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NASA NTRS2024

Northern Rockies Ecological Conservation: Leveraging Earth Observations to Monitor and Predict Populations of Federally Threatened Whitebark Pine (Pinus albicaulis) across the Intermountain West

Whitebark pine (WBP; Pinus albicaulis) is an ecologically important species in North America. As a federally listed threatened species, an understanding of WBP habitat, distribution, and health is important for the natural resource managers of the National Park Service, United States Forest Service, Bureau of Land Management, Fish and Wildlife Service, and non-profit organizations such as the Whitebark Pine Ecosystem Foundation. Previous attempts to develop models of WBP habitat suitability and distribution lack confidence in their validity and integrity for these organizations. The updated models of habitat suitability and distribution developed by this study would provide managers with a capability to be employed in the conservation and future research direction for WBP. Thus, we developed a habitat suitability model of WBP at a high spatial resolution (Landsat 9 Operational Land Image-2, National Land Cover Database, NASA Shuttle Radar Topography Mission; 30m pixels) using a generalized logistic regression with an area under the curve value of 0.754. We extracted spectral reflectance signatures from overlapped ground sample points and Sentinel-2 Multispectral Instrument. The spectral signature analysis indicates WBP is separable from other tree species. We also utilized a visual validation approach and random forest (RF) modeling to separate WBP from limber pine. Through visual validation the RF classifier successfully identified 8out of 10 WBP trees gathered through ground truth points. Additionally, we achieved an overall accuracy of 91%in our confusion matrix for the distribution model using a dependent validation approach. The derived products from this study allow project partners to assess current suitable habitat and apparent health status in areas of identified WBP occurrence, providing data to aid future research regarding WBP health.

Sentinel-2↗

NASA NTRS1983

Comparison of winter-nocturnal geostationary satellite infrared-surface temperature with shelter-height temperature in Florida

Geostationary satellite surface temperatures derived from a Visible and Infrared Spin Scan Radiometer (VISSR) sensor (10.5 to 12.6 microns) were compared with 1.5-m air temperatures collected by a thermocouple on a traversing vehicle along rural highway transects in Florida, and with two fixed thermographs located in rural and agricultural areas. Statistical comparisons between satellite and 1.5-m observations yielded a mean correlation coefficient of 0.87 and an average sample standard deviation from regression of 1.57 C during clear nights for four winters (1978-1981). The satellite-temperature image of Lake Okeechobee was compared with its geographic outline for areal image registration. Manual overlays of temporal images were repeatable to within one pixel. Satellite-sensed water temperature of Lake Okeechobee was used as an indicator of satellite radiometer repeatability and stability.

Chen, E.↗

NASA NTRS2020

Review: Strategies for Using Satellite-Based Products in Modeling PM2.5 and Short-Term Pollution Episodes

Short-term air pollution episodes motivate improved understanding of the association between air pollution and acute morbidity and mortality episodes, and triggers required mitigation plans. A variety of methods have been employed to estimate exposure to air pollution episodes, including GIS-based dispersion models, interpolation between sparse monitoring sites, land-use regression models, optimization models, line- or area-dispersion plume models, and models using information from imaging satellites, often including land-use and meteorological variables. There has been increasing use of satellite-borne aerosol products for assessing short-term air quality events. They provide better spatial coverage, but currently at the price of low temporal coverage and rather crude spatial resolution. This brief review of using satellite data for modeling short-term air quality and pollution events. The review can be pursued as a practical guide for modeling air quality with satellite-based products, as it includes important questions that should be considered in both the study design as well as the model development stages. Progress in this field is detailed and includes published models and their use in environmental and health studies. Both current and future satellite-borne capabilities are covered. It also provides links to access and download relevant datasets and some R code for data processing and modeling.

Meytar Sorek-Hamer↗

NASA NTRS1984

LANDSAT instruments characterization

Work performed for the LANDSAT instrument characterization task in the areas of absolute radiometry, coherent noise analysis, and between-date smoothing is reported. Absolute radiometric calibration for LANDSAT-5 TM under ambient conditions was performed. The TM Radiometric Algorithms and Performance Program (TRAPP) was modified to create optional midscan data files and to match the TM Image Processing System (TIPS) algorithm for pulse determination. Several data reduction programs were developed, including a linear regression and its plotted result. A fast Fourier transformation study was conducted on the resequenced TM data. Subscenes of homogeneous water within scenes over Pensacola, Florida were used for testing the FFT on the resequenced data. Finally, a gain and pulse height stability study of LANDSAT 5 TM spectral bands was performed.

Lee, Y.↗

NASA NTRS1991

A preliminary comparison of Landsat Thematic Mapper and SPOT-1 HRV multispectral data for estimating coniferous forest volume

Digital Landsat Thematic Mapper (TM) and SPOT high-resolution visible (HRV) images of coniferous forest canopies were compared in their relationship to forest wood volume using correlation and regression analyses. Significant inverse relationships were found between softwood volume and the spectral bands from both sensors (P less than 0.01). The highest correlations were between the log of softwood volume and the near-infrared bands.

Ripple, W. J.↗

NASA NTRS1995

A preliminary comparison of Landsat Thematic Mapper and SPOT-1 HRV multispectral data for estimating coniferous forest volume

Digital Landsat Thematic Mapper (TM) and Satellite Probatoire d'Observation de la Terre (SPOT) High Resolution Visible (HRV) images of coniferous forest canopies were compared in their relationship to forest wood volume using correlation and regression analyses. Significant inverse relationships were found between softwood volume and the spectral bands from both sensors (P less than 0.01). The highest correlations were between the log of softwood volume and the near-infrared bands (HRV band 3, r = -0.89; TM band 4, r = -0.83).

Ripple, William J.↗

NASA NTRS1982

A method for diagnosing surface parameters using geostationary satellite imagery and a boundary-layer model

A method for diagnosing surface parameters on a regional scale via geosynchronous satellite imagery is presented. Moisture availability, thermal inertia, atmospheric heat flux, and total evaporation are determined from three infrared images obtained from the Geostationary Operational Environmental Satellite (GOES). Three GOES images (early morning, midafternoon, and night) are obtained from computer tape. Two temperature-difference images are then created. The boundary-layer model is run, and its output is inverted via cubic regression equations. The satellite imagery is efficiently converted into output-variable fields. All computations are executed on a PDP 11/34 minicomputer. Output fields can be produced within one hour of the availability of aligned satellite subimages of a target area.

Polansky, A. C.↗

NASA NTRS2020

Southern Colorado Disasters: Using NASA Observations to Map Aspen Extent and Recovery Due to Wildfire

Quaking aspen (Populus tremuloides) is an important species for wildlife, watershed health, and ecosystem resilience across its range. Heavy ungulate browsing and factors influenced by a changing climate including seasonal temperature changes and moisture deficit have led to reduced post-fire aspen regeneration rates in southern Colorado. This project partnered with Trinchera Ranch and the Colorado State Forest Service to estimate aspen recovery after the Spring Creek Fire, which ignited in June of 2018. The Southern Colorado Disasters team utilized field measurements and satellite imagery from Landsat Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and the Shuttle Radar Topography Mission (SRTM) to train and run several random forest models that detect pre- and post-fire aspen extent. Ocular sampling of over 500 points on high-resolution pre-fire and post-fire images identified percentage aspen cover in 30 x 30-meter grid cells. This process provided training data for regression models, which were able to detect aspen across the landscape for both time periods using multiple remote sensing vegetation health indices. In addition, landscape suitability for aspen regeneration was modeled to provide a guide for managers on where to monitor for aspen regeneration post-fire.

DEVELOP Project Summary↗

NASA NTRS2020

Southern Colorado Disasters: Using NASA Earth Observations to Map Aspen Extent and Recovery Due to Wildfire

DEVELOP Technical Paper↗

NASA NTRS1986

Optimized retrievals of precipitable water fields from combinations of VAS satellite and conventional surface observations

VISSR (visible and infrared spin-scan radiometer) atmospheric sounder (VAS) radiances and conventional surface temperature and dewpoint data are used in several combinations within a regression approach to determine the optimum resolution and accuracy of precipitable water (PW) fields retrieved from satellite observations. Point retrievals at radiosonde stations are used to determine the numerical accuracy of each retrieval technique, and image sequences of the retrieved PW fields are used to determine the temporal stability and spatial coherence of mesoscale PW features. VAS channels 5, 6, 7, and 8 and the surface dewpoint contribute the most information to regression-based retrievals of PW. The most accurate PW retrievals are obtained when radiances are averaged to a resolution of 15 to 60 km. A physical 'split-window' approach provides better PW estimates than regression when only the 11- and 12-micron VAS channels are available or when radiosonde-based training is limited to only one time period.

Robinson, W. D.↗

NASA NTRS1989

Remote sensing of geobotanical trends in East Africa

The spatial and phenological distribution of vegetation was examined in a remote sensing geobotanical study of the East Africa Rift region. Six normalized difference vegetation index (NDVI) AVHRR (advanced very high resolution radiometer) scenes (February, June, and September of both 1984 and 1987) were used as a measure of vegetation presence. NOAA ETOPO-5 elevation and geographic coordinate data were coregistered with the multitemporal NDVI images. Univariate and multivariate statistics indicate that the NDVI values are significantly associated with elevation, latitude, and longitude. This supports the concept of quantifiable, regional gradients that affect large-scale geobotanical studies. A quadratic regression line was fitted to the NDVI, elevation, latitude, and longitudinal data. In this way a regional trend, as affected by elevation, was determined. The deviation of the actual data from this regional trend was displayed as an image, and shows the local climatic and geological influences.

Warner, Timothy A.↗

NASA NTRS1979

Recession of Martian north polar cap - 1977-1978 Viking observations

The regression curve for the 1977-1978 sublimation of the north polar cap of Mars is extracted from Viking orbiter imaging data. The recession observed during the spring season is quite consistent with previous observations and is in excellent agreement with telescopic data obtained concurrently. A receding late winter boundary is identified, but owing to hood obscuration this boundary cannot be definitely attributed to a surface cap.

James, P. B.↗

NASA NTRS1980

Application of remote sensing techniques to hydrography with emphasis on bathymetry

Remote sensing techniques are utilized for the determination of hydrographic characteristics, with emphasis in bathymetry. Two sensor systems were utilized: the Metric Camera Wild RC-10 and the Multispectral Scanner of LANDSAT Satellite (MSS-LANDSAT). From photographs of the metric camera, data of photographic density of points with known depth are obtained. A correlation between the variables density x depth is calculated through a regression straight line. From this line, the depth of points with known photographic density is determined. The LANDSAT MSS images are interpreted automatically in the Iterative Multispectral Analysis System (I-100) with the obtention of point subareas with the same gray level. With some simplifications done, it is assumed that the depth of a point is directly related with its gray level. Subareas with points of the same depth are then determined and isobathymetric curves are drawn. The coast line is obtained through the sensor systems already mentioned. Advantages and limitations of the techniques and of the sensor systems utilized are discussed and the results are compared with ground truth.

Dejesusparada, N.↗

NASA NTRS2004

Mapping Surface Soil Organic Carbon for Crop Fields with Remote Sensing

The organic C concentration of surface soil can be used in agricultural fields to vary crop production inputs. Organic C is often highly spatially variable, so that maps of soil organic C can be used to vary crop production inputs using precision farming technology. The objective of this research was to demonstrate the feasibility of mapping soil organic C on three fields, using remotely sensed images of the fields with a bare surface. Enough soil samples covering the range in soil organic C must be taken from each field to develop a satisfactory relationship between soil organic C content and image reflectance values. The number of soil samples analyzed in the three fields varied from 22 to 26. The regression equations differed between fields, but gave highly significant relationships with R2 values of 0.93, 0.95, and 0.89 for the three fields. A comparison of predicted and measured values of soil organic C for an independent set of 2 soil samples taken on one of the fields gave highly satisfactory results, with a comparison equation of % organic C measured + 1.02% organic C predicted, with r2 = 0.87.

Chen, Feng↗

NASA NTRS2007

Physical Interpretation of the Correlation Between Multi-Angle Spectral Data and Canopy Height

Recent empirical studies have shown that multi-angle spectral data can be useful for predicting canopy height, but the physical reason for this correlation was not understood. We follow the concept of canopy spectral invariants, specifically escape probability, to gain insight into the observed correlation. Airborne Multi-Angle Imaging Spectrometer (AirMISR) and airborne Laser Vegetation Imaging Sensor (LVIS) data acquired during a NASA Terrestrial Ecology Program aircraft campaign underlie our analysis. Two multivariate linear regression models were developed to estimate LVIS height measures from 28 AirMISR multi-angle spectral reflectances and from the spectrally invariant escape probability at 7 AirMISR view angles. Both models achieved nearly the same accuracy, suggesting that canopy spectral invariant theory can explain the observed correlation. We hypothesize that the escape probability is sensitive to the aspect ratio (crown diameter to crown height). The multi-angle spectral data alone therefore may not provide enough information to retrieve canopy height globally

Schull, M. A.↗

NASA NTRS2011

Analysis of Association Between Remotely Sensed (RS) Data and Soil Transmitted Helminthes Infection Using Geographical Information Systems (GIS): Boaco, Nicaragua

Soil-transmitted helminths are intestinal nematodes that can infect all members of a population but specially school-age children living in poverty. Infection can be significantly reversed with anthelmintic drug treatments and sanitation improvement. Implementation of effective public health programs requires reliable and updated information to identify areas at higher risk and to calculate amount of drug required. Geo-referenced in situ prevalence data will be overlaid over an ecological map derived from RS data using ARC Map 9.3 (ESRI). Prevalence data and RS data matching at the same geographical location will be analyzed for correlation and those variables from RS data that better correlate with prevalence will be included in a multivariate regression model. Temperature, vegetation, and distance to bodies of water will be inferred using data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat TM and ETM+. Elevation will be estimated with data from The Shuttle Radar Topography Mission (SRTM). Prevalence and intensity of infections are determined by parasitological survey (Kato Katz) of children enrolled in rural schools in Boaco, Nicaragua, in the communities of El Roblar, Cumaica Norte, Malacatoya 1, and Malacatoya 2). This study will demonstrate the importance of an integrated GIS/RS approach to define sampling clusters without the need for any ground-based survey. Such information is invaluable to identify areas of high risk and to geographically target control programs that maximize cost-effectiveness and sanitation efforts.

MorenoMadrinan, Max J.↗

NASA NTRS2023

Comparative Analysis of Empirical and Machine Learning Models for Chla Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges

Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra () from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, -fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.

algal blooms↗

NASA NTRS2001

A semi-automatic method for left ventricle volume estimate: an in vivo validation study

This study aims to the validation of the left ventricular (LV) volume estimates obtained by processing volumetric data utilizing a segmentation model based on level set technique. The validation has been performed by comparing real-time volumetric echo data (RT3DE) and magnetic resonance (MRI) data. A validation protocol has been defined. The validation protocol was applied to twenty-four estimates (range 61-467 ml) obtained from normal and pathologic subjects, which underwent both RT3DE and MRI. A statistical analysis was performed on each estimate and on clinical parameters as stroke volume (SV) and ejection fraction (EF). Assuming MRI estimates (x) as a reference, an excellent correlation was found with volume measured by utilizing the segmentation procedure (y) (y=0.89x + 13.78, r=0.98). The mean error on SV was 8 ml and the mean error on EF was 2%. This study demonstrated that the segmentation technique is reliably applicable on human hearts in clinical practice.

NASA Discipline Cardiopulmonary↗