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NASA NTRSDate not supplied

Autoencoders for Denoising Atmospheric Profiles from ICESat-2

Abstract: The 2nd generation Ice, Cloud, and land Elevation Satellite (ICESat-2) is an altimetry mission designed primarily for measuring ice sheet elevation and sea ice thickness, provides atmospheric profiles of clouds and aerosols at 532 nm using a photo counting detection approach. While highly sensitive for the detection of tenuous aerosol and cloud features, during the day signal-to-noise-ratio (SNR) photon counting detectors are adversely impacted by solar contributions to the total signal. Averaging the data to coarser horizontal resolutions has been the standard way to increase SNR and thus allow clouds and aerosols to be more easily detectable. Recent work has demonstrated success in boosting SNR without decreasing resolution using advanced filtering techniques [Yorks et al., 2021], however, rapid advancements in Deep Learning based image denoising algorithms can further improve the SNR. Here, we present results using a state-of-the-art Deep Learning autoencoder applied to noisy ICESat-2 data to improve daytime SNR and discuss implications for atmospheric feature detection, classification, and optical property retrievals.

denoising↗
NASA NTRS1983

Study and simulation results for video landmark acquisition and tracking technology (Vilat-2)

The results of several investigations and hardware developments which supported new technology for Earth feature recognition and classification are described. Data analysis techniques and procedures were developed for processing the Feature Identification and Location Experiment (FILE) data. This experiment was flown in November 1981, on the second Shuttle flight and a second instrument, designed for aircraft flights, was flown over the United States in 1981. Ground tests were performed to provide the basis for designing a more advanced version (four spectral bands) of the FILE which would be capable of classifying clouds and snow (and possibly ice) as distinct features, in addition to the features classified in the Shuttle experiment (two spectral bands). The Shuttle instrument classifies water, bare land, vegetation, and clouds/snow/ice (grouped).

Lowrie, J. W.↗
NASA NTRS1987

Narrow-line Seyfert galaxies with permitted Fe II emission Markarian 507, 5C 3.100, and I Zw 1

Optical, X-ray and and/or IR spectra of Mrk 507, 5C 3.100 and I Zw 1 are compared in a discussion of the bases for classifications of Seyfert 1 and 2 galaxies. Einstein Observatory X ray data indicate that the Seyfert designation requires a continuum extending to X-rays in order to account for permitted Fe II lines observed in Seyfert galaxies. It is suggested that the IR luminosity of I Zw 1, which exceeds the X-ray luminosity by two orders of magnitude, may be accounted for by interaction between companion galaxies. The presence or absence of dense clouds such as those in I Zw 1 is identified as the factor which separates Seyfert galaxies into two mutually exclusive ranges in X-ray luminosity.

Halpern, J. P.↗
NASA NTRS1994

The 1994 International Geoscience and Remote Sensing Symposium (IGARSS 1994)

The papers presented at the symposium focus on remote sensing, particularly on global monitoring of the earth with emphasis on the solution of environmental problems. Topics discussed include remote sensing of clouds and earth troposphere, sea ice remote sensing, optical remote sensing, land monitoring and thermal sensing, atmospheric sounding and monitoring, atmospheric correction, and satellite imaging data. Other subject areas are ecosystems and vegetation monitoring; ocean winds and surface scattering; ocean waves, currents and bathymetry; satellite oceanography; SAR for remote sensing; neural nets application to remote sensing; geographical information systems; and electromagnetic wave propagation. Also discussed environmental monitoring using ERS-1; Topex/Poseidon results; spaceborne instruments; image processing and classification algorithms; and future space missions.

Source record↗
NASA NTRS2022

Identifying Chemical Aerosol Signatures Using Optical Suborbital Observations: How Much Can Optical Properties Tell us about Aerosol Composition?

Improvements in air quality and Earth’s climate predictions require improvements of the aerosol speciation in chemical transport models, using observational constraints. Aerosol speciation (e.g., organic aerosols, black carbon, sulfate, nitrate, ammonium, dust or sea salt) is typically determined using in situ instrumentation. Continuous, routine surface network aerosol composition measurements are not uniformly widespread over the globe. Satellites, on the other hand, can provide a maximum coverage of the horizontal and vertical atmosphere but observe aerosol optical properties (and not aerosol speciation) based on remote sensing instrumentation. Combinations of satellite-derived aerosol optical properties can inform on air mass aerosol types (AMTs e.g., clean marine, dust, polluted continental). However, these AMTs are subjectively defined, might often be misclassified and are hard to relate to the critical parameters that need to be refined in models. In this paper, we derive AMTs that are more directly related to sources and hence to speciation. They are defined, characterized, and derived using simultaneous in situ gas-phase, chemical and optical instruments on the same aircraft during the Study of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS, US, summer of 2013). First, we prescribe well-informed AMTs that display distinct aerosol chemical and optical signatures to act as a training AMT dataset. These in situ observations reduce the errors and ambiguities in the selection of the AMT training dataset. We also investigate the relative skill of various combinations of aerosol optical properties to define AMTs and how much these optical properties can capture dominant aerosol speciation. We find distinct optical signatures for biomass burning (from agricultural or wildfires), biogenic and dust-influence AMTs. Useful aerosol optical properties to characterize these signatures are the extinction angstrom exponent (EAE), the single scattering albedo, the difference of single scattering albedo in two wavelengths, the absorption coefficient, the absorption angstrom exponent (AAE), and the real part of the refractive index (RRI). We find that all four AMTs studied when prescribed using mostly airborne in situ gas measurements, can be successfully extracted from at least three combinations of airborne in situ aerosol optical properties (e.g., EAE, AAE and RRI) over the US during SEAC4RS. However, we find that the optically based classifications for BB from agricultural fires and polluted dust include a large percentage of misclassifications that limit the usefulness of results relating to those classes. The technique and results presented in this study are suitable to develop a representative, robust and diverse source-based AMT database. This database could then be used for widespread retrievals of AMTs using existing and future remote sensing suborbital instruments/networks. Ultimately, it has the potential to provide a much broader observational aerosol data set to evaluate chemical transport and air quality models than is currently available by direct in situ measurements. This study illustrates how essential it is to explore existing airborne datasets to bridge chemical and optical signatures of different AMTs, before the implementation of future spaceborne missions (e.g., the next generation of Earth Observing System (EOS) satellites addressing Aerosol, Cloud, Convection and Precipitation (ACCP) designated observables).

Meloe S F Kacenelenbogen↗
NASA NTRS1986

Spectral evolution of young stellar objects

An evolutionary sequence, from protostars to pre-main-sequence stars, is suggested for the classification of young stellar objects. This sequence is derived by comparing the predictions of the theoretical models of Adams and Shu with the morphological classification scheme of Lada and Wilking. We first define the spectral index in the near- and mid-infrared, n is identical to d log(nu F sub nu)/d log nu, and then interpret the class of sources with negative spectral indices as protostars. The inferred mass infall rates for these objects are generally consistent with the measured gas temperatures of approx. 35 K in Ophiuchus, and of approx. 10 K in Taurus. Fitting the data requires us to adopt cloud rotation rates in Ophiuchus which are typically an order of magnitude greater than in Taurus, and we speculate on the mechanistic origin for this difference. Next, we consider a subclass of T Tauri stars with near- and mid-infrared excesses and positive or zero spectral indices. We find that the objects with the steeper indices can be understood as the post-infall products from the collapse of rotating cloud cores, where the infrared excesses arise from the simple reprocessing of visible stellar photons in optically thick but spatially thin disks. The sources with flatter spectra may require massive accretion disks. Given the existence of protostars and naked star/disk systems, there is a natural interpretation of another subclass of T Tauri stars, those with two peaks in their emergent spectral energy distributions. These are readily explained as intermediate cases in which dust envelopes still surround the stars and disks. Finally, we find that the theory can be extended to explain the spectral energy distribution of FU Orionis, a famous outburst source. Our model suggests that FU Orionis has a disk, but it offers no discrimination between the competing ideas that the outburst took place on the star or in the disk.

Adams, F. C.↗
NASA NTRSDate not supplied

Transcribing Air Traffic Control System Command Center Planning Telecons Using Cloud-Based Automatic Speech Recognition

This paper addresses the challenge of using Automatic Speech Recognition (ASR) technology to transcribe regular teleconferences that happen between FAA Air Traffic Control System Command Center (ATCSCC) planners, stakeholders and air users. These planning teleconferences (aka telecons or planning webinars) are an integral part of managing air traffic in the U.S. National Airspace System (NAS). In particular, the meetings facilitate the creation and modification of various traffic management initiatives (TMIs), that are used to regulate the flow of air traffic. This is typically a human intensive process, requiring specialists to listen to the entire meeting audio (10-20 minutes duration) and inferring the state of the NAS (e.g., weather phenomenon) that was discussed. It would be advantageous to have digital transcripts of the audio and have useful information (e.g., related to TMIs) automatically extracted from the transcripts. In this regard, we are exploring the adoption of state-of-the-art speech to text and Natural Language Processing (NLP) tools that will achieve our objective of digitizing the webinar audio. Unfortunately, the highly technical phraseology present in the audio and limited data availability for model building make ASR difficult. To overcome this challenge, we have taken the critical first step in creating a human transcription dataset from ~20 hours of speech in the ATCSCC audio with the help of subject matter experts. A novelty of our work is the creation of a ground truth transcription dataset for ATCSCC teleconference webinars, which is particularly important for Aviation domain-specific NLP tasks. Using Microsoft Speech Studio, a cloud-based ASR platform, we have fine-tuned the English pre-trained ASR models (available in speech studio) and achieved an average word error rate (WER) of 6.81%. The baseline ASR also provides a digital version of each planning webinar, making it accessible and text-searchable for future references. Additionally, the transcriptions can serve as a bridge between raw audio data and a range of text-based NLP tasks, such as named entity recognition (NER) and intent classification, potentially enhancing the digital footprint of the webinars and other connected data sources. Our work has several potential applications. Firstly, the transcriptions can be analyzed to understand the complex decision process of creating, implementing and modifying TMIs and may also contribute to TMI prediction services. Secondly, our dataset and model can be used to develop more accurate ASR systems for aviation-specific language, which can bring about digital communication in the aviation industry (and aid current “voice only” communications, which are inherently error-prone). Lastly, the transcriptions themselves can be used as a valuable resource for training other NLP models.

Stephen S. B. Clarke↗
NASA NTRSDate not supplied

Toward The Development of Hailstorm Climatologies Derived From Reanalyses and Infared/Passive Microwave Satellite Imagers

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) and Meteosat series, provide both historical and near-real-time observations of cloud top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to United States Next Generation Weather Radar- (NEXRAD-) estimated Maximum Expected Size of Hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are specifically designed to make hail likelihood distinctions based on satellite-indicated points of deep convection within environments favorable for storm development. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. This presentation demonstrates that statistical distributions of convective parameters from satellite and reanalysis show separation between non-severe/severe hailstorm classes for predictors including overshooting cloud top temperature and area characteristics, convective available potential energy, vertical wind shear, 500 hPa temperature, mid-level lapse rate, precipitable water, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a hail likelihood metric with a critical success index of 0.504 and Heidke skill score of 0.403, which is exceptional among recent analogous hail studies. Furthermore, applications of the DNN to select case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-year GOES-12/13 image database to derive a hail frequency and severity climatology, which denotes the Central Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied. Opportunities for training and applying DNN-based hailstorm predictions to recently developed GOES-8/10/12/13/16 and Meteosat Second Generation convective storm detection and characterization climatologies over South America and South Africa, respectively, will also be presented.

