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Big Cypress Water Resources: Using Earth Observations to Assess Water Quality in Big Cypress Reservation, FL
Upstream agricultural development and runoff are driving harmful algal blooms in the Big Cypress Reservation. The Seminole Tribe of Florida Environmental Resource Management Department monitors water quality in the Big Cypress Reservation, located on the north end of Big Cypress National Preserve. The Environmental Resource Management Department aims to incorporate Earth observations into its investigative approach, which consists of Geographic Information Systems and in situ water sampling. We assessed the feasibility of using Earth observations to measure harmful algal blooms over time and identify vulnerable areas. We analyzed data from multiple remote sensing platforms and sensors, including Landsat 8 Thermal Infrared Spectrometer(TIRS), Landsat 9 TIRS-2, Landsat 9 Operational Land Imager 2, Sentinel-2 MultiSpectral Instrument, and Sentinel-3 Ocean and Land Color Instrument. We determined that it was challenging to use Sentinel-3, Landsat 8, and Landsat 9 imagery for monitoring the canals within the Reservation, and that Sentinel-2 was the most capable platform for the study area. Using Sentinel-2, we created spectral indices for the detection of algal blooms. We used these indices to measure algal blooms at 8 stations across the Big Cypress Reservation. We then created time series and seasonal decompositions of in situ water sample data and indices to visualize relationships between datasets, along with correlation matrices. Our correlations were not conclusive. However, we found that the spectral indices have the potential to detect algal blooms. Lastly, we evaluated Maxar WorldView-3 imagery and found that using data from sensors with higher spatial resolutions would improve results.
Big Bend Ecological Forecasting: Integrating Earth Observations Into Invasive Species Management Decisions in Big Bend National Park in Texas
Located in Brewster County Texas, U.S., along the Texas-Mexico border, Big Bend National Park is 3,243 square kilometers of desert, mountains, and rivers. NASA’s MSFC Spring 2024 DEVELOP Team partnered with the National Park Service (NPS), to address the environmental concern of perineal invasive grasses in Big Bend National Park. Buffelgrass (Cenchrus ciliaris), introduced to the park in the 1940’s, poses an ongoing threat, and causes habitat destruction for many of the park’s native ecosystems. Buffelgrass amplifies fire risk in the park, aids in the destruction of historic structures, and alters stream channels. To address the rising concern of Buffelgrass presence, unique advanced spatial technique applications were necessary to construct a habitat suitability model and perform a comprehensive fire risk assessment. A habitat suitability model was developed considering climate, vegetation, and phenological variables, in addition to physical and topographical variables. Subsequently, a fire risk model was developed, taking into account fire history data, accessibility factors, climate trends and predictions, along with developed areas. Multi-Source Land Surface Phenology (LSP) Sentinel-2 and Landsat 8 Operational Land Imager (OLI) imagery were used to predict Buffelgrass hotspot locations throughout the park. These analyses allowed the identification of optimal Buffelgrass habitat and hotspot locations, as well as park zones that reflect the greatest risk for future Buffelgrass invasion and fire risk. The collective results of the habitat suitability model, fire risk assessment, and Buffelgrass hot spot identification will allow the NPS to facilitate efficient mitigation measures and management strategies, and improved resource allocation where it’s most needed.
Big Comsats for big jobs at low user cost
Three examples are used to illustrate what is possible with large space systems: (1) personal communications using wrist telephones, (2) electronic transmission of mail, and (3) wide dissemination of educational TV. Design concepts and costs are explored and compared to alternative ground-based concepts.
How Big is Too Big for Hubs: Marginal Profitability in Hub-and-Spoke Networks
Increasing the scale of hub operations at major airports has led to concerns about congestion at excessively large hubs. In this paper, we estimate the marginal cost of adding spokes to an existing hub network. We observe entry/non-entry decisions on potential spokes from existing hubs, and estimate both a variable profit function for providing service in markets using that spoke as well as the fixed costs of providing service to the spoke. We let the fixed costs depend upon the scale of operations at the hub, and find the hub size at which spoke service costs are minimized.
