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NASA NTRS2014

Aggregation Tool to Create Curated Data albums to Support Disaster Recovery and Response

Despite advances in science and technology of prediction and simulation of natural hazards, losses incurred due to natural disasters keep growing every year. Natural disasters cause more economic losses as compared to anthropogenic disasters. Economic losses due to natural hazards are estimated to be around $6-$10 billion dollars annually for the U.S. and this number keeps increasing every year. This increase has been attributed to population growth and migration to more hazard prone locations such as coasts. As this trend continues, in concert with shifts in weather patterns caused by climate change, it is anticipated that losses associated with natural disasters will keep growing substantially. One of challenges disaster response and recovery analysts face is to quickly find, access and utilize a vast variety of relevant geospatial data collected by different federal agencies such as DoD, NASA, NOAA, EPA, USGS etc. Some examples of these data sets include high spatio-temporal resolution multi/hyperspectral satellite imagery, model prediction outputs from weather models, latest radar scans, measurements from an array of sensor networks such as Integrated Ocean Observing System etc. More often analysts may be familiar with limited, but specific datasets and are often unaware of or unfamiliar with a large quantity of other useful resources. Finding airborne or satellite data useful to a natural disaster event often requires a time consuming search through web pages and data archives. Additional information related to damages, deaths, and injuries requires extensive online searches for news reports and official report summaries. An analyst must also sift through vast amounts of potentially useful digital information captured by the general public such as geo-tagged photos, videos and real time damage updates within twitter feeds. Collecting and aggregating these information fragments can provide useful information in assessing damage in real time and help direct recovery efforts. The search process for the analyst could be made much more efficient and productive if a tool could go beyond a typical search engine and provide not just links to web sites but actual links to specific data relevant to the natural disaster, parse unstructured reports for useful information nuggets, as well as gather other related reports, summaries, news stories, and images. This presentation will describe a semantic aggregation tool developed to address similar problem for Earth Science researchers. This tool provides automated curation, and creates "Data Albums" to support case studies. The generated "Data Albums" are compiled collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; information about the event contained in news reports, and images or videos to supplement research analysis. An ontology-based relevancy-ranking algorithm drives the curation of relevant data sets for a given event. This tool is now being used to generate a catalog of Hurricane Case Studies at Global Hydrology Resource Center (GHRC), one of NASA's Distribute Active Archive Centers. Another instance of the Data Albums tool is currently being created in collaboration with NASA/MSFC's SPoRT Center, which conducts research on unique NASA products and capabilities that can be transitioned to the operational community to solve forecast problems. This new instance focuses on severe weather to support SPoRT researchers in their model evaluation studies

Ramachandran, Rahul↗
NASA NTRS1998

Polar Hydra Data Analysis

The science activities are: 1) Hydra is still operating successfully on orbit. 2) A large amount of analysis and discovery has occurred with the Hydra ground data processing this past year. 3) Full interdetector calibration has been implemented and documented. This intercalibration was necessitated by the incorrect installation of bias resistors in the pre-acceleration stage to the electron channeltrons. This had the effect of making the counting efficiency for electrons energy dependent as well as channeltron specific. The nature of the error had no impact on the ion detection efficiency since they have a different bias arrangement. This intercalibration is so effective, that the electron and ion moment densities are routinely produced with a level of agreement better than 20%. 4) The data processing routinely removes glint in the sensors and produces public energy time spectrograms on the web overnight. 6) Routine, but more intensive computer processing codes are operational that determine for electrons and ions, the density, the flow vector, the pressure tensor and the heat flux by numerical integration. These codes use the magnetic field to sustain the quality of their output. To gain access to this high quality magnetic field within our data stream we have monitored Russell's web page for zero levels and timing files (since his data acquisition is not telemetry synchronous) and have a local reconstruction of B for our use. We have also detected a routine anomaly in the magnetometer data stream that we have documented to Chris Russell and developed an editing algorithm to intercept these "hits" and remove them from the geophysical analysis.

Scudder, J. D.↗
NASA NTRS1995

Investigation of cloud properties and atmospheric stability with MODIS

In the past six months several milestones were accomplished. The MODIS Airborne Simulator (MAS) was flown in a 50 channel configuration for the first time in January 1995 and the data were calibrated and validated; in the same field campaign the approach for validating MODIS radiances using the MAS and High resolution Interferometer Sounder (HIS) instruments was successfully tested on GOES-8. Cloud masks for two scenes (one winter and the other summer) of AVHRR local area coverage from the Gulf of Mexico to Canada were processed and forwarded to the SDST for MODIS Science Team investigation; a variety of surface and cloud scenes were evident. Beta software preparations continued with incorporation of the EOS SDP Toolkit. SCAR-C data was processed and presented at the biomass burning conference. Preparations for SCAR-B accelerated with generation of a home page for access to real time satellite data related to biomass burning; this will be available to the scientists in Brazil via internet on the World Wide Web. The CO2 cloud algorithm was compared to other algorithms that differ in their construction of clear radiance fields. The HIRS global cloud climatology was completed for six years. The MODIS science team meeting was attended by five of the UW scientists.

Menzel, Paul↗
NASA NTRS1975

Read-write holographic memory with iron-doped lithium niobate

The response of iron doped lithium niobate under conditions corresponding to hologram storage and retrieval is described, and the material's characteristics are discussed. The optical sensitivity can be improved by heavy chemical reduction of lightly doped crystals such that most of the iron is in the divalent state, the remaining part being trivalent. The best reduction process found to be reproducible so far is the anneal of the doped crystal in the presence of a salt such as lithium carbonate. It is shown by analysis and simulation that a page-oriented read-write holographic memory with 1,000 bits per page would have a cycle time of about 60 ms and a signal-to-noise ratio of 27 db. This cycle time, although still too long for a practical system, represents an improvement of two orders of magnitude over that of previous laboratory prototypes using different storage media.

Alphonse, G. A.↗
NASA NTRS1976

Iron-doped lithium niobate as a read-write holographic storage medium

The response of iron-doped lithium niobate under conditions corresponding to hologram storage and retrieval is described, and the material characteristics are discussed. The optical sensitivity can be improved by heavy chemical reduction of lightly doped crystals such that most of the iron is in the divalent state, the remaining part being trivalent. The best reduction process found to be reproducible so far is the anneal of the doped crystal in the presence of a salt such as lithium carbonate. It is shown by analysis and simulation that a page-oriented read-write holographic memory with 1000 bits per page would have a cycle time of about 60 msec and a signal-to-noise ratio of 27 dB. This cycle time, although still too long for a practical memory, represents an improvement of two orders of magnitude over that of previous laboratory prototypes using a thermoplastic storage medium

Alphonse, G. A.↗
NASA NTRS1982

Solar-Radiation Measuring Equipment and Glossary

1976 listing of commercially available solar-radiation measuring equipment is presented in 50-page report. Sensor type, response time, cost data, and comments concerning specifications and intended usage are listed for 145 instruments from 38 manufactures.

Carter, E. A.↗
NASA NTRS2002

Comparison of VDL Modes in the Aeronautical Telecommunications Network

VHF Digital Link (VDL) has been identified as a method of communication between aircraft and ground stations in the Aeronautical Telecommunications Network (ATN). Three different modes of VDL have been suggested for implementation. Simulations were conducted to compare the data transfer capabilities of VDL Modes 2, 3, and 4. These simulations focus on up to 50 aircraft communicating with a single VDL ground station. The data traffic is generated by the standard File Transfer Protocol (FTP) and Hyper Text Transfer Protocol (HTTP) applications in the aircraft. Comparisons of the modes are based on the number of files and pages transferred and the response time.

