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Carbon dust management is vital to the continuous operation of the Plasma Pyrolysis Assembly (PPA), which supports the Carbon Dioxide Reduction Assembly (CRA) on the International Space Station (ISS). A developmental filter system known as the Cold Trap Carbon Capture Filter is being prototyped and tested to determine its performance under different challenge aerosols.
This report details one of three experiments performed during FY 2007 for the NASA RPC (Rapid Prototyping Capability) at Stennis Space Center. This RPC experiment assesses the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) and MODIS (Moderate Resolution Imaging Spectroradiometer) data for detecting and monitoring forest defoliation from the non-native Eurasian gypsy moth (Lymantria dispar). The intent of the RPC experiment was to assess the degree to which VIIRS data can provide forest disturbance monitoring information as an input to a forest threat EWS (Early Warning System) as compared to the level of information that can be obtained from MODIS data. The USDA Forest Service (USFS) plans to use MODIS products for generating broad-scaled, regional monitoring products as input to an EWS for forest health threat assessment. NASA SSC is helping the USFS to evaluate and integrate currently available satellite remote sensing technologies and data products for the EWS, including the use of MODIS products for regional monitoring of forest disturbance. Gypsy moth defoliation of the mid-Appalachian highland region was selected as a case study. Gypsy moth is one of eight major forest insect threats listed in the Healthy Forest Restoration Act (HFRA) of 2003; the gypsy moth threatens eastern U.S. hardwood forests, which are also a concern highlighted in the HFRA of 2003. This region was selected for the project because extensive gypsy moth defoliation occurred there over multiple years during the MODIS operational period. This RPC experiment is relevant to several nationally important mapping applications, including agricultural efficiency, coastal management, ecological forecasting, disaster management, and carbon management. In this experiment, MODIS data and VIIRS data simulated from MODIS were assessed for their ability to contribute broad, regional geospatial information on gypsy moth defoliation. Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data were used to assess the quality of gypsy moth defoliation mapping products derived from MODIS data and from simulated VIIRS data. The project focused on use of data from MODIS Terra as opposed to MODIS Aqua mainly because only MODIS Terra data was collected during 2000 and 2001-years with comparatively high amounts of gypsy moth defoliation within the study area. The project assessed the quality of VIIRS data simulation products. Hyperion data was employed to assess the quality of MODIS-based VIIRS simulation datasets using image correlation analysis techniques. The ART (Application Research Toolbox) software was used for data simulation. Correlation analysis between MODIS-simulated VIIRS data and Hyperion-simulated VIIRS data for red, NIR (near-infrared), and NDVI (Normalized Difference Vegetation Index) image data products collectively indicate that useful, effective VIIRS simulations can be produced using Hyperion and MODIS data sources. The r(exp 2) for red, NIR, and NDVI products were 0.56, 0.63, and 0.62, respectively, indicating a moderately high correlation between the 2 data sources. Temporal decorrelation from different data acquisition times and image misregistration may have lowered correlation results. The RPC experiment also generated MODIS-based time series data products using the TSPT (Time Series Product Tool) software. Time series of simulated VIIRS NDVI products were produced at approximately 400-meter resolution GSD (Ground Sampling Distance) at nadir for comparison to MODIS NDVI products at either 250- or 500-meter GSD. The project also computed MODIS (MOD02) NDMI (Normalized Difference Moisture Index) products at 500-meter GSD for comparison to NDVI-based products. For each year during 2000-2006, MODIS and VIIRS (simulated from MOD02) time series were computed during the peak gypsy moth defoliation time frame in the study area (approximately June 10 through July 27). Gypsy moth defoliation mapping products from simated VIIRS and MOD02 time series were produced using multiple methods, including image classification and change detection via image differencing. The latter enabled an automated defoliation detection product computed using percent change in maximum NDVI for a peak defoliation period during 2001 compared to maximum NDVI across the entire 2000-2006 time frame. Final gypsy moth defoliation mapping products were assessed for accuracy using randomly sampled locations found on available geospatial reference data (Landsat and ASTER data in conjunction with defoliation map data from the USFS). Extensive gypsy moth defoliation patches were evident on screen displays of multitemporal color composites derived from MODIS data and from simulated VIIRS vegetation index data. Such defoliation was particularly evident for 2001, although widespread denuded forests were also seen for 2000 and 2003. These visualizations were validated using aforementioned reference data. Defoliation patches were visible on displays of MODIS-based NDVI