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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Development and Validation of Sensorimotor Assessment Tasks Using Spaceflight Analogs

INTRODUCTION Exploration class missions will require a new level of crew autonomy to self-assess and treat their sensorimotor dysfunction around periods of gravitational transition, where sensorimotor disturbances are at their highest. There is a need to define sensorimotor assessment thresholds that indicate when performance in early extravehicular activities (EVAs) might be impacted or unsafe. The purpose of this study was to develop and validate a set of assessment tasks using sensorimotor spaceflight analogs including the Sensorimotor Disorientation Analog (SDA) and +3G x sustained centrifugation. METHODS For the SDA, forty-one healthy non-astronaut subjects (21 males, 20 females; Mean ± SD, Age: 34.0 ± 7.8 years) volunteered to participate in this study. Subjects attended two testing sessions: 1) familiarization to the tasks and baseline data collection, and 2) tasks completed under two levels of sensorimotor disorientation. Subjects performed a series of seven sensorimotor assessment tasks followed by two operationally relevant EVA analog tasks. The sensorimotor assessments tasks, ordered from least to most provocative of the sensorimotor system, included: postural stability, step test, prone/supine recovery from fall (RFF), four-square-step-test, obstacle negotiation with turns (OTT), kneel-and-reach, and augmented reality full-body coordination. The operational analog tasks included a top-hatch capsule egress and a 5-minute EVA traversal. Disorientation was applied via the SDA which utilized galvanic vestibular stimulation and weighted trunk, wrists, and ankles to target the vestibular and proprioceptive systems such that performance while wearing the SDA mimicked astronaut postflight performance at R+0 and R+1. Sixteen healthy non-astronaut subjects (10 males, 6 females; Age: 35.0 ± 9.5 years) completed +3G x 60-minute sustained centrifugation as an established spaceflight analog replicating the vestibular disruptions seen postflight. Subjects completed a pre-session which included familiarization to the tasks and a 5-minute centrifuge spin. The data collection session included, in order, a baseline data collection, 60-minute centrifuge spin, immediate post-centrifuge data collection, 45-minute seated recovery, and a final data collection. The same assessment and operational analog tasks were conducted as described above. RESULTS Pearsons’ correlation r-values were calculated between assessment and operational analog tasks. The two assessment tasks with the strongest correlations to the operational analog tasks were the RFF (SDA: r≥ 0.57, Centrifuge: r ≥0.55) and OTT (SDA: r≥ 0.66, Centrifuge: r ≥ 0.49). For brevity, repeated measures ANOVA with follow-up paired t-tests are discussed for these two assessment tasks. The SDA found significant differences between all three levels of disorientation for both RFF and OTT (p < 0.001). Significant differences were also found between baseline and immediate post centrifuge testing for both the RFF (p≤ 0.011) and OTT (p = 0.001). Additional results are forthcoming for both disorientation paradigms. DISCUSSION These validation studies provide evidence for down-selection of the assessment tasks that were able to distinguish between levels of sensorimotor disorientation and correlate to performance in operationally relevant tasks for use in upcoming Artemis missions.

S C Moudy↗
NASA NTRS1995

A satellite based telemetry link for a UAV application

The requirements for a satellite based communication facility to service the needs of the Geographical Information System (GIS) data collection community are addressed in this paper. GIS data is supplied in the form of video imagery at sub-television rates in one or more spectral bands / polarizations laced with a position correlated data stream. The limitations and vicissitudes of using a terrestrial based telecommunications link to collect GIS data are illustrated from actual mission scenarios. The expectations from a satellite based communications link by the geophysical data collection community concerning satellite architecture, operating bands, bandwidth, footprint agility, up link and down link hardware configurations on the UAV, the Mobile Control Vehicle and at the Central Command and Data Collection Facility comprise the principle issues discussed in the first section of this paper. The final section of the paper discusses satellite based communication links would have an increased volume and scope of services the GIS data collection community could make available to the GIS user community, and the price the data collection community could afford to pay for access to the communication satellite described in the paper.

