Open Data andTools Continuation

GeoAvalanche is an ecosystem of integrated applications for the avalanche risk management which fosters the use of geographical information, Earth Observation and crowdsourced data. It is a unique system capable of providing near real-time information on the avalanche risk with the highest level of accuracy and precision. Its algorithms can consume elevation models, crowdsourcing and Earth Observation data about snowpack trend (Snow Cover, Snow Water Equivalent, Snow Surface Temperature) at a resolution of 30m. Public authorities and Avalanche Warning Services can build spatial data infrastructures and geoportals for sharing snow avalanche information and maps of situational awareness like the public demo (http://geoavalanche.org). At the core there is GeoAvalanche server, a custom GeoServer (https://github.com/geoavalanche/geoavalanche-server) which has been extended with several geoprocessing published through a set of standard OGC WPS web services and the app-schema extension for sharing avalanche data in compliance with the GML profile of CAAML. All GeoAvalanche WPS processes (Slope, Aspect, Curvature, SnowPack, Crowd) have been atomically designed to achieve specific functionalities and can be orchestrated within a workflow for performing more complex processes like Avalanche Terrain Exposure or the Avalanche Risk Index of each requested feature collection. GeoAvalanche server can also be used as geospatial back-end of GeoNode for building snow avalanche geoportals through the development of custom GeoNode projects which exploit those WPS processes toward avalanche risk insight tools like the web mapping client (http://geoavalanche.org/mygeoss/public/) awarded by the MyGEOSS competition. All the software is released under the GeoAvalanche organization (https://github.com/geoavalanche) in GitHub with an open source license.

In the era of open data policies, a vast amount of data is provided freely and openly accessible to its users. In this framework the European Union started the Copernicus Programme aiming at the development of a European information services based on satellite Earth Observation and in situ (non-space) data that are freely and openly accessible to its users. This data is intended to provide information to help service providers, public authorities, international organisations as well as SMEs, to improve the quality of life for the citizens of Europe. However, there are several issues that still need to be addressed. On one hand, there are several entities that could take great advantages from the use of open EO data in their specific fields of interests but do not have the right instruments, experience or knowledge to extract relevant information from EO data. On the other hand, the creation of valuable content from large and growing volume of EO derived data is by Research organizations, governments and companies, do not always find a market exploitation. Moreover, the cooperation between industry and research centers is not always easy, leading to a sort of short-circuit in the complete development of space economy. In order to bring order to such a disordered market, we introduce K2SPACE with the intent to define protocols and standards to facilitate connection, coordination, and collaboration between entities. K2SPACE is a platform, defined as a business model that allows multiple sides to interact by providing an infrastructure to connect them. In particular we subdivided the interacting entities in peer producers (Space SMEs, Research institutions and Universities), and peer consumers (Non-space SMEs). The aim of the K2SPACE platform is to revolutionize the space economy in Europe. Operating as hub, K2SPACE “organizes” the interaction, skills, resources outside of traditional organization boundaries and shapes of the markets. This is made possible by providing reduced barriers to entry, a shared storefront and an overall enabling set of services to all sides of a market. This approach will give advantages to both peer producers and consumers. In particular, through the use of K2SPACE, peer producers will acknowledge the developed know-how, will have access to external funding and consequently will increase revenues, improve company visibility and be open to new market opportunities. Similarly, the peer consumers that interact with other entities via the K2SPACE platform will have access to high-level technologies allowing the improvement of the quality of the offered services, resulting in reduced expenses and increase in revenues.

The increasing availability of very high spatial resolution satellite images has opened the door to systematic studies of smallholder systems from space. In this work, we present a database of crop characteristics plus a web-based open data exploration tool produced in the context of the STARS project (www.stars-project.org). STARS aims to address the information scarcity around the heterogeneous smallholder farming systems that are common in Africa and Asia. For this, we conducted a number of studies in sites in West and East Africa as well as South Asia, which brought together fieldwork- and image-derived characteristics of farm fields into a central database known as the Crop Spectrotemporal Signature Library (CSSL). The CSSL does not hold image data but statistical characterizations derived from analyzing both multispectral and panchromatic images through a fully automated workflow. This workflow allowed us to calculate and extract around one hundred field-specific characteristics. These include spectral characteristics (including common vegetation indices), their in-field variability, and a number of GLCM-based textural characteristics (with different lags, different angles). We continue to enrich that list with other image-based methods, and we apply a.o. machine-learning techniques to analyze these. At the same time, STARS project teams conducted fieldwork that provided in situ agronomic measurements, helping to characterize crop development and field management. The field and image data was semi-synchronously collected throughout the crop season at regular two-week intervals. Our hypothesis is that a collection of this nature can support studies in crop identification, farm field delineation, farm practice detection and other crop-related phenomena in smallholder contexts. In addition to the CSSL, we present an online tool that allows the exploration of spectral, spatial and temporal characteristics and their relationships as detected in classification studies. This comes with an invitation the wider scientific community to use our collection of field- and image-derived characteristics. Our data exploration tool accommodates the comparison of time series, for instance, between different vegetation indices and textural or in situ measurements. Both the data and the tool will become free and open products so that other scientists can use them in their smallholder farming projects. We believe that the CSSL and the accompanying web tool can contribute to define agricultural baselines and we hope that we can continue to enrich the CSSL database with more crops, more years, and a wider geographic coverage.

