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Twenty-three processing functions aid in utilizing LANDSAT data for forest resource management. Designed to work primarily with digital data obtained from measurements recorded by multispectral remote sensors mounted on aerospace platforms. communication between processing functions, simplicity of control, and commonality of data files in LARSFRIS enhance usefulness of system as tool for research and development of remote sensing systems.
Background: The transmission of vectorborne infectious diseases is often influenced by environmental, meteorological and climatic parameters, because the vector life cycle depends on these factors. For example, the geophysical parameters relevant to malaria transmission include precipitation, surface temperature, humidity, elevation, and vegetation type. Because these parameters are routinely measured by satellites, remote sensing is an important technological tool for predicting, preventing, and containing a number of vectorborne infectious diseases, such as malaria, dengue, West Nile virus, etc. Methods: A variety of NASA remote sensing data can be used for modeling vectorborne infectious disease transmission. We will discuss both the well known and less known remote sensing data, including Landsat, AVHRR (Advanced Very High Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer), TRMM (Tropical Rainfall Measuring Mission), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), EO-1 (Earth Observing One) ALI (Advanced Land Imager), and SIESIP (Seasonal to Interannual Earth Science Information Partner) dataset. Giovanni is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center. It provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science remote sensing data. After remote sensing data is obtained, a variety of techniques, including generalized linear models and artificial intelligence oriented methods, t 3 can be used to model the dependency of disease transmission on these parameters. Results: The processes of accessing, visualizing and utilizing precipitation data using Giovanni, and acquiring other data at additional websites are illustrated. Malaria incidence time series for some parts of Thailand and Indonesia are used to demonstrate that malaria incidences are reasonably well modeled with generalized linear models and artificial intelligence based techniques. Conclusions: Remote sensing data relevant to the transmission of vectorborne infectious diseases can be conveniently accessed at NASA and some other websites. These data are useful for vectorborne infectious disease surveillance and modeling.
The importance of using remote sensing techniques, in the visible and near-infrared ranges, for mapping, inventory, conservation and management of natural ecosystems is discussed. Some examples realized in Brazil or other countries are given to evaluate the products from orbital platform (MSS and RBV imagery of LANDSAT) and aerial level (photography) for ecosystems study. The maximum quantitative and qualitative information which can be obtained from each sensor, at different level, are discussed. Based on the developed experiments it is concluded that the remote sensing technique is a useful tool in mapping vegetation units, estimating biomass, forecasting and evaluation of fire damage, disease detection, deforestation mapping and change detection in land-use. In addition, remote sensing techniques can be used in controling implantation and planning natural/artificial regeneration.
Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
The results of the research performed by the university principal investigators are herein compiled. OhioView's general goals were: 1) To increase remote sensing education for Ohio s undergraduate and graduate students, and also enhancing curriculum in the mathematics and science for K-12 students using the capabilities of remote sensing; 2) To conduct advanced research to develop novel remote sensing applications, i.e. to turn data into information for more applications; 3) To maximize the use of remote sensing technology by the general public through outreach and the development of tools for more user-friendly access to remote sensing data.
Quantum computing technology has grown rapidly in recent years, with new technologies being explored, error rates being reduced, and quantum processor’s qubit capacity growing. However, near-term quantum algorithms are still unable to be induced without compounding consequential levels of noise, leading to non-trivial erroneous results. Quantum Error Correction (in-situ error mitigation) and Quantum Error Mitigation (post-induction error mitigation) are promising fields of research within the quantum algorithm scene, aiming to alleviate quantum errors, increasing the overall fidelity and hence the overall quality of circuit induction. Earlier this year, a pioneering work, namely HAMMER, published in ASPLOS-22 demonstrated the existence of a latent structure regarding post-circuit induction errors when mapping to the Hamming spectrum. However, they intuitively assumed that errors occur in local clusters, and that at higher average Hamming distances this structure falls away. In this work, we show that such a correlation structure is not only local but extends certain non-local clustering patterns which can be precisely described by a Poisson distribution model taking the input circuit, the device run time status (i.e., calibration statistics) and qubit topology into consideration. Using this quantum error characterizing model, we developed an iterative algorithm over the generated Bayesian network state-graph for post-induction error mitigation. Thanks to more precise modeling of the error distribution latent structure and the new iterative method, our Q-Beep approach provides state of the art performance and can boost circuit execution fidelity by up to 234.6% on Bernstein-Vazirani circuits and on average 71.0% on QAOA solution quality, using 16 practical IBMQ quantum processors. For other benchmarks such as those in QASMBench, the fidelity improvement is up to 17.8%. Q-Beep is a light-weight post-processing technique that can be performed offline and remotely, making it a useful tool for quantum vendors to integrate and provide more reliable circuit induction results.
The objectives of this study were centered around the demonstration of remote sensing as an inventory tool and researching the multiple uses of riparian vegetation. Specific study objectives were to: (1) map riparian vegetation along the Gila River, San Simon Creek, San Pedro River, Pantano Wash, (2) determine the feasibility of automated mapping using LANDSAT-1 computer compatible tapes, (3) locate and summarize existing mpas delineating riparian vegetation, (4) summarize data relevant to Southern Arizona's riparian products and uses, (5) document recent riparian vegetation changes along a selected portion of the San Pedro River, (6) summarize historical changes in composition and distribution of riparian vegetation, and (7) summarize sources of available photography pertinent to Southern Arizona.
A coordinated microwave applications development program was initiated to improve the capability to: (1) identify, monitor, and assess the earth's resources; and (2) monitor the earth's environment and predict significant changes. The program consists of the scientific, technical, and programmatic activities required to develop microwave remote sensing into an operational tool for systematic earth observations.
The paper applies iterated and non-iterated extended Kalman filters to solve two practical meteorological inversion problems. Passive microwave satellite soundings are used to infer vertical temperature profiles and cloud parameters. In both cases it is shown that improvements can be obtained over presently used techniques. Finally, the results suggest that modern multivariate nonlinear recursive estimation techniques based in a Bayesian methodology can be a valuable tool in the area of remote sounding of atmospheric parameters.
This project demonstrated the feasibility of using laser remote sensing technology as a tool for leak survey work in natural gas distribution systems. A laboratory device was assembled using a pair of helium neon (HeNe) lasers to measure methane. One HeNe laser emits radiation at a wavelength of 3.3922 micrometers, which corresponds to a strong absorption feature of methane, while the other emits radiation at a wavelength of 3.3911 micrometers, which corresponds to a weak absorption by methane. As a particular area is scanned for leaks, the laser is pointed at convenient topographic targets within its operating range, about 25 m. A portion of the backscattered radiation is collected by a receiver and focused onto an indium antimonide (InSb) photodetector, cooled to 77K. Methane concentrations were determined from the differential absorption at the two wavelengths for the backscattered radiation.
The airborne imaging spectrometer (AIS) has provided for the first time, the possibility to map mineralogical constituents in the Earth's surface and thus has enormously increased the value of remote-sensing data as a tool in the solution of geologic problems. The question addressed with AIS at Cuprite was how well could the mineral components at the surface of a hydrothermal alteration zone be detected, identified and mapped? The question was answered positively and is discussed. A relatively rare mineral, buddingtonie, that could not have been detected by conventional means, was discovered and mapped by the use of AIS.
Synthetic aperture radar remote sensing is a promising tool for detection of flooding on forested floodplains. The brigtht appearance of flooded forests on radar images results from double-bounce reflections between smooth water surfaces and tree trunks or branches. Enhanced backscattering at L-band has been shown to occur in a wide variety of forest types, including cypress-tupelo swamps, temperate bottomland hardwoods, spruce bogs, mangroves and tropical floodplain forests. Lack of enhancement is a function of both stand density and branching structure. According to models and measurements, the magnitude of the enhancement is about 3 to 10 dB. Steep incidence angles (20-30 deg) are optimal for detection of flooding, since some forest types exhibit bright returns only at steeper angles. P-band should prove useful for floodwater mapping in dense stands, and multifrequency polarimetric analysis should allow flooded forests to be distinguished from marshes.
A method has been developed to determine the oceanic daily mean mixed layer depth from satellite observations and a mixed layer thermal inertia (MLTI) model. The algorithms were developed to use remotely-sensed values of sea surface temperature, albedo, and surface wind speeds to calculate the thermal inertia and to predict changes in subsurface diurnal mixed layer depth. The MLTI model, based on a mixed layer model of the upper ocean, has been used to simulate the diurnal mixing process and thermal inertia distribution in the Sargasso Sea around 34 deg N, 70 deg W. Sea surface temperature and albedo have been obtained from the NOAA7-AVHRR images. Surface wind speeds have been derived from the Scanning Multichannel Microwave Radiometer (SMMR) aboard Nimbus 7. Image processing was performed for images gathered between June and July 1982. The daily mean mixed layer depths predicted by the MLTI model agree well with data gathered at the LOTUS mooring located in the Sargasso Sea. This suggests that vertical mixing is the dominant physical process that controls the thermal inertia distribution in the midocean, far from major current systems, and that remote sensing is a promising tool to study such upper ocean processes.
The ability to map changes over large surface areas over time is one of the advantages in using remote sensing as a monitoring tool. Temporal changes in the surface may be gradual, making them difficult to detect in the short-term, and because they commonly occur at the subpixel scale, they may be difficult to detect in the long-term as well. Also, subtle changes may be real or merely an artifact of image noise. It is, therefore, necessary to understand the factors that limit the detection of surface materials in evaluating temporal data. The spectral detectability of vegetation and soil in the 1990 July and October Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data of Jasper Ridge, CA was evaluated and compared.
Satellite remote sensing has provided powerful tools for constructing long-term records of observations of many of Earth's geophysical processes. Geophysicists studying climage change would like to construct time series of data which are as long, accurate, and consistent as possible. While technological advances continue to improve the quality of observations from space, they also present consistency problems. The scanning multichannel microwave radiometer (SMMR), which operated from October 1978 to August 1987, and the special sensor microwave imager (SSMI), which overlapped with the SMMR for nearly two months in 1987, have provided microwave radiances which have been used to monitor long-term changes in sea ice concentration and snow accumulation, among other things. Inspection has shown, however, that brightness temperatures from the two sensors over the same polar firn-covered scene can differ by as much as 14 K. Calibration corrections for polar firn have been derived; in this paper, we present calculations addressing the physics of the small frequency and viewing angle differences between the SMMR and the SSMI on microwave emission from polar scenes containing free water. We focus here only on surface emission. We use a surface scattering model and study the effects of surface roughness, snow wetness, snow density, and young sea ice concentration. We also compare data from Antarctica with our modeled results. We find that for most scenes, surface roughness dominates over the effects of frequency and viewing angle differences, but that for open long-term times series, data must be calibrated and constructed at the geophysical product level rather than at the level of measured radiances.
In 1996, the NASA Administrator Dan Goldin and Rhode Island Congressman Patrick Kennedy challenged researchers in the Department of Geological Sciences at Brown University to developed a series of projects to apply remotely sensed data to problems of immediate concern to the State of Rhode Island. The result of that challenge was the project Narragansett Bay from Space: A Perspective for the 21st Century. The goals of the effort were to a) identify problems in coordination with state and local agencies, b) apply NASA technology to the problems and c) to involve small business that would benefit from incorporating remotely sensed data into their business operations. The overall effort was to serve two functions: help provide high quality science results based on remotely sensed data and increase the capacity of environmental managers and companies to use remotely sensed data. The effort has succeeded on both these fronts by providing new, quantitative information on the extent of environmental problems and developing a greater awareness and acceptance of remotely sensed data as a tool for monitoring and research.
The Automated Portable Test (APT) System is a notebook-sized, computer-based, human-performance and subjective-status assessment system. It is now being used in a wide range of environmental studies (e.g., simulator aftereffects, flight tests, drug effects, and hypoxia). Three questionnaires and 15 performance tests have been implemented, and the adaptation of 30 more tests is underway or is planned. The APT System is easily transportable, is inexpensive, and has the breadth of expansion options required for field and laboratory applications. The APT System is a powerful and expandable tool for human assessment in remote and unusual environments.
Hydrothermal processes have been suggested to explain a number of observations for Mars, including D/H ratios of water extracted from Martian meteorites, as a means for removing CO2 from the Martian atmosphere and sequestering it in the crust as carbonates, and as a possible origin for iron oxide-rich spectral units on the floors of some rifted basins (chasmata). There are numerous examples of Martian channels formed by discharges of subsurface water near potential magmatic heat sources, and hydrothermal processes have also been proposed as a mechanism for aquifer recharge needed to sustain long term erosion of sapping channels. The following geological settings have been identified as targets for ancient hydrothermal systems on Mars: channels located along the margins of impact crater melt sheets and on the slopes of ancient volcanoes; chaotic and fretted terranes where shallow subsurface heat sources are thought to have interacted with ground ice; and the floors of calderas and rifted basins (e.g. chasmata). On Earth, such geological environments are often a locus for hydrothermal mineralization. But we presently lack the mineralogical information needed for a definitive evaluation of hypotheses. A preferred tool for identifying minerals by remote sensing methods on Earth is high spatial resolution, hyperspectral, near-infrared spectroscopy, a technique that has been extensively developed by mineral explorationists. Future efforts to explore Mars for ancient hydrothermal systems would benefit from the application of methods developed by the mining industry to look for similar deposits on Earth. But Earth-based exploration models must be adapted to account for the large differences in the climatic and geological history of Mars. For example, it is likely that the early surface environment of Mars was cool, perhaps consistently below freezing, with the shallow portions of hydrothermal systems being dominated by magma-cryosphere interactions. Given the smaller gravitational field, declining atmospheric pressure, and widespread, permeable megaregolith on Mars, volatile outgassing and magmatic cooling would have been more effective than on Earth. Thus, hydrothermal systems are likely to have had much lower average surface temperatures than comparable geological settings on Earth. The likely predominance of basaltic crust on Mars suggests that hydrothermal fluids and associated deposits should be enriched in Fe, Mg, Si and Ca, with surficial deposits being dominated by lower temperature, mixed iron oxide and carbonate mineralogies.