Addressing and Presenting Quality of Satellite Data via Web-Based Services

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project

NASA Global Satellite and Model Data Products and Services for Tropical Cyclone Research

The lack of observations over vast tropical oceans is a major challenge for tropical cyclone research. Satellite observations and model reanalysis data play an important role in filling these gaps. Established in the mid-1980s, the Goddard Earth Sciences Data and Information Services Center (GES DISC), as one of the 12 NASA data centers, archives and distributes data from several Earth science disciplines such as precipitation, atmospheric dynamics, atmospheric composition, and hydrology, including well-known NASA satellite missions (e.g., TRMM, GPM) and model assimilation projects (MERRA-2). Acquiring datasets suitable for tropical cyclone research in a large data archive is a challenge for many, especially for those who are not familiar with satellite or model data. Over the years, the GES DISC has developed user-friendly data services. For example, Giovanni is an online visualization and analysis tool, allowing users to visualize and analyze over 2000 satellite- and model-based variables with a Web browser, without downloading data and software. In this chapter, we will describe data and services at the GES DISC with emphasis on tropical cyclone research. We will also present two case studies and discuss future plans

Use of the NASA Giovanni Data System for Geospatial Public Health Research: Example of Weather-Influenza Connection

The NASA Giovanni data analysis system has been recognized as a useful tool to access and analyze many different types of remote sensing data. The variety of environmental data types has allowed the use of Giovanni for different application areas, such as agriculture, hydrology, and air quality research. The use of Giovanni for researching connections between public health issues and Earths environment and climate, potentially exacerbated by anthropogenic influence, has been increasingly demonstrated. In this communication, the pertinence of several different data parameters to public health will be described. This communication also provides a case study of the use of remote sensing data from Giovanni in assessing the associations between seasonal influenza and meteorological parameters. In this study, logistic regression was employed with precipitation, temperature and specific humidity as predictors. Specific humidity was found to be associated (p 0.05) with influenza activity in both temperate and tropical climate. In the two temperate locations studied, specific humidity was negatively correlated with influenza; conversely, in the three tropical locations, specific humidity was positively correlated with influenza. Influenza prediction using the regression models showed good agreement with the observed data (correlation coefficient of 0.50.83)

Global Satellite Observations for Smart Cities

The smart city approach requires collection of interdisciplinary data and information from multiple sources and integration with modern technologies to provide a new and cost-effective way for researchers and decision makers to study and manage cities. In this book chapter, we introduce NASA satellite-based global and regional observations with emphasis on the hydrologic cycle (e.g., precipitation, wind, temperature, soil moisture) for smart cities. These products, consisting of both near-real-time and historical datasets, are publicly available free of charge and can be used for global and regional research and applications. Examples of using these datasets in smart cities are included. The chapter is organized as follows, first, a brief overview of NASA global satellite-based data products, followed by data services and tools, two examples of using satellite-based datasets in megacities, and finally summary and future plans

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

Climate data analysis and visualization information system

У роботі проводиться аналіз наукових досліджень та існуючих програм для аналізу та візуалізації кліматичних даних, в результаті чого доведено актуальність та новизну дослідницької роботи. Для методів обрані програмні засоби для розробки та планування архітектури додатків клієнт-сервер, методи аналізу та узагальнення MS SQL Server для створення баз даних, C # та JavaScript (стандарт ECMAScript 6) та ReactJS v.16.12.0. аналізу та узагальнення для клієнта для програми. Експериментально обґрунтовано застосування об’єктно-орієнтованого підходу для проектування та розробки клієнтської частини програми. Була спланована та створена комп’ютерна система для аналізу та візуалізації даних; наукова новизна дослідження була доведена експериментом та узагальненням. Зокрема, розроблений додаток містить логічні операції з аналізу історичних кліматичних показників у клієнтській частині, що прискорило процес візуалізації інформації для користувача.In the bachelors thesis the analysis of scientific researches and existing programs for the analysis and visualization of climatic data is carried out as a result of which urgency and novelty of research work has been proved. Software tools for development and planning of client-server application architecture, methods of analysis and generalization of MS SQL Server for database creation, C # and JavaScript (ECMAScript 6 standard) and ReactJS v.16.12.0 framework based on it are chosen for methods of analysis and generalization for the client for the application. The application of object-oriented approach for the design and development of the client part of the program is experimentally substantiated. A computer system for data analysis and visualization was planned and created; the scientific novelty of the research was proved by experiment and generalization. In particular, the developed application features logical operations on analysis of historical climatic indicators in the client part, which accelerated the process of information visualization for the user.INTRODUCTION 8 1. CLIMATOLOGY, METEOROLOGY AND EXISTING SYSTEMS FOR DATA ANALYSIS AND VISUALIZATION 11 1.1. CLIMATOLOGY AND METEOROLOGY AND THEIR RESEARCHERS. 11 1.2 TYPES OF CLIMATIC INDICATORS WITH THEIR CALCULATION 14 1.3 THE GENERAL REVIEW OF EXISTING SYSTEMS FOR ANALYZING AND VISUALIZING OF CLIMATE DATA 23 1.4 CONCLUSIONS TO THE SECTION 30 2 SELECTION OF SOFTWARE AND DEVELOPMENT OF THE SERVER PART 31 2.1. JAVASCRIPT PROGRAMMING LANGUAGE AND REACT FRAMEWORK 31 2.2. MARKUP LANGUAGES AND HTML / CSS 35 2.3. C # PROGRAMMING LANGUAGE 36 2.4. DEVELOPMENT OF THE SERVER PART AND CREATION OF A DATABASE FOR THE PROGRAM 38 2.5 CONCLUSIONS TO THE SECTION 44 3 DEVELOPMENT OF THE CLIENT PART OF THE SYSTEM OF ANALYSIS AND VISUALIZATION OF CLIMATE DATA 46 3.1 CREATE CLIENT PART FORM 46 3.2. CONNECT APEXCHARTS AND CREATE CHARTS TO VISUALIZE DATA 58 4 LIFE SAFETY 67 4.1 GENERAL CHARACTERISTICS OF THE ROOM AND WORKPLACE 67 4.2 ANALYSIS OF POTENTIALLY DANGEROUS AND HARMFUL PRODUCTION FACTORS IN THE WORKPLACE 70 4.3 CONCLUSIONS TO THE SECTION 71 CONCLUSIONS 73 REFERENCES 7