and NDMI data. The viewing of apparent defoliation patches on all of these products necessitated adoption of a specialized temporal data processing method (e.g., maximum NDVI during the peak defoliation time frame). The frequency of cloud cover necessitated this approach. Multitemporal simulated VIIRS and MODIS Terra data both produced effective general classifications of defoliated forest versus other land cover. For 2001, the MOD02-simulated VIIRS 400-meter NDVI classification produced a similar yet slightly lower overall accuracy (87.28 percent with 0.72 Kappa) than the MOD02 250-meter NDVI classification (88.44 percent with 0.75 Kappa). The MOD13 250-meter NDVI classification had a lower overall accuracy (79.13 percent) and a much lower Kappa (0.46). The report discusses accuracy assessment results in much more detail, comparing overall classification and individual class accuracy statistics for simulated VIIRS 400-meter NDVI, MOD02 250-meter NDVI, MOD02-500 meter NDVI, MOD13 250-meter NDVI, and MOD02 500-meter NDMI classifications. Automated defoliation detection products from simulated VIIRS and MOD02 data for 2001 also yielded similar, relatively high overall classification accuracy (85.55 percent for the VIIRS 400-meter NDVI versus 87.28 percent for the MOD02 250-meter NDVI). In contrast, the USFS aerial sketch map of gypsy moth defoliation showed a lower overall classification accuracy at 73.64 percent. The overall classification Kappa values were also similar for the VIIRS (approximately 0.67 Kappa) versus the MOD02 (approximately 0.72 Kappa) automated defoliation detection product, which were much higher than the values exhibited by the USFS sketch map product (overall Kappa of approximately 0.47). The report provides additional details on the accuracy of automated gypsy moth defoliation detection products compared with USFS sketch maps. The results suggest that VIIRS data can be effectively simulated from MODIS data and that VIIRS data will produce gypsy moth defoliation mapping products that are similar to MODIS-based products. The results of the RPC experiment indicate that VIIRS and MODIS data products have good potential for integration into the forest threat EWS. The accuracy assessment was performed only for 2001 because of time constraints and a relative scarcity of cloud-free Landsat and ASTER data for the peak defoliation period of the other years in the 2000-2006 time series. Additional work should be performed to assess the accuracy of gypsy moth defoliation detection products for additional years.The study area (mid-Appalachian highlands) and application (gypsy moth forest defoliation) are not necessarily representative of all forested regions and of all forest threat disturbance agents. Additional work should be performed on other inland and coastal regions as well as for other major forest threats.
The International Space Station (ISS) is a manned laboratory operating in orbit around the Earth that was built and is currently operated by several countries across the world. The ISS is a platform for novel scientific research as well as a testbed for technologies that will be required for the next step in space exploration. In order for astronauts to live on ISS for an extended period of time, it is vital that on board systems consistently provide a clean atmosphere. One contaminant that must be removed from the atmosphere is carbon dioxide (CO2). CO2 levels on ISS are higher than those on Earth and can cause crew members to experience symptoms such as headaches, lethargy and mental slowness. A variety of systems exist on ISS to remove carbon dioxide, including adsorbent technologies which can be reused and testbed technologies for future space vehicles.
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Research efforts for effectively and consistently monitoring terrestrial carbon are increasing in number. As such, there is a need to define carbon monitoring and how it relates to carbon cycle science and carbon management. There is also a need to identify capabilities of a carbon monitoring system and the system components needed to develop the capabilities. Capabilities that enable the effective application of a carbon monitoring system for monitoring and management purposes may include: reconciling carbon stocks and fluxes, developing consistency across spatial and temporal scales, tracking horizontal movement of carbon, attribution of emissions to originating sources, cross-sectoral accounting, uncertainty quantification, redundancy and policy relevance. Focused research is needed to integrate these capabilities for sustained estimates of carbon stocks and fluxes. Additionally, if monitoring is intended to inform management decisions, management priorities should be considered prior to development of a monitoring system.
CO2 is the principal human generated driver of climate change. Accurate forecasting of future climate requires an improved understanding of the global carbon cycle and its interaction with the climate system. The Orbiting Carbon Observatory (OCO) will make global, space-based observations of atmospheric CO2 with the precision, resolution, and coverage needed to understand sources and sinks. OCO data will provide critical information for decision makers including the scientific basis for policy formulation, guide for carbon management strategies and treaty monitoring.