Bloise, Anthony↗
NASA NTRS1992

SEDS1 mission software verification using a signal simulator

The first flight of the Small Expendable Deployer System (SEDS1) is schedule to fly as the secondary payload of a Delta 2 in March, 1993. The objective of the SEDS1 mission is to collect data to validate the concept of tethered satellite systems and to verify computer simulations used to predict their behavior. SEDS1 will deploy a 50 lb. instrumented satellite as an end mass using a 20 km tether. Langley Research Center is providing the end mass instrumentation, while the Marshall Space Flight Center is designing and building the deployer. The objective of the experiment is to test the SEDS design concept by demonstrating that the system will satisfactorily deploy the full 20 km tether without stopping prematurely, come to a smooth stop on the application of a brake, and cut the tether at the proper time after it swings to the local vertical. Also, SEDS1 will collect data which will be used to test the accuracy of tether dynamics models used to stimulate this type of deployment. The experiment will last about 1.5 hours and complete approximately 1.5 orbits. Radar tracking of the Delta II and end mass is planned. In addition, the SEDS1 on-board computer will continuously record, store, and transmit mission data over the Delta II S-band telemetry system. The Data System will count tether windings as the tether unwinds, log the times of each turn and other mission events, monitor tether tension, and record the temperature of system components. A summary of the measurements taken during the SEDS1 are shown. The Data System will also control the tether brake and cutter mechanisms. Preliminary versions of two major sections of the flight software, the data telemetry modules and the data collection modules, were developed and tested under the 1990 NASA/ASEE Summer Faculty Fellowship Program. To facilitate the debugging of these software modules, a prototype SEDS Data System was programmed to simulate turn count signals. During the 1991 summer program, the concept of simulating signals produced by the SEDS electronics systems and circuits was expanded and more precisely defined. During the 1992 summer program, the SEDS signal simulator was programmed to test the requirements of the SEDS Mission software, and this simulator will be used in the formal verification of the SEDS Mission Software. The formal test procedures specification was written which incorporates the use of the signal simulator to test the SEDS Mission Software and which incorporates procedures for testing the other major component of the SEDS software, the Monitor Software.

Pierson, William E.↗
NASA NTRS1986

Trace gas exchanges and transports over the Amazonian rain forest

Early results are presented from a program to model deep convective transport of chemical species by means of in situ data collection and numerical models. Data were acquired during the NASA GTE Amazon Boundary Layer Experiment in July-August 1985. Airborne instrumentation, including a UV-DIAL system, collected data on the O3, CO, NO, temperature and water vapor profiles from the surface to 400 mb altitude, while GOES imagery tracked convective clouds over the study area. A two-dimensional cloud model with small amplitude random temperature fluctuations at low levels, which simulated thermals, was used to describe the movements of the chemical species sensed in the convective atmosphere. The data was useful for evaluating the accuracy of the cloud model, which in turn was effective in describing the circulation of the chemical species.

Garstang, Michael↗
NASA NTRS1993

Software quality: Process or people

This paper will present data related to software development processes and personnel involvement from the perspective of software quality assurance. We examine eight years of data collected from six projects. Data collected varied by project but usually included defect and fault density with limited use of code metrics, schedule adherence, and budget growth information. The data are a blend of AFSCP 800-14 and suggested productivity measures in Software Metrics: A Practioner's Guide to Improved Product Development. A software quality assurance database tool, SQUID, was used to store and tabulate the data.

Palmer, Regina↗
NASA NTRSDate not supplied

Aero-Propulsive Modeling for eVTOL Aircraft Using Wind Tunnel Testing with Multisine Inputs

A novel approach for modeling the aero-propulsive characteristics of an electric vertical takeoff and landing (eVTOL) aircraft was developed and demonstrated in wind tunnel testing. The approach was applied to the NASA LA-8 tandem tilt-wing eVTOL aircraft, using an efficient, hybrid experiment design composed of a static I-optimal response surface design for slowly-varying test variables, and dynamic orthogonal phase-optimized multisine excitations for the control surfaces and electric propulsors. Both the static and dynamic experiment designs were executed simultaneously to collect informative data for model identification. Statistically-weighted multivariate orthogonal function modeling was used to combine local modeling results computed in the frequency domain using data collected with dynamic excitations operating on the control effectors to form an aggregate aero-propulsive model. The final identified model exhibited good predictive capability when compared to validation data acquired separately from the data used to develop the model. The required test time using these new techniques was reduced by at least a factor of five compared to previous static wind tunnel testing for the LA-8 aircraft, while providing more informative data, greater parameterization flexibility, and high-quality models.