NASA's Deep Space Network: Big Antennas with a Big Job
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Big Bend Ecological Conservation Integrating Earth Observations into Invasive Species Management Decisions in Big Bend National Park in Texas
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Big Cypress Water Resources: Using Earth Observations to Assess Water Quality in Big Cypress Reservation, FL
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Earth Science Data Analysis in the Era of Big Data
Anyone with even a cursory interest in information technology cannot help but recognize that "Big Data" is one of the most fashionable catchphrases of late. From accurate voice and facial recognition, language translation, and airfare prediction and comparison, to monitoring the real-time spread of flu, Big Data techniques have been applied to many seemingly intractable problems with spectacular successes. They appear to be a rewarding way to approach many currently unsolved problems. Few fields of research can claim a longer history with problems involving voluminous data than Earth science. The problems we are facing today with our Earth's future are more complex and carry potentially graver consequences than the examples given above. How has our climate changed? Beside natural variations, what is causing these changes? What are the processes involved and through what mechanisms are these connected? How will they impact life as we know it? In attempts to answer these questions, we have resorted to observations and numerical simulations with ever-finer resolutions, which continue to feed the "data deluge." Plausibly, many Earth scientists are wondering: How will Big Data technologies benefit Earth science research? As an example from the global water cycle, one subdomain among many in Earth science, how would these technologies accelerate the analysis of decades of global precipitation to ascertain the changes in its characteristics, to validate these changes in predictive climate models, and to infer the implications of these changes to ecosystems, economies, and public health? Earth science researchers need a viable way to harness the power of Big Data technologies to analyze large volumes and varieties of data with velocity and veracity. Beyond providing speedy data analysis capabilities, Big Data technologies can also play a crucial, albeit indirect, role in boosting scientific productivity by facilitating effective collaboration within an analysis environment. To illustrate the effects of combining a Big Data technology with an effective means of collaboration, we relate the (fictitious) experience of an early-career Earth science researcher a few years beyond the present, interlaced and contrasted with reminiscences of its recent past (i.e., the present).
Anticipated Changes in Conducting Scientific Data-Analysis Research in the Big-Data Era
A Big-Data environment is one that is capable of orchestrating quick-turnaround analyses involving large volumes of data for numerous simultaneous users. Based on our experiences with a prototype Big-Data analysis environment, we anticipate some important changes in research behaviors and processes while conducting scientific data-analysis research in the near future as such Big-Data environments become the mainstream. The first anticipated change will be the reduced effort and difficulty in most parts of the data management process. A Big-Data analysis environment is likely to house most of the data required for a particular research discipline along with appropriate analysis capabilities. This will reduce the need for researchers to download local copies of data. In turn, this also reduces the need for compute and storage procurement by individual researchers or groups, as well as associated maintenance and management afterwards. It is almost certain that Big-Data environments will require a different "programming language" to fully exploit the latent potential. In addition, the process of extending the environment to provide new analysis capabilities will likely be more involved than, say, compiling a piece of new or revised code.We thus anticipate that researchers will require support from dedicated organizations associated with the environment that are composed of professional software engineers and data scientists. A major benefit will likely be that such extensions are of higherquality and broader applicability than ad hoc changes by physical scientists. Another anticipated significant change is improved collaboration among the researchers using the same environment. Since the environment is homogeneous within itself, many barriers to collaboration are minimized or eliminated. For example, data and analysis algorithms can be seamlessly shared, reused and re-purposed. In conclusion, we will be able to achieve a new level of scientific productivity in the Big-Data analysis environments.
MERRA Analytic Services: Meeting the Big Data Challenges of Climate Science Through Cloud-enabled Climate Analytics-as-a-service
Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however, we it see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a service. These elements are essential because in the aggregate they lead to generativity, a capacity for self-assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Services (MERRAAS) is an example of cloud-enabled CAaaS built on this principle. MERRAAS enables MapReduce analytics over NASAs Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection. The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing importance to scientists doing climate change research and a wide range of decision support applications. MERRAAS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capabilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRAAS has been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides the agility required to meet our customers' increasing and changing needs. Cloud Computing is providing a new tier in the data services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. For climate science, Cloud Computing's capacity to engage communities in the construction of new capabilies is perhaps the most important link between Cloud Computing and Big Data.
Climatespark: an In-Memory Distributed Computing Framework for Big Climate Data Analytics
The unprecedented growth of climate data creates new opportunities for climate studies, and yet big climate data pose a grand challenge to climatologists to efficiently manage and analyze big data. The complexity of climate data content and analytical algorithms increases the difficulty of implementing algorithms on high performance computing systems. This paper proposes an in-memory, distributed computing framework, ClimateSpark, to facilitate complex big data analytics and time-consuming computational tasks. Chunking data structure improves parallel I/O efficiency, while a spatiotemporal index is built for the chunks to avoid unnecessary data reading and preprocessing. An integrated, multi-dimensional, array-based data model (ClimateRDD) and ETL operations are developed to address big climate data variety by integrating the processing components of the climate data lifecycle. ClimateSpark utilizes Spark SQL and Apache Zeppelin to develop a web portal to facilitate the interaction among climatologists, climate data, analytic operations and computing resources (e.g., using SQL query and Scala/Python notebook). Experimental results show that ClimateSpark conducts different spatiotemporal data queries/analytics with high efficiency and data locality. ClimateSpark is easily adaptable to other big multiple- dimensional, array-based datasets in various geoscience domains.
