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The visible/infrared spin scan radiometer (VISSR) atmospheric sounder (VAS) rawinsonde field program is discussed. Specific items covered include: planning, personnel requirements and training, operational requirement and procedures, sounding times and dates, methods of data processing, data inventory, and status of data processing.
Contents include the following: Introduction. A Producer Perspective on Earth Science Data. Data Producers as Members of a Scientific Community. Some Unique Characteristics of Scientific Data. Spatial and Temporal Sampling for Earth (or Space) Science Data. The Influence of the Data Production System Architecture. The Spatial and Temporal Structures Underlying Earth Science Data. Earth Science Data File (or Relation) Schemas. Data Producer Configuration Management Complexities. The Topology of Earth Science Data Inventories. Some Thoughts on the User Perspective. Science Data User Communities. Spatial and Temporal Structure Needs of Different Users. User Spatial Objects. Data Search Services. Inventory Search. Parameter (Keyword) Search. Metadata Searches. Documentation Search. Secondary Index Search. Print Technology and Hypertext. Inter-Data Collection Configuration Management Issues. An Archive View. Producer Data Ingest and Production. User Data Searching and Distribution. Subsetting and Supersetting. Semantic Requirements for Data Interchange. Tentative Conclusions. An Object Oriented View of Archive Information Evolution. Scientific Data Archival Issues. A Perspective on the Future of Digital Archives for Scientific Data. References Index for this paper.
A computer-generated graphic display of land use data was developed. The level II inventory data for Sarpy County, Nebraska, was placed on magnetic tape. This data could then be displayed in a map format for comparative analysis of amount and distribution of the various categories of land use. The presentation scale can be varied and thus utilized as a direct guide for cartographic purposes during preparation for publication. In addition, the inventory and classification system was further refined.
Planetary data include non-imaging remote sensing data, which includes spectrometric, radiometric, and polarimetric remote sensing observations. Also included are in-situ, radio/radar data, and Earth based observation. Also discussed is development of a planetary data system. A catalog to identify observations will be the initial entry point for all levels of users into the data system. There are seven distinct data support services: encyclopedia, data index, data inventory, browse, search, sample, and acquire. Data systems for planetary science users must provide access to data, process, store, and display data. Two standards will be incorporated into the planetary data system: Standard communications protocol and Standard format data unit. The data system configuration must combine a distributed system with those of a centralized system. Fiscal constraints have made prioritization important. Activities include saving previous mission data, planning/cost analysis, and publishing of proceedings.
We estimate the overall CO2, CH4, and CO flux from the South Coast Air Basin using an inversion that couples Total Carbon Column Observing Network (TCCON) and Orbiting Carbon Observatory-2 (OCO-2) observations, with the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Using TCCON data we estimate the direct net CO2 flux from the So-CAB to be 104±26 Tg CO2 yr(exp -1) for the study period of July 2013–August 2016. We obtain a slightly higher estimate of 120±30 Tg CO2 yr(exp -1) using OCO-2 data. These CO2 emission estimates are on the low end of previous work. Our net CH4 (360±90 Gg CH4 y(exp -1)) flux estimate is in agreement with central values from previous top-down studies going back to 2010 (342–440 Gg CH4 yr(exp -1)). CO emissions are estimated at 487±122 Gg CO yr(exp -1), much lower than previous top-down estimates (1440 Gg CO yr(exp -1)). Given the decreasing emissions of CO, this finding is not unexpected. We perform sensitivity tests to estimate how much errors in the prior, errors in the covariance, different inversion schemes, or a coarser dynamical model influence the emission estimates. Overall, the uncertainty is estimated to be 25%, with the largest contribution from the dynamical model. Lessons learned here may help in future inversions of satellite data over urban areas.
Digital data for the Washington, D.C. scene from the LANDSAT-4 Multispectral Scanner (MSS) and the LANDSAT-4 Thematic Mapper (TM) are compared. Classification success for the TM and MSS data sets was determined by a per pixel comparison with digitized ground verification data (GVD). These GVD were comprised of Level I land cover (developed, agriculture, forest, water, wetlands, and barren) for four USGS 7.5-minute topographic quadrangle maps. Classification accuracy was computed as an average value and for each cover type. Accuracy was expressed two ways: (1) as the percent correspondence with GVD (% correct) and (2) as the percent correspondence relative to both the GVD and LANDSAT classification schemes. Errors of omission and commission associated with the LANDSAT classifications were also computed. Specific results are discussed.
Digital data for the Washington, DC scene simultaneously acquired by the LANDSAT-4 Multispectral Scanner (MSS) and the LANDSAT-4 thematic mapper (TM) was compared. Classification success for the TM and MSS data sets was determined by a per pixel comparison with digitized ground verification data (GVD). These GVD were comprised of Level 7 land cover (developed, agriculture, forest, water, wetlands, and barren) for four USGS 7.5 minute topographic quadrangle maps. The relative improvement in classification success for TM was between 11% and 14%, or about a factor of 1.3, for these data. This represents a meaningful improvement in accuracy for Level 7 land cover categorization for TM relative to MSS, particularly when errors of omission and commission were considered.
Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of AGB and estimate AGB growth using LiDAR data. The proposed model accommodates temporal misalignment between field measurements and remotely sensed data-a problem pervasive in such settings-by including multiple time-indexed measurements at plot locations to estimate AGB growth. We pursue a Bayesian modeling framework that allows for appropriately complex parameter associations and uncertainty propagation through to prediction. Specifically, we identify a space-varying coefficients model to predict and map AGB and its associated growth simultaneously. The proposed model is assessed using LiDAR data acquired from NASA Goddard's LiDAR, Hyper-spectral & Thermal imager and field inventory data from the Penobscot Experimental Forest in Bradley, Maine. The proposed model outperformed the time-invariant counterpart models in predictive performance as indicated by a substantial reduction in root mean squared error. The proposed model adequately accounts for temporal misalignment through the estimation of forest AGB growth and accommodates residual spatial dependence. Results from this analysis suggest that future AGB models informed using remotely sensed data, such as LiDAR, may be improved by adapting traditional modeling frameworks to account for temporal misalignment and spatial dependence using random effects.
Scientists at NASA's Goddard Space Flight Center have developed an ultra-portable, low-cost, multi-sensor remote sensing system for studying the form and function of terrestrial ecosystems. G-LiHT integrates two LIDARs, a 905 nanometer single beam profiler and 1550 nm scanner, with a narrowband (1.5 nanometers) VNIR imaging spectrometer and a broadband (8-14 micrometers) thermal imager. The small footprint (approximately 12 centimeters) LIDAR data and approximately 1 meter ground resolution imagery are advantageous for high resolution applications such as the delineation of canopy crowns, characterization of canopy gaps, and the identification of sparse, low-stature vegetation, which is difficult to detect from space-based instruments and large-footprint LiDAR. The hyperspectral and thermal imagery can be used to characterize species composition, variations in biophysical variables (e.g., photosynthetic pigments), surface temperature, and responses to environmental stressors (e.g., heat, moisture loss). Additionally, the combination of LIDAR optical, and thermal data from G-LiHT is being used to assess forest health by sensing differences in foliage density, photosynthetic pigments, and transpiration. Low operating costs (approximately $1 ha) have allowed us to evaluate seasonal differences in LiDAR, passive optical and thermal data, which provides insight into year-round observations from space. Canopy characteristics and tree allometry (e.g., crown height:width, canopy:ground reflectance) derived from G-LiHT data are being used to generate realistic scenes for radiative transfer models, which in turn are being used to improve instrument design and ensure continuity between LiDAR instruments. G-LiHT has been installed and tested in aircraft with fuselage viewports and in a custom wing-mounted pod that allows G-LiHT to be flown on any Cessna 206, a common aircraft in use throughout the world. G-LiHT is currently being used for forest biomass and growth estimation in the CONUS and Mexico in support of NASA's Carbon Monitoring System (CMS) and AMIGA-Carb (AMerican Icesat Glas Assessment of Carbon). For NASA's CMS, wall-to-wall G-LiHT data have been acquired over intensive study sites with historic LiDAR datasets, dense inventory data, stem maps and flux tower observations. For AMIGA-Carb, G-LiHT transects have been acquired over ICESat tracks and USDA-FS inventory plots throughout the CONUS, and similar data will be acquired in Mexico during 2013. This talk will highlight recent science results from continental-scale transects landscape-scale deployments of G-LiHT, as well as seasonal forest dynamics from repeat pass G-LiHT acquisitions.
Worldwide data inventory of rock features associated with meteorite impact structures, including photomicrographs of petrographic features
The boreal forest is the Earth's largest terrestrial biome, covering some 12 million km2 and accounting for about one third of this planet's total forest area. Mapping of boreal forest's type, structure parameters and biomass are critical for understanding the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. Ground based forest inventories, have much uncertainty in the inventory data, particularly in remote areas of Siberia where sampling is sparse and/or lacking. In addition, many of the forest inventories that do exist for Siberia are now a decade or more old. Thus, available forest inventories fail to capture the current conditions. Changes in forest structure in a particular forest-type and region can change significantly due to changing environment conditions, and natural and anthropogenic disturbance. Remote sensing methods can potentially overcome these problems. Multispectral sensors can be used to provide vegetation cover maps that show a timely and accurate geographic distribution of vegetation types rather than decade old ground based maps. Lidar sensors can be used to directly obtain measurements that can be used to derive critical forest structure information (e.g., height, density, and volume). These in turn can used to estimate biomass components using allometric equations without having to use out dated forest inventory. Finally, remote sensing data is ideally suited to provide a sampling basis for a rigorous statistical estimate of the variance and error bound on forest structure measures. In this study, new remote sensing methods were applied to develop estimates timber volume using NASA's MODerate resolution Imaging Spectroradiometer (MODIS) and unique waveform data of the geoscience laser altimeter system (GLAS) for a 10 deg x 10 deg area in central Siberia. Using MODIS and GLAS data, maps were produced for cover type and timber volume for 2003, and a realistic variance (error bound) for timber volume was calculated for the study area. In this 'study we used only GLAS footprints that had a slope value of less than 10 deg. This was done to avoid large errors due to the effect of slope on the GLAS models. The method requires the integration of new remote sensing methods with available ground studies of forest timber volume conducted in Russian forests. The results were compared to traditional ground forest inventory methods reported in the literature and to ground truth collected in the study area.
Over 700 climate related datasets were curated by subject matter experts into 9 thematic areas as a part of the Climate Data Initiative (CDI). NASA was tasked with maintaining the collection’s data inventory and supporting web pages at data.gov/climate. Today, the Data Curation for Discovery (DCD) team at MSFC continues to support the CDI collection. In order to expose the collection to a new and growing user community, the DCD team has partnered with GeoPlatform.gov to develop the Resilience community. The Resilience community serves as an interactive, topically-focused web portal that further promotes and shares CDI web content, datasets, services, maps, and other tools relevant to global resilience and change. This poster focuses on the team’s efforts to leverage GeoPlatform’s semantic applications to link CDI objects within the platform to improve discoverability. This poster also provides insights as to how this effort may serve as an example for building and expanding future Geoplatform.gov communities..
