A Topology Based Spatio-Temporal Map Algebra for Big Data Analysis

Earth observation (EO) data cubes have gained significance for their potential in providing comprehensive insights into dynamic earth phenomena. However, a significant challenge in their utilization is the interoperability among EO data cubes with varying dimensionalities. Existing efforts have primarily focused on enhancing analysis ready data cubes, yet cross-dimensional adaptivity remains relatively unexplored. This study proposes a novel approach to address this issue. The proposed method is based on a 3D adaptive data cube model constructed in a 3D adaptive space. The main objective is to achieve interoperability between EO data cubes of different dimensions while adhering to common geospatial standards. The core methodology involves cross-dimensional mapping, considering a 3D adaptive space containing spatial axes and adaptive axes. Importantly, the method applies to data cubes with the same coordinate reference system, resolution, and data type. To enable cross-dimensional mapping, a key requirement is the existence of a mapping function between the multidimensional space domain and the 3D adaptive space domain. When these conditions are met, data cubes with three or more dimensions can be interoperable. This becomes feasible by applying suitable serialization algorithms. This study demonstrates that data cubes can be successfully mapped into the proposed 3D adaptive data cube, achieving interoperability under specific conditions. This finding has significant implications in the field of EO data analysis, enabling seamless interaction between data cubes with different dimensions. The method not only facilitates cross-dimensional adaptability but also aligns with mainstream geospatial service standards. It is particularly suitable for multidimensional geospatial raster services based on the OGC WCS 2.0 standard. In conclusion, this study addresses a critical challenge in EO data analysis by proposing a 3D adaptive data cube model that promotes interoperability between data cubes of varying dimensions.

Environmental sustainability is nowadays a major global issue that requires efficient and effective responses from governments. Essential variables (EV) have emerged in different scientific communities as a means to characterize and follow environmental changes through a set of measurements required to support policy evidence. To help track these changes, our planet has been under continuous observation from satellites since 1972. Currently, petabytes of satellite Earth observation (EO) data are freely available. However, the full information potential of EO data has not been yet realized because many big data challenges and complexity barriers hinder their effective use. Consequently, facilitating the production of EVs using the wealth of satellite EO data can be beneficial for environmental monitoring systems. In response to this issue, a comprehensive list of EVs that can take advantage of consistent time-series satellite data has been derived. In addition, a set of use-cases, using an Earth Observation Data Cube (EODC) to process large volumes of satellite data, have been implemented to demonstrate the practical applicability of EODC to produce EVs. The proposed approach has been successfully tested showing that EODC can facilitate the production of EVs at different scales and benefiting from the spatial and temporal dimension of satellite EO data for enhanced environmental monitoring.

In recent years, the need to protect and conserve biodiversity has become more critical than ever before, as a prerequisite for both sustainable development and the very survival of the human species. This has made it a priority for the scientific community to develop technological solutions that provide data and information for monitoring, directly or indirectly, biodiversity and the drivers of change. A new era of satellite earth observation upgrades the potential of Remote Sensing (RS) to support, at relatively low cost, but with high accuracy the extraction of information over large areas, at regular intervals, and over extended periods of time. Also, the recent development of the Earth Observation Data Cubes (EODC) framework facilitates EO data management and information extraction, enabling the mapping and monitoring of temporal and spatial patterns on the Earth’s surface. This submission presents the ELBIOS EODC, specifically developed to support the biodiversity management and conservation over Greece. Based on the Open Data Cube (ODC) framework, it exploits multi-spectral optical Copernicus Sentinel-2 data and provides a series of Satellite Earth Observation (SEO) biodiversity products and spectral indices nationwide.

Geospatial technologies are central to spatial decision making and governance, but gaining equitable access to these is still difficult for traditionally marginalized communities. We contend that the dominance of proprietary GIS software has contributed to this digital divide, as these are inherently disempowering to marginalized social groups. Their high purchasing cost and licensing fees pose access barriers to resource‐poor citizens. Design of proprietary software may also not be appropriate for marginalized groups who are neither trained in GIS, nor represent the needs of dominant market base. Therefore, “free and open source software for geospatial” (FOSS4G) and open GIS provide new opportunities in democratizing GIS, as these are open code and free of purchasing and licensing costs. This paper aims to discuss the role of open GIS in advancing the goals of public participation GIS (PPGIS). We first discuss the origins of the FOSS movement, and explore the ways it has shaped the FOSS4G and open GIS movements. Next, we examine how a community information system built with open GIS software is being successfully utilized by an environmental organization in Milwaukee, to contest urban poverty. Our research demonstrates that open source GIS offers unique opportunities in advancing PPGIS research and practice.

