From crowdsourcing environmental measurements to their integration in the GEOSS portal

2020 ◽  
Author(s):  
Valantis Tsiakos ◽  
Maria Krommyda ◽  
Athanasia Tsertou ◽  
Angelos Amditis
Keyword(s):  
Environmental Monitoring ◽  
Citizen Science ◽  
Low Cost ◽  
Earth Observation ◽  
Observation System ◽  
Observation Data ◽  
The European Union ◽  
Science Data ◽  
Portable Sensors ◽  
Areas Of Interest

<p>Environmental monitoring is based on time-series of data collected over long periods of time from expensive and hard to maintain in-situ sensors available only in specific areas. Due to the climate change it is important to monitor extended areas of interest. This need has raised the question of whether such monitoring can be complemented or replaced by Citizen Science.</p><p>Crowdsourced measurements from low-cost and easy to use portable sensors and devices can facilitate the collection of the needed information with the support of volunteers, enabling the monitoring of environmental ecosystems and extended areas of interest. In particular, during the last years there has been a rapid increase of citizen-generated knowledge that has been facilitated by the wider use of mobile devices and low-cost portable sensors. To enable their easy integration to existing models and systems as well as their utilisation in the context of new applications, citizen science data should be easily discoverable, re-usable, accessible and available for future use.</p><p>The Global Earth Observation System of Systems (GEOSS) offers a single access point to Earth Observation data (GEOSS Portal), connecting users to various environmental monitoring systems around the world while promoting the use of common technical standards to support their utilisation. </p><p>Such a connection was demonstrated in the context of SCENT project. SCENT is a EU project which has implemented an integrated toolbox of smart collaborative and innovating technologies that allows volunteers to collect environmental measurements as part of their everyday activities.</p><p>These measurements may include images that include information about the land cover and land use of the area, air temperature and soil moisture measurements from low-cost portable environmental sensors or river measurements, water level and water velocity extracted from multimedia, images and video, through dedicated tools.</p><p>The collected measurements are provided to policy makers and scientists to facilitate the decision making regarding needed actions and infrastructure improvements as well as the monitoring of environmental phenomena like floods through the crowdsourced information.</p><p>In order to ensure that the provided measurements are of high quality, a dedicated control mechanism has been implemented that uses a custom mechanism, based on spatial and temporal clustering, to identify biased or low quality contributions and remove them from the system.</p><p>Finally, recognising the importance of making the collected data available all the validated measurements are modelled, stored and provisioned using the Open Geospatial Consortium (OGC) standards Web Feature Service (WFS) and Web Map Service (WMS) as applicable.</p><p>This allows the spatial and temporal discovery of information among the collected measurements, encourages their re-usability and allows their integration to systems and platforms utilizing the same standards. The data collected by the SCENT Campaigns organized at the Kifisos river basin and the Danube Delta can be found at the GEOSS portal under the WFS here https://www.geoportal.org/?f:sources=wfsscentID and under the WMS here https://www.geoportal.org/?f:sources=wmsSCENTID.  </p><p>This activity is showcased as part of WeObserve project that has received funding from the European Union’ s Horizon 2020 research and innovation programme under grant agreement No 776740.</p>

scholarly journals Pull vs. Push: How OmniEarth Delivers Better Earth Observation Information to Subscribers

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1083-1087
Author(s):  
C. Fish ◽  
S. Slagowski ◽  
L. Dyrud ◽  
J. Fentzke ◽  
B. Hargis ◽  
...  
Keyword(s):  
Low Cost ◽  
Contextual Information ◽  
Earth Observation ◽  
Observation Data ◽  
Science Data ◽  
Bottom Line ◽  
Commercial Use ◽  
Mobile Interfaces ◽  
Observation Systems ◽  
Earth Observation Data