Scale aware modeling and monitoring of the urban energy chain

With energy modeling at different complexity levels for smart cities and the concurrent data availability revolution from connected devices, a steady surge in demand for spatial knowledge has been observed in the energy sector. This transformation occurs in population centers focused on efficient energy use and quality of life. Energy-related services play an essential role in this mix, as they facilitate or interact with all other city services. This trend is primarily driven by the current age of the Ger.: Energiewende or energy transition, a worldwide push towards renewable energy sources, increased energy use efficiency, and local energy production that requires precise estimates of local energy demand and production. This shift in the energy market occurs as the world becomes aware of human-induced climate change, to which the building stock has a significant contribution (40% in the European Union). At the current rate of refurbishment and building replacement, of the buildings existing in 2050 in the European Union, 75% would not be classified as energy-efficient. That means that substantial structural change in the built environment and the energy chain is required to achieve EU-wide goals concerning environmental and energy policy. These objectives provide strong motivation for this thesis work and are generally made possible by energy monitoring and modeling activities that estimate the urban energy needs and quantify the impact of refurbishment measures. To this end, a modeling library called aEneAs was developed in the scope of this thesis that can perform city-wide building energy modeling. The library performs its tasks at the level of a single building and was a first in its field, using standardized spatial energy data structures that allow for portability from one city to another. For data input, extensive use was made of digital twins provided from CAD, BIM, GIS, architectural models, and a plethora of energy data sources. The library first quantifies primary thermal energy demand and then the impact of refurbishment measures. Lastly, it estimates the potential of renewable energy production from solar radiation. aEneAs also includes network modeling components that consider energy distribution in the given context, showing a path toward data modeling and simulation required for distributed energy production at the neighborhood and district level. In order to validate modeling activities in solar radiation and green façade and roof installations, six spatial models were coupled with sensor installations. These digital twins are included in three experiments that highlight this monitoring side of the energy chain and portray energy-related use cases that utilize the spatially enabled web services SOS-SES-WNS, SensorThingsAPI, and FIWARE. To this author\u27s knowledge, this is the first work that surveys the capabilities of these three solutions in a unifying context, each having its specific design mindset. The modeling and monitoring activity and their corresponding literature review indicated gaps in scientific knowledge concerning data science in urban energy modeling. First, a lack of standardization regarding the spatial scales at which data is stored and used in urban energy modeling was observed. In order to identify the appropriate spatial levels for modeling and data aggregation, scale is explored in-depth in the given context and defined as a byproduct of resolution and extent, with ranges provided for both parameters. To that end, a survey of the encountered spatial scales and actors in six different geographical and cultural settings was performed. The information from this survey was used to put forth a standardized spatial scales definition and create a scale-dependent ontology for use in urban energy modeling. The ontology also provides spatially enabled persistent identifiers that resolve issues encountered with object relationships in modeling for inheritance, dependency, and association. The same survey also reveals two significant issues with data in urban energy modeling. These are data consistency across spatial scales and urban fabric contiguity. The impact of these issues and different solutions such as data generalization are explored in the thesis. Further advancement of scientific knowledge is provided specifically with spatial standards and spatial data infrastructure in urban energy modeling. A review of use cases in the urban energy chain and a taxonomy of the standards were carried out. These provide fundamental input for another piece of this thesis: inclusive software architecture methods that promote data integration and allow for external connectivity to modern and legacy systems. In order to reduce time-costly extraction, transformation, and load processes, databases and web services to ferry data to and from separate data sources were used. As a result, the spatial models become central linking elements of the different types of energy-related data in a novel perspective that differs from the traditional one, where spatial data tends to be non-interoperable / not linked with other data types. These distinct data fusion approaches provide flexibility in an energy chain environment with inconsistent data structures and software. Furthermore, the knowledge gathered from the experiments presented in this thesis is provided as a synopsis of good practices