system identification↗
NASA NTRS1992

The FAA/NASA flight loads monitoring program - The prototype system and its benefits for the aviation community

The FAA established the flight load monitoring program to collect a data base of typical flight operational loads experienced by commercial transports. This system will provide a comprehensive monitoring of aircraft loading conditions with over 20 flight parameters being recorded simultaneously. NASA is designing and testing a prototype data collection and analysis system which will be implemented into an FAA operational program. This paper presents the program's objectives and the proposed development testing on a commercial Boeing 737-400. The prototype system, its data processing schemes, and reports are described. The searching criteria or flight attributes generated for each flight are listed. The data processing system will provide the aviation community with a powerful tool for the study of transport flight loading conditions and the system's flexibility will accommodate individual studies and specialized concerns.

Whitehead, Julia H.↗
NASA NTRS1997

Summary Report of Mission Acceleration Measurements for STS-79. Launched 16 Sep. 1996

The Space Acceleration Measurement System (SAMS) collected acceleration data in support of the Mechanics of Granular Materials experiment during the STS-79 Mir docking mission, September 1996. STS-79 was the first opportunity to record SAMS data on an Orbiter while it was docked to Mir. Crew exercise activities in the Atlantis middeck and the Mir base module are apparent in the data. The acceleration signals related to the Enhanced Orbiter Refrigerator Freezer had different characteristics when comparing the data recorded on Atlantis on STS-79 with the data recorded on Mir during STS-74. This is probably due, at least in part, to different transmission paths and SAMS sensor head mounting mechanisms. Data collected on Atlantis during the STS-79 docking indicate that accelerations due to vehicle and solar array structural modes from Mir transfer to Atlantis and that the structural modes of the Atlantis-Mir complex are different from those of either vehicle independently. A 0.18 Hz component of the SAMS data, present while the two vehicles were docked, was probably caused by the Mir solar arrays. Compared to Atlantis structural modes of about 3.9 and 4.9 Hz, the Atlantis-Mir complex has structural components of about 4.5 and 5.1 Hz. After docking, apparent structural modes appeared in the data at about 0.8 and 1.8 Hz. The appearance, disappearance, and change in the structural modes during the docking and undocking phases of the joint Atlantis-Mir operations indicates that the structural modes of the two spacecraft have an effect on the microgravity environment of each other. The transfer of structural and equipment related accelerations between vehicles is something that should be considered in the International Space Station era.

Rogers, Melissa J. B.↗
NASA NTRS2021

Characterization of the NASA Glenn Research Center 8- by 6-Foot Supersonic Wind Tunnel (2019 Test)

There were several major modifications made to the NASA Glenn Research Center 8- by 6-Foot Supersonic Wind Tunnel between 2016 and 2019, including the 9- by 15-Foot Low Speed Wind Tunnel Acoustic Improvement Modifications. Following completion of these facility modifications, a characterization test was conducted in the 8- by 6-Foot Supersonic Wind Tunnel test section. This test entry collected data for a single test section porosity configuration across the entire operating range of the facility. Flow field data were collected at a cross-sectional plane and axially along test section center-line for use in quantifying the test section flow quality. Off-nominal supersonic conditions were surveyed through variation of balance chamber pressure at each of the discrete flexible wall nozzle settings. Additionally, a baseline check calibration data set was acquired during this entry.

