Search NASA⌕ Search

SEARCH · Search NASA

Results for “hyperspectral imagery”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Remote Sensing of CDOM, CDOM Spectral Slope, and Dissolved Organic Carbon in the Global Ocean

A Global Ocean Carbon Algorithm Database (GOCAD) has been developed from over 500 oceanographic field campaigns conducted worldwide over the past 30 years including in situ reflectances and coincident satellite imagery, multi- and hyperspectral Chromophoric Dissolved Organic Matter (CDOM) absorption coefficients from 245–715 nm, CDOM spectral slopes in eight visible and ultraviolet wavebands, dissolved and particulate organic carbon (DOC and POC, respectively), and inherent optical, physical, and biogeochemical properties. From field optical and radiometric data and satellite measurements, several semi-analytical, empirical, and machine learning algorithms for retrieving global DOC, CDOM, and CDOM slope were developed, optimized for global retrieval, and validated. Global climatologies of satellite-retrieved CDOM absorption coefficient and spectral slope based on the most robust of these algorithms lag seasonal patterns of phytoplankton biomass belying Case 1 assumptions, and track terrestrial runoff on ocean basin scales. Variability in satellite retrievals of CDOM absorption and spectral slope anomalies are tightly coupled to changes in atmospheric and oceanographic conditions associated with El Niño Southern Oscillation (ENSO), strongly covary with the multivariate ENSO index in a large region of the tropical Pacific, and provide insights into the potential evolution and feedbacks related to sea surface dissolved carbon in a warming climate. Further validation of the DOC algorithm developed here is warranted to better characterize its limitations, particularly in mid-ocean gyres and the southern oceans.

Dissolved organic carbon↗
NASA NTRS2001

Derivative Analysis of AVIRIS Hyperspectral Data for the Detection of Plant Stress

A remote sensing campaign was conducted over a U.S. Department of Agriculture test site at Shelton, Nebraska. The test field was set off in blocks that were differentially treated with nitrogen. Four replicates of 0-kg/ha to 200-kg/ha, in 50-kg/ha increments, were present. Low-altitude AVIRIS hyperspectral data were collected over the site in 224 spectral bands. Simultaneously, ground data were collected to support the airborne imagery. In an effort to evaluate published, derivative-based algorithms for the detection of plant stress, different derivative-based approaches were applied to the collected AVIRIS image cube. The results indicate that, given good quality hyperspectral imagery, derivative techniques compare favorably with simple, well known band ratio algorithms for detection of plant stress.

Estep, Lee↗
NASA NTRS2009

Radiometric Characterization of Hyperspectral Imagers using Multispectral Sensors

The Remote Sensing Group (RSG) at the University of Arizona has a long history of using ground-based test sites for the calibration of airborne and satellite based sensors. Often, ground-truth measurements at these test sites are not always successful due to weather and funding availability. Therefore, RSG has also automated ground instrument approaches and cross-calibration methods to verify the radiometric calibration of a sensor. The goal in the cross-calibration method is to transfer the calibration of a well-known sensor to that of a different sensor, This work studies the feasibility of determining the radiometric calibration of a hyperspectral imager using multispectral a imagery. The work relies on the Moderate Resolution Imaging Spectroradiometer (M0DIS) as a reference for the hyperspectral sensor Hyperion. Test sites used for comparisons are Railroad Valley in Nevada and a portion of the Libyan Desert in North Africa. Hyperion bands are compared to MODIS by band averaging Hyperion's high spectral resolution data with the relative spectral response of M0DlS. The results compare cross-calibration scenarios that differ in image acquisition coincidence, test site used for the calibration, and reference sensor. Cross-calibration results are presented that show agreement between the use of coincident and non-coincident image pairs within 2% in most brands as well as similar agreement between results that employ the different MODIS sensors as a reference.

McCorkel, Joel↗
NASA NTRSDate not supplied

CyanoSCape: Freshwater Phytoplankton and Floating Aquatic Vegetation Biodiversity

In Southern Africa, the impacts of anthropogenic activities on biodiversity and ecosystem services are exacerbated by the climate crisis. Rapid land use change and the lack of emphasis on environmentally sustainable agricultural practices has hindered hydrological processes and compromised riverine and aquatic ecosystems. This poses obvious risks to natural/indigenous aquatic biodiversity and long-term ecosystem sustainability. Phytoplankton serve as the foundation of the freshwater food web with zooplankton as consumers, which feed fish, invertebrates, and so on up the food chain that comprises the biodiversity of the freshwater system that serves as habitat for biodiversity as well. The diversity of phytoplankton (microscopic organisms) includes photosynthesizing bacteria (cyanobacteria), plant-like diatoms, dinoflagellates, and green algae. Nutrient run-off from agricultural fertilizers and urban overflows, warm temperatures, abundant light availability and compromised hydrological systems provide an ideal environment for cyanobacteria to flourish. Increased prevalence of cyanobacteria, due to both natural and anthropogenic causes, can incur significant effects on the biodiversity of the overall phytoplankton assemblage. These bloom forming algae can significantly outcompete other phytoplankton classes in warmer and eutrophic waters where they are quick to dominate the freshwater system. Eutrophication and toxic cyanobacteria blooms (cyanoHABs) in the inland waters of the Greater Cape Floristic Region (GCFR) incur significant effects on the biodiversity of the overall phytoplankton assemblage and provide a favorable environment forthe overgrowth of floating aquatic vegetation (FAV), which is often invasive and associated with reduced aquatic biodiversity. The algal biodiversity of the GCFR’s freshwater systems is not well characterized. Hyperspectral optical observations are expected to facilitate the improvement of current phytoplankton functional type retrievals significantly, as the sensitivity is sufficient that the distinctive, fine spectral features of different phytoplankton groups can be detected. This will enable testing emerging algorithms and inform the development of new algorithms for use with upcoming hyperspectral satellite missions in this decade. Innovations in optical sensor sensitivity and next generation machine learning capabilities considerably enhance the potential for accurate and rapid detection of phytoplankton, namely the presence, extent, and diversity of cyanobacteria present in cyanoHABs and additionally, invasive FAV. Upcoming hyperspectral satellite missions such as NASA’s Surface Biology and Geology (SBG), Plankton, Aerosol, Cloud, ocean Ecosystem (PACE), and the European Space Agency’s Copernicus Hyperspectral Imaging Mission (CHIME) will provide imagery with unprecedented spectral and spatial resolution which will further enable the discovery of linkages between the seasonality and dynamics of HABs and FAV. The overarching goal of this project is to utilize hyperspectral data, with recently developed and next-generation algorithms, to determine the biodiversity of freshwater systems phytoplankton assemblage with emphasis on genus level distinction, as well as monitor the prevalence and diversity of FAV.

CyanoSCape↗
NASA NTRS2001

Synthesis of Multispectral Bands from Hyperspectral Data: Validation Based on Images Acquired by AVIRIS, Hyperion, ALI, and ETM+

Multispectral data requirements for Earth science applications are not always studied rigorously studied before a new remote sensing system is designed. A study of the spatial resolution, spectral bandpasses, and radiometric sensitivity requirements of real-world applications would focus the design onto providing maximum benefits to the end-user community. To support systematic studies of multispectral data requirements, the Applications Research Toolbox (ART) has been developed at NASA's Stennis Space Center. The ART software allows users to create and assess simulated datasets while varying a wide range of system parameters. The simulations are based on data acquired by existing multispectral and hyperspectral instruments. The produced datasets can be further evaluated for specific end-user applications. Spectral synthesis of multispectral images from hyperspectral data is a key part of the ART software. In this process, hyperspectral image cubes are transformed into multispectral imagery without changes in spatial sampling and resolution. The transformation algorithm takes into account spectral responses of both the synthesized, broad, multispectral bands and the utilized, narrow, hyperspectral bands. To validate the spectral synthesis algorithm, simulated multispectral images are compared with images collected near-coincidentally by the Landsat 7 ETM+ and the EO-1 ALI instruments. Hyperspectral images acquired with the airborne AVIRIS instrument and with the Hyperion instrument onboard the EO-1 satellite were used as input data to the presented simulations.