Until very recently, the commercialization of Earth observation systems has largely occurred in two ways: either through the detuning of government satellites or the repurposing of NASA (or other science) data for commercial use. However, the convergence of cloud computing and low-cost satellites is enabling Earth observation companies to tailor observation data to specific markets. Now, underserved constituencies, such as agriculture and energy, can tap into Earth observation data that is provided at a cadence, resolution and cost that can have a real impact to their bottom line. To connect with these markets, OmniEarth fuses data from a variety of sources, synthesizes it into useful and valuable business information, and delivers it to customers via web or mobile interfaces. The “secret sauce” is no longer about having the highest resolution imagery, but rather it is about using that imagery – in conjunction with a number of other sources – to solve complex problems that require timely and contextual information about our dynamic and changing planet. OmniEarth improves subscribers’ ability to visualize the world around them by enhancing their ability to see, analyze, and react to change in real time through a solutions-as-a-service platform.

scholarly journals A Sentinel-2 Dataset for Uganda

Data ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 35
Author(s):  
Jonas Ardö
Keyword(s):  
Environmental Monitoring ◽  
Input Data ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Observation Data ◽  
Output Data ◽  
Radiometric Correction ◽  
Clear Sky ◽  
Earth Observation Data ◽  
Sentinel 2

Earth observation data provide useful information for the monitoring and management of vegetation- and land-related resources. The Framework for Operational Radiometric Correction for Environmental monitoring (FORCE) was used to download, process and composite Sentinel-2 data from 2018–2020 for Uganda. Over 16,500 Sentinel-2 data granules were downloaded and processed from top of the atmosphere reflectance to bottom of the atmosphere reflectance and higher-level products, totalling > 9 TB of input data. The output data include the number of clear sky observations per year, the best available pixel composite per year and vegetation indices (mean of EVI and NDVI) per quarter. The study intention was to provide analysis-ready data for all of Uganda from Sentinel-2 at 10 m spatial resolution, allowing users to bypass some basic processing and, hence, facilitate environmental monitoring.

scholarly journals Copernicus: the European Earth Observation programme

2020 ◽  
Author(s):  
S. Jutz ◽  
M.P. Milagro-Pérez
Keyword(s):  
Numerical Models ◽  
Earth Observation ◽  
Environmental Data ◽  
Observation Data ◽  
The European Union ◽  
Global Environmental ◽  
In Situ Sensors ◽  
Earth Observation Data ◽  
Observation Programme

<span>The European Union-led Copernicus programme, born with the aim of developing space-based global environmental monitoring services to ensure a European autonomous capacity for Earth Observation, comprises a Space Component, Core Services, and In-situ measurements. The Space Component, coordinated by ESA, has seven Sentinel satellites in orbit, with further missions planned, and is complemented by contributing missions, in-situ sensors and numerical models, and delivers many terabytes of accurate climate and environmental data, free and open, every day to hundreds of thousands of users. This makes Copernicus the biggest provider of Earth Observation data in the world.</span>

Copernicus Program of the European Union for Environmental Monitoring and Civil Security

2017 ◽  
Vol 11 (1) ◽  
pp. 53-55
Author(s):  
Vasile Loghin
Keyword(s):  
European Union ◽  
Environmental Monitoring ◽  
International Organizations ◽  
Scientific Community ◽  
Earth Observation ◽  
Public Institutions ◽  
The European Union ◽  
Network Measurements ◽  
Operational Program ◽  
Earth Observation Satellites

Abstract Copernicus is an operational program of the European Union for environmental monitoring and civil security. It provides services for collecting, processing and distributing data from Earth observation satellites and network measurements (records) onsite. Copernicus services covers six thematic areas: atmosphere, climate, oceans and seas, the continental emergencies and civil security. Information can be accessed free of charge on specific websites (www.copernicus.eu) by public institutions / national and international organizations, the scientific community, to be used in developing appropriate policies on the environment and civil security.

scholarly journals Advancing in Citizen Science Interoperability by testing standard components between Citizen Observatories

2020 ◽  
Author(s):  
Joan Masó ◽  
Ester Prat ◽  
Andy Cobley ◽  
Andreas Matheus ◽  
Núria Julià ◽  
...  
Keyword(s):  
Citizen Science ◽  
Assessment Tool ◽  
European Space Agency ◽  
Lessons Learned ◽  
Quality Data ◽  
Observation System ◽  
Second Phase ◽  
International Institute ◽  
Science Data ◽  
The Eu

