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All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

Comprehensive Observations and Geostatistics of Slope Streaks within the Olympus Mons Aureole

Slope streaks (SS) are enigmatic linear features characterized by relatively low-albedo features that appear and fade on high-albedo slopes on Mars. Despite numerous hypotheses proposed to explain their formation, the primary mechanism behind SS remains elusive. Here, we examine 702 SS features using 32 multitemporal Context Imager (CTX) images and mesoscale modeling data obtained from a site (centered at 31.230°N, 216.281°E) in the Olympus Mons Aureole region. Our investigation revealed several key findings that shed light on the dynamics of SS formation and fading. We discovered a significant preference for SS formation on south-facing (equator-facing) slopes compared to north-facing slopes, with SS being over seven times more likely to occur on the former. Furthermore, SS formation was found to be seasonal with significantly enhanced by a factor of ∼6 near the equinoxes (from solar longitude Ls 337°-42° and 136°-227°) compared to other times of the year. Our analysis also revealed a correlation between the rates of SS formation and fading, with scree slopes exhibited the fastest-fading SS also experiencing the highest rates of newly-formed SS. Additionally, we measured the median starting and stopping slopes of SS to be 23.4° and 14.9°, respectively, significantly below the angle of repose of sand. These low slopes suggest the necessity for an energetic trigger mechanism to initiate SS formation. Infrared spectroscopy revealed that the principal distinction between the material inside and outside of a SS lies in the reduced abundance of dust within the streak. Notably, this site demonstrates the highest rates of SS formation (a 29.6% increase in new SS per Mars year) and fading (a 12% fading of SS per Mars year) ever quantified. These elevated rates may be attributed to the site's topography, which facilitates calm surficial nighttime winds throughout the year, leading to widespread dust deposition. Daytime downhill winds near the ridgelines of S-facing slopes may then trigger movement of newly deposited dust aggregates or alternatively, a Knudsen pump phenomenon could serve as a potential trigger for SS. We propose that SS is inhibited during the northern summer solstice season due to the relatively clear aphelion atmosphere, which limits dust deposition. Similarly, the lack of triggering activity during the winter solstice is likely due to more moderate daytime winds. Overall, our findings are consistent with the wind-triggered dry avalanche hypothesis as a plausible explanation for SS formation.

D E Stillman↗
NASA NTRS1999

Canopy Cover Predictions using Ground Observations and Remotely Sensed Data

Maps of vegetation status are needed at many scales, from the field level to monitor ecosystem condition to the global level to understand the carbon cycle. Status is quantified by such variables as leaf area index, biomass, and fraction of canopy cover. Current methods of predicting vegetation variables use remote sensing data to provide a spatially exhaustive data source. In a study in western Montana, several hundred ground observations made by the US Forest Service on tenth-acre conifer plots were used to develop aspatial regression and geostatistical prediction models. Normalized Difference Vegetation Index (NDVI) values from Landsat Thematic Mapper images were used as ancillary data. These models were then used to predict canopy cover at unsampled locations in a 97 square kilometer region on the boundary of the Flathead National Forest and the Bob Marshall Wilderness. Independent data from two dates six years apart were used for validation. Given the assumption that actual canopy cover remained relatively unchanged within this time period, partial validation can be achieved by measuring the correspondence of the two maps. This criterion results in ranking the aspatial regression maps as less accurate than the geostatistically generated maps. The geostatistical approach emphasizes ground measurements more heavily than does aspatid regression. Geostatistical simulations of canopy cover also provide a means of describing uncertainty about the patterns of canopy cover.