Web service-based exploration of Earth Observation time-series data for analyzing environmental changes

The increasing amount of Earth observation (EO) data requires a tremendous change, in order to property handle the number of observations and storage size thereof. Due to open data strategies and the increasing size of data archives, a new market has been developed to provide analysis and application-ready data, services, and platforms. It is not only scientists and geospatial processing specialists who work with EO data; stakeholders, thematic experts, and software developers do too. There is thus a great demand for improving the discovery, access, and analysis of EO data in line with new possibilities of web-based infrastructures. With the aim of bridging the gap between users and EO data archives, various topics have been researched: 1) user requirements and their relation to web services and output formats; 2) technical requirements for the discovery and access of multi-source EO time-series data, and 3) management of EO time-series data focusing on application-ready data. Web services for EO data discovery and access, time-series data processing, and EO platforms have been reviewed and related to the requirements of users. The diversity of data providers and web services requires specific knowledge of systems and specifications. Although service specifications for the discovery of EO data exist, improvements are still necessary to meet the requirements of different user personas. For the processing of EO time-series data, various data formats and processing steps need to be handled. Still, there remains a gap between EO time-series data access and analysis tools, which needs to be addressed to simplify work with such data. Within this thesis, web services for the discovery, access, and analysis of EO time-series data have been described and evaluated based on different user requirements. Standardized web services specifications, output and data formats are proposed, introduced and described to meet the needs of the different user personas

Smart data management with BIM for Architectural Heritage

In the last years smart buildings topic has received much attention as well as Building Information Modelling (BIM) and interoperability as independent fields. Linking these topics is an essential research target to help designers and stakeholders to run processes more efficiently. Working on a smart building requires the use of Innovation and Communication Technology (ICT) to optimize design, construction and management. In these terms, several technologies such as sensors for remote monitoring and control, building equipment, management software, etc. are available in the market. As BIM provides an enormous amount of information in its database and theoretically it is able to work with all kind of data sources using interoperability, it is essential to define standards for both data contents and format exchange. In this way, a possibility to align research activity with Horizon 2020 is the investigation of energy saving using ICT. Unfortunately, comparing the Architecture Engineering and Construction (AEC) Industry with other sectors it is clear how in the building field advanced information technology applications have not been adopted yet. However in the last years, the adoption of new methods for the data management has been investigated by many researchers. So, basing on the above considerations, the main purpose of this thesis is investigate the use of BIM methodology relating to existing buildings concerning on three main topics: • Smart data management for architectural heritage preservation; • District data management for energy reduction; • The maintenance of highrises. For these reasons, data management acquires a very important value relating to the optimization of the building process and it is considered the most important goal for this research. Taking into account different kinds of architectural heritage, the attention is focused on the existing and historical buildings that usually have characterized by several constraints. Starting from data collection, a BIM model was developed and customized in function of its objectives, and providing information for different simulation tests. Finally, data visualization was investigated through the Virtual Reality(VR) and Augmented Reality (AR). Certainly, the creation of a 3D parametric model implies that data is organized according to the use of individual users that are involved in the building process. This means that each 3D model can be developed with different Levels of Detail/Development (LODs) basing on the goal of the data source. Along this thesis the importance of LODs is taken into account related to the kind of information filled in a BIM model. In fact, basing on the objectives of each project a BIM model can be developed in a different way to facilitate the querying data for the simulations tests.\ud The three topics were compared considering each step of the building process workflow, highlighting the main differences, evaluating the strengths and weaknesses of BIM methodology. In these terms, the importance to set a BIM template before the modelling step was pointed out, because it provides the possibility to manage information in order to be collected and extracted for different purposes and by specific users. Moreover, basing on the results obtained in terms of the 3D parametric model and in terms of process, a proper BIM maturity level was determined for each topic. Finally, the value of interoperability was arisen from these tests considering that it provided the opportunity to develop a framework for collaboration, involving all parties of the building industry

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium