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Water turbidity detection using ERTS-1 imagery
Water turbidity detection in Kansas using ERTS-1 imagery
Detection of water bodies in Saline County, Kansas
The author has identified the following significant results. A total of 2,272 water bodies were mapped in Saline County, Kansas in 1972 using ERTS-1 imagery. A topographic map of 1955 shows 1,056 water bodies in the county. The major increase took place in farm ponds. Preliminary comparison of image and maps indicates that water bodies larger than ten acres in area proved consistently detectable. Most water areas between four and ten acres are also detectable, although occasionally image context prevents detection. Water areas less than four acres in extent are sometimes detected, but the number varies greatly depending on image context and the individual interpretor.
Application of Data Cubes for Improving Detection of Water Cycle Extreme Events
As part of an ongoing NASA-funded project to remove a longstanding barrier to accessing NASA data (i.e., accessing archived time-step array data as point-time series), for the hydrology and other point-time series-oriented communities, "data cubes" are created from which time series files (aka "data rods") are generated on-the-fly and made available as Web services from the Goddard Earth Sciences Data and Information Services Center (GES DISC). Data cubes are data as archived rearranged into spatio-temporal matrices, which allow for easy access to the data, both spatially and temporally. A data cube is a specific case of the general optimal strategy of reorganizing data to match the desired means of access. The gain from such reorganization is greater the larger the data set. As a use case of our project, we are leveraging existing software to explore the application of the data cubes concept to machine learning, for the purpose of detecting water cycle extreme events, a specific case of anomaly detection, requiring time series data. We investigate the use of support vector machines (SVM) for anomaly classification. We show an example of detection of water cycle extreme events, using data from the Tropical Rainfall Measuring Mission (TRMM).
Identification of Phyllosilicates in Mudstone Samples Using Water Releases Detected by the Sample Analysis at Mars (SAM) Instrument in Gale Crater, Mars
The Sample Analysis at Mars (SAM) instrument on board the Curiosity Rover has detected high temperature water releases from mud-stones in the areas of Yellowknife Bay, Pahrump Hills, Naukluft Plateau, and Murray Buttes in Gale crater. Dehydroxylation of phyllosilicates may have caused the high temperature water releases observed in these samples. Because each type of phyllosilicate undergoes dehydroxylation at distinct temperatures, these water releases can be used to help constrain the type of phyllosilicate present in each sample.
Unmanned Ground Vehicle Perception Using Thermal Infrared Cameras
The ability to perform off-road autonomous navigation at any time of day or night is a requirement for some unmanned ground vehicle (UGV) programs. Because there are times when it is desirable for military UGVs to operate without emitting strong, detectable electromagnetic signals, a passive only terrain perception mode of operation is also often a requirement. Thermal infrared (TIR) cameras can be used to provide day and night passive terrain perception. TIR cameras have a detector sensitive to either mid-wave infrared (MWIR) radiation (3-5?m) or long-wave infrared (LWIR) radiation (8-12?m). With the recent emergence of high-quality uncooled LWIR cameras, TIR cameras have become viable passive perception options for some UGV programs. The Jet Propulsion Laboratory (JPL) has used a stereo pair of TIR cameras under several UGV programs to perform stereo ranging, terrain mapping, tree-trunk detection, pedestrian detection, negative obstacle detection, and water detection based on object reflections. In addition, we have evaluated stereo range data at a variety of UGV speeds, evaluated dual-band TIR classification of soil, vegetation, and rock terrain types, analyzed 24 hour water and 12 hour mud TIR imagery, and analyzed TIR imagery for hazard detection through smoke. Since TIR cameras do not currently provide the resolution available from megapixel color cameras, a UGV's daytime safe speed is often reduced when using TIR instead of color cameras. In this paper, we summarize the UGV terrain perception work JPL has performed with TIR cameras over the last decade and describe a calibration target developed by General Dynamics Robotic Systems (GDRS) for TIR cameras and other sensors.
Detection of Water Hazards for Autonomous Robotic Vehicles
Four methods of detection of bodies of water are under development as means to enable autonomous robotic ground vehicles to avoid water hazards when traversing off-road terrain. The methods involve processing of digitized outputs of optoelectronic sensors aboard the vehicles. It is planned to implement these methods in hardware and software that would operate in conjunction with the hardware and software for navigation and for avoidance of solid terrain obstacles and hazards. The first method, intended for use during the day, is based on the observation that, under most off-road conditions, reflections of sky from water are easily discriminated from the adjacent terrain by their color and brightness, regardless of the weather and of the state of surface waves on the water. Accordingly, this method involves collection of color imagery by a video camera and processing of the image data by an algorithm that classifies each pixel as soil, water, or vegetation according to its color and brightness values (see figure). Among the issues that arise is the fact that in the presence of reflections of objects on the opposite shore, it is difficult to distinguish water by color and brightness alone. Another issue is that once a body of water has been identified by means of color and brightness, its boundary must be mapped for use in navigation. Techniques for addressing these issues are under investigation. The second method, which is not limited by time of day, is based on the observation that ladar returns from bodies of water are usually too weak to be detected. In this method, ladar scans of the terrain are analyzed for returns and the absence thereof. In appropriate regions, the presence of water can be inferred from the absence of returns. Under some conditions in which reflections from the bottom are detectable, ladar returns could, in principle, be used to determine depth. The third method involves the recognition of bodies of water as dark areas in short-wavelength infrared (SWIR) images. This method is based on the fact, well known among experts in remote sensing, that water bodies of any appreciable depth appear very dark in near-infrared, overhead imagery. Even under a thick layer of marine fog, SWIR illumination is present. Hence, this method may work even in the presence of clouds, though it is unlikely to work at night. Snow and ice also exhibit very strong absorption at wavelengths greater than about 1.4 m. Hence, the wavelength range of about 1.5 to 1.6 m might be useable in this method for recognizing water, snow, and ice. One notable drawback of this method is that useful look-ahead distance could be limited by surface reflections. The fourth method, intended for use at night, involves the contrast between water and terrain in thermal-infrared (medium-wavelength infrared) imagery. This method is based on the fact that at night, water is usually warmer than the adjacent terrain. Look-ahead distance could be limited in this method because, for reasons not yet fully understood, water appears to darken in the thermal infrared with increasing distance.
Comments on the detection of water and ice clouds on Venus
Problems in detecting ice and water clouds on Venus
Detection of water in interstellar regions by its microwave radiation.
Water in interstellar regions detected by microwave emission from 6 sub 16 to 5 sub 23 rotational transition
LAVA Subsystem Integration and Testing for the RESOLVE Payload of the Resource Prospector Mission: Mass Spectrometers and Gas Chromatography
The Regolith and Environment Science & Oxygen and Lunar Volatile Extraction (RESOLVE) payload is part of Resource Prospector (RP) along with a rover and a lander that are expected to launch in 2020. RP will identify volatile elements that may be combined and collected to be used for fuel, air, and water in order to enable deeper space exploration. The Resource Prospector mission is a key part of In-Situ Resource Utilization (ISRU). The demand for this method of utilizing resources at the site of exploration is increasing due to the cost of resupply missions and deep space exploration goals. The RESOLVE payload includes the Lunar Advanced Volatile Analysis (LAVA) subsystem. The main instrument used to identify the volatiles evolved from the lunar regolith is the Gas Chromatograph-Mass Spectrometer (GC-MS). LAVA analyzes the volatiles emitted from the Oxygen and Volatile Extraction Node (OVEN) Subsystem. The objective of OVEN is to obtain, weigh, heat and transfer evolved gases to LAVA through the connection between the two subsystems called the LOVEN line. This paper highlights the work completed during a ten week internship that involved the integration, testing, data analysis, and procedure documentation of two candidate mass spectrometers for the LAVA subsystem in order to aid in determining which model to use for flight. Additionally, the examination of data from the integrated Resource Prospector '15 (RP' 15) field test will be presented in order to characterize the amount of water detected from water doped regolith samples.
Lunar Advanced Volatile Analysis Integration and Testing
The Regolith and Environment Science Oxygen and Lunar Volatile Extraction (RESOLVE) payload is part of Resource Prospector (RP) along with a rover and a lander that are expected to launch in 2020. RP will identify volatile elements that may be combined and collected to be used for fuel, air, and water in order to enable deeper space exploration. The Resource Prospector mission is a key part of In-Situ Resource Utilization (ISRU). The demand for this method of utilizing resources at the site of exploration is increasing due to the cost of resupply missions and deep space exploration goals. The RESOLVE payload includes the Lunar Advanced Volatile Analysis (LAVA) subsystem. The main instrument used to identify the volatiles evolved from the lunar regolith is the Gas Chromatograph-Mass Spectrometer (GC-MS). LAVA analyzes the volatiles emitted from the Oxygen and Volatile Extraction Node (OVEN) Subsystem. The objective of OVEN is to obtain, weigh, heat and transfer evolved gases to LAVA through the connection between the two subsystems called the LOVEN line. This paper highlights the work completed during a ten week internship that involved the integration, testing, data analysis, and procedure documentation of two candidate mass spectrometers for the LAVA subsystem in order to aid in determining which model to use for flight. Additionally, the examination of data from the integrated Resource Prospector '15 (RP' 15) field test will be presented in order to characterize the amount of water detected from water doped regolith samples.