Now that the Galileo and Copernicus satellite programmes are entering their operational phase, innovation possibilities in the field of satellite data driven applications are getting wider. Thanks to these two massive investments in technology, European and worldwide companies are starting to benefit from increasing, regular and cheaper (not to say free of charge) data flows, which could lead to the development of new and innovative applications and services in an incredibly vast range of markets, including non-space markets [1]. The exploitation of satellite data, as well as open data (from public authorities in particular) has the potential to generate a lot of innovative solutions. In this context the FabSpace 2.0 project aims at putting the Universities at the front line for the take-off of Earth Observation based applications in Europe and worldwide. This can be pursued by hosting and animating open places dedicated to space and geodata-driven innovation where young developers from the civil society, experienced developers from industry or academic and research institutes, public administrations as well as civil organizations can meet, work together and co-create new tools and business models. They can create an ecosystem fitting (and developed according to) the particularities of geodata-driven innovation, in particular for the emergence of Space data downstream services. In this innovative environment, innovation is driven by the needs of users through the involvement of civil society in the innovation process and in the definition of new challenges. Moreover the actors making innovation will be anonymous civilians (students and researchers in particular) and will thus be at the same time developers and end-users of the applications they develop. That is why the FabSpace 2.0 project is expected to improve the capacity of Universities to generate more innovations and generate positive socio-economic impacts. All partner universities are centers of excellence in research in the field of geomatics and space based information. They are not only offering a highly-qualified human capital likely to generate innovation, but also providing open access to data generated within previous research works. Thus the FabSpace 2.0 project can be a particularly relevant opportunity for research teams to make a step forward towards Science 2.0. [1] Harris H. and I. Baumann, “Open data policies and satellite Earth observation,” Space Policy, Vol. 32, May 2015, pp. 44-53

The aim of this work is to leverage the global-scale analysis capabilities of Google Earth Engine (GEE) [1] to study the temporal variations of the Urban Heat Island (UHI) effect at large-scale. GEE is indeed the computing platform recently released by Google “for petabyte-scale scientific analysis and visualization of geospatial datasets”. Using a dedicated HPC (High Performance Computing) infrastructure, it enables researchers to easily and quickly access more than thirty years of free and public data archives, including historical imagery and scientific datasets, for global and large scale remote sensing applications. In this way, many of the limitations related to the data downloading, storage and processing, that usually occur when a such large amount of information (Big Data) is analyzed, are effortlessly overcome. Specifically, the work was focused on the Phoenix Metropolitan Area (PMA) (Ariziona, USA) which, from 1983 to 2010, was subjected to a significant expansion, changing from a mostly agricultural region to a metropolis predominantly characterized by residential suburbs [2]. In fact, an UHI is an urban or metropolitan area significantly warmer than its surrounding rural region (the temperature difference usually is larger at night than during the day, and is most evident when winds are weak) and it is widely acknowledged that this phenomenon is due to the impact of the alterations of land use and land cover caused by human activities. In particular, the Climate Engine Application [3], powered by GEE, was used to compute the annual mean of the Land Surface Temperature (LST) from Landsat Top of Atmosphere Reflectance Data for every year of the temporal range comprised between the 1992 and the 2011 on a Region Of Interest (ROI) corresponding to the PMA. The USGS National Land Cover Database (NCD) was directly retrieved from GEE for the same temporal range and ROI. At a first stage, a pixel-wise analysis was performed through dedicated scripts developed in python; for every pixel of the ROI, the parameters of a simple linear model describing the LST trend as a function of time were robustly estimated. Overall, a positive trend for LST was retrieved, but with rates variable with locations. Therefore, a spatial analysis was developed to cluster the pixels with similar rates, in order to highlight areas with homogeneous behaviors and to investigate their relationship with the most significant urban expansion areas. The obtained results allow to provide possible predictions for future trends, thus giving valuable indications to address the urban planning of the city. [1] Google Earth Engine Team. Google Earth Engine: A Planetary-scale Geospatial Analysis Platform, (2015), https://earthengine-google.com. [2] Lee, T-W., J. Y. Lee, and Zhi-Hua Wang. "Scaling of the urban heat island intensity using time-dependent energy balance." Urban Climate. Volume 2 (2012), 16-24. [3] Huntington, J. L., Hegewisch, K. C., Daudert, B., Morton, C. G., Abatzoglou, J. T., McEvoy, D. J., and Erickson, T. "Climate Engine: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding." Bulletin of the American Meteorological Society, (2017).

