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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Midwest Food Security & Agriculture II: Leveraging NASA Earth Observations to Analyze and Display Crop Phenology Data and Weather Conditions to Support Expansion of Small Grain Crops in the Midwest

Agriculture in the Midwest is dominated by monoculture systems that strip the soil of nutrients, decrease yields, and worsen water quality. Crop diversification and cultivating small grains is economically and ecologically advantageous, but limited in practice due to a lack of data about small-grain crop performance as well as monoculture-favoring insurance coverage, economic incentives, and sociocultural traditions. Practical Farmers of Iowa (PFI) strives to create more sustainable agriculture and supports Midwestern farmers in adding small grains into their crop rotations. The DEVELOP team partnered with the USDA Agricultural Research Service and the USA National Phenology Network to assist PFI in applying NASA Earth observation data to track small-grain crop performance. This project developed a Google Earth Engine user interface that enables PFI farmers to compare satellite-assessed vegetation production and climatic conditions of their farm with other PFI farmers. The team assessed the crop performance through Normalized Difference Vegetation Index (NDVI) values derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI). Additionally, the team created a regression model to analyze the predictive properties of vegetation indices and crop yield, ultimately establishing a relationship with mean NDVI from the early growing season. PFI farmers can utilize these products to make informed decisions about their crop production as well as advocate for more expansive insurance policies to protect small-grain crops.

Sophie Barrowman↗
NASA NTRS1999

The Fringe-Imaging Skin Friction Technique PC Application User's Manual

A personal computer application (CXWIN4G) has been written which greatly simplifies the task of extracting skin friction measurements from interferograms of oil flows on the surface of wind tunnel models. Images are first calibrated, using a novel approach to one-camera photogrammetry, to obtain accurate spatial information on surfaces with curvature. As part of the image calibration process, an auxiliary file containing the wind tunnel model geometry is used in conjunction with a two-dimensional direct linear transformation to relate the image plane to the physical (model) coordinates. The application then applies a nonlinear regression model to accurately determine the fringe spacing from interferometric intensity records as required by the Fringe Imaging Skin Friction (FISF) technique. The skin friction is found through application of a simple expression that makes use of lubrication theory to relate fringe spacing to skin friction.

Zilliac, Gregory G.↗
NASA NTRS2013

Sally Ride EarthKAM - Automated Image Geo-Referencing Using Google Earth Web Plug-In

Sally Ride EarthKAM is an educational program funded by NASA that aims to provide the public the ability to picture Earth from the perspective of the International Space Station (ISS). A computer-controlled camera is mounted on the ISS in a nadir-pointing window; however, timing limitations in the system cause inaccurate positional metadata. Manually correcting images within an orbit allows the positional metadata to be improved using mathematical regressions. The manual correction process is time-consuming and thus, unfeasible for a large number of images. The standard Google Earth program allows for the importing of KML (keyhole markup language) files that previously were created. These KML file-based overlays could then be manually manipulated as image overlays, saved, and then uploaded to the project server where they are parsed and the metadata in the database is updated. The new interface eliminates the need to save, download, open, re-save, and upload the KML files. Everything is processed on the Web, and all manipulations go directly into the database. Administrators also have the control to discard any single correction that was made and validate a correction. This program streamlines a process that previously required several critical steps and was probably too complex for the average user to complete successfully. The new process is theoretically simple enough for members of the public to make use of and contribute to the success of the Sally Ride EarthKAM project. Using the Google Earth Web plug-in, EarthKAM images, and associated metadata, this software allows users to interactively manipulate an EarthKAM image overlay, and update and improve the associated metadata. The Web interface uses the Google Earth JavaScript API along with PHP-PostgreSQL to present the user the same interface capabilities without leaving the Web. The simpler graphical user interface will allow the public to participate directly and meaningfully with EarthKAM. The use of similar techniques is being investigated to place ground-based observations in a Google Mars environment, allowing the MSL (Mars Science Laboratory) Science Team a means to visualize the rover and its environment.

Andres, Paul M.↗
NASA NTRS2024

Soil Salinity Level Assessment and Prediction Integrating UAV-borne Hyperspectral Imaging and Machine Learning Algorithms to Combat Desertification

In response to the ongoing global food crisis, the United Nations has identified “Zero Hunger” as one of its Sustainable Development Goals. A central contributor to the crisis is the process in which agricultural lands go through desertification. Research has shown a direct correlation between soil salinity and desertification - increased salinity levels indicate a higher risk for desertification. Furthermore, researchers have explored various techniques to map soil salinity, but these methods are oftentimes inefficient and don’t address future salinity predictions. To improve desertification monitoring, soil salinity can be observed via hyperspectral imaging on unmanned aerial vehicles (UAVs) to predict the risk of agricultural desertification using artificial intelligence (AI) and machine learning (ML) techniques. A significant gap exists in past research that applies ML and imaging techniques to soil salinity: convolutional neural networks (CNNs) and regression models are rarely leveraged together, despite the efficiency and accuracy of these models. To compensate for this gap, the proposed system leverages the use of these AI and ML models to improve soil assessment and prediction techniques. This approach involves three steps - data collection, image analysis, and future prediction. Using hyperspectral cameras on UAVs to collect the data from the region, a trained CNN model will output estimated soil salinity levels at a specific time. The estimations will then be analyzed by a regression model to assess the accuracy of future soil salinity predictions. The proposed system will identify regions at risk of desertification to help farmers mitigate agricultural loss, in turn helping alleviate the food crisis.

UAV systems↗
NASA NTRS1994

A new machine classification method applied to human peripheral blood leukocytes

Human beings judge images by complex mental processes, whereas computing machines extract features. By reducing scaled human judgments and machine extracted features to a common metric space and fitting them by regression, the judgments of human experts rendered on a sample of images may be imposed on an image population to provide automatic classification.

Rorvig, Mark E.↗
NASA NTRS1977

Application of LANDSAT to the surveillance of lake eutrophication in the Great Lakes basin

The author has identified the following significant results. A step-by-step procedure for establishing and monitoring the trophic status of inland lakes with the use of LANDSAT data, surface sampling, laboratory analysis, and aerial observations were demonstrated. The biomass was related to chlorophyll-a concentrations, water clarity, and trophic state. A procedure was developed for using surface sampling, LANDSAT data, and linear regression equations to produce a color-coded image of large lakes showing the distribution and concentrations of water quality parameters, causing eutrophication as well as parameters which indicate its effects. Cover categories readily derived from LANDSAT were those for which loading rates were available and were known to have major effects on the quality and quantity of runoff and lake eutrophication. Urban, barren land, cropland, grassland, forest, wetlands, and water were included.

Rogers, R. H.↗
NASA NTRS1999

Cerebrovascular regulation in the postural orthostatic tachycardia syndrome (POTS)

Patients with the postural orthostatic tachycardia syndrome (POTS) have symptoms of orthostatic intolerance despite having a normal orthostatic blood pressure (BP), which suggests some impairment of cerebrovascular regulation. Cerebrovascular autoregulation refers to the maintenance of normal cerebral blood flow in spite of changing BP. Mechanisms of autoregulation include myogenic, metabolic and neurogenic vasoregulation. Beat-to-beat recording of blood-flow velocity (BFV) is possible using transcranial Doppler imaging. It is possible to evaluate autoregulation by regressing deltaBFV to deltaBP during head-up tilt. A number of dynamic methods, relating deltaBFV to deltaBP during sudden induced changes in BP by occluding then releasing peripheral arterial flow or by the Valsalva maneuver. The deltaBFV to deltaBP provides an index of autoregulation. In orthostatic hypotension, the autoregulated range is typically expanded. In contrast, paradoxical vasoconstriction occurs in POTS because of an increased depth of respiration, resulting in hypocapnic cerebrovascular constriction, and impaired autoregulation.

Review↗
NASA NTRS1983

Use of the VAS for analysis of a prethunderstorm environment

The information content of the VAS radiances can be converted to meteorological parameters useful for analyzing a severe weather environment. The method by which the VAS variances are converted to vertical profiles of temperature, dewpoints, and equivalent potential temperature involves a basic regression technique using the most local radiosonde data available for establishing a correlation matrix. The results indicate that mesoscale features apparent within images of the radiances can be converted to usable temperature and moisture fields using regression when surface temperature and dewpoint observations are included within the total data base. In addition, results indicate that surface data are very important for better defining lower tropospheric structure that the VAS radiances alone cannot properly resolve. Analyses of these retrievals distinctly show mesoscale structure in the temperature and moisture fields derived with VAS radiances collected every 3 hours, and 0000 GMT. The retrievals capture the moisture structure. More important, convective instability is clearly detected immediately before the onset of convection. The results indicate that the VAS is capable of providing valuable mesoscale information suitable for analyzing a preconvective environment that is generally clear.

Uccellini, L. W.↗
NASA NTRS2011

Forest Biomass Mapping From Lidar and Radar Synergies

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed. In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R(exp 2) of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI Mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R2 of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar- SAR synergy 63 was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.

Sun, Guoqing↗
NASA NTRS1974

Application of ERTS-1 data to the harvest model of the US menhaden fishery

The author has identified the following significant results. The project was conducted in Mississippi Sound in the north-central Gulf of Mexico. It utilized conventional surface data, obtained from fishing and other vessels, as well as aircraft and spacecraft remote data. A relationship was established between surface measured water transparency, temperature and salinity, and commercial fish-stock availability. Numerical models of the relationships were derived. A multiple regression was performed relating ERTS-1 MSS Band 5 image density to measured transparency and water depth. It is concluded that remotely acquired data can play a role in harvest decisions of commercial fisheries.

Maughan, P. M.↗
NASA NTRS2004

An Orbital "Virtual Radar" from TRMM Passive Microwave and Lightning Observations

The retrieval of vertical structure from joint passive microwave and lightning observations is demonstrated. Three years of data from the TRMM (Tropical Rainfall Measuring Mission) are used as a training dataset for regression and classification neural networks; the TMI (TRMM Microwave Imager) and LIS (Lightning Imaging Sensor) provide the inputs, the PR (Precipitation Radar) provides the training targets. Both vertical reflectivity profile categorization (into 9 convective, 7 stratiform, 2 mixed and 6 anvil types) and geophysical parameters (surface rainfall, vertically integrated liquid (VIL), ice water content (IWC) and echo tops) are retrieved. Retrievals are successful over both land and ocean surfaces. The benefit of using lightning observations as inputs to these retrievals is quantitatively demonstrated; lightning essentially provides an additional convective/stratiform discriminator, and is most important for isolation of midlevel (tops in the mixed phase region) convective profile types (this is because high frequency passive microwave observations already provide good convective/stratiform discrimination for deep convective profiles). This is highly relevant as midlevel convective profiles account for an extremely large fraction of tropical rainfall, and yet are most difficult to discriminate from comparable-depth stratiform profile types using passive microwave observations alone.

Boccippio, Dennis J.↗
NASA NTRS1980

Incorporation of star measurements for the determination of orbit and attitude parameters of a geosynchronous satellite: An iterative application of linear regression

Currently on NOAA/NESS's VIRGS system at the World Weather Building star images are being ingested on a daily basis. The image coordinates of the star locations are measured and stored. Subsequently, the information is used to determine the attitude, the misalignment angles between the spin axis and the principal axis of the satellite, and the precession rate and direction. This is done for both the 'East' and 'West' operational geosynchronous satellites. This orientation information is then combined with image measurements of earth based landmarks to determine the orbit of each satellite. The method for determining the orbit is simple. For each landmark measurement one determines a nominal position vector for the satellite by extending a ray from the landmark's position towards the satellite and intersecting the ray with a sphere with center coinciding with the Earth's center and with radius equal to the nominal height for a geosynchronous satellite. The apparent motion of the satellite around the Earth's center is then approximated with a Keplerian model. In turn the variations of the satellite's height, as a function of time found by using this model, are used to redetermine the successive satellite positions by again using the Earth based landmark measurements and intersecting rays from these landmarks with the newly determined spheres. This process is performed iteratively until convergence is achieved. Only three iterations are required.

Phillips, D.↗
NASA NTRS1992

A linear spectral matching technique for retrieving equivalent water thickness and biochemical constituents of green vegetation

Over the last decade, technological advances in airborne imaging spectrometers, having spectral resolution comparable with laboratory spectrometers, have made it possible to estimate biochemical constituents of vegetation canopies. Wessman estimated lignin concentration from data acquired with NASA's Airborne Imaging Spectrometer (AIS) over Blackhawk Island in Wisconsin. A stepwise linear regression technique was used to determine the single spectral channel or channels in the AIS data that best correlated with measured lignin contents using chemical methods. The regression technique does not take advantage of the spectral shape of the lignin reflectance feature as a diagnostic tool nor the increased discrimination among other leaf components with overlapping spectral features. A nonlinear least squares spectral matching technique was recently reported for deriving both the equivalent water thicknesses of surface vegetation and the amounts of water vapor in the atmosphere from contiguous spectra measured with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The same technique was applied to a laboratory reflectance spectrum of fresh, green leaves. The result demonstrates that the fresh leaf spectrum in the 1.0-2.5 microns region consists of spectral components of dry leaves and the spectral component of liquid water. A linear least squares spectral matching technique for retrieving equivalent water thickness and biochemical components of green vegetation is described.

Gao, Bo-Cai↗
NASA NTRS1993

Predicting LANDSAT MSS endmember signatures from corresponding higher resolution TM fraction images

The approach to predicting Landsat Multispectral Scanner System (MSS) endmember signatures from fraction images derived from high resolution Landsat Thematic Mapper (TM) data is presented. The purpose, conducted in the Mogi-Guacu study site located in Sao Paulo State, Brazil, was to determine if information derived from a mixture model applied to higher resolution TM data could serve as ground truth for the lower resolution MSS sensor. The Constrained Least Squares (CLS) method was used to generate vegetation, soil, and shade fraction images from the TM data. The resulting images were then used to estimate the endmember spectral response for MSS data by regressing each MSS spectral channel against the corresponding proportion values estimated for the same resolution cells from the TM mixture model. The evaluation of the predicted signatures was performed by comparing the corresponding fraction images derived from both the MSS and TM data acquired on 14 Sep. 1986. The techniques serves as a potential tool for integrating information in global studies where remote sensors with different spectral and spatial resolutions were used.

Shimabukuro, Yosio E.↗
NASA NTRS1988

Two dimensional convolute integers for machine vision and image recognition

Machine vision and image recognition require sophisticated image processing prior to the application of Artificial Intelligence. Two Dimensional Convolute Integer Technology is an innovative mathematical approach for addressing machine vision and image recognition. This new technology generates a family of digital operators for addressing optical images and related two dimensional data sets. The operators are regression generated, integer valued, zero phase shifting, convoluting, frequency sensitive, two dimensional low pass, high pass and band pass filters that are mathematically equivalent to surface fitted partial derivatives. These operators are applied non-recursively either as classical convolutions (replacement point values), interstitial point generators (bandwidth broadening or resolution enhancement), or as missing value calculators (compensation for dead array element values). These operators show frequency sensitive feature selection scale invariant properties. Such tasks as boundary/edge enhancement and noise or small size pixel disturbance removal can readily be accomplished. For feature selection tight band pass operators are essential. Results from test cases are given.

Edwards, Thomas R.↗
NASA NTRS2001

The Statistical Consulting Center for Astronomy (SCCA)

The process by which raw astronomical data acquisition is transformed into scientifically meaningful results and interpretation typically involves many statistical steps. Traditional astronomy limits itself to a narrow range of old and familiar statistical methods: means and standard deviations; least-squares methods like chi(sup 2) minimization; and simple nonparametric procedures such as the Kolmogorov-Smirnov tests. These tools are often inadequate for the complex problems and datasets under investigations, and recent years have witnessed an increased usage of maximum-likelihood, survival analysis, multivariate analysis, wavelet and advanced time-series methods. The Statistical Consulting Center for Astronomy (SCCA) assisted astronomers with the use of sophisticated tools, and to match these tools with specific problems. The SCCA operated with two professors of statistics and a professor of astronomy working together. Questions were received by e-mail, and were discussed in detail with the questioner. Summaries of those questions and answers leading to new approaches were posted on the Web (www.state.psu.edu/ mga/SCCA). In addition to serving individual astronomers, the SCCA established a Web site for general use that provides hypertext links to selected on-line public-domain statistical software and services. The StatCodes site (www.astro.psu.edu/statcodes) provides over 200 links in the areas of: Bayesian statistics; censored and truncated data; correlation and regression, density estimation and smoothing, general statistics packages and information; image analysis; interactive Web tools; multivariate analysis; multivariate clustering and classification; nonparametric analysis; software written by astronomers; spatial statistics; statistical distributions; time series analysis; and visualization tools. StatCodes has received a remarkable high and constant hit rate of 250 hits/week (over 10,000/year) since its inception in mid-1997. It is of interest to scientists both within and outside of astronomy. The most popular sections are multivariate techniques, image analysis, and time series analysis. Hundreds of copies of the ASURV, SLOPES and CENS-TAU codes developed by SCCA scientists were also downloaded from the StatCodes site. In addition to formal SCCA duties, SCCA scientists continued a variety of related activities in astrostatistics, including refereeing of statistically oriented papers submitted to the Astrophysical Journal, talks in meetings including Feigelson's talk to science journalists entitled "The reemergence of astrostatistics" at the American Association for the Advancement of Science meeting, and published papers of astrostatistical content.

Akritas, Michael↗
NASA NTRS1993

Computer assisted analysis of auroral images obtained from high altitude polar satellites

Automatic techniques that allow the extraction of physically significant parameters from auroral images were developed. This allows the processing of a much larger number of images than is currently possible with manual techniques. Our techniques were applied to diverse auroral image datasets. These results were made available to geophysicists at NASA and at universities in the form of a software system that performs the analysis. After some feedback from users, an upgraded system was transferred to NASA and to two universities. The feasibility of user-trained search and retrieval of large amounts of data using our automatically derived parameter indices was demonstrated. Techniques based on classification and regression trees (CART) were developed and applied to broaden the types of images to which the automated search and retrieval may be applied. Our techniques were tested with DE-1 auroral images.

Samadani, Ramin↗
NASA NTRS1971

Techniques for generation of control and guidance signals derived from optical fields, part 2

The development is reported of a high resolution technique for the detection and identification of landmarks from spacecraft optical fields. By making use of nonlinear regression analysis, a method is presented whereby a sequence of synthetic images produced by a digital computer can be automatically adjusted to provide a least squares approximation to a real image. The convergence of the method is demonstrated by means of a computer simulation for both elliptical and rectangular patterns. Statistical simulation studies with elliptical and rectangular patterns show that the computational techniques developed are able to at least match human pattern recognition capabilities, even in the presence of large amounts of noise. Unlike most pattern recognition techniques, this ability is unaffected by arbitrary pattern rotation, translation, and scale change. Further development of the basic approach may eventually allow a spacecraft or robot vehicle to be provided with an ability to very accurately determine its spatial relationship to arbitrary known objects within its optical field of view.

Hemami, H.↗