Kristopher Bedka↗
NASA NTRS1987

Monsoon flood boundary delineation and damage assessment using space borne imaging radar and Landsat data

Space-borne synthetic aperture radar (SAR) data acquired by the Shuttle Imaging Radar-B (SIR-B) Program and Landsat Multispectral Scanner Subsystem (MSS) Data from Landsat 4 were used to map flood boundaries for the assessment of flood damage in the Peoples Republic of Bangladesh. The cloud penetrating capabilities of the L-band radar provided a clear picture of the hydrologic conditions of the surface during a period of inclement weather at the end of the wet phase of the 1984 monsoon. The radar image data were digitally processed to geometrically rectify the pixel geometry and were filtered to subdue radar image speckle effects. Contrast enhancement techniques and density slicing were used to create discrete land-cover categories corresponding to surface conditions present at the time of the Shuttle overflight. The radar image classification map was digitally registered to a spectral signature classification map of the area derived from Landsat MSS data collected two weeks prior to the SIR-B mission. Classification accuracy comparisons were made between the radar and MSS classification maps, and flood boundary and flood damage assessment measurements were made with the merged data by adding the classifications and inventorying the land-cover classes inundated at the time of flooding.

Imhoff, Marc L.↗
NASA NTRS2001

Resolution Enhancement of Spaceborne Radiometer Images

Our progress over the last year has been along several dimensions: 1. Exploration and understanding of Earth Observatory System (EOS) mission with available data from NASA. 2. Comprehensive review of state of the art techniques and uncovering of limitations to be investigated (e.g. computational, algorithmic ...). and 3. Preliminary development of resolution enhancement algorithms. With the advent of well-collaborated satellite microwave radiometers, it is now possible to obtain long time series of geophysical parameters that are important for studying the global hydrologic cycle and earth radiation budget. Over the world's ocean, these radiometers simultaneously measure profiles of air temperature and the three phases of atmospheric water (vapor, liquid, and ice). In addition, surface parameters such as the near surface wind speed, the sea surface temperature, and the sea ice type and concentration can be retrieved. The special sensor microwaves imager SSM/I has wide application in atmospheric remote sensing over the ocean and provide essential inputs to numerical weather-prediction models. SSM/I data has also been used for land and ice studies, including snow cover classification measurements of soil and plant moisture contents, atmospheric moisture over land, land surface temperature and mapping polar ice. The brightness temperature observed by SSM/I is function of the effective brightness temperature of the earth's surface and the emission scattering and attenuation of the atmosphere. Advanced Microwave Scanning Radiometer (AMSR) is a new instrument that will measure the earth radiation over the spectral range from 7 to 90 GHz. Over the world's ocean, it will be possible to retrieve the four important geographical parameters SST, wind speed, vertically integrated water vapor, vertically integrated cloud liquid water L.

Krim, Hamid↗
NASA NTRS2020

Names Don't Fly: Smart Filters for Profanity Detection and Classification in User-Generated Content

Generally, names associate with a person’s identity. But what if in the pretext of a legitimate name and given the opportunity, users of software provide names to online web forms that carry along offensive language, slurs, and other profanity that is then sent to Mars ? The answer is simple: they don’t fly. In this paper,we perform model explorations to detect and classify inappropriate content in the names submitted from people across the world to ‘Send Your Names to MARS’ public engagement campaign.We propose a novel pipeline approach, that can effectively overcome the issues of lack of negative samples, noisy labels by gathering expert knowledge over time with human(s) in the loop and data augmentation, and achieve high accuracy in classifying inappropriate names with very little or no context. We describe cloud-based infrastructure to deploy our application and run predictions on large-scale data through our pipeline and achieve significant speedup over offline processes, with enhanced reliability and security.