8x6 SWT↗
NASA NTRSDate not supplied

Volumetric Assessment of UPRITE Exercises From Marker-Based Motion Capture

BACKGROUND Lack of volumetric data on full-body movement of exercises presents a challenge to ensuring the fit of crew member’s full range of motion on the International Space Station (ISS). The Upright Proprioception Retention via In-flight Training and Evaluation (UPRITE) is a sensorimotor countermeasure device designed for maintaining crew members’ proprioception in a microgravity environment. A footplate—attached to a static base—rotates in two degrees of freedom (pitch and roll) up to a 20 deg angle. An initial volumetric assessment assuming an upright standing posture produced a cone-like shape with a narrow bottom and wide top. Such general volumetric assessments risk creating an overly conservative volume estimate, taking up more space than is necessary on the already limited interior space of the ISS, and neglecting necessary volume due to oversimplifying assumptions. Rather, higher-fidelity volumetric assessments offer more comprehensive insights in an environment where every area counts. The main objective of this work is to provide the spatial parameters of exercises on the UPRITE such that it is placed on the ISS according to its volumetric demands or that usage is adjusted to fit the available space. METHODS In 2023, a data collection was performed originally to inform loads and dynamics of system use and was recently leveraged for volumetric assessment. Three human subjects representing different body types (~63-76 inches in stature) performed a variety of board manipulations using UPRITE with body weight offload. The test collected the 3D positional data of a modified full-body Plug-in Gait marker set [1] via a 16-camera OptiTrack MoCap system. After processing – filling marker gaps and trimming data – in OptiTrack Motive, the recorded marker location data, which included device markers, was exported to a readable trajectory file. To accurately represent the full volume defining landmarks, additional markers were digitally added to an unscaled Modified Full Body Model [2]. The model was then scaled according to its subject parameters upon which an inverse kinematics analysis was performed. A custom plugin yielded model marker location data files. Volumetric analyses were performed on the recorded trajectory and model trajectory files using a custom Python-built tool that extracted the marker location data and plotted it in a 3D space. Concerned with only the maximum volume of the motion, a 3D convex hull analysis was applied to the plot, extracting the vertices or external points of the eventual 3D CAD output, dubbed aptly as a “volume shell”. This overall approach was based on guidance in a NASA-STD-3001 Technical Brief [3]. RESULTS AND DISCUSSION Batch volumetric assessment on the exercises for each subject was performed, producing high-fidelity volume shells in minimal time. Preliminary results highlighted the value in higher-fidelity volumes based on collected data when possible. For example, revolving a single posture in the cone assessment would not have sufficiently captured a single leg stance; rather, it would need to involve swinging the leg both forward and back. Additional observations and the maximal dimensions of the volumes, including those based on scaled data for ISS anthropometric requirements, will be presented at the Human Research Program Investigator’s Workshop. CONCLUSIONS While this work’s primary objective was for the UPRITE-to-ISS integration, the tool built to conduct this analysis has wide applications for future exercise systems as an informational tool for optimal device placement. The tool and its findings also have implications for exercise device design and spacecraft interior considerations on Gateway, the Lunar Pressurized Rover, and beyond. REFERENCES [1] Bell, C. A., et al. (2023) Recent Improvements and Verification of a Full Body Model in OpenSim. NASA Human Research Program Investigator’s Workshop. https://ntrs.nasa.gov/citations/20230001080 [2] Lostroscio, K., et al (2023) The Digital Astronaut Simulation. AHFE International Conference on Human Factors in Design, Engineering, and Computing for All. [3] Exercise Overview. (2023) NASA-STD-3001 Technical Brief. https://www.nasa.gov/wp-content/uploads/2023/12/ochmo-tb-031-exercise-overview.pdf?emrc=9d454c?emrc=9d454c

L D Quinto↗
NASA NTRS2011

Collaborative Supervised Learning for Sensor Networks

Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

Wagstaff, Kiri L.↗
NASA NTRS2020

TCL4 UTM (UAS Traffic Management) Nevada 2019 Flight Tests, Airspace Operations Laboratory (AOL) Report

The Unmanned Aircraft Systems (UAS) Traffic Management (UTM) research project has been developing and testing concept ideas for enabling small UAS (sUAS) operations in low altitude airspace (ground to 400 feet). To do this, NASA has organized a series of flight test demonstrations. Technology Capability Level-4 (TCL4) flight tests were conducted at a Nevada, USA test site, during June 2019. The testing resulted in over 300 data collection flights using eight live rotorcraft, 15 simulated vehicles, involving six flight crews and five Unmanned Aerial System (UAS) Service Suppliers (USS). The TCL4 approach was designed to demonstrate five scenarios that set up five diverse sets of UAS events and activities. The Nevada test site focused on three of these scenarios: an incoming weather front, a concert event with an incident requiring an emergency response, and a scenario where multiple vehicles experienced Communication, Navigation, and Surveillance (CNS) issues. The test site created their scenarios to each have three phases and were required to complete three executions of each scenario, for a total of nine missions per Nevada vehicle per scenario. This document presents data collected from participants during the TCL4-Nevada flight test that provides information about how much and how well operators were able to make use of UTM functions and information, with the goal of exploring what the minimum information requirements and/or best practices might be in TCL4 operations. The driving enquiry was: how do UTM tools and features support (human) operators leading to safe and effective conduct of large-scale beyond visual line of sight (BVLOS) sUAS operations in “urban canyon” environments? As with the data collected during previous similar tests (e.g., TCL3, Martin et al., 2019), the quality of the UTM information exchanged, and the meaningfulness and therefore usefulness of this information, were all focal points of the questions asked and the data collected. Data aligned with five human-system attributes to indicate that UTM provided information that contributed to users’ ability to operate safely and effectively within UTM, but that information was not always complete and was sometimes unclear.