Bretmersky, Steven↗
NASA NTRS2001

New Data Source for Studying and Modelling the Topside Ionosphere

The existing uncertainties about density profiles in the topside ionosphere, i.e., in the height regime from hmF2 to approx. 2000 km, requires the search for new data sources. Millions of ionograms had been recorded by the ISIS and Alouette satellites in the sixties and seventies, that never were analyzed in terms of electron density profiles. In recent years an effort started to digitize the analog recordings to prepare the ionograms for computerized analysis. This paper shows how the digital ionograms are processed and the electron density profiles (from satellite orbit altitude, 1400 km for ISIS-2, down to the F peak) are calculated. The most difficult part of the task is the automatic scaling of the echo traces in the ISIS ionograms. Unlike the ionograms from modern ionosondes, the ISIS ionograms do not identify the wave polarization of the different echo traces, so physical logic must be applied to identify the ordinary ()) and extraordinary (X) traces, and this is not always successful. Characteristic resonance features seen in the topside ionograms occur at the gyro and plasma frequencies. An elaborate scheme was developed to identify these resonance frequencies in order to determine the local plasma and gyrofrequencies. This information helps in the identification of the O and X traces, and it provides the starting density of the electron density profile. The inversion of the echo traces into electron density profiles uses the same modified Chebyshev polynomial fitting technique that is successfully applied in the ground-based Digisonde network. The automatic topside ionogram scaler with true height algorithm TOPIST is successfully scaling approx. 70% of the ionograms. An 'editing process' is available to manually scale the more difficult ionograms. The home page for the ISIS project is at http://nssdc.gsfc.nasa.gov/space/isis/isis-status.html. It provides access to as of January 2001, 3000,000 digitized ISIS ionogram data and to related software. A search page lets users select data location, time, and a host of other search criteria. The automated processing of the ISIS ionograms will begin later this year and the electron density profiles will be made available from the project home page. The ISIS data restoration efforts are supported through NASA's Applied Systems and Information Research Program.

Huang, Xue-Qin↗
NASA NTRS2009

Cooperative Three-Robot System for Traversing Steep Slopes

Teamed Robots for Exploration and Science in Steep Areas (TRESSA) is a system of three autonomous mobile robots that cooperate with each other to enable scientific exploration of steep terrain (slope angles up to 90 ). Originally intended for use in exploring steep slopes on Mars that are not accessible to lone wheeled robots (Mars Exploration Rovers), TRESSA and systems like TRESSA could also be used on Earth for performing rescues on steep slopes and for exploring steep slopes that are too remote or too dangerous to be explored by humans. TRESSA is modeled on safe human climbing of steep slopes, two key features of which are teamwork and safety tethers. Two of the autonomous robots, denoted Anchorbots, remain at the top of a slope; the third robot, denoted the Cliffbot, traverses the slope. The Cliffbot drives over the cliff edge supported by tethers, which are payed out from the Anchorbots (see figure). The Anchorbots autonomously control the tension in the tethers to counter the gravitational force on the Cliffbot. The tethers are payed out and reeled in as needed, keeping the body of the Cliffbot oriented approximately parallel to the local terrain surface and preventing wheel slip by controlling the speed of descent or ascent, thereby enabling the Cliffbot to drive freely up, down, or across the slope. Due to the interactive nature of the three-robot system, the robots must be very tightly coupled. To provide for this tight coupling, the TRESSA software architecture is built on a combination of (1) the multi-robot layered behavior-coordination architecture reported in "An Architecture for Controlling Multiple Robots" (NPO-30345), NASA Tech Briefs, Vol. 28, No. 10 (October 2004), page 65, and (2) the real-time control architecture reported in "Robot Electronics Architecture" (NPO-41784), NASA Tech Briefs, Vol. 32, No. 1 (January 2008), page 28. The combination architecture makes it possible to keep the three robots synchronized and coordinated, to use data from all three robots for decision- making at each step, and to control the physical connections among the robots. In addition, TRESSA (as in prior systems that have utilized this architecture) , incorporates a capability for deterministic response to unanticipated situations from yet another architecture reported in Control Architecture for Robotic Agent Command and Sensing (NPO-43635), NASA Tech Briefs, Vol. 32, No. 10 (October 2008), page 40. Tether tension control is a major consideration in the design and operation of TRESSA. Tension is measured by force sensors connected to each tether at the Cliffbot. The direction of the tension (both azimuth and elevation) is also measured. The tension controller combines a controller to counter gravitational force and an optional velocity controller that anticipates the motion of the Cliffbot. The gravity controller estimates the slope angle from the inclination of the tethers. This angle and the weight of the Cliffbot determine the total tension needed to counteract the weight of the Cliffbot. The total needed tension is broken into components for each Anchorbot. The difference between this needed tension and the tension measured at the Cliffbot constitutes an error signal that is provided to the gravity controller. The velocity controller computes the tether speed needed to produce the desired motion of the Cliffbot. Another major consideration in the design and operation of TRESSA is detection of faults. Each robot in the TRESSA system monitors its own performance and the performance of its teammates in order to detect any system faults and prevent unsafe conditions. At startup, communication links are tested and if any robot is not communicating, the system refuses to execute any motion commands. Prior to motion, the Anchorbots attempt to set tensions in the tethers at optimal levels for counteracting the weight of the Cliffbot; if either Anchorbot fails to reach its optimal tension level within a specified time, it sends message to the other robots and the commanded motion is not executed. If any mechanical error (e.g., stalling of a motor) is detected, the affected robot sends a message triggering stoppage of the current motion. Lastly, messages are passed among the robots at each time step (10 Hz) to share sensor information during operations. If messages from any robot cease for more than an allowable time interval, the other robots detect the communication loss and initiate stoppage.

