Application of advanced technology to space automation

Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits

Air-snow exchange investigations at Summit, Greenland: An overview

The Greenland Ice Sheet Project 2 (GISP2) and Greenland Ice Core Project (GRIP) deep drilling programs at Summit, Greenland included support (both logistical and scientific) of extensive investigation of atmospheric transport and air-snow exchange processes of gases and particles relevant to the interpretation of the ice-core records. Much of the sampling for the air-snow exchange investigations was conducted at a unique solar-powered camp 30 km southwest of the GISP2 drill camp (even further from the GRIP camp) and was characterized by a high degree of international collaboration and cooperation. The wide range of expertise and analytical capabilities of the 20-plus investigators participating in these studies has provided important insight into the meteorological, physical, and chemical processes which interact to determine the composition of snow and firn at Summit. Evolving understanding of this system will allow improved reconstruction of the composition of the atmosphere over Greenland in the past from the detailed Summit ice-core records. This paper provides an overview of air-snow exchange investigations at Summit, including their development through the course of the drilling programs (1989–1993), significant findings related to both air-snow exchange issues and the present state of the Arctic free troposphere, as well as the major outstanding questions which are being addressed in ongoing experiments at Summit

Integration Of Multi-Sensory Earth Observations For Characterization Of Air Quality Events

In order to characterize air quality events, such as dust storms or smoke events from fires, a wide variety of Earth observations are needed from satellites, surface monitors and models. Traditionally, the burden of data access and processing was placed on the data user. These challenges of finding, accessing and merging data are overcome through the principles of Service Oriented Architecture. This thesis describes the collaborative, service-oriented approach now available for air quality event analysis, where datasets are turned into services that can be accessed by tools through standard queries. This thesis extends AQ event evidence to include photos, videos and personal observations gathered from social media websites such as Flickr, Twitter and YouTube. In this thesis, the service-oriented approach is demonstrated using two case studies. The first explains the benefits of data reuse in real-time event analysis focusing on the 2009 Southern California Smoke event. The second case study highlights post-event analysis for EPAΓÇÖs Exceptional Event Rule. The thesis concludes with a first attempt to quantify the benefits of data reuse by identifying all of the different user requirements for Earth observation data. We found that the real-time and post-event analysis had 68 unique Earth observation requirements making it an ideal example for illustrating the benefits of service oriented architecture for air quality analysis. While this thesis focuses on the air quality domain, the tools and methods can be applied to any area that needs distributed data

Defining Safe Training Datasets for Machine Learning Models Using Ontologies

Machine Learning (ML) models have been gaining popularity in recent years in a wide variety of domains, including safety-critical domains. While ML models have shown high accuracy in their predictions, they are still considered black boxes, meaning that developers and users do not know how the models make their decisions. While this is simply a nuisance in some domains, in safetycritical domains, this makes ML models difficult to trust. To fully utilize ML models in safetycritical domains, there needs to be a method to improve trust in their safety and accuracy without human experts checking each decision. This research proposes a method to increase trust in ML models used in safety-critical domains by ensuring the safety and completeness of the model’s training dataset. Since most of the complexity of the model is built through training, ensuring the safety of the training dataset could help to increase the trust in the safety of the model. The method proposed in this research uses a domain ontology and an image quality characteristic ontology to validate the domain completeness and image quality robustness of a training dataset. This research also presents an experiment as a proof of concept for this method where ontologies are built for the emergency road vehicle domain

Application of ESE Data and Tools to Air Quality Management: Services for Helping the Air Quality Community use ESE Data (SHAirED)

The goal of this REASoN applications and technology project is to deliver and use Earth Science Enterprise (ESE) data and tools in support of air quality management. Its scope falls within the domain of air quality management and aims to develop a federated air quality information sharing network that includes data from NASA, EPA, US States and others. Project goals were achieved through a access of satellite and ground observation data, web services information technology, interoperability standards, and air quality community collaboration. In contributing to a network of NASA ESE data in support of particulate air quality management, the project will develop access to distributed data, build Web infrastructure, and create tools for data processing and analysis. The key technologies used in the project include emerging web services for developing self describing and modular data access and processing tools, and service oriented architecture for chaining web services together to assemble customized air quality management applications. The technology and tools required for this project were developed within DataFed.net, a shared infrastructure that supports collaborative atmospheric data sharing and processing web services. Much of the collaboration was facilitated through community interactions through the Federation of Earth Science Information Partners (ESIP) Air Quality Workgroup. The main activities during the project that successfully advanced DataFed, enabled air quality applications and established community-oriented infrastructures were: develop access to distributed data (surface and satellite), build Web infrastructure to support data access, processing and analysis create tools for data processing and analysis foster air quality community collaboration and interoperability