UTM↗
NASA NTRS1982

Classification of the Johnson Space Center stratospheric dust collection

Data on the nature of dust in the upper atmosphere have been provided by a stratospheric dust collection program. This program makes it possible to study in detail materials related to early solar system history. A comprehensive overview of the stratospheric collection program provides a basis for a better understanding of stratospheric dust origins. It is important for the development of this understanding that easily accessible criteria for classification be established. The present investigation is concerned with the development of a suitable classification scheme for the obtained stratospheric materials. Three sets of eight collectors were flown on WB57 high-altitude aircraft during August and September, 1981, as part of the considered collection program. Three of the 24 impactors (flags) have undergone preliminary examination. The current investigation is concerned with selected data on materials from flags W7010, W7017, and W7029.

Mackinnon, I. D. R.↗
NASA NTRSDate not supplied

Human Factors and Behavioral Performance Exploration Measures Harmonized Across HERA, NEK, and ISS: Teams Risk

BACKGROUNDThe Human Factors and Behavioral Performance Exploration Measures (HFBP-EM) suite is a set of standardized measures to assess behavioral health and performance risk related to future exploration class missions, and to support reduction of the Human Research Program’s (HRP) Behavioral Medicine (BMed), Team, Sleep, and Human Systems Integration Architecture risks. HFBP-EM were collected during Human Exploration Research Analogs (HERA) campaigns 4 (C4) and 5 (C5), and during SIRIUS 17 and 19 missions in the Russian Ground Based Experiment Complex, NEK, to document the feasibility, flexibility, and acceptability of these measures in analogsof the spaceflight environment. A subset of the HFBP-EM suite was collected during spaceflight as part HRP’s Standard Measures in Spaceflight Project. Whenever possible, the HFBP-EM protocol and measures are kept the same across studies, however, differences across research settings (e.g., experimental manipulations, mission scenarios, mission length) and implementation of the measures require the data are harmonized to ensure comparable views across missions. The purpose of our project is to develop a harmonized database of HFBP-EM data from different settings, and to summarize the trajectory of behavioral health and performance within and between research settings. In this presentation, we will summarize the harmonized dataset and the trajectory of measures related to the Team Risk, including team performance, team cohesion, team processes, and psychological safety, over time and between and within settings.METHODSWe followed best practices for data harmonization. Characteristics of each research setting were assessed for harmonization potential, and we deemed NEK-SIRIUS 17 as inappropriate due to study aims, length, and data quality. Common variables of interest were identified. Study characteristics and key variables with which datafiles could be merged were defined as “meta-data.” HFBP-EM data from all settings were processed under a common format. We then created a harmonized team-level database designed to facilitate analyses that address HRP research gaps related to HRP’s Team risk. In this database, team cohesion, processes, performance, psychological safety, and group living were operationalized as the team mean of the crew responses for each data collection (e.g., on mission day 7). Data collected on the International Space Station (ISS) included a subset of scale items administered to participants. Data collected from ISS team members within +/- 7 days of the first data collection and every following 30 +/-4 days were aggregated to the team level. We generated figures that display the mean and variability of team constructs across research settings. We also generated plots of changes in team constructs with mission day and with percentage of the mission completed. Where possible, we compared data from spaceflight analogs with data from astronauts aboard the ISS. RESULTS AND DISCUSSIONThe team-level harmonized database was structured such that each row represents data for a team on a specific mission day. Team constructs (e.g., team cohesion) were included as columns (i.e., wide format) with repeated measures across mission days given in rows (i.e., long format). The database included data from 18 crews: five, 4-person American crews in HERA C4, four 4-person American crews in HERA C5, one 6-person multinational crew in NEK-SIRIUS 19, and eight, 2–11-person multinational crews aboard the ISS. Results provide insights into mission and campaign differences in team functioning and performance. For example, between campaign differences were observed for team processes—the interdependent team actions that orchestrate taskwork in pursuit of the team’s goals [1]. Greater between-team variability on team processes was detected during HERA C4 than during C5, and a downward trend was observed over the duration of C4, but not C5 or SIRIUS 19. This may be due to the campaign-level differences such as the sleep deprivation implemented during C4. Responses on the subset of team process items administered on the ISS indicate between-crew variability more like those observed in C4 than C5 and NEK, with some, but not all ISS crews demonstrating a downward trend over time. Additional findings will be presented. REFERENCESMarks, M. A., et al (2001) Academy of Management Review, 26(3), 356-376.