Carbon in forest canopies represents about 85% of the total carbon in the Earth's aboveground biomass (Olson et al., 1983). A major source of uncertainty in global carbon budgets derives from large errors in the current estimates of these carbon stocks (IPCC, 2001). The magnitudes and distributions of terrestrial carbon storage along with changes in sources and sinks for atmospheric C02 due to land use change remain the most significant uncertainties in Earth's carbon budget. These uncertainties severely limit accurate terrestrial carbon accounting; our ability to evaluate terrestrial carbon management schemes; and the veracity of atmospheric C02 projections in response to further fossil fuel combustion and other human activities. Measurements of vegetation three-dimensional (3D) structural characteristics over the Earth's land surface are needed to estimate biomass and carbon stocks and to quantify biomass recovery following disturbance. These measurements include vegetation height, the vertical profile of canopy elements (i.e., leaves, stems, branches), andlor the volume scattering of canopy elements. They are critical for reducing uncertainties in the global carbon budget. Disturbance by natural phenomena, such as fire or wind, as well as by human activities, such as forest harvest, and subsequent recovery, complicate the quantification of carbon storage and release. The resulting spatial and temporal heterogeneity of terrestrial biomass and carbon in vegetation make it very difficult to estimate terrestrial carbon stocks and quantify their dynamics. Vegetation height profiles and disturbance recovery patterns are also required to assess ecosystem health and characterize habitat. The three-dimensional structure of vegetation provides habitats for many species and is a control on biodiversity. Canopy height and structure influence habitat use and specialization, two fundamental processes that modify species richness and abundance across ecosystems. Accurate and consistent 3D measurements of forest structure at the landscape scale are needed for assessing impacts to animal habitats and biodiversity following disturbance.
Background: Carbon stocks and fluxes in tropical forests remain large sources of uncertainty in the global carbon budget. Airborne lidar remote sensing is a powerful tool for estimating aboveground biomass, provided that lidar measurements penetrate dense forest vegetation to generate accurate estimates of surface topography and canopy heights. Tropical forest areas with complex topography present a challenge for lidar remote sensing. Results: We compared digital terrain models (DTM) derived from airborne lidar data from a mountainous region of the Atlantic Forest in Brazil to 35 ground control points measured with survey grade GNSS receivers. The terrain model generated from full-density (approx. 20 returns/sq m) data was highly accurate (mean signed error of 0.19 +/-0.97 m), while those derived from reduced-density datasets (8/sq m, 4/sq m, 2/sq m and 1/sq m) were increasingly less accurate. Canopy heights calculated from reduced-density lidar data declined as data density decreased due to the inability to accurately model the terrain surface. For lidar return densities below 4/sq m, the bias in height estimates translated into errors of 80-125 Mg/ha in predicted aboveground biomass. Conclusions: Given the growing emphasis on the use of airborne lidar for forest management, carbon monitoring, and conservation efforts, the results of this study highlight the importance of careful survey planning and consistent sampling for accurate quantification of aboveground biomass stocks and dynamics. Approaches that rely primarily on canopy height to estimate aboveground biomass are sensitive to DTM errors from variability in lidar sampling density.
The Healthy Forest Restoration Act of 2003 mandated that a national forest threat Early Warning System (EWS) be developed. The USFS (USDA Forest Service) is currently building this EWS. NASA is helping the USFS to integrate remotely sensed data into the EWS, including MODIS data for monitoring forest disturbance at broad regional scales. This RPC experiment assesses the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) and MODIS (Moderate Resolution Imaging Spectroradiometer) data for contribution to the EWS. In doing so, the RPC project employed multitemporal simulated VIIRS and MODIS data for detecting and monitoring forest defoliation from the non-native Eurasian gypsy moth (Lymantria despar). Gypsy moth is an invasive species threatening eastern U.S. hardwood forests. It is one of eight major forest insect threats listed in the Healthy Forest Restoration Act of 2003. This RPC experiment is relevant to several nationally important mapping applications, including carbon management, ecological forecasting, coastal management, and disaster management