Soderstrom, Tomas↗
NASA NTRS1975

The LANDSAT-1 multispectral scanner as a tool in the classification of inland lakes

Relationships between LANDSAT-1 multispectral scanner (MSS) data and the trophic status of a group of lakes in the north-northeastern part of the United States were studied by predicting the magnitudes of two trophic state indicators, estimating lake position on a multivariate trophic scale, and automatically classifying lakes according to their trophic state. Initially, the principal component ordination was employed with 100 lakes. MSS data for some 20 lakes was then extracted from computer-compatible tapes (CCT) using a binary marking technique. The output was in the form of descriptive statistics and photographic concatenations. Color ratios were incorporated into regression models for the prediction of Secchi disc transparency, chlorophyll a, and lake position on the tropic scale. Results indicate that the LANDSAT-1 system, although handicapped by low spectral and spatial resolutions as well as excessive cloud cover, can be used as a supplemental data source in lake survey programs.

Boland, D. H. P.↗
NASA NTRS1979

Spectral feature classification and spatial pattern recognition

This paper introduces a spatial pattern recognition processing concept involving the use of spectral feature classification technology and coherent optical correlation. The concept defines a hybrid image processing system incorporating both digital and optical technology. The hybrid instrument provides simplified pseudopattern images as functions of pixel classification from information embedded within a real-scene image. These pseudoimages become simplified inputs to an optical correlator for use in a subsequent pattern identification decision useful in executing landmark pointing, tracking, or navigating functions. Real-time classification is proposed as a research tool for exploring ways to enhance input signal-to-noise ratio as an aid in improving optical correlation. The approach can be explored with developing technology, including a current NASA Langley Research Center technology plan that involves a series of related Shuttle-borne experiments. A first-planned experiment, Feature Identification and Location Experiment (FILE), is undergoing final ground testing, and is scheduled for flight on the NASA Shuttle (STS2/flight OSTA-1) in 1980. FILE will evaluate a technique for autonomously classifying earth features into the four categories: bare land; water; vegetation; and clouds, snow, or ice.

Sivertson, W. E., Jr.↗
NASA NTRS1993

Glaciological studies in the central Andes using AIRSAR/TOPSAR

The interaction of climate and topography in mountainous regions is dramatically expressed in the spatial distribution of glaciers and snowcover. Monitoring existing alpine glaciers and snow extent provides insight into the present mountain climate system and how it is changing, while mapping the positions of former glaciers as recorded in landforms such as cirques and moraines provide a record of the large past climate change associated with the last glacial maximum. The Andes are an ideal mountain range in which to study the response of snow and ice to past and present climate change. Their expansive latitudinal extent offers the opportunity to study glaciers in diverse climate settings from the tropical glaciers of Peru and Bolivia to the ice caps and tide-water glaciers of sub-polar Patagonia. SAR has advantages over traditional passive remote sensing instruments for monitoring present snow and ice and differentiating moraine relative ages. The cloud penetrating ability of SAR is indispensable for perennially cloud covered mountains. Snow and ice facies can be distinguished from SAR's response to surface roughness, liquid water content and grain size distribution. The combination of SAR with a coregestered high-resolution DEM (TOPSAR) provides a promising tool for measuring glacier change in three dimensions, thus allowing ice volume change to be measured directly. The change in moraine surface roughness over time enables SAR to differentiate older from younger moraines. Polarimetric SAR data have been used to distinguish snow and ice facies and relatively date moraines. However, both algorithms are still experimental and require ground truth verification. We plan to extend the SAR classification of snow and ice facies and moraine age beyond the ground truth sites to throughout the Cordillera Real to provide a regional view of past and present snow and ice. The high resolution DEM will enhance the SAR moraine dating technique by discriminating relative ages based on moraine slope degradation.