Stroupe, Ashley↗
NASA NTRS2014

A Biological Perspective on the Meaning of Time

We have become impatient waiting for a web page to load, but the first member of our species evolved about 150,000 years ago - a geological instant as brief and as transitory as a text message. The shortest generation time of a bacterium is a sprint at under ten minutes, whereas a 200-year old whale, turtle or tree is not unknown. Life is a phenomenon that integrates processes ranging from the near instantaneous reactions of photosynthesis to the more stately pace of evolution. Here I will elucidate these processes with radically different time scales that go to creating and maintaining the diversity of life on earth, the clocks that nature uses to time them, and how modern biology is being used to alter the natural time scales.

Time↗
NASA NTRS2008

Mars Reconnaissance Orbiter Uplink Analysis Tool

This software analyzes Mars Reconnaissance Orbiter (MRO) orbital geometry with respect to Mars Exploration Rover (MER) contact windows, and is the first tool of its kind designed specifically to support MRO-MER interface coordination. Prior to this automated tool, this analysis was done manually with Excel and the UNIX command line. In total, the process would take approximately 30 minutes for each analysis. The current automated analysis takes less than 30 seconds. This tool resides on the flight machine and uses a PHP interface that does the entire analysis of the input files and takes into account one-way light time from another input file. Input flies are copied over to the proper directories and are dynamically read into the tool s interface. The user can then choose the corresponding input files based on the time frame desired for analysis. After submission of the Web form, the tool merges the two files into a single, time-ordered listing of events for both spacecraft. The times are converted to the same reference time (Earth Transmit Time) by reading in a light time file and performing the calculations necessary to shift the time formats. The program also has the ability to vary the size of the keep-out window on the main page of the analysis tool by inputting a custom time for padding each MRO event time. The parameters on the form are read in and passed to the second page for analysis. Everything is fully coded in PHP and can be accessed by anyone with access to the machine via Web page. This uplink tool will continue to be used for the duration of the MER mission's needs for X-band uplinks. Future missions also can use the tools to check overflight times as well as potential site observation times. Adaptation of the input files to the proper format, and the window keep-out times, would allow for other analyses. Any operations task that uses the idea of keep-out windows will have a use for this program.

Khanampompan, Teerapat↗
NASA NTRS1997

Archiving of Wideband Plasma Wave Data

Beginning with the third year of funding, we began a more ambitious archiving production effort, minimizing work on new software and concentrating on building representative archives of the missions mentioned above, recognizing that only a small percentage of the data from any one mission can be archived with reasonable effort. We concentrated on data from Dynamics Explorer and ISEE 1, archiving orbits or significant fractions of orbits which attempt to capture the essence of the mission and provide data which will hopefully be sufficient for ongoing and new research as well as to provide a reference to upcoming and current ISTP missions which will not fly in the same regions of space as the older missions and which will not have continuous wideband data. We archived approximately 181 Gigabytes of data, accounting for some 1582 hours of data. Included in these data are all of the AMPTE chemical releases, all of the Spacelab 2/PDP data obtained during the free-flight portion of its mission, as well as significant portions of the S3, DE-1, Imp-6, Hawkeye, Injun 5, and ISEE 1 and 2 data sets. Table 1 summarizes these data. All of the data archived are summarized in gif-formatted images of frequency-time spectrograms which are directly accessible via the internet. Each of the gif files are identified by year, day, and time as described in the Web page. This provides a user with a specific date/time in mind a way of determining very quickly if there is data for the interval in question and, by clicking on the file name, browsing the data. Alternately, a user can browse the data for interesting features and events simply by viewing each of the gif files. When a user finds data of interest, he/she can notify us by email of the time period involved. Based on the user's needs, we can provide data on a convenient medium or by ftp, or we can mount the appropriate data and provide access to our analysis tools via the network. We can even produce products such as plots or spectrograms in hardcopy form based on the specific request of the user.

Kurth, William S.↗
NASA NTRS1983

On Why It Is Impossible to Prove that the BDX90 Dispatcher Implements a Time-sharing System

The Software Implemented Fault Tolerance SIFT system, is written in PASCAL except for about a page of machine code. The SIFT system implements a small time sharing system in which PASCAL programs for separate application tasks are executed according to a schedule with real time constraints. The PASCAL language has no provision for handling the notion of an interrupt such as the B930 clock interrupt. The PASCAL language also lacks the notion of running a PASCAL subroutine for a given amount of time, suspending it, saving away the suspension, and later activating the suspension. Machine code was used to overcome these inadequacies of PASCAL. Code which handles clock interrupts and suspends processes is called a dispatcher. The time sharing/virtual machine idea is completely destroyed by the reconfiguration task. After termination of the reconfiguration task, the tasks run by the dispatcher have no relation to those run before reconfiguration. It is impossible to view the dispatcher as a time-sharing system implementing virtual BDX930s running concurrently when one process can wipe out the others.

Boyer, R. S.↗
NASA NTRS2000

TAILSIM Users Guide

The TAILSIM program uses a 4th order Runge-Kutta method to integrate the standard aircraft equations-of-motion (EOM). The EOM determine three translational and three rotational accelerations about the aircraft's body axis reference system. The forces and moments that drive the EOM are determined from aerodynamic coefficients, dynamic derivatives, and control inputs. Values for these terms are determined from linear interpolation of tables that are a function of parameters such as angle-of-attack and surface deflections. Buildup equations combine these terms and dimensionalize them to generate the driving total forces and moments. Features that make TAILSIM applicable to studies of tailplane stall include modeling of the reversible control System, modeling of the pilot performing a load factor and/or airspeed command task, and modeling of vertical gusts. The reversible control system dynamics can be described as two hinged masses connected by a spring. resulting in a fifth order system. The pilot model is a standard form of lead-lag with a time delay applied to an integrated pitch rate and/or airspeed error feedback. The time delay is implemented by a Pade approximation, while the commanded pitch rate is determined by a commanded load factor. Vertical gust inputs include a single 1-cosine gust and a continuous NASA Dryden gust model. These dynamic models. coupled with the use of a nonlinear database, allow the tailplane stall characteristics, elevator response, and resulting aircraft response, to be modeled. A useful output capability of the TAILSIM program is the ability to display multiple post-run plot pages to allow a quick assessment of the time history response. There are 16 plot pages currently available to the user. Each plot page displays 9 parameters. Each parameter can also be displayed individually. on a one plot-per-page format. For a more refined display of the results the program can also create files of tabulated data. which can then be used by other plotting programs. The TAILSIM program was written straightforwardly assuming the user would want to change the database tables, the buildup equations, the output parameters. and the pilot model parameters. A separate database file and input file are automatically read in by the program. The use of an include file to set up all common blocks facilitates easy changing of parameter names and array sizes.

Hiltner, Dale W.↗
DOE OSTI2023

SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2022

This data set consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming (Hanson et al. 2017) in August 2015 through March 31 of 2023, with start- and end-of-season phenological transition dates derived through the end of autumn 2022. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating (1) Picea trees (vegetation type EN, for evergreen needleleaf); (2) Larix trees (vegetation type DN, for deciduous needleleaf); and (3) the mixed shrub layer (vegetation type SH). This data set consists of three sets of data files: 3-day summary product files: One file for each camera and each ROI (i.e. vegetation type), characterizing vegetation color at a 3-day time step Contains 35 files in *.csv format inside a compressed (*.zip) file. Transition date file: Estimates “greenness rising” (spring) and “greenness falling” (autumn) transition dates derived from the smoothed daily green chromatic coordinate (GCC) values, for each camera and each ROI (i.e. vegetation type) Contains one file in *.csv format Snow flag files: Indicate days with snow on trees or snow on ground for each experimental enclosure Contains two files in *.csv format, one for snow on trees and one for snow on ground This data set consists of two sets of companion files: Accompanying HTML files show the 90th quantiles of the mean GCC plotted together with transition dates for each vegetation type and plot. Contains three files in HTML format, one for each vegetation type One additional file in HTML format with the transition dates plotted for each vegetation type, by year R files for processing Phenocam files and flags. Contains five files in R file (*.R) format in one compressed (*.zip) file User Note: All imagery is posted in near-real time to the PhenoCam Project web page (http://phenocam.sr.unh.edu/), where it is publicly available. Scroll to “spruce” in the Gallery or link directly to the 29 SPRUCE cameras at https://tinyurl.com/y7z5mau7. The data reported here are based on the complete camera record from SPRUCE and supersedes the previously released phenocam datasets (see Related Data Sets). The estimated transition dates for previously released datasets may differ slightly (in most cases, by ±3 days or less), because following standard PhenoCam processing protocols (Richardson et al. 2018, Scientific Data), smoothing and interpolation, outlier removal, and transition date estimation are always conducted using the full data record.

SPRUCE Experiment, Marcell Experimental Forest, Sp↗
DOE OSTI2024

SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery, 2015-2023

This data set consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming (Hanson et al. 2017) in August 2015 through March 31 of 2024, with start- and end-of-season phenological transition dates derived through the end of autumn 2023. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating (1) Picea trees (vegetation type EN, for evergreen needleleaf); (2) Larix trees (vegetation type DN, for deciduous needleleaf); and (3) the mixed shrub layer (vegetation type SH). This data set consists of three sets of data files: (1) 3-day summary product files: One file for each camera and each ROI (i.e. vegetation type), characterizing vegetation color at a 3-day time step • Contains 36 files in *.csv format inside a compressed (*.zip) file. (2) Transition date file: Estimates “greenness rising” (spring) and “greenness falling” (autumn) transition dates derived from the smoothed daily green chromatic coordinate (GCC) values, for each camera and each ROI (i.e. vegetation type) • Contains one file in *.csv format (3) Snow flag files: Indicate days with snow on trees or snow on ground for each experimental enclosure • Contains two files in *.csv format, one for snow on trees and one for snow on ground This data set consists of two sets of companion files: (1) Accompanying HTML files show the 90th quantiles of the mean GCC plotted together with transition dates for each vegetation type and plot. • Contains three files in HTML format, one for each vegetation type • One additional file in HTML format with the transition dates plotted for each vegetation type, by year (2) R files for processing Phenocam files and flags. • Contains five files in R file (*.R) format in one compressed (*.zip) file User Note: All imagery is posted in near-real time to the PhenoCam Project web page (http://phenocam.sr.unh.edu/), where it is publicly available. Scroll to “spruce” in the Gallery or link directly to the 29 SPRUCE cameras at https://tinyurl.com/sprucecams. The data reported here are based on the complete camera record from SPRUCE and supersedes the previously released data inclusive of the 2015-2022 data (see Related Data Sets). The estimated transition dates for previously released datasets may differ slightly (in most cases, by ±3 days or less), because following standard PhenoCam processing protocols (Richardson et al. 2018, Scientific Data), smoothing and interpolation, outlier removal, and transition date estimation are always conducted using the full data record.