About 2.8 billion people in developing countries dependent on biomass fuels (e.g. fuel wood, charcoal and animal dung) to meet their energy needs for cooking food (IEA, 2010). Fuel wood plays a vital role in ensuring the food security of millions of people and its consumption must be better understood in order to address resource shortages and forest decline (MacDonald, Adamowicz and Luckert, 2001). However, the supply and use of fuel wood are often embedded in complex systems that include external factors of a non-forestry nature, which influence the capacity to provide forestry-based solutions (FAO 1983). Natural resources including fuel wood must therefore be carefully managed and monitored to meet current demands and ensure sustainability (Warner, 2000). Tree and shrub cover are often the primary sources of deadwood used as fuel wood, however fuel wood can also be acquired through pruning. In the case of dense forest with high canopy cover, a coarser resolution may provide reliable estimates like Landsat satellite images, while in areas with open woodland high resolution images are the best reliable option. Detailed study of the degradation of tree and shrub cover and area change requires a minimum spatial resolution of 1.5 m (FAO, 2016). The current study focuses on woody biomass assessment around two communities Auno and Dusuman communities, in the Borno state of Nigeria, where refugees are hosted by local communities and the primary source of fuelwood is woody biomass in the surroundings. Nigeria’s landscape consist on Sambisa forest where sparse vegetation is the norm. The forest consists of a mixture of open woodland and sections of very dense vegetation of short trees about two meters high and thorny bushes. Multiple factors hinder detailed field inventory measurements in the area, among these factors the most important being security issues. In this scenario, the use of high resolution remote sensing images along with field data can provide detailed analysis of change in resources and can lead to the development of plans for sustainable use of existing resource to guarantee food security. References FAO. 1983. Wood fuel surveys: Forestry for local community development programme. Rome (available at www.fao.org/docrep/Q1085e/Q1085e00.htm). FAO.2016. Assessing wood fuel supply and demand in displacement settings, A technical handbook. Rome (available at http://www.fao.org/3/a-i5762e.pdf). IEA. 2010. World Energy Outlook. Paris, International Energy Agency (available at www.worldenergyoutlook.org/media/weo2010.pdf). MacDonald, D., Adamowicz, W. & Luckert, M. 2001. Fuelwood collection in North-Eastern Zimbabwe: valuation and caloric expenditures. J Forest Econ, 7: 29-52 (available at www.cabdirect.org/abstracts/20013019924.html;jsessionid=C10ADF3785ECEFAFC4AF8CAEF619E5D2?freeview=true) Warner, K. 2000. Forestry and sustainable livelihoods. Unasylva, 51: 3-12 (available at www.fao.org/docrep/x7273e/x7273e02.htm#P0_0).

CitySmart is a suite of open source tools to benefit city operations, such as management of urban infrastructure (utilities, traffic, services, etc.) for more efficient operations and with ultimate consideration for increasing sustainability and quality of urban life. The framework is architected to continually add and advance functionalities serving urban management. Almost every city needs the same data management ^tools^ as every other city. If cities are able to share their solutions with each other, this would multiply their investment by the number of cities participating, massively increasing Earth’s collective productivity for planet livability. This CitySmart application provides the basis for that enterprise.

Copernicus, the most ambitious earth observation programme to date, is now possible to be exploited on Google Earth Engine - an open cloud computing platform which is boosting (even more) the usage of Sentinel data. This comprises a very interesting tool for tackling global and regional issues with short and intuitive scripts since all Sentinel-2 data and Sentinel-2 GRD scenes are among a growing set of free datasets on Earth Engine.