Forster, Richard R.↗
NASA NTRS2021

Mapping Boreal Forest Spruce Beetle Health Status at the Individual Crown Scale Using Fused Spectral and Structural Data

The frequency and severity of spruce bark beetle outbreaks are increasing in boreal forests leading to widespread tree mortality and fuel conditions promoting extreme wildfire. Detection of beetle infestation is a forest health monitoring (FHM) priority but is hampered by the challenges of detecting early stage (“green”) attack from the air. There is indication that green stage might be detected from vertical gradients of spectral data or from shortwave infrared information distributed within a single crown. To evaluate the efficacy of discriminating “non-infested”, “green”, and “dead” health statuses at the landscape scale in Alaska, USA, this study conducted spectral and structural fusion of data from: (1) Unoccupied aerial vehicle (UAV) multispectral (6 cm) + structure from motion point clouds (~700 pts per sq. m); and (2) Goddard Lidar Hyperspectral Thermal (G-LiHT) hyperspectral (400 to 1000 nm, 0.5 m) + SWIR-band lidar (~32 pts per sq.m). We achieved 78% accuracy for all three health statuses using spectral + structural fusion from either UAV or G-LiHT and 97% accuracy for non-infested/dead using G-LiHT. We confirm that UAV 3D spectral (e.g., greenness above versus below median height in crown) and lidar apparent reflectance metrics (e.g., mean reflectance at 99th percentile height in crown), are of high value, perhaps capturing the vertical gradient of needle degradation. In most classification exercises, UAV accuracy was lower than G-LiHT indicating that collecting ultra-high spatial resolution data might be less important than high spectral resolution information. While the value of passive optical spectral information was largely confined to the discrimination of non-infested versus dead crowns, G-LiHT hyperspectral band selection (~400, 675, 755, and 940 nm) could inform future FHM mission planning regarding optimal wavelengths for this task. Interestingly, the selected regions mostly did not align with the band designations for our UAV multispectral data but do correspond to, e.g., Sentinel-2 red edge bands, suggesting a path forward for moderate scale bark beetle detection when paired with suitable structural data.

Janice Cessna↗
NASA NTRSDate not supplied

Building a Bilingual Google Earth Engine Dashboard to Increase Accessibility to Long-term Time Series Remote Sensing Data for Monitoring Saline System Changes in Chile’s Atacama Desert

Saline systems, consisting of salt flats, ponds, and marshes, provide vital water resources to wildlife and communities in northern Chile’s Atacama Desert, one of the driest regions in the world. Mining is extensive in the Atacama, which contains 30% of the world’s lithium reserves and is abundant in potassium and boron. The groundwater that feeds into salt marshes and ponds is extracted in large volumes for mining operations, limiting the availability of water for ecosystems. However, identifying long-term and large-scale environmental impacts from local lithium mining on the saline systems is limited by region inaccessibility and terrain variability. Open access satellite imagery and cloud computing technology has made studying Atacama saline systems feasible and allowed for collaboration across different agencies and countries. The NASA DEVELOP Program partnered with Chile’s la Universidad de La Serena and Servicio Nacional de Geología y Minería (SERNAGEOMIN) to create the Saline Analysis Tool (SalT) in Google Earth Engine (GEE). SalT is used to analyze the extent and distribution of remote saline systems in the Atacama from 1986 to the present day. The tool filters Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) data from GEE’s data catalog and creates a single composite image per year for analysis. Additional output analyses include land cover classification, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) raster images that can be displayed on the map interface or exported. The tool can also generate time-lapse videos and charts displaying NDVI, NDWI, and land cover over time. A key feature of the tool is the use of a bilingual graphical user interface to make analysis accessible and customizable to different users’ needs—SalT provides options to select an analysis area, analysis time period, and outputs to display or export. The tool also incorporates new Earth observations as they are added to GEE’s catalog. The ability to easily visualize and analyze long-term remote sensing imagery will enable SERNAGEOMIN and la Universidad de la Serena to continually monitor changes in these saline systems and inform future land management policy.

