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Computer processing procedures and programs applied to Multispectral Scanner data from LANDSAT are described. The output product produced is a level 1 land use map in conformance with a Universal Transverse Mercator projection. The region studied was a five-county area in north Alabama.
Development of ARIS - Alabama Resources Information System is summarized. Development of data bases, system simplification for user access, and making information available to personnel having a need to use ARIS or in the process of developing ARIS type systems are discussed.
The history of a comprehensive solar energy system analysis data base developed by NASA/Marshall Space Flight Center and the University of Alabama is presented, along with its current status. The Marshall Information Retrieval and Data Storage (MIRADS) system was chosen for the data base, and feedback systems were arranged to cope with changes in the needs of the program management for the type of data gathered. The final structure of the data base consists of 22 files divided into 6 topical sections: summaries, climatological, utility rates, architectural, equipment, and economics. The data base offers help to the solar industry in two ways: it provides information and it serves as a model for users trying to establish the climatic and socioeconomic variables they should take into account when they examine a potential market for solar energy equipment.
Final report on solar-heating demonstration project in Huntsville, Alabama, is rich in technical data, planning considerations, test and maintenance data, and other information. It can be useful reference for those planning similar systems.
Solar-assisted hot-water system for a new 122-unit motor inn in Mobile, Alabama, generates more than half the energy needed for hot-water heating at motel each year. System consists of 93 flat-plate collectors, 2,500 gallon (9,500 1) insulated storage tank located outdoors, heat exchangers and controls. Electronic thermometers, measuring the temperatures at 22 locations monitor system performance.
A computer-implemented classification has been derived from Landsat-4 Thematic Mapper data acquired over Baldwin County, Alabama on January 15, 1983. One set of spectral signatures was developed from the data by utilizing a 3x3 pixel sliding window approach. An analysis of the classification produced from this technique identified forested areas. Additional information regarding only the forested areas. Additional information regarding only the forested areas was extracted by employing a pixel-by-pixel signature development program which derived spectral statistics only for pixels within the forested land covers. The spectral statistics from both approaches were integrated and the data classified. This classification was evaluated by comparing the spectral classes produced from the data against corresponding ground verification polygons. This iterative data analysis technique resulted in an overall classification accuracy of 88.4 percent correct for slash pine, young pine, loblolly pine, natural pine, and mixed hardwood-pine. An accuracy assessment matrix has been produced for the classification.
Seasonally compatible data collected by SIR-A and by Landsat 4 TM over the lower coastal plain in Alabama were coregistered, forming a SIR-A/TM multichannel data set with 30 m x 30 m pixel size. Spectral signature plots and histogram analysis of the data were used to observe data characteristics. Radar returns from pine forest classes correlated highly with the tree ages, suggesting the potential utility of microwave remote sensing for forest biomass estimation. As compared with the TM-only data set, the use of SIR-A/TM data set improved classification accuracy of the seven land cover types studied. In addition, the SIR-A/TM classified data support previous finding by Engheta and Elachi (1982) that microwave data appear to be correlated with differing bottomland hardwood forest vegetation as associated with varying water regimens (i.e., wet versus dry).
The University of Alabama experiment which flew on the STS-8 mission had several objectives which were mostly of a speculative nature since so little was known of the processes of interest. The experiment provided original data on: (1) oxidation of metal surfaces; (2) reaction rates of atomic oxygen with carbon and other surfaces and the dependence of these rates on temperature; and (3) the angular distribution of 5 eV atoms scattered off a solid surface. A review of the results is provided.
The urban heat island represents a case of inadvertent human modification of climate in an urban environment. Urbanization changes the nature of the surface and atmospheric properties of a region. As a result, radiation balance in the urban areas is altered and sensible heat is added to the point that urban areas are warmer than surrounding rural areas. At the boundary between the rural and urban area, a sharp rise in temperature occurs, culminating to a peak temperature at the central business district of the city, hence the name 'urban heat island'. The extent and intensity of the urban heat island are a function of population size, land use, and topography. Because the urban heat island exhibits spatial variations of temperatures, the use of Geographic Information System (GIS) is appropriate. The research on the urban heat island focuses on the acquisition of 15 bands of visible and thermal infrared data (ranging from 0.45 to 12.2 microns) from an aerial platform using NASA's ATLAS (Airborne Thermal/Visible Land Application Sensor) over Huntsville, Alabama. The research reported in this paper is an analysis of the impact of population, land use, and topography on the shape of the urban heat island that could be developed in Huntsville using the GIS approach. The outcome of this analysis can then be verified using the acquired remotely sensed data.