Dungan, Jennifer L.↗
NASA NTRS2021

Weekly Mapping of Sea Ice Freeboard in the Ross Sea from ICESat-2

NASA’s ICESat-2 has been providing sea ice freeboard measurements across the polar regions since October 2018. In spite of the outstanding spatial resolution and precision of ICESat-2, the spatial sparsity of the data can be a critical issue for sea ice monitoring. This study employs a geostatistical approach (i.e., ordinary kriging) to characterize the spatial autocorrelation of the ICESat-2 freeboard measurements (ATL10) to estimate weekly freeboard variations in 2019 for the entire Ross Sea area, including where ICESat-2 tracks are not directly available. Three variogram models (exponential, Gaussian, and spherical) are compared in this study. According to the cross-validation results, the kriging-estimated freeboards show correlation coefficients of 0.56–0.57, root mean square error (RMSE) of ~0.12 m, and mean absolute error (MAE) of ~0.07 m with the actual ATL10 freeboard measurements. In addition, the estimated errors of the kriging interpolation are low in autumn and high in winter to spring, and low in southern regions and high in northern regions of the Ross Sea. The effective ranges of the variograms are 5–10 km and the results from the three variogram models do not show significant differences with each other. The southwest (SW) sector of the Ross Sea shows low and consistent freeboard over the entire year because of the frequent opening of wide polynya areas generating new ice in this sector. However, the southeast (SE) sector shows large variations in freeboard, which demonstrates the advection of thick multiyear ice from the Amundsen Sea into the Ross Sea. Thus, this kriging-based interpolation of ICESat-2 freeboard can be used in the future to estimate accurate sea ice production over the Ross Sea by incorporating other remote sensing data.

Satellite altimeter↗
NASA NTRS1988

Estimating the signal-to-noise ratio of AVIRIS data

To make the best use of narrowband airborne visible/infrared imaging spectrometer (AVIRIS) data, an investigator needs to know the ratio of signal to random variability or noise (signal-to-noise ratio or SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption; random noise comprises sensor noise and intrapixel variability (i.e., variability within a pixel). The three existing methods for estimating the SNR are inadequate, since typical laboratory methods inflate while dark current and image methods deflate the SNR. A new procedure is proposed called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intrapixel variability using the semi-variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season and could be applied to remotely sensed data from broadband sensors.

Curran, Paul J.↗
NASA NTRS1988

Zones of information in the AVIRIS spectra

To make the best use of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data an investigator needs to know the ratio of signal to random variability or noise (S/N ratio). The signal is land-cover dependent and decreases with both wavelength and atmospheric absorption and random noise comprises sensor noise and intra-pixel variability. The three existing methods for estimating the S/N ratio are inadequate as typical laboratory methods inflate, while dark current and image methods deflate the S/N ratio. We propose a new procedure called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intra-pixel variability using the semi-variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season.

Curran, Paul J.↗
NASA NTRS1989

Autocorrelation and regularization in digital images. II - Simple image models

The variogram function used in geostatistical analysis is a useful statistic in the analysis of remotely sensed images. Using the results derived by Jupp et al. (1988), the basic second-order, or covariance, properties of scenes modeled by simple disks of varying size and spacing after imaging into disk-shaped pixels are analyzed to explore the relationship betwee image variograms and discrete object scene structure. The models provide insight into the nature of real images of the earth's surface and the tools for a complete analysis of the more complex case of three-dimensional illuminated discrete-object images.

Jupp, David L. B.↗
NASA NTRS1989

Estimation of signal-to-noise - A new procedure applied to AVIRIS data

To make the best use of narrowband airborne visible/infrared imaging spectrometer (AVIRIS) data, an investigator needs to know the ratio of signal to random variability or noise (signal-to-noise ratio or SNR). The signal is land cover dependent and varies with both wavelength and atmospheric absorption; random noise comprises sensor noise and intrapixel variability (i.e., variability within a pixel). The three existing methods for estimating the SNR are inadequate, since typical laboratory methods inflate while dark current and image methods deflate the SNR. A new procedure is proposed called the geostatistical method. It is based on the removal of periodic noise by notch filtering in the frequency domain and the isolation of sensor noise and intrapixel variability using the semi-variogram. This procedure was applied easily and successfully to five sets of AVIRIS data from the 1987 flying season and could be applied to remotely sensed data from broadband sensors.

Curran, Paul J.↗
NASA NTRS1991

Estimating The SNR Of AVIRIS Data

Combination of established statistical and filtering techniques yields improved estimates of signal-to-noise ratio (SNR) of multispectral imagery produced by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Method yields more accurate estimate of random variability in AVIRIS imagery. Combination of techniques called "geostatistical" method. In first step of method, periodic noise removed by notch filtering in frequency domain. In subsequent steps, sensor noise and variability within picture elements isolated by use of semi-variance of picture elements.

Curran, Paul J.↗
NASA NTRS1989

Decorrelation distance of snow in the Colorado River Basin

The problem of estimating areal averages from point measurement has been extensively studied by mining engineers and hydrologists. Its application to satellite measurements has recently been introduced. The semivariaogram has been used in many geostatistical applications to estimate spatial structures of observed properties, such as mineral distributions. An examination is made of snow variations in Colorado from daily snow data collected in 11 SNOTEL stations. The associated semivariogram is estimated. The objective is to estimate the spatial structure of the snow field so that the point data can be used for comparison with, and validation for, satellite measurements.

Chang, A. T. C.↗
NASA NTRS1991

Environmental analysis using integrated GIS and remotely sensed data - Some research needs and priorities

This paper discusses some basic scientific issues and research needs in the joint processing of remotely sensed and GIS data for environmental analysis. Two general topics are treated in detail: (1) scale dependence of geographic data and the analysis of multiscale remotely sensed and GIS data, and (2) data transformations and information flow during data processing. The discussion of scale dependence focuses on the theory and applications of spatial autocorrelation, geostatistics, and fractals for characterizing and modeling spatial variation. Data transformations during processing are described within the larger framework of geographical analysis, encompassing sampling, cartography, remote sensing, and GIS. Development of better user interfaces between image processing, GIS, database management, and statistical software is needed to expedite research on these and other impediments to integrated analysis of remotely sensed and GIS data.

Davis, Frank W.↗
NASA NTRS1993

Functional patterns in an annual grassland during an AVIRIS overflight

This study relates Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery to ground measurements of vegetation distribution, physiology, and productivity at Stanford University's Jasper Ridge Biological Preserve. Primary efforts focused on a 9-ha region of annual grassland where we completed a detailed ground-based study in conjunction with a 15 May 1991 AVIRIS overflight. Spectral mixture analysis and the normalized difference vegetation index (NDVI) calculated from AVIRIS data were used to evaluate spatial patterns of vegetation type, productivity, and potential physiological activity. Concurrent ground sampling revealed a high degree of correlation between NDVI and estimates of canopy chemistry, structure, productivity, and CO2 flux, supporting the use of imaging spectrometry to estimate spatial and temporal trends in vegetation physiology and productivity in this relatively simple grassland ecosystem. Geostatistical analyses of both ground and AVIRIS data supported the conclusion that the AVIRIS pixel size was suitable for describing the influence of major landscape features in this grassland and that spatial detail would be lost at slightly larger pixel sizes typical of other imaging spectrometers.

Gamon, John A.↗
NASA NTRS1996

New results from mapping Antarctica at high resolution from radar altimeter data

The limits of the altimeter data evaluation were analyzed using geostatistical methods. It is often argued that satellite radar altimeter data over ice can not be used to map ice surfaces with a slope exceeding 0.5 deg. The maps presented, obtained from the Geosat satellite geodetic mission, concern the Antarctica north of 72.1 deg South. The grids constructed from other satellite observations facilitate the analysis of the changes in the Antarctic ice stream/ice shelf systems. The evaluation was applied to the Lambert Glacier/Amery Ice Shelf system.

Matassa, Michael↗
NASA NTRS1997

Evaluation of GIS Technology in Assessing and Modeling Land Management Practices

There is an increasing concern of land owners to protect and maintain healthy and sustainable agroecosystems through the implementation of best management practices (BMP). The objectives of this study were: (1) To develop and evaluate the use of a Geographic Information System (GIS) technology for enhancing field-scale management practices; (2) evaluate the use of 2-dimensional displays of the landscape and (3) define spatial classes of variables from interpretation of geostatistical parameters. Soil samples were collected to a depth of 2 m at 15 cm increments. Existing data from topographic, land use, and soil survey maps of the Winfred Thomas Agricultural Research Station were converted to digital format. Additional soils data which included texture, pH, and organic matter were also generated. The digitized parameters were used to create a multilayered field-scale GIS. Two dimensional (2-D) displays of the parameters were generated using the ARC/INFO software. The spatial distribution of the parameters evaluated in both fields were similar which could be attributed to the similarity in vegetation and surface elevation. The ratio of the nugget to total semivariance, expressed as a percentage, was used to assess the degree of spatial variability. The results indicated that most of the parameters were moderate spatially dependent Biophysical constraint maps were generated from the database layers, and used in multiple combination to visualize results of the BMP. Understanding the spatial relationships of physical and chemical parameters that exists within a field should enable land managers to more effectively implement BMP to ensure a safe and sustainable environment.