Application of ERTS-1 data for inventory of critical land resources for regional land use planning in Wisconsin
The State of Wisconsin has embarked on a Critical Resources Information Program aimed at defining, establishing the units of measurement, inventorying, and monitoring natural and cultural spatial resource elements which are of statewide or regional significance. This paper presents the results of using ERTS data to inventory certain significant natural resources, such as agricultural land, forests, surface water and wetlands. Computer-generated spatial and statistical comparisons of resource data derived from conventional sources, RB-57 photographs, and ERTS images of Wisconsin suggest that certain resources can be inventoried and monitored on a statewide basis using ERTS images. Preliminary results of data extraction using ERTS digital tapes are also presented. The interpretation of certain natural and cultural resource information from ERTS images is anticipated to be an operational part of Wisconsin's Critical Resources Information Program.
We describe the Harvard-NASA Emission Component version 1.0 (HEMCO), a stand-alone software component for computing emissions in global atmospheric models. HEMCO determines emissions from different sources, regions, and species on a user-defined grid and can combine, overlay, and update a set of data inventories and scale factors, as specified by the user through the HEMCO configuration file. New emission inventories at any spatial and temporal resolution are readily added to HEMCO and can be accessed by the user without any preprocessing of the data files or modification of the source code. Emissions that depend on dynamic source types and local environmental variables such as wind speed or surface temperature are calculated in separate HEMCO extensions. HEMCO is fully compliant with the Earth System Modeling Framework (ESMF) environment. It is highly portable and can be deployed in a new model environment with only few adjustments at the top-level interface. So far, we have implemented HEMCO in the NASA Goddard Earth Observing System (GEOS-5) Earth system model (ESM) and in the GEOS-Chem chemical transport model (CTM). By providing a widely applicable framework for specifying constituent emissions, HEMCO is designed to ease sensitivity studies and model comparisons, as well as inverse modeling in which emissions are adjusted iteratively. The HEMCO code, extensions, and the full set of emissions data files used in GEOS-Chem are available at http: //wiki.geos-chem.org/HEMCO.
Mapping of boreal forest's type, structure parameters and biomass are critical for understanding the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. The biggest deficiency of the existing ground based forest inventories is the uncertainty in the inventory data, particularly in remote areas of Siberia where sampling is sparse, lacking, and often decades old. Remote sensing methods can help overcome these problems. In this joint US and Russian study, we used the moderate resolution imaging spectroradiometer (MODIS) and unique waveform data of the geoscience laser altimeter system (GLAS) and produced a map of timber volume for a 10degx12deg area in Central Siberia. Using these methods, the mean timber volume for the forested area in the total study area was 203 m3/ ha. The new remote sensing methods used in this study provide a truly independent estimate of forest structure, which is not dependent on traditional ground forest inventory methods.
An overview of the capabilities and software architecture of the IPAD information processor (IPIP) is presented. IPIP is a state-of-the-art data base management system that satisfies engineering requirements not addressed by present day commercial systems. It also significantly advances a number of capabilities that are offered commercially. IPIP capabilities range from support for multiple schemas and data models to support for distributed processing, configuration control, and data inventory management. IPIP exploits semantic commonality in features offered in various forms at different user interfaces in today's commercial systems. An integrated software architecture supports all user interfaces: programming languages, interactive data manipulation, and schema languages. This approach promotes simplicity and compactness in software and permits features to be offered symmetrically across all appropriate user interfaces.
A brief overview of the philosophy, design, and organization of the Pilot Climate Data System (PCDS) is presented. The design has evolved, over time, to deal with numerous data sets, provide appropriate tools for the manipulation and display of such data, and support a wide variety of users. The user interface to the system is provided by the Transportable Applications Executive (TAE). A user can choose one of the five major subsystems from the root menu display. These subsystems are CATALOG, INVENTORY, DATA ACCESS, DATA MANIPULATION, and GRAPHICS. Each subsystem was discussed and examples were given, demonstrating the flexibility provided by the existing PCDS software, Version 3.3. Three of these subsystems are presently being redesigned to provide a more flexible and friendly structure, to allow more consistency and facilitate the ability to support new data sets. Ongoing developmental efforts to migrate from the Version 3.3 prototype phase of PCDS to the operational Version 4.0 stress increased flexibility and data independence.