S T Bell↗
NASA NTRS1973

Preliminary geologic investigation of the Apollo 17 landing site

A geological investigation of the Apollo 17 lunar landing site was conducted. The Taurus-Littrow valley is interpreted as a deep graben formed by structural adjustment of lunar crustal material to the Serenitatis impact. Materials of the valley fill were sampled at many stations. Ejecta around many craters on the valley floor consist of basalt, showing that the graben was partly filled by lava flows. The geological objectives of the Apollo 17 mission are divided into orbital and lunar surface data collection. The data obtained for both types of investigation are presented in tables, photographs, and drawings.

Muehlberger, W. R.↗
NASA NTRS1974

ERTS operations and data processing

The overall communications and data flow between the ERTS spacecraft and the ground stations and processing centers are generally described. Data from the multispectral scanner and the return beam vidicon are telemetered to a primary ground station where they are demodulated, processed, and recorded. The tapes are then transferred to the NASA Data Processing Facility (NDPF) at Goddard. Housekeeping data are relayed from the prime ground stations to the Operations Control Center at Goddard. Tracking data are processed at the ground stations, and the calculated parameters are transmitted by teletype to the orbit determination group at Goddard. The ERTS orbit has been designed so that the same swaths of the ground coverage pattern viewed during one 18-day coverage cycle are repeated by the swaths viewed on all subsequent cycles. The Operations Control Center is the focal point for all communications with the spacecraft. NDPF is a job-oriented facility which processes and stores all sensor data, and which disseminates large quantities of these data to users in the form of films, computer-compatible tapes, and data collection system data.

Gonzales, L.↗
NASA NTRS1975

Earth resources data acquisition sensor study

The minimum data collection and data processing requirements are investigated for the development of water monitoring systems, which disregard redundant and irrelevant data and process only those data predictive of the onset of significant pollution events. Two approaches are immediately suggested: (1) adaptation of a presently available ambient air monitoring system developed by TVA, and (2) consideration of an air, water, and radiological monitoring system developed by the Georgia Tech Experiment Station. In order to apply monitoring systems, threshold values and maximum allowable rates of change of critical parameters such as dissolved oxygen and temperature are required.

Grohse, E. W.↗
NASA NTRS1975

An investigation of the waters in the lower Chesapeake Bay area

Since October of 1972 the Langley Research Center and the Old Dominion University have been engaged in a cooperative effort to investigate the waters of the lower Chesapeake Bay area. During the first year an intensive effort was made to collect water data at the time of Landsat-1 overpasses. Bands 5 and 6 of the multispectral scanner (MSS) were shown to be useful for monitoring total particles, although a daily calibration was required. Band 5 had a high correlation with sediment and under some conditions an internal correction for atmospheric interference was possible. The relation of sediment to particles was established by using the MSS radiance values, since the two parameters were not monitored at the same stations. A contouring program was developed to display the sediment variation in the lower Bay at the time of the Jan. 26, 1973, overpass. Attenuation coefficients monitored continuously by ship along three baselines were cross-correlated with radiance values on three days. Evidence for tidal effects was obtained by showing that correlations improved when ship data collected during the nearest 'tidal time' instead of physical time were used

Bowker, D. E.↗