The Power of the Pixel- A Thermodynamic Paradigm for Studying Disease Vectors Habitats Life Cycles Using NASA's Remote Sensing Data
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remote sensing↗
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Neural network approaches versus statistical methods in classification of multisource remote sensing data
Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.
Benediktsson, Jon A.↗
An examination of applications of remote sensing data to Metropolitan Washington Council of Governments' planning requirements
A comprehensive inventory of a series of remote sensing applications for a variety of regional planning programs in metropolitan Washington was undertaken. Examples of application, methods for data utilization, and corresponding photographic illustrations are provided illustrating how remote sensing would prove particularly useful as a unique and/or supplemental data source.
Mallon, H. J.↗
Remote sensing data handbook
A digest of information on remote sensor data systems is given. It includes characteristics of spaceborne sensors and the supportive systems immediately associated therewith. It also includes end-to-end systems information that will assist the user in appraising total data system impact produced by a sensor. The objective is to provide a tool for anticipating the complexity of systems and potential data system problems as new user needs are generated. Materials in this handbook span sensor systems from the present to those planned for use in the 1990's. Sensor systems on all planned missions are presented in digest form, condensed from data as available at the time of compilation. Projections are made of anticipated systems.
Source record↗
Remote sensing applications in forestry - Analysis of remote sensing data for evaluating forest and range resources Annual progress report
Analysis of remote forestry sensing research program
Colwell, R. N.↗
Bhutan Agriculture II: Creating a Graphical User Interface, Crop Mask, and Data Collection Protocol for Analysis of Rice Crop in Bhutan Using Remotely Sensed Data
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Wangdrak Dorji↗
Spatial reasoning in remotely sensed data
Photointerpreters employ a variety of implicit spatial models to provide interpretations from remotely sensed aerial or satellite imagery. In this paper one application is illustrated: how ridges and valleys can be automatically interpreted from Landsat imagery of a mountainous area, and how a relative elevation terrain model can be constructed from this interpretation. How to examine valleys for the possible presence of streams or rivers is shown, and how a spatial relational model can be set up to make a final interpretation of the river drainage network is explored.
Campbell, J.↗
High Spatial Resolution Thermal Infrared Remote Sensing Data for Analysis of the Atlanta, Georgia, Urban Heat Island Effect and Its Impacts on the Environment
The twenty-first century is the first "urban century" according to the United Nations Development Program. The focus of cities reflects awareness of the growing percentage of the world's population that lives in urban areas. In environmental terms, cities are the original producers of many of the global problems related to waste disposal, air and water pollution, and associated environmental and ecological challenges. Expansion of cities, both in population and areal extent, is a relentless process. In 2000, approximately 3 billion people representing about 40% of the global population, resided in urban areas. Urban population will continue to rise substantially over the next several decades according to UN estimates, and most of this growth will Occur in developing countries. The UN estimates that by 2025, 60% of the world's population will live in urban areas. As a consequence, the number of"megacities" (those cities with populations of 10 million inhabitants or more) will increase by 100 by 2025. Thus, there is a critical need to understand urban areas and what their impacts are on environmental, ecological and hydrologic resources, as well as on the local, regional, and even global climate. One of the more egregious side effects of urbanization is the increase in surface and air temperatures that lead to deterioration in air quality. In the United States, under the more stringent air quality guidelines established by the U.S. Environmental Protection Agency in 1997, nearly 300 counties in 34 states will not meet these new air quality standards for ground level ozone. Mitigation of the urban heat island (UHI) effect is actively being evaluated as a possible way to reduce ground ozone levels in cities and assist states in improving air quality. Foremost in the analysis of how the UHI affects air quality and other environmental factors is the use of remote sensing technology and data to characterize urban land covers in sufficient detail to quantifiably measure the impact of increased urban heating on air quality. The urban landscape impacts surface thermal energy exchanges that determine development of the UHI. This paper will illustrate how we are using high spatial remote sensing data collected over the Atlanta, Georgia metropolitan area in conjunction with other geographic information, to perform a detailed urban land cover classification and to determine the contribution of these land covers to the urban heat island effect. Also, the spatial arrangement of the land covers and the impact on urban heating from these selected patterns of development are evaluated. Additionally, this paper will show how these data are being used as inputs to improve air quality modeling for Atlanta, including potential benefits from UHI mitigation.
Quattrochi, Dale A.↗
Mare Crisium geologic units - Implications of additional remote sensing data
Additional spectral reflectance data are presented for the Mare Crisium region. This new remote sensing information supports and supplements the subdivision of Mare Crisium into three major basalt units and shows further that: (1) the Luna 24 landing site is on a small region apparently surrounded by distinctly different units, but is clearly correlated with basalt Group IIA of Head et al. (1978); (2) exposures of Group IIB basalt in the northern and southern parts of the basin are spectrally similar; and (3) Group I basalts along the eastern part of the basin, which have been proposed to be relatively young (Boyce and Johnson, 1977), are spectrally similar to older Group I basalts.
Pieters, C. M.↗
A pointable, helicopter-based remote sensing data acquisition system for collecting bidirectional reflectance data
Discussed here is a new configuration of a helicopter-borne remote sensing system to support field measurements programs such as studies of forest decline damage associated with atmospheric deposition and the Forest Ecosystems Dynamics project. In this new configuration, the sensor payload is mounted externally on a hinged pallet located on the starboard side of the Bell Iroquois UH-1B (HUEY) helicopter. The sensor payload includes a set of bore-sighted instruments consisting of a Barnes Modular Multiband Radiometer (MMR), a Spectron Engineering (SE) 590 spectroradiometer, an Everest infrared temperature sensor, and a Sony CCD video camera with a 10x zoom lens. The system was tested and excellent data was acquired in 1990.
Williams, Darrel L.↗
Analysis of Upper Air, Ground and Remote Sensing Data For the ATLAS Field Campaign in San Juan, Puerto Rico
The Atlas San Juan Mission was conducted in February 2004 with the main objectives of observing the Urban Heat Island of San Juan, providing high resolution data of the land use for El Yunque Rain Forest and for calibrating remote sensors. The mission was coordinated with NASA staff members at Marshall, Stennis, Goddard, and Glenn. The Airborne Thermal and Land Applications Sensor (ATLAS) from NASA/Stennis, that operates in the visual and IR bands, was used as the main sensor and was flown over Puerto Rico in a Lear 23 jet plane. To support the data gathering effort by the ATLAS sensor, remote sensing observations and upper air soundings were conducted along with the deployment of a number of ground based weather stations and temperature sensors. This presentation focuses in the analysis of this complementary data for the Atlas San Juan Mission. Upper air data show that during the days of the mission the Caribbean mid and high atmospheres were relatively dry and highly stable reflecting positive surface lifted index, a necessary condition to conduct this suborbital campaign. Surface wind patterns at levels below 850mb were dominated by the easterly trades, while the jet stream at the edge of the troposphere dominated the westerly wind at levels above 500mb. The jet stream remained at high latitudes reducing the possibility of fronts. In consequence, only 8.4 mm of precipitation were reported during the entire mission. Observation of soundings located about 150 km apart reflected minimum variations of the boundary layer across the island for levels below 850 meters and a uniform atmosphere for higher levels. The weather stations and the temperature sensors were placed at strategic locations to observe variations across the urban and rural landscapes. Time series plot of the stations' data show that heavily urbanized commercial areas have higher air temperatures than urban and suburban residential areas, and much higher temperatures than rural areas. Temperature differences [dT(U-R)] were obtained by subtracting the values of several stations from a reference urban station, located in the commercial area of San Juan. These time series show that the UHI peaks during the morning between 10:00am and noon to an average of 4.5 C, a temporal pattern not previously observed in similar studies for continental cities. It is also observed a high variability of the UHI with the precipitation patterns even for short events. These results may be a reflection of a large land use density by low level buildings with an apparent absence of significant heat storage effects in the urban areas, and the importance of the surrounding soil and vegetation moisture in controlling the urban tropical climate. The ATLAS data was used to determine albedo and surface temperature patterns on a 10m scale for the study area. These data were used to calibrate the spatial distribution of the surface temperature when using remote sensing images from MODIS (Moderate Resolution Imaging Spectroradiometer). Surface temperatures were estimated using the land surface temperature product MOD11_L2 distributed by the Land Process Distributed Active Archive Center (LP DAAC). These results show the maximum, minimum and average temperatures in San Juan and in the entire Island at a resolution of 1 km. The information retrieved from MODIS for land surface temperatures reflected similar temporal and spatial variations as the weather stations and ATLAS measurements with a highest absolute offset of about 5 C due to the differences between surface and air temperatures.