NASA DEVELOP↗
NASA NTRS2019

2018 NISAR Applications Workshop: Forest and Disturbance; Workshop Report

Forest lands cover the globe and are important sources for providing ecosystem services including: carbon sequestration, biodiversity, timber, air and water quality. As such, counties around the world have dedicated programs for managing them. Accurate and timely information concerning the status of these forests (moisture, biomass, disturbance type, etc.) is essential to those Nations’ human and ecological health as well as economy. The joint NASA/US Forest Service workshop focused on arming forest land managers with observations and remote sensing information from the upcoming NASA-ISRO (Indian Space Research Organization) SAR (Synthetic Aperture Radar) (NISAR) satellite mission (expected to launch early 2022). Participants included representatives from different US Federal Agencies, private sector, and non-governmental organizations (NGO) that are key players in facilitating integration of Earth Observations (EO) into forest management and decision support workflows. They included scientists, technicians, and program managers with a responsibility for data acquisition and exploitation such as product development, delivery, and use, and capacity building. Discussions were held over two days to convey the broader forest and disturbance community information needs for various representative participants and programs and to facilitate the delivery of NISAR mission geospatial products and observational capabilities. Case studies were presented to demonstrate the current state of practice in the use of SAR remote sensing for applications of direct importance to forest and disturbance land management community. Eleven organizations presented their information requirements in response to a set of questions provided by the NASA team, then the NASA team responded by describing the degree to which NISAR could meet these requirements. Discussion ensued about needed data product specifications to increase utility (e.g., projection, latency, etc.), tools and capacity building. The general findings of this workshop were that (a) NISAR observations will be particularly useful to the global forest carbon and disturbance monitoring applications, but that certain data product design decisions (projections and radiometric and terrain corrections) need to be considered to increase utility; b) the biomass and disturbance detection algorithms meet many of the community needs, however there are other information products of value (e.g., soil moisture or disturbance classification, not just detection) and all products should be compliant with existing community standards for reporting uncertainty; c) providing SAR education to the community will be key specifically thinking about putting the information first and the SAR theory second, providing a simple guide of standard data processing steps (e.g., dB (decibel) to power conversion and speckle filtering); d) the community needs a user-friendly interface for finding free, archived data over their geographic regions of interest; e) user-friendly tools that connect to open-sources GIS (Global Information System) software (e.g., QGIS (Quantum GIS)) that include a graphical user interface (GUI) for SAR processing that enables both download and cloud processing. To integrate these findings and prepare the community before NISAR launches, it was suggested that there be a dedicated NISAR Forest and Disturbance Applications Working Group (as per the specifications in the NISAR Utilization Plan). After launch, it was decided that the community continue capacity building activities.

Stavros, Natasha↗
NASA NTRS2014

A Classification Scheme for Young Stellar Objects Using the WIDE-FIELD INFRARED SURVEY EXPLORER ALLWISE Catalog: Revealing Low-Density Star Formation in the Outer Galaxy

We present an assessment of the performance of WISE and the AllWISE data release in a section of the Galactic Plane. We lay out an approach to increasing the reliability of point source photometry extracted from the AllWISE catalog in Galactic Plane regions using parameters provided in the catalog. We use the resulting catalog to construct a new, revised young star detection and classification scheme combining WISE and 2MASS near and mid-infrared colors and magnitudes and test it in a section of the Outer Milky Way. The clustering properties of the candidate Class I and II stars using a nearest neighbor density calculation and the two-point correlation function suggest that the majority of stars do form in massive star forming regions, and any isolated mode of star formation is at most a small fraction of the total star forming output of the Galaxy. We also show that the isolated component may be very small and could represent the tail end of a single mechanism of star formation in line with models of molecular cloud collapse with supersonic turbulence and not a separate mode all to itself.

WIDE-FIELD↗