Fusion of VNIR Optical and C-Band Polarimetric SAR Satellite Data for Accurate Detection of Temporal Changes in Vegetated Areas

In this paper, we propose a processing chain jointly employing Sentinel-1 and Sentinel-2 data, aiming to monitor changes in the status of the vegetation cover by integrating the four 10 m visible and near-infrared (VNIR) bands with the three red-edge (RE) bands of Sentinel-2. The latter approximately span the gap between red and NIR bands (700 nm–800 nm), with bandwidths of 15/20 nm and 20 m pixel spacing. The RE bands are sharpened to 10 m, following the hypersharpening protocol, which holds, unlike pansharpening, when the sharpening band is not unique. The resulting 10 m fusion product may be integrated with polarimetric features calculated from the Interferometric Wide (IW) Ground Range Detected (GRD) product of Sentinel-1, available at 10 m pixel spacing, before the fused data are analyzed for change detection. A key point of the proposed scheme is that the fusion of optical and synthetic aperture radar (SAR) data is accomplished at level of change, through modulation of the optical change feature, namely the difference in normalized area over (reflectance) curve (NAOC), calculated from the sharpened RE bands, by the polarimetric SAR change feature, achieved as the temporal ratio of polarimetric features, where the latter is the pixel ratio between the co-polar and the cross-polar channels. Hyper-sharpening of Sentinel-2 RE bands, calculation of NAOC and modulation-based integration of Sentinel-1 polarimetric change features are applied to multitemporal datasets acquired before and after a fire event, over Mount Serra, in Italy. The optical change feature captures variations in the content of chlorophyll. The polarimetric SAR temporal change feature describes depolarization effects and changes in volumetric scattering of canopies. Their fusion shows an increased ability to highlight changes in vegetation status. In a performance comparison achieved by means of receiver operating characteristic (ROC) curves, the proposed change feature-based fusion approach surpasses a traditional area-based approach and the normalized burned ratio (NBR) index, which is widespread in the detection of burnt vegetation

Impacts of a Changing Climate and Land Use on Reindeer Pastoralism: Indigenous Knowledge and Remote Sensing

The Arctic is home to many indigenous peoples, including those who depend on reindeer herding for their livelihood, in one of the harshest environments in the world. For the largely nomadic peoples, reindeer not only form a substantial part of the Arctic food base and economy, but they are also culturally important, shaping their way of life, mythologies, festivals and ceremonies. Reindeer pastoralism or husbandry has been practiced by numerous peoples all across Eurasia for thousands of years and involves moving herds of reindeer, which are very docile animals, from pasture to pasture depending on the season. Thus, herders must adapt on a daily basis to find optimal conditions for their herds according to the constantly changing conditions. Climate change and variability plus rapid development are increasingly creating major changes in the physical environment, ecology, and cultures of these indigenous reindeer herder communities in the North, and climate changes are occurring significantly faster in the Arctic than the rest of the globe, with correspondingly dramatic impacts (Oskal, 2008). In response to these changes, Eurasian reindeer herders have created the EALAT project, a comprehensive new initiative to study these impacts and to develop local adaptation strategies based upon their traditional knowledge of the land and its uses - in targeted partnership with the science and remote sensing community - involving extensive collaborations and coproduction of knowledge to minimize the impacts of the various changes. This chapter provides background on climate and development challenges to reindeer husbandry across the Arctic and an overview of the EALAT initiative, with an emphasis on indigenous knowledge, remote sensing, Geographic Information Systems (GIS), and other scientific data to 'co-produce' datasets for use by herders for improved decision-making and herd management. It also provides a description of the EALAT monitoring data integration and sharing system and portal being developed for reindeer pastoralism. In addition, the chapter provides some preliminary results from the EALAT Project, including some early remote sensing research results