This paper describes features of a language approach for map algebra based on the use of algebraic expressions that satisfy a concise formalism. To be consistent with formal approaches such as geoalgebra and image algebra, the proposed algebraic expressions are suitable not only for the usual modeling of layers but also to describe variable neighborhoods and zones. As a compromise between language and implementation issues we present an implementation strategy based on the theory of automata. The result is an efficient way of implementing map algebra that simplifies its use on environmental and dynamic models without going too far from its well-known paradigm.

The role of Earth observation (EO) data in addressing societal problems from environmental through to humanitarian should not be understated. Recent innovation in EO means provision of analysis ready data and data cubes, which allows for rapid use of EO data. This in combination with processing technologies, such as Google Earth Engine and open source algorithms/software for EO data integration and analyses, has afforded an explosion of information to answer research questions and/or inform policy making. However, there is still a need for both training and validation data within EO projects – often this can be challenging to obtain. It has been suggested that citizen science can help here to provide these data, yet there is some perceived hesitancy in using citizen science within EO projects. This paper reports on the Citizen Science 4 EO (Citizens4EO) project that aimed to obtain an in-depth understanding of researchers’ and practitioners’ experiences with citizen science data in EO within the UK. Through a mixed methods approach (online and in-depth surveys and a spotlight case study) it was found that although the benefits of using citizen science data in EO projects were many (and highlighted in the spotlighted “Slavery from Space” case study), there were a number of common concerns around using citizen science. These were around the mechanics of deploying citizen science and the unreliability of a potentially misinformed or undertrained citizen base. As such, comparing the results of this study with those of a similar survey undertaken in 2016, it is apparent that progress towards optimizing citizen science use in EO has been incremental but positive with evidence of the realization of the benefits of citizen science for EO (Citizens4EO). As such, we conclude by offering priority action areas to support further use of citizen science by the EO community within the UK, which ultimately should be adopted further afield.

Legislation was introduced in Portugal to enable the Portuguese Forest Services (DGF) to implement a management strategy to control forest plantation in forest burned areas. Under this legislation, the forest owners have to notify DGF after a fire in order to replant the same species, or to seek DGF’s permission if the previous species is to be replaced. We developed a methodology that uses Earth Observation (EO) data to identify the species composition of the forests before being burned, and to yearly supervise the burnt areas in order to identify new reforestations and check if the forest owners are in compliance with the Portuguese legislation on burnt areas. Our methodology is based on vegetation indices differencing and map algebra leading to the production of maps where potential illegal areas are identified. These areas are then field checked to identify the forest species that was planted and compare it with the pre-fire forest cover to detect legal and illegal situations. The methodology was successfully tested in a study area in central Portugal with an extension of 640 km2. The results are encouraging for an operational implementation of the methodology by DGF, leading to an efficient application of this specific legislation.

<p>Operational Earth Observation (EO) satellite missions are entering their 5<sup>th</sup> lifetime decade, and the need to access historical data has strongly increased, particularly for long-term science and environmental monitoring applications. This trend that drives users to request long time-series of data will increase even more in the future, in particular regarding the interest on global change assessment and monitoring to support policy makers decisions on atmosphere, ocean, cryosphere, carbon and other biogeochemical cycles safeguard.</p><p>The Copernicus initiative (https://www.copernicus.eu) is playing a unique and unprecedented role form the point of view of amount, relevance and quality of provided environmental data. In the frame of the European Commission funded activities, the Data and Information Access Service (DIAS) are operated by five different consortia to acquire, process, archive and distribute data from Copernicus and Third-Party Missions.</p><p>With this enormous availability of past, present, and future geospatial environmental data, there is the need to make users able to identify the datasets that best fit with their needs and obtain these data in fastest and easiest-to-use possible way. The Advanced geospatial DAta Management - ADAM platform (https://adamplatform.eu/) provides discovery, access, processing and visualization services for data in the distributed cloud environment, significantly reducing the burden of data usability.</p><p>ADAM allows the exploitation of the content of EO data archives extended from a few years to decades and therefore makes their continuously increasing scientific value fully accessible. The advances in satellite sensor characteristics (spatial resolution, temporal frequency, spectral sensors) as well as in all related technical aspects (data and metadata format, storage, infrastructures) underline the strong need to preserve the EO space data without time constraints and to keep them accessible and exploitable, as they constitute a humankind asset. This is a typical big data challenge that ADAM can face.  </p><p>This paper describes the ADAM platform and various application domains supported with its data science analytics and visualization capabilities.</p>

While free and open source software becomes increasingly important in geospatial research and industry, open science perspectives are generally less reflected in universities’ educational programs. We present an example of how free and open source software can be incorporated into geospatial education to promote open and reproducible science. Since 2008 graduate students at North Carolina State University have the opportunity to take a course on geospatial modeling and analysis that is taught with both proprietary and free and open source software. In this course, students perform geospatial tasks simultaneously in the proprietary package ArcGIS and the free and open source package GRASS GIS. By ensuring that students learn to distinguish between geospatial concepts and software specifics, students become more flexible and stronger spatial thinkers when choosing solutions for their independent work in the future. We also discuss ways to continually update and improve our publicly available teaching materials for reuse by teachers, self-learners and other members of the GIS community. Only when free and open source software is fully integrated into geospatial education, we will be able to encourage a culture of openness and, thus, enable greater reproducibility in research and development applications.

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing number of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9 and Sentinel-2, and planned future missions, like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA)’s Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from reference systems to client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.

Web services provide users ever better methods to access and use Earth Observation data. Development of a web services architecture for the Global Earth Observing System of Systems was conducted using an evolutionary development process that depended on standards for geospatial web services. Standards for geospatial web services provide interfaces that can be scaled to global deployment. The GEOSS Architecture Implementation Pilot defined an evolutionary development process and reusable architecting methods for applying web services to a global scale system of systems for Earth observations. ISO/IEC, OGC and other standards are used by the AIP architecture to meet the objectives of GEOSS to enable comprehensive, coordinated and sustained observation of the Earth and also efficient publication and use of the collected information.

Relations of multiple zeta values and their algebraic expression

Spatial Data Infrastructures (SDI) have been widely accepted to exchange geospatial data among organizations. Today SDIs main focus lies on the provision of geospatial data in the form of distributed spatial web services, the retrieval through catalogues, and visualization in the form of Web Map Services (WMS). The hypothesis presented in this paper takes SDI's one step further by providing a method to process geodata in an Open Geospatial Consortium (OGC) compliant way into information. Two case studies present the potential of standardized geoprocessing services. In addition, this paper addresses the problem of service chaining by providing a system architecture to implement complex geoprocessing models and workflows based on web services using Web Service Orchestration (WSO). The proposed methods utilize spatial standards provided by OGC, the International Organization for Standardization (ISO) and ‘mainstream IT’ standards provided by the World Wide Web Consortium (W3C) and the Organization for the Advancement of Structured Information Standards (OASIS) to establish a generic web service architecture for providing common geoprocessing capabilities (e.g. spatial algorithms, map algebra, etc.) for usage in SDIs.

Robust rectification of aerial photographs in an open source environment

Earth observation (EO) data play a crucial role in monitoring ecosystems and environmental processes. Time series of satellite data are essential for long-term studies in this context. Working with large volumes of satellite data, however, can still be a challenge, as the computational environment with respect to storage, processing and data handling can be demanding, which sometimes can be perceived as a barrier when using EO data for scientific purposes. In particular, open-source developments which comprise all components of EO data handling and analysis are still scarce. To overcome this difficulty, we present Tools for Analyzing Time Series of Satellite Imagery (TATSSI), an open-source platform written in Python that provides routines for downloading, generating, gap-filling, smoothing, analyzing and exporting EO time series. Since TATSSI integrates quality assessment and quality control flags when generating time series, data quality analysis is the backbone of any analysis made with the platform. We discuss TATSSI’s 3-layered architecture (data handling, engine and three application programming interfaces (API)); by allowing three APIs (a native graphical user interface, some Jupyter Notebooks and the Python command line) this development is exceptionally user-friendly. Furthermore, to demonstrate the application potential of TATSSI, we evaluated MODIS time series data for three case studies (irrigation area changes, evaluation of moisture dynamics in a wetland ecosystem and vegetation monitoring in a burned area) in different geographical regions of Mexico. Our analyses were based on methods such as the spatio-temporal distribution of maxima over time, statistical trend analysis and change-point decomposition, all of which were implemented in TATSSI. Our results are consistent with other scientific studies and results in these areas and with related in-situ data.