Alexandrite lidar for the atmospheric water vapor detection and development of powerful tunable sources in IR
New tunable solid-state lasers, such as alexandrite and Ti-sapphire lasers, provide a powerful technique to detect various molecules in the atmosphere whose absorption bands are in the infrared region. The differential absorption lidar (DIAL) system to measure the tropospheric water vapor has been investigated by many authors, in an early stage, by dye and ruby lasers. Using the alpha band of water vapor, the longest detection range can be obtained with high accuracy, and the alexandrite laser is the most suitable laser for this purpose. In this paper, we describe the detection of water vapor in the atmosphere by an alexandrite lidar, and the development of powerful tunable sources based on Raman lasers in the infrared region.
Rapid and Ultrasensitive Short-Chain PFAS (GenX) Detection in Water via Surface-Enhanced Raman Spectroscopy with a Hierarchical Nanofibrous Substrate
GenX, the trade name of hexafluoropropylene oxide dimer acid (HFPO-DA) and its ammonium salt, is a short-chain PFAS that has emerged as a substitute for the legacy PFAS perfluorooctanoic acid (PFOA). However, GenX has turned out to be more toxic than people originally thought. In order to monitor and regulate water quality according to recently issued drinking water standards for GenX, rapid and ultrasensitive detection of GenX is urgently needed. For the first time, this study reports ultrasensitive (as low as 1 part per billion (ppb)) and fast detection (in minutes) of GenX in water via surface-enhanced Raman spectroscopy (SERS) using a hierarchical nanofibrous SERS substrate, which was prepared by assembling ~60 nm Ag nanoparticles on electrospun nylon-6 nanofibers through a “hot start” method. The findings in this research highlight the potential of the engineered hierarchical nanofibrous SERS substrate for enhanced detection of short-chain PFASs in water, contributing to the improvement of environmental monitoring and management strategies for PFASs.
Rapid toxicity detection in water quality control utilizing automated multispecies biomonitoring for permanent space stations
The objective of this study was to evaluate proposed design characteristics and applications of automated biomonitoring devices for real-time toxicity detection in water quality control on-board permanent space stations. Simulated tests in downlinking transmissions of automated biomonitoring data to Earth-receiving stations were simulated using satellite data transmissions from remote Earth-based stations.
Electronics for a Martian water vapor detection system
Electronic circuitry for aluminum oxide detector of water content in Martian atmosphere and surface
Electromagnetic detection of lunar subsurface water
Radar detection of lunar subsurface water from lunar orbiting satellite
Supporting Aquaculture in the Chesapeake Bay Using Artificial Intelligence to Detect Poor Water Quality with Remote Sensing
Reliable information on water quality is not currently available at the space and time scales that are required for aquaculture and other resource management needs. For example, shellfish growing areas maybe impacted by harmful algal blooms or runoff from land that increases turbidity, lowers salinity, or introduces contaminants. Shellfish resource managers in the Chesapeake Bay are especially concerned with sources of bacteria from land such as failing onsite waste systems, failing wastewater infrastructure, and concentrated animal feeding operations. There is an urgent need for remote sensing of water quality indicators beyond chlorophyll-a and suspended sediments to augment field sampling programs. Artificial Intelligence trained with simultaneous in situ and satellite observations is explored in preparation for future hyperspectral satellite missions, which offer potential to detect additional water quality indicators not previously possible. This first step identifies and develops a method to harmonize disparate, unlinked aquatic datasets to derive information about where water quality is likely degraded.
Detection of water vapor in Halley's comet
Gaseous, neutral H2O was detected in the coma of comet Halley on 22.1 and 24.1 December 1985 Universal Time. Nine spectral lines of the nus band (2.65 micrometers) were found by means of a Fourier transform spectrometer on the NASA-Kuiper Airborne Observatory. The water production rate was about 6 x 10 to the 28th molecules per second on 22.1 December and 1.7 x 10 to the 29th molecules per second on 24.1 December UT. The numbers of spectral lines and their intensities are in accord with nonthermal-equilibrium cometary models. Rotational populations are derived from the observed spectral line intensities and excitation conditions are discussed. The ortho-para ratio was found to be 2.66 + or - 0.13, corresponding to a nuclear-spin temperature of 32 K (+5 K, -2 K), possibly indicating that the observed water vapor originated from a low-temperature ice.