Blonksi, Slawomir↗
NASA NTRS2014

Detecting Trend and Seasonal Changes in Bathymetry Derived from HICO Imagery: A Case Study of Shark Bay, Western Australia

The Hyperspectral Imager for the Coastal Ocean (HICO) aboard the International Space Station has offered for the first time a dedicated space-borne hyperspectral sensor specifically designed for remote sensing of the coastal environment. However, several processing steps are required to convert calibrated top-of-atmosphere radiances to the desired geophysical parameter(s). These steps add various amounts of uncertainty that can cumulatively render the geophysical parameter imprecise and potentially unusable if the objective is to analyze trends and/or seasonal variability. This research presented here has focused on: (1) atmospheric correction of HICO imagery; (2) retrieval of bathymetry using an improved implementation of a shallowwater inversion algorithm; (3) propagation of uncertainty due to environmental noise through the bathymetry retrieval process; (4) issues relating to consistent geo-location of HICO imagery necessary for time series analysis, and; (5) tide height corrections of the retrieved bathymetric dataset. The underlying question of whether a temporal change in depth is detectable above uncertainty is also addressed. To this end, nine HICO images spanning November 2011 to August 2012, over the Shark Bay World Heritage Area, Western Australia, were examined. The results presented indicate that precision of the bathymetric retrievals is dependent on the shallow water inversion algorithm used.Within this study, an average of 70% of pixels for the entire HICO-derived bathymetry dataset achieved a relative uncertainty of less than +/-20%. A per-pixel t-test analysis between derived bathymetry images at successive timestamps revealed observable changes in depth to as lowas 0.4 m. However, the present geolocation accuracy of HICO is relatively poor and needs further improvements before extensive time series analysis can be performed.

Geolocations accuracy↗
NASA NTRS2014

Detecting Trend and Seasonal Changes in Bathymetry Derived from HICO Imagery: A Case Study of Shark Bay, Western Australia

The Hyperspectral Imager for the Coastal Ocean (HICO) aboard the International Space Station has offered for the first time a dedicated space-borne hyperspectral sensor specifically designed for remote sensing of the coastal environment. However, several processing steps are required to convert calibrated top-of-atmosphere radiances to the desired geophysical parameter(s). These steps add various amounts of uncertainty that can cumulatively render the geophysical parameter imprecise and potentially unusable if the objective is to analyze trends and/or seasonal variability. This research presented here has focused on: (1) atmospheric correction of HICO imagery; (2) retrieval of bathymetry using an improved implementation of a shallow water inversion algorithm; (3) propagation of uncertainty due to environmental noise through the bathymetry retrieval process; (4) issues relating to consistent geo-location of HICO imagery necessary for time series analysis, and; (5) tide height corrections of the retrieved bathymetric dataset. The underlying question of whether a temporal change in depth is detectable above uncertainty is also addressed. To this end, nine HICO images spanning November 2011 to August 2012, over the Shark Bay World Heritage Area, Western Australia, were examined. The results presented indicate that precision of the bathymetric retrievals is dependent on the shallow water inversion algorithm used. Within this study, an average of 70% of pixels for the entire HICO-derived bathymetry dataset achieved a relative uncertainty of less than +/-20%. A per-pixel t-test analysis between derived bathymetry images at successive timestamps revealed observable changes in depth to as low as 0.4 m. However, the present geolocation accuracy of HICO is relatively poor and needs further improvements before extensive time series analysis can be performed.

coastal marine↗
NASA NTRS2020

Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar

Warming in arctic and boreal regions is increasing shrub cover and biomass. In southcentral Alaska, willow (Salix spp.) and alder (Alnus spp.) shrubs grow taller than many tree species and account for a substantial proportion of aboveground biomass, yet they are not individually measured as part of the operational Forest Inventory and Analysis (FIA) Program. The goal of this research was to test methods for landscape-scale mapping of tall shrub biomass in upper montane and subalpine environments using FIA-type plot measurements (n = 51) and predictor variables from imagery-based structure-from-motion (SfM) and airborne lidar. Specifically, we compared biomass models constructed from imagery acquired by unmanned aerial vehicle (UAV; ~1.7 cm pixels), imagery from the NASA Goddard's Lidar, Hyperspectral, and Thermal Airborne Imager (G-LiHT; ~3.1 cm pixels), and concomitant G-LiHT small-footprint lidar. Tall shrub biomass was most accurately predicted at 5 m resolution (R^2 = 0.81, RMSE = 1.09 kg/sq. m) using G-LiHT SfM color and structure variables. Lidar-only models had lower precision (R^2 = 0.74, RMSE = 1.26 kg/sq. m), possibly due to reduced model information content from variable multicollinearity or lower data density. Separate models for upper montane zones with trees and shrubs and subalpine zones with only shrubs were always chosen over single models based on minimization of Akaike's Information Criterion, indicating the need for variable sets robust to overhanging tree canopy. Decreasing point density from UAV (5000–8000 pts./sq. m) to the G-LiHT SfM point cloud (500–2000 pts./sq. m) had little impact on model fit, suggesting that high-resolution airborne imagery can extend SfM approaches well beyond line-of-sight restrictions for UAV platforms. Overall, our results confirmed that SfM from high-resolution imagery is a viable approach to estimate shrub biomass in the boreal region, especially when an existing lidar terrain model and local field calibration data are available to quantify uncertainty in the SfM point cloud and landscape-scale estimates of shrub biomass.

Michael Alonzo↗
NASA NTRS1993

Unmixing of Spectral Components Affecting AVIRIS Imagery of Tampa Bay

Models have been developed for use with hyperspectral remote-sensing reflectance data collected just above the air-sea interface for the West Florida Shelf 1,2,3,4,. They respond to variations in pigment, detrital, and gelbstoff absorption, chlorophyll a and gelbstoff flourescence, water Raman Scattering, Backscattering by water and particulate, and bottom depth and albedo.

Imagery↗
NASA NTRS2012

G-LiHT: Goddard's LiDAR, Hyperspectral and Thermal Airborne Imager

Scientists at NASA's Goddard Space Flight Center have developed an ultra-portable, low-cost, multi-sensor remote sensing system for studying the form and function of terrestrial ecosystems. G-LiHT integrates two LIDARs, a 905 nanometer single beam profiler and 1550 nm scanner, with a narrowband (1.5 nanometers) VNIR imaging spectrometer and a broadband (8-14 micrometers) thermal imager. The small footprint (approximately 12 centimeters) LIDAR data and approximately 1 meter ground resolution imagery are advantageous for high resolution applications such as the delineation of canopy crowns, characterization of canopy gaps, and the identification of sparse, low-stature vegetation, which is difficult to detect from space-based instruments and large-footprint LiDAR. The hyperspectral and thermal imagery can be used to characterize species composition, variations in biophysical variables (e.g., photosynthetic pigments), surface temperature, and responses to environmental stressors (e.g., heat, moisture loss). Additionally, the combination of LIDAR optical, and thermal data from G-LiHT is being used to assess forest health by sensing differences in foliage density, photosynthetic pigments, and transpiration. Low operating costs (approximately $1 ha) have allowed us to evaluate seasonal differences in LiDAR, passive optical and thermal data, which provides insight into year-round observations from space. Canopy characteristics and tree allometry (e.g., crown height:width, canopy:ground reflectance) derived from G-LiHT data are being used to generate realistic scenes for radiative transfer models, which in turn are being used to improve instrument design and ensure continuity between LiDAR instruments. G-LiHT has been installed and tested in aircraft with fuselage viewports and in a custom wing-mounted pod that allows G-LiHT to be flown on any Cessna 206, a common aircraft in use throughout the world. G-LiHT is currently being used for forest biomass and growth estimation in the CONUS and Mexico in support of NASA's Carbon Monitoring System (CMS) and AMIGA-Carb (AMerican Icesat Glas Assessment of Carbon). For NASA's CMS, wall-to-wall G-LiHT data have been acquired over intensive study sites with historic LiDAR datasets, dense inventory data, stem maps and flux tower observations. For AMIGA-Carb, G-LiHT transects have been acquired over ICESat tracks and USDA-FS inventory plots throughout the CONUS, and similar data will be acquired in Mexico during 2013. This talk will highlight recent science results from continental-scale transects landscape-scale deployments of G-LiHT, as well as seasonal forest dynamics from repeat pass G-LiHT acquisitions.

Cook, Bruce↗
NASA NTRS2001

Road Extraction from AVIRIS Using Spectral Mixture and Q-Tree Filter Techniques

Accurate road location and condition information are of primary importance in road infrastructure management. Additionally, spatially accurate and up-to-date road networks are essential in ambulance and rescue dispatch in emergency situations. However, accurate road infrastructure databases do not exist for vast areas, particularly in areas with rapid expansion. Currently, the US Department of Transportation (USDOT) extends great effort in field Global Positioning System (GPS) mapping and condition assessment to meet these informational needs. This methodology, though effective, is both time-consuming and costly, because every road within a DOT's jurisdiction must be field-visited to obtain accurate information. Therefore, the USDOT is interested in identifying new technologies that could help meet road infrastructure informational needs more effectively. Remote sensing provides one means by which large areas may be mapped with a high standard of accuracy and is a technology with great potential in infrastructure mapping. The goal of our research is to develop accurate road extraction techniques using high spatial resolution, fine spectral resolution imagery. Additionally, our research will explore the use of hyperspectral data in assessing road quality. Finally, this research aims to define the spatial and spectral requirements for remote sensing data to be used successfully for road feature extraction and road quality mapping. Our findings will facilitate the USDOT in assessing remote sensing as a new resource in infrastructure studies.

Gardner, Margaret E.↗
NASA NTRS2015

Simulation of Wake Vortex Radiometric Detection via Jet Exhaust Proxy

This paper describes an analysis of the potential of an airborne hyperspectral imaging IR instrument to infer wake vortices via turbine jet exhaust as a proxy. The goal was to determine the requirements for an imaging spectrometer or radiometer to effectively detect the exhaust plume, and by inference, the location of the wake vortices. The effort examines the gas spectroscopy of the various major constituents of turbine jet exhaust and their contributions to the modeled detectable radiance. Initially, a theoretical analysis of wake vortex proxy detection by thermal radiation was realized in a series of simulations. The first stage used the SLAB plume model to simulate turbine jet exhaust plume characteristics, including exhaust gas transport dynamics and concentrations. The second stage used these plume characteristics as input to the Line By Line Radiative Transfer Model (LBLRTM) to simulate responses from both an imaging IR hyperspectral spectrometer or radiometer. These numerical simulations generated thermal imagery that was compared with previously reported wake vortex temperature data. This research is a continuation of an effort to specify the requirements for an imaging IR spectrometer or radiometer to make wake vortex measurements. Results of the two-stage simulation will be reported, including instrument specifications for wake vortex thermal detection. These results will be compared with previously reported results for IR imaging spectrometer performance.

Daniels, Taumi S.↗
NASA NTRS1995

Mine Discrimination Using Multispectral Imagery With Feedforward Neural Networks

Simulated mine detection was performed on a polarimetric hyperspectral imaging dataset collected by using an acousto-optic tunable filter camera. A feedforward artificial neural network was programmed to recognize predefined spectral "templates." The simulation results are provided along with the preprocessing steps and window sizes leading to mine detection without false alarms.

polarimetric↗
NASA NTRS2012

Hardware Implementation of Lossless Adaptive Compression of Data From a Hyperspectral Imager

Efficient onboard data compression can reduce the data volume from hyperspectral imagers on NASA and DoD spacecraft in order to return as much imagery as possible through constrained downlink channels. Lossless compression is important for signature extraction, object recognition, and feature classification capabilities. To provide onboard data compression, a hardware implementation of a lossless hyperspectral compression algorithm was developed using a field programmable gate array (FPGA). The underlying algorithm is the Fast Lossless (FL) compression algorithm reported in Fast Lossless Compression of Multispectral- Image Data (NPO-42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), p. 26 with the modification reported in Lossless, Multi-Spectral Data Comressor for Improved Compression for Pushbroom-Type Instruments (NPO-45473), NASA Tech Briefs, Vol. 32, No. 7 (July 2008) p. 63, which provides improved compression performance for data from pushbroom-type imagers. An FPGA implementation of the unmodified FL algorithm was previously developed and reported in Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System (NPO-46867), NASA Tech Briefs, Vol. 36, No. 5 (May 2012) p. 42. The essence of the FL algorithm is adaptive linear predictive compression using the sign algorithm for filter adaption. The FL compressor achieves a combination of low complexity and compression effectiveness that exceeds that of stateof- the-art techniques currently in use. The modification changes the predictor structure to tolerate differences in sensitivity of different detector elements, as occurs in pushbroom-type imagers, which are suitable for spacecraft use. The FPGA implementation offers a low-cost, flexible solution compared to traditional ASIC (application specific integrated circuit) and can be integrated as an intellectual property (IP) for part of, e.g., a design that manages the instrument interface. The FPGA implementation was benchmarked on the Xilinx Virtex IV LX25 device, and ported to a Xilinx prototype board. The current implementation has a critical path of 29.5 ns, which dictated a clock speed of 33 MHz. The critical path delay is end-to-end measurement between the uncompressed input data and the output compression data stream. The implementation compresses one sample every clock cycle, which results in a speed of 33 Msample/s. The implementation has a rather low device use of the Xilinx Virtex IV LX25, making the total power consumption of the implementation about 1.27 W.

Keymeulen, Didlier↗
NASA NTRS2015

International Space Station Data Collection for Disaster Response

Remotely sensed data acquired by orbital sensor systems has emerged as a vital tool to identify the extent of damage resulting from a natural disaster, as well as providing near-real time mapping support to response efforts on the ground and humanitarian aid efforts. The International Space Station (ISS) is a unique terrestrial remote sensing platform for acquiring disaster response imagery. Unlike automated remote-sensing platforms it has a human crew; is equipped with both internal and externally-mounted remote sensing instruments; and has an inclined, low-Earth orbit that provides variable views and lighting (day and night) over 95 percent of the inhabited surface of the Earth. As such, it provides a useful complement to autonomous sensor systems in higher altitude polar orbits. NASA remote sensing assets on the station began collecting International Disaster Charter (IDC) response data in May 2012. The initial NASA ISS sensor systems responding to IDC activations included the ISS Agricultural Camera (ISSAC), mounted in the Window Observational Research Facility (WORF); the Crew Earth Observations (CEO) Facility, where the crew collects imagery using off-the-shelf handheld digital cameras; and the Hyperspectral Imager for the Coastal Ocean (HICO), a visible to near-infrared system mounted externally on the Japan Experiment Module Exposed Facility. The ISSAC completed its primary mission in January 2013. It was replaced by the very high resolution ISS SERVIR Environmental Research and Visualization System (ISERV) Pathfinder, a visible-wavelength digital camera, telescope, and pointing system. Since the start of IDC response in 2012 there have been 108 IDC activations; NASA sensor systems have collected data for thirty-two of these events. Of the successful data collections, eight involved two or more ISS sensor systems responding to the same event. Data has also been collected by International Partners in response to natural disasters, most notably JAXA and Roscosmos/Energia through the Urugan program.

Stefanov, William L.↗
NASA NTRS1993

Spectral variations in a collection of AVIRIS imagery

The advent of AVIRIS presents to the scientific community the first opportunity to examine high quality hyperspectral data over areas of interest, while recent developments promise even better data in the future. With these data we may address questions such as: (1) Is such high spectral resolution (0.01 micron) necessary?; and (2) How can we take advantage of the AVIRIS experience in specifying an improved future Landsat satellite? In this paper we describe the use of relatively broadband measurements (20-50 nm) to represent spectral variability. The problem of generality is addressed through consideration of 28 AVIRIS scenes, although analysis of additional areas is clearly desirable. Section 2 defines the expansion of spectra in terms of basis functions, section 3 describes the application to AVIRIS data, and the conclusion, section 4, relates these preliminary results to possible future remote sensing instrumentation such as the planned Landsat 8.

Price, John C.↗
NASA NTRS2000

Wavelet and Fractal Analysis of Remotely Sensed Surface Temperature with Applications to Estimation of Surface Sensible Heat Flux Density

Wavelet and fractal analyses have been used successfully to analyze one-dimensional data sets such as time series of financial, physical, and biological parameters. These techniques have been applied to two-dimensional problems in some instances, including the analysis of remote sensing imagery. In this respect, these techniques have not been widely used by the remote sensing community, and their overall capabilities as analytical tools for use on satellite and aircraft data sets is not well known. Wavelet and fractal analyses have the potential to provide fresh insight into the characterization of surface properties such as temperature and emissivity distributions, and surface processes such as the heat and water vapor exchange between the surface and the lower atmosphere. In particular, the variation of sensible heat flux density as a function of the change In scale of surface properties Is difficult to estimate, but - in general - wavelets and fractals have proved useful in determining the way a parameter varies with changes in scale. We present the results of a limited study on the relationship between spatial variations in surface temperature distribution and sensible heat flux distribution as determined by separate wavelet and fractal analyses. We analyzed aircraft imagery obtained in the thermal infrared (IR) bands from the multispectral TIMS and hyperspectral MASTER airborne sensors. The thermal IR data allows us to estimate the surface kinetic temperature distribution for a number of sites in the Midwestern and Southwestern United States (viz., San Pedro River Basin, Arizona; El Reno, Oklahoma; Jornada, New Mexico). The ground spatial resolution of the aircraft data varied from 5 to 15 meters. All sites were instrumented with meteorological and hydrological equipment including surface layer flux measuring stations such as Bowen Ratio systems and sonic anemometers. The ground and aircraft data sets provided the inputs for the wavelet and fractal analyses, and the validation of the results.

Schieldge, John↗