Gonzalez, J. E.↗
Analysis of Upper Air, Ground and Remote Sensing Data for the ATLAS Field Campaign in San Juan, Puerto Rico
The Atlas San Juan Mission was conducted in February 2004 with the main objectives of observing the Urban Heat Island of San Juan, providing high resolution data of the land use for El Yunque Rain Forest and for calibrating remote sensors. The mission was coordinated with NASA staff members at Marsha& Stennis, Goddard, and Glenn. The Airborne Thermal and Land Applications Sensor (ATLAS) from NASA/Stennis, that operates in the visual and IR bands, was used as the main sensor and was flown over Puerto Rico in a Lear 23 jet plane. To support the data gathering effort by the ATLAS sensor, remote sensing observations and upper air soundings were conducted along with the deployment of a number of ground based weather stations and temperature sensors. This presentation focuses in the analysis of this complementary data for the Atlas San Juan Mission. Upper air data show that during the days of the mission the Caribbean mid and high atmospheres were relatively dry and highly stable reflecting positive surface lifted index, a necessary condition to conduct this suborbital campaign. Surface wind patterns at levels below 850mb were dominated by the easterly trades, while the jet stream at the edge of the troposphere dominated the westerly wind at levels above 500mb. The jet stream remained at high latitudes reducing the possibility of fronts. In consequence, only 8.4 mm of precipitation were reported during the entire mission. Observation of soundings located about 150 km apart reflected minimum variations of the boundary layer across the Island for levels below 850 meters and a uniform atmosphere for higher levels. The weather stations and the temperature sensors were placed at strategic locations to observe variations across the urban and rural landscapes. Time series plot of the stations' data show that heavily urbanized commercial areas have higher air temperatures than urban and suburban residential areas, and much higher temperatures than rural areas. Temperature differences [dT(U-R)] were obtained by subtracting the values of several stations h m a reference urban station, located m the commercial area of San Juan. These time series show that the UHI peaks during the morning between 10:00am and noon to an average of 4.5 C, a temporal pattern not previously observed in similar studies for continental cities. It is also observed a high variability of the UHI with the precipitation patterns even for short events. These results may be a reflection of a large land use density by low level buildings with an apparent absence of significant heat storage effects in the urban areas, and the importance of the surrounding soil and vegetation moisture in controlling the urban tropical climate. The ATLAS data was used to determine albedo and surface temperature patterns on a 10m scale for the study area. These data were used to calibrate the spatial distribution of the surface temperature when using remote sensing images from MODIS (Moderate Resolution Imaging Spectradiometer). Surface temperatures were estimated using the land surface temperature product MODII-L2 distributed by the Land Process Distributed Active Archive Center(LP DAAC). These results show the maximum, minimum and average temperatures in San Juan and in the entire Island at a resolution of 1 km. The information retrieved from MODIS for land surface temperatures reflected similar temporal and spatial variations as the weather stations and ATLAS measurements with a highest absolute offset of about 5 C due to the differences between surface and air temperatures.
Gonzalez, Jorge E.↗
Using Remote Sensing Data to Evaluate Surface Soil Properties in Alabama Ultisols
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.
Sullivan, Dana G.↗
Region-based modeling algorithms for remotely-sensed data
Five algorithms being developed for performing region-based modeling operations on classified remotely-sensed images are described. The first algorithm converts images from standard grid structure into topological grid structure, while the remaining algorithms act upon topologically grid-structured images to perform region-based relabelling, overlaying, distance searching, and neighborhood scanning operations. The use of precomputed topological information, through the use of topological grid structure, makes region-based algorithms highly accessible to earth scientists.
Goldberg, M.↗
Effective use of principal component analysis with high resolution remote sensing data to delineate hydrothermal alteration and carbonate rocks
Methods of applying principal component (PC) analysis to high resolution remote sensing imagery were examined. Using Airborne Imaging Spectrometer (AIS) data, PC analysis was found to be useful for removing the effects of albedo and noise and for isolating the significant information on argillic alteration, zeolite, and carbonate minerals. An effective technique for using PC analysis using an input the first 16 AIS bands, 7 intermediate bands, and the last 16 AIS bands from the 32 flat field corrected bands between 2048 and 2337 nm. Most of the significant mineralogical information resided in the second PC. PC color composites and density sliced images provided a good mineralogical separation when applied to a AIS data set. Although computer intensive, the advantage of PC analysis is that it employs algorithms which already exist on most image processing systems.
Feldman, Sandra C.↗
Needs Assessment for the Use of NASA Remote Sensing Data for Regulatory Water Quality
This slide presentation reviews the assessment of the needs that NASA can use for the remote sensing of water quality. The goal of this project is to provide information for decision-making activities (water quality standards) using remotely sensed/satellite based water quality data from MODIS and Landsat data.
Spiering, Bruce↗
Statistical evaluation of the composition, physical properties, and surface configuration of terrestrial test sites and their correlation with remotely sensed data Final report
Utilizing spatial filtering for analyzing structural configuration of Michigan Basin for application to remote sensing
Beckman, W. A., Jr.↗
Estimation of atmospheric species concentrations from remote sensing data
The atmospheric species concentration distribution-estimation problem that arises from the measurement configuration of remote sensing, which involves a field of view that moves across the region, is solved by means of the partial differential equation for atmospheric diffusion and the Wiener-Hopf theory. A numerical study is also undertaken for the estimation of concentration distribution downwind of a hypothetical, continuous ground-level source of pollutants.
Omatu, S.↗