Two Landsat Thematic Mapper (TM) images, from July 1984 and July 1992, were used to identify land use/cover changes in the urban and suburban fringe of the city of Huntsville, Alabama. Image difference was the technique used to quantify the change between the two dates. The eight-year period showed a 16% change, mainly from agricultural lands to urban areas generated by the settlement of industrial, commercial, and residential areas. Visual analysis of the change map (i.e., difference image) supported this phenomenon by showing that most changes were occurring in the vicinity of the major roads and highways across the city.
The Severe Thunderstorm Observations, Research, and Monitoring network (STORMnet) became operational in 2001 as a test bed to infuse new science and technologies into the severe and hazardous weather forecasting and warning process. STORMnet is collaboration among NASA scientists, National Weather Service (NWS) forecasters, emergency managers and other partners. STORMnet integrates total lightning observations from a ten-station 3-D VHF regional lightning mapping array, the National Lightning Detection Network (NLDN), real-time regional NEXRAD Doppler radar, satellite visible and infrared imagers, and a mobile atmospheric profiling system to characterize storms and their evolution. The storm characteristics and life-cycle trending are accomplished in real-time through the second generation Lightning Imaging Sensor Demonstration and Display (LISDAD II), a distributed processing system with a JAVA-based display application that allows anyone, anywhere to track individual storm histories within the Tennessee Valley region of north Alabama and Tennessee, a region of the southeastern U.S. well known for abundant severe weather.
Two approaches are used to characterize how accurately the North Alabama Lightning Mapping Array (LMA) is able to locate lightning VHF sources in space and in time. The first method uses a Monte Carlo computer simulation to estimate source retrieval errors. The simulation applies a VHF source retrieval algorithm that was recently developed at the NASA-MSFC and that is similar, but not identical to, the standard New Mexico Tech retrieval algorithm. The second method uses a purely theoretical technique (i.e., chi-squared Curvature Matrix theory) to estimate retrieval errors. Both methods assume that the LMA system has an overall rms timing error of 50ns, but all other possible errors (e.g., multiple sources per retrieval attempt) are neglected. The detailed spatial distributions of retrieval errors are provided. Given that the two methods are completely independent of one another, it is shown that they provide remarkably similar results, except that the chi-squared theory produces larger altitude error estimates than the (more realistic) Monte Carlo simulation.
Two approaches are used to characterize how accurately the North Alabama Lightning Mapping Array (LMA) is able to locate lightning VHF sources in space and in time. The first method uses a Monte Carlo computer simulation to estimate source retrieval errors. The simulation applies a VHF source retrieval algorithm that was recently developed at the NASA-MSFC and that is similar, but not identical to, the standard New Mexico Tech retrieval algorithm. The second method uses a purely theoretical technique (i.e., chi-squared Curvature Matrix theory) to estimate retrieval errors. Both methods assume that the LMA system has an overall rms timing error of 50ns, but all other possible errors (e.g., multiple sources per retrieval attempt) are neglected. The detailed spatial distributions of retrieval errors are provided. Given that the two methods are completely independent of one another, it is shown that they provide remarkably similar results, except that the chi-squared theory produces larger altitude error estimates than the (more realistic) Monte Carlo simulation.
Two approaches are used to characterize how accurately the North Alabama Lightning Mapping Array (LMA) is able to locate lightning VHF sources in space and in time. The first method uses a Monte Carlo computer simulation to estimate source retrieval errors. The simulation applies a VHF source retrieval algorithm that was recently developed at the NASA Marshall Space Flight Center (MSFC) and that is similar, but not identical to, the standard New Mexico Tech retrieval algorithm. The second method uses a purely theoretical technique (i.e., chi-squared Curvature Matrix Theory) to estimate retrieval errors. Both methods assume that the LMA system has an overall rms timing error of 50 ns, but all other possible errors (e.g., multiple sources per retrieval attempt) are neglected. The detailed spatial distributions of retrieval errors are provided. Given that the two methods are completely independent of one another, it is shown that they provide remarkably similar results. However, for many source locations, the Curvature Matrix Theory produces larger altitude error estimates than the (more realistic) Monte Carlo simulation.
Knowledge of surface soil properties is used to assess past erosion and predict erodibility, determine nutrient requirements, and assess surface texture for soil survey applications. This study was designed to evaluate high resolution IKONOS multispectral data as a soil- mapping tool. Imagery was acquired over conventionally tilled fields in the Coastal Plain and Tennessee Valley physiographic regions of Alabama. Acquisitions were designed to assess the impact of surface crusting, roughness and tillage on our ability to depict soil property variability. Soils consisted mostly of fine-loamy, kaolinitic, thermic Plinthic Kandiudults at the Coastal Plain site and fine, kaolinitic, thermic Rhodic Paleudults at the Tennessee Valley site. Soils were sampled in 0.20 ha grids to a depth of 15 cm and analyzed for % sand (0.05 - 2 mm), silt (0.002 -0.05 mm), clay (less than 0.002 mm), citrate dithionite extractable iron (Fe(sub d)) and soil organic carbon (SOC). Four methods of evaluating variability in soil attributes were evaluated: 1) kriging of soil attributes, 2) co-kriging with soil attributes and reflectance data, 3) multivariate regression based on the relationship between reflectance and soil properties, and 4) fuzzy c-means clustering of reflectance data. Results indicate that co-kriging with remotely sensed data improved field scale estimates of surface SOC and clay content compared to kriging and regression methods. Fuzzy c-means worked best using RS data acquired over freshly tilled fields, reducing soil property variability within soil zones compared to field scale soil property variability.
Evaluation of surface soil properties via remote sensing could facilitate soil survey mapping, erosion prediction and allocation of agrochemicals for precision management. The objective of this study was to evaluate the relationship between soil spectral signature and surface soil properties in conventionally managed row crop systems. High-resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil organic carbon, particle size distribution, and citrate dithionite extractable iron content. Surface roughness, soil water content, and crusting were also measured during sampling. Two methods of analysis were evaluated: 1) multiple linear regression using common spectral band ratios, and 2) partial least squares regression. Our data show that thermal infrared spectra are highly, linearly related to soil organic carbon, sand and clay content. Soil organic carbon content was the most difficult to quantify in these highly weathered systems, where soil organic carbon was generally less than 1.2%. Estimates of sand and clay content were best using partial least squares regression at the Valley site, explaining 42-59% of the variability. In the Coastal Plain, sandy surfaces prone to crusting limited estimates of sand and clay content via partial least squares and regression with common band ratios. Estimates of iron oxide content were a function of mineralogy and best accomplished using specific band ratios, with regression explaining 36-65% of the variability at the Valley and Coastal Plain sites, respectively.
The Lightning Imaging Sensor (LIS) aboard the TRMM satellite has been collecting optical lightning data since November 1997. A Lightning Mapping Array (LMA) that senses VHF impulses from lightning was installed in North Alabama in the Fall of 2001. A dataset has been compiled to compare data from both instruments for all times when the LIS was passing over the domain of our LMA. We have algorithms for both instruments to group pixels or point sources into lightning flashes. This study presents the comparison statistics of the flash data output (flash duration, size, and amplitude) from both algorithms. We will present the results of this comparison study and show "point-level" data to explain the differences. AS we head closer to realizing a Global Lightning Mapper (GLM) on GOES-R, better understanding and ground truth of each of these instruments and their respective flash algorithms is needed.
The NASA Marshall Space Flight Center Lightning Nitrogen Oxides Model (LNOM) is applied to August 2006 North Alabama Lightning Mapping Array (LMA) data to estimate the raw (i.e., unmixed and otherwise environmentally unmodified) vertical profile of lightning nitrogen oxides, NOx = NO + NO 2 . This is part of a larger effort aimed at building a more realistic lightning NOx emissions inventory for use by the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality (CMAQ) modeling system. Data from the National Lightning Detection Network TM (NLDN) is also employed. Overall, special attention is given to several important lightning variables including: the frequency and geographical distribution of lightning in the vicinity of the LMA network, lightning type (ground or cloud flash), lightning channel length, channel altitude, channel peak current, and the number of strokes per flash. Laboratory spark chamber results from the literature are used to convert 1-meter channel segments (that are located at a particular known altitude; i.e., air density) to NOx concentration. The resulting raw NOx profiles are discussed.