Extraplanetary surface habitat life support systems (LSS) on the Moon and Mars, as well as long-duration space travel, will require novel capabilities to withstand anticipated unique, harsh conditions. In order to provide safe, habitable environments for the crew, water purification and waste processing systems will be required to treat all sources of water (condensate, Sabatier, urine, hygiene, fecal, food waste) in order to achieve the necessary levels of recovery needed to sustain life over the long-duration missions. The ability to recycle organic wastes creates an opportunity to recover critical elements (e.g., C, H, O, N, P) for subsequent food production, water purification, and atmospheric regeneration. Bioregenerative systems mimic functions of nature in engineered systems, or bioreactors, utilizing combination of prokaryotes, eukaryotes and archaea. While these systems are commonly used on Earth for wastewater treatment, bioreactors for space travel face additional challenges. Terrestrial bioreactors often rely on gravitational settling of dense flocs and granules for cell retention. For micro- or partial-gravity environments, density differential alone will not be adequate for cell retention; a gravity-independent means for cell retention is crucial. The membrane bioreactor represents the state of the art in wastewater treatment. This hybrid system combines biological processes with membrane filtration to achieve performance beyond what each can accomplish individually. The complete cell retention in an MBR allows for the decoupling of hydraulic retention time (HRT) and solids retention time (SRT), which result in a high-thruput, compact, treatment system. The Bioregenerative Water Technology Team at NASA Kennedy Space Center and the University of South Florida has developed a bioregenerative platform based on the hybrid MBR technology. The overall architecture is compact, modular, flexible, and adaptable to mission evolutions. The main subsystems of the bioregenerative architecture include: 1) Anaerobic membrane bioreactor (AnMBR): Also termed the Organic processor assembly (OPA), the function of the AnMBR is to treat organic wastes such as fecal and food wastes. These wastes are characterized by a concentration of suspended solids comprised of carbohydrates, proteins and lipids. The assigned function of the AnMBR is to break down and covert suspended solids to biogas (methane, hydrogen and carbon dioxide), reduce effluent chemical oxygen demand (COD), liberate organically-bound nutrients, and remove pathogenic organisms. 2) Phototrophic membrane bioreactor (PMBR): The PMBR is comprised of a co-culture of microalgae and bacteria. The assigned function of the PMBR is to polish the permeate of the AnMBR to further remove dissolved organic carbon, manage nutrients (nitrogen transformation, load dampening), and perform air revitalization. 3) Food processor assembly (FPA): The FPA is a food production platform (prokaryotic or eukaryotic), fueled by outputs from the AnMBR, or PMBR. For the presentation, we will describe each step of the bioregenerative architecture, and present performance data from extended trials treating analog and real metabolic wastes.
SERVIR is a joint NASA and USAID program that partners with countries and organizations to support locally led efforts to strengthen climate resilience, food and water security, forest and carbon management, and air quality. This unique program integrates NASA’s world class scientists and data with USAID’s development expertise and network of partners and relationships around the world. SERVIR harnesses the power of satellite data and science collaboration to support healthy, sustainable communities, livelihoods and environments. SERVIR’s name is derived from Latin, meaning "to serve.” We serve and partner with leading local, national, and regional institutions in Africa, Asia, and Latin America and in partnership with scientists and subject matter experts to co-develop and implement activities called “services.” Each of SERVIR’s services provide a comprehensive suite of geospatial data, software, and training materials, all of which are tailored to the unique needs of SERVIR’s end users. Each service is collaboratively designed and implemented with the help of partners and users, such as local governments and NGOs, to better support decision-making. When SERVIR plans new services, it prioritizes long-term dialogue and engagement with communities to ensure that services are sustainable, socially inclusive, and suited to local needs. SERVIR strives to make the power of Earth science more accessible and inclusive. All web tools are made publicly available to help promote greater uptake and long-term use of SERVIR’s services.
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Advanced carbon carbon composites were joined to copper-clad molybdenum (Cu/Mo) using four active metal brazes containing Ti (Cu ABA, Cusin-1 ABA, Ticuni, and Ticusil) for potential use in thermal management applications. The brazed joints were characterized using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and Knoop microhardness measurements across the joint region. Metallurgically sound C-C/Cu/Mo joints, devoid of interfacial cracks formed in all cases. The joint interfaces were preferentially enriched in Ti, with Cu ABA joints exhibiting the largest interfacial Ti concentrations. The microhardness measurements revealed hardness gradients across the joint region, with a peak hardness of 300-350 KHN in Cusin-1 ABA and Ticusil joints and 200-250 KHN in Cu ABA and Ticuni joints, respectively.
This document serves as the Final Report for Iowa State University's Space Habitat group to fulfill the specifications of NASA's eXploration Systems and Habitation (X-Hab) 2019 Academic Innovation Challenge for the 'Implementation of Advanced Sorbents in a Carbon Dioxide Management Unit' portion of the challenge. The scope of this document includes a description of the current Carbon Dioxide management systems implemented on ISS, a description of the group’s design, a description of the operational environment and scenarios, risks and mitigations, performance and testing results of the system, outreach, and future work.