoric times to the present. It emphasizes interdisciplinary research to develop ecological baselines through the use of remotely sensed imagery, in situ field work, and the modeling of human population dynamics. It utilizes cultural and biological data from dated archaeological sites to assess the subsistence and settlement patterns of human societies in response to changing climatic and environmental conditions. The utilization of remote sensing techniques in archaeology is relatively new, exciting, and opens many doors.

Lowry, James D., Jr.↗

NASA NTRS2003

Mapping a Better Vintage

Vineyards such as those located in California's Napa Valley tend to be subdivided into relatively large fields or blocks that often encompass heterogeneous physical conditions. Since growers typically treat the entire block as a single minimum management unit for cultivation and harvest, mapping and monitoring the variability within a block is a concern. Over the last decade, an increasing number of vineyard managers have utilized digital remote sensing and geographic information systems (GIS) to visualize the variability within their blocks. With computer software designed to overlay remotely sensed imagery with environmental and agronomic geographic data on a map, GIS helps growers recognize and explain patterns that might not have been obvious otherwise. GIS can also serve as a valuable archiving mechanism for future reference. To further develop the use of image technology and GIS for vineyard management support, NASA's Earth Science Enterprise partnered with the U.S. wine and commercial remote sensing industries for a project known as the Viticultural Integration of NASA Technologies for Assessment of the Grapevine Environment (VINTAGE). With project investigators from NASA's Ames Research Center, the California State University at Monterey Bay, and the University of Montana, several prototype products have been developed to support agricultural decisions concerning canopy management and irrigation practice. One key VINTAGE aspect involved the evaluation of satellite and airborne multispectral imagery for delineation of sub-block management zones within a vineyard.

Source record↗

NASA NTRS1970

Summary: Remote sensing soil moisture research

During the 1969 and 1970 growing seasons research was conducted to investigate the relationship between remote sensing imagery and soil moisture. The research was accomplished under two completely different conditions: (1) cultivated cropland in east central South Dakota, and (2) rangeland in western South Dakota. Aerial and ground truth data are being studied and correlated in order to evaluate the moisture supply and water use. Results show that remote sensing is a feasible method for monitoring soil moisture.

Schmer, F. A.↗

NASA NTRS1981

Estimating reforestation by means of remote sensing

LANDSAT imagery at the scale of 1:250.000 and obtained from bands 5 and 7 as well as computer compatible tapes were used to evaluate the effectiveness of remotely sensed orbital data in inventorying forests in a 462,100 area of Brazil emcompassing the cities of Ribeirao, Altinopolis Cravinhos, Serra Azul, Luis Antonio, Sao Simao, Santa Rita do Passa Quatro, and Santa Rosa do Viterbo. Visual interpretation of LANDSAT imagery shows that 37,766 hectares (1977) and 38,003.75 hectares (1979) were reforested areas of pine and eucalyptus species. An increment of 237.5 hectares was found during this two-year time lapse.

Dejesusparada, N.↗

NASA NTRS2003

Improved Prediction of Momentum and Scalar Fluxes Using MODIS Imagery

There are remote sensing and science objectives. The remote sensing objectives are: To develop and test a theoretical method for estimating local momentum aerodynamic roughness length, z(sub 0m), using satellite multispectral imagery. To adapt the method to the MODIS imagery. To develop a high-resolution (approx. 1km) gridded dataset of local momentum roughness for the continental United States and southern Canada, using MODIS imagery and other MODIS derived products. The science objective is: To determine the sensitivity of improved satellite-derived (MODIS-) estimates of surface roughness on the momentum and scalar fluxes, within the context of 3-D atmospheric modeling.

Crago, Richard D.↗

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 NTRSDate not supplied

Satellite-Derived Imagery and Transfer Learning: A Novel Technique for Land Cover Classification

Land cover classification is a continuing research topic due to its relevance to land use and land cover changes from impacts such as climate change, agriculture, urbanization, and hazardous weather. Simple access to frequently changing land cover classifications could provide knowledge and decision support to various researchers and agencies across the globe for each of above-mentioned and related influences. This research aims to provide a novel technique for land cover classification of remote sensing imagery; harnessing Artificial Neural Networks and transfer learning (TL). Knowledge sharing techniques within machine learning are typically utilized when training datasets are sparse or transitions between data modalities is required. In this case, two data modalities, multi and hyperspectral data, are considered for knowledge transfer. The large number of continuous spectral bands available from hyperspectral sensors typically provide increased sophisticated land classification capability compared to more traditional multispectral imagery with limited discrete spectral bands. However, large-scale, frequent access to hyperspectral imagery is relatively limited. The proposed classification technique would therefore prove useful in regions in which hyperspectral data are not readily available for classification but multispectral data are. Image segmentation models are trained on each multi and hyperspectral datasets. Knowledge sharing techniques are then applied to each model to understand what knowledge, if any, is gained when moving between the data modalities. The datasets utilized for training and testing a U-Net model include the European Space Agency’s multispectral imager Sentinel-2 and the hyperspectral German Aerospace Center’s Earth Sensing Imaging Spectrometer (DESIS). Initially, only two classes, land and water, are classified for simplicity. However, more complex classes can be added if knowledge sharing is successful. The workflow for image classification through supervised image segmentation with a U-Net model will be discussed along with metrics calculated before and after TL for both Sentinel-2 and DESIS data are applied. Additionally, future steps to advance the sophistication of this technique as well as other applicable methodologies will be explored.

Emily Foshee↗