&lt;p&gt;The first phase of the citizen science Interoperability Experiment organized by the Interoperability Community of Practice in the EU H2020 WeObserve project under the Open Geospatial Consortium (OGC) innovation program and supported by the four H2020 Citizen Observatories projects (SCENT, GROW, LandSense &amp; GroundTruth 2.0) as well as the EU H2020 NEXTGEOSS project has finalized with the release of an Engineering Report in the OGC website. The activity, initiated by&amp;#160;the European Space Agency (ESA), EC Joint Research Center (JRC), the Wilson Center, International Institute for Applied Systems Analysis (IIASA) and CREAF wanted to&amp;#160;covered aspects of data sharing architectures for citizen science data, data quality, data definitions and user authentication.&amp;#160;&lt;/p&gt;&lt;p&gt;The final aim is to propose solutions for Citizen Science data to be integrated in the Global Earth Observation System of Systems (GEOSS).&amp;#160;The solution is necessarily a combination of technical and networking components, being the first ones the focus of this work. The applications of international geospatial standards in current citizen science and citizen observatory projects to improve interoperability and foster innovation is one of the main tasks in during the experiment to achieve the final aim.&lt;/p&gt;&lt;p&gt;The main result was to demonstrate that OGC Sensor Observing Service (SOS) standard can be used for citizen science data (as already proposed in the OGC SWE4CS discussion paper) by implementing it in servers that were combined by visualization clients showing Citizen Science observations from different projects together. The adoption of SOS opened new opportunities for creating interoperable components such as a quality assessment tool. In parallel, an authentication server was used to federate three project observers in a single community. Lessons learned will be used to define an architecture for the H2020 COS4Cloud project. The second phase&amp;#160;of the Interoperability Experiment has already started and developments and tests will be conducted by participants in the next 9 months.&amp;#160;Some open issues identified and document in the Engineering Report will be addressed in the second phase of the experiment, including the use of a Definitions Server and the adoption of the OGC SensorThings API as an alternative to SOS.&amp;#160;The second phase&amp;#160;will finalize in September 2020 with a presentation in the Munich OGC Technical Committee meeting. The call for participation and additional contributions will remain for the whole duration of the activity&lt;/p&gt;

Ensuring Scientific Reproducibility within the Earth Observation Community: Standardized Algorithm Documentation for Improved Scientific Data Understanding

2020 ◽  
Author(s):  
Aaron Kaulfus ◽  
Kaylin Bugbee ◽  
Alyssa Harris ◽  
Rahul Ramachandran ◽  
Sean Harkins ◽  
...  
Keyword(s):  
Earth Science ◽  
Scientific Data ◽  
Earth Observation ◽  
Data Reuse ◽  
Observation Data ◽  
Science Data ◽  
Dynamic Documents ◽  
Common Information ◽  
Earth Observation Data ◽  
Earth Science Data

&lt;p&gt;Algorithm Theoretical Basis Documents (ATBDs) accompany Earth observation data generated from algorithms. ATBDs describe the physical theory, mathematical procedures and assumptions made for the algorithms that convert radiances received by remote sensing instruments into geophysical quantities. While ATBDs are critical to scientific reproducibility and data reuse, there have been technical, social and informational issues surrounding the creation and maintenance of these key documents. A standard ATBD structure has been lacking, resulting in inconsistent documents of varying levels of detail. Due to the lack of a minimum set of requirements, there has been very little formal guidance on the ATBD publication process.&amp;#160; Additionally, ATBDs have typically been provided as static documents that are not machine readable, making search and discovery of the documents and the content within the documents difficult for users. To address the challenges surrounding ATBDs, NASA has prototyped the Algorithm Publication Tool (APT), a centralized cloud-based publication tool that standardizes the ATBD content model and streamlines the ATBD authoring process. This presentation will describe our approach in developing a common information model for ATBDs and our efforts to provide ATBDs as dynamic