BIG MAC: A bolometer array for mid-infrared astronomy, Center Director's Discretionary Fund
The infrared array referred to as Big Mac (for Marshall Array Camera), was designed for ground based astronomical observations in the wavelength range 5 to 35 microns. It contains 20 discrete gallium-doped germanium bolometer detectors at a temperature of 1.4K. Each bolometer is irradiated by a square field mirror constituting a single pixel of the array. The mirrors are arranged contiguously in four columns and five rows, thus defining the array configuration. Big Mac utilized cold reimaging optics and an up looking dewar. The total Big Mac system also contains a telescope interface tube for mounting the dewar and a computer for data acquisition and processing. Initial astronomical observations at a major infrared observatory indicate that Big Mac performance is excellent, having achieved the design specifications and making this instrument an outstanding tool for astrophysics.
Limits to the primordial helium abundance in the baryon-inhomogeneous big bang
The parameter space for baryon inhomogeneous big bang models is explored with the goal of determining the minimum helium abundance obtainable in such models while still satisfying the other light-element constraints. We find that the constraint of (D + He-3)/H less than 10 exp -4 restricts the primordial helium mass fraction from baryon-inhomogeneous big bang models to be greater than 0.231 even for a scenario which optimizes the effects of the inhomogeneities and destroys the excess lithium production. Thus, this modification to the standard big bang as well as the standard homogeneous big bang model itself would be falsifiable by observation if the primordial He-4 abundance were observed to be less than 0.231. Furthermore, a present upper limit to the observed helium mass fraction of Y(obs)(p) less than 0.24 implies that the maximum baryon-to-photon ratio allowable in the inhomogeneous models corresponds to eta less than 2.3 x 10 exp -9 (omega(b) h-squared less than 0.088) even if all conditions are optimized.
From the Big Bang to the Nobel Prize and on to James Webb Space Telescope
The history of the universe in a nutshell, from the Big Bang to now. and on to the future - John Mather will tell the story of how we got here, how the Universe began with a Big Bang, how it could have produced an Earth where sentient beings can live, and how those beings are discovering their history. Mather was Project Scientist for NASA's Cosmic Background Explorer (COBE) satellite, which measured the spectrum (the color) of the heat radiation from the Big Bang, discovered hot and cold spots in that radiation, and hunted for the first objects that formed after the great explosion. He will explain Einstein's biggest mistake, show how Edwin Hubble discovered the expansion of the universe, how the COBE mission was built, and how the COBE data support the Big Bang theory. He will also show NASA's plans for the next great telescope in space, the James Webb Space Telescope. It will look even farther back in time than the Hubble Space Telescope, and will look inside the dusty cocoons where stars and planets are being born today. Planned for launch in 2013, it may lead to another Nobel Prize for some lucky observer.
From the Big Bang to the Nobel Prize and on to James Webb Space Telescope
The history of the universe in a nutshell, from the Big Bang to now, and on to the future - John Mather will tell the story of how we got here, how the Universe began with a Big Bang, how it could have produced an Earth where sentient beings can live, and how those beings are discovering their history. Mather was Project Scientist for NASA's Cosmic Background Explorer (COBE) satellite, which measured the spectrum (the color) of the heat radiation from the Big Bang, discovered hot and cold spots in that radiation, and hunted for the first objects that formed after the great explosion. He will explain Einstein's biggest mistake, show how Edwin Hubble discovered the expansion of the universe, how the COBE mission was built, and how the COBE data support the Big Bang theory. He will also show NASA's plans for the next great telescope in space, the James Webb Space Telescope. It will look even farther back in time than the Hubble Space Telescope, and will look inside the dusty cocoons where stars and planets are being born today. Planned for launch in 2013, it may lead to another Nobel Prize for some lucky observer.
From the Big Bang to the Nobel Prize and on to the James Webb Space Telescope
The history of the universe in a nutshell, from the Big Bang to now. and on to the future - John Mather will tell the story of how we got here, how the Universe began with a Big Bang, how it could have produced an Earth where sentient beings can live, and how those beings are discovering their history. Mather was Project Scientist for NASA's Cosmic Background Explorer (COBE) satellite, which measured the spectrum (the color) of the heat radiation from the Big Bang, discovered hot and cold spots in that radiation, and hunted for the first objects that formed after the great explosion. He will explain Einstein's biggest mistake, show how Edwin Hubble discovered the expansion of the univerre, how the COBE mission was built, and how the COBE data support the Big Bang theory. He will also show NASA's plans for the next great telescope in space, the Jarnes Webb Space Telescope. It will look even farther back in time than the Hubble Space Telescope, and will look inside the dusty cocoons where rtars and planets are being born today. Planned for launch in 2013, it may lead to another Nobel Prize for some lucky observer.