Spruce and Peatland Responses Under Changing Envir↗
DOE OSTI2025

SPRUCE Vegetation Phenology in Experimental Plots from PhenoCam Imagery, 2015-2024

This data set consists of PhenoCam data from the SPRUCE experiment from the beginning of whole ecosystem warming (Hanson et al. 2017) in August 2015 through March 31 of 2025 (2015-08-24 to 2025-03-31), with start- and end-of-season phenological transition dates derived through the end of autumn 2024. Digital cameras, or phenocams, installed in each SPRUCE enclosure track seasonal variation in vegetation “greenness”, a proxy for vegetation phenology and associated physiological activity. Three separate regions of interest (ROIs) were defined for each camera field of view, corresponding to different vegetation types and demarcating (1) Picea trees (vegetation type EN, for evergreen needleleaf); (2) Larix trees (vegetation type DN, for deciduous needleleaf); and (3) the mixed shrub layer (vegetation type SH). This data set consists of three sets of data files: (1) 3-day summary product files: One file for each camera and each ROI (i.e. vegetation type), characterizing vegetation color at a 3-day time step. • Contains 36 files in *.csv format inside a compressed (*.zip) file. (2) Transition date file: Estimates “greenness rising” (spring) and “greenness falling” (autumn) transition dates derived from the smoothed daily green chromatic coordinate (GCC) values, for each camera and each ROI (i.e., vegetation type). • Contains one file in *.csv format. (3) Snow flag files: Indicate days with snow on trees or snow on ground for each experimental enclosure. • Contains two files in *.csv format, one for snow on trees and one for snow on ground. This data set consists of two sets of companion files: (1) Accompanying HTML files show the 90th quantiles of the mean GCC plotted together with transition dates for each vegetation type and plot. • Contains three files in HTML format, one for each vegetation type. • One additional file in HTML format with the transition dates plotted for each vegetation type, by year. (2) R files for processing PhenoCam files and flags. • Contains five files in R file(*.R) format and the components of the phenocamr package (Version 1.1.4) used for calculating transition dates for 2015-2024. These are contained in a compressed (*.zip) file. User Note: All imagery is posted in near-real time to the PhenoCam Project web page (https://phenocam.nau.edu), where it is publicly available. Scroll to “spruce” in the Gallery or link directly to the 29 SPRUCE cameras at https://tinyurl.com/sprucecams. This data set is based on the complete camera record from SPRUCE and supersedes all previously released PhenoCam datasets (see Related Data Sets). The estimated transition dates for previously released datasets may differ slightly (in most cases, by ±3 days or less), because following standard PhenoCam processing protocols (Richardson et al. 2018, Scientific Data), smoothing and interpolation, outlier removal, and transition date estimation are always conducted using the full data record.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1972

Apollo 16 far-ultraviolet camera/spectrograph - Earth observations.

A far-ultraviolet camera/spectrograph experiment was operated on the lunar surface during the Apollo 16 mission. Among the data obtained were images and spectra of the terrestrial atmosphere and geocorona in the wavelength range below 1600 angstroms. These gave the spatial distributions and relative intensities of emissions due to atomic hydrogen, atomic oxygen, molecular nitrogen, and other species - some observed spectrographically for the first time.

Carruthers, G. R.↗