Archer, F.↗
NASA NTRS2001

Investigating Uncertainty in Predicting Carbon Dynamics in North American Biomes: Putting Support-Effect Bias in Perspective

A fundamental strategy in NASA's Earth Observing System's (EOS) monitoring of vegetation and its contribution to the global carbon cycle is to rely on deterministic, process-based ecosystem models to make predictions of carbon flux over large regions. These models are parameterized (that is, the input variables are derived) using remotely sensed images such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS), ground measurements and interpolated maps. Since early applications of these models, investigators have noted that results depend partly on the spatial support of the input variables. In general, the larger the support of the input data, the greater the chance that the effects of important components of the ecosystem will be averaged out. A review of previous work shows that using large supports can cause either positive or negative bias in carbon flux predictions. To put the magnitude and direction of these biases in perspective, we must quantify the range of uncertainty on our best measurements of carbon-related variables made on equivalent areas. In other words, support-effect bias should be placed in the context of prediction uncertainty from other sources. If the range of uncertainty at the smallest support is less than the support-effect bias, more research emphasis should probably be placed on support sizes that are intermediate between those of field measurements and MODIS. If the uncertainty range at the smallest support is larger than the support-effect bias, the accuracy of MODIS-based predictions will be difficult to quantify and more emphasis should be placed on field-scale characterization and sampling. This talk will describe methods to address these issues using a field measurement campaign in North America and "upscaling" using geostatistical estimation and simulation.

Dungan, Jennifer L.↗
NASA NTRS2002

Visualizing 2D Probability Distributions from Satellite Image-Derived Data

Creating maps of biophysical and geophysical variables using Earth Observing System (EOS) satellite image data is an important component of Earth science. These 2D maps have a single value at every location and standard techniques are used to visualize them. Current tools fall short, however, when it is necessary to describe a distribution of values at each location. Distributions may represent a frequency of occurrence over time, frequency of occurrence from multiple runs of an ensemble forecast or possible values from an uncertainty model. 'Distribution data sets' are described, then a case study is presented to visualize such 2D distributions. Distribution data sets are different from multivariate data sets in the sense that the values are for a single variable instead of multiple variables. Our case study data consists of multiple realizations of percent forest cover, generated using a geostatistical technique that combines ground measurements and satellite imagery to model uncertainty about forest cover. We present several approaches for analyzing and visualizing such data sets. The first is a pixel-wise analysis of the probability density functions for the 2D image while the second is an analysis of features identified within the image. Such pixel-wise and feature-wise views will give Earth scientists a more complete understanding of distribution data sets.

Kao, David↗
NASA NTRS2003

Fractals and Spatial Methods for Mining Remote Sensing Imagery

The rapid increase in digital remote sensing and GIS data raises a critical problem -- how can such an enormous amount of data be handled and analyzed so that useful information can be derived quickly? Efficient handling and analysis of large spatial data sets is central to environmental research, particularly in global change studies that employ time series. Advances in large-scale environmental monitoring and modeling require not only high-quality data, but also reliable tools to analyze the various types of data. A major difficulty facing geographers and environmental scientists in environmental assessment and monitoring is that spatial analytical tools are not easily accessible. Although many spatial techniques have been described recently in the literature, they are typically presented in an analytical form and are difficult to transform to a numerical algorithm. Moreover, these spatial techniques are not necessarily designed for remote sensing and GIS applications, and research must be conducted to examine their applicability and effectiveness in different types of environmental applications. This poses a chicken-and-egg problem: on one hand we need more research to examine the usability of the newer techniques and tools, yet on the other hand, this type of research is difficult to conduct if the tools to be explored are not accessible. Another problem that is fundamental to environmental research are issues related to spatial scale. The scale issue is especially acute in the context of global change studies because of the need to integrate remote-sensing and other spatial data that are collected at different scales and resolutions. Extrapolation of results across broad spatial scales remains the most difficult problem in global environmental research. There is a need for basic characterization of the effects of scale on image data, and the techniques used to measure these effects must be developed and implemented to allow for a multiple scale assessment of the data before any useful process-oriented modeling involving scale-dependent data can be conducted. Through the support of research grants from NASA, we have developed a software module called ICAMS (Image Characterization And Modeling System) to address the need to develop innovative spatial techniques and make them available to the broader scientific communities. ICAMS provides new spatial techniques, such as fractal analysis, geostatistical functions, and multiscale analysis that are not easily available in commercial GIS/image processing software. By bundling newer spatial methods in a user-friendly software module, researchers can begin to test and experiment with the new spatial analysis methods and they can gauge scale effects using a variety of remote sensing imagery. In the following, we describe briefly the development of ICAMS and present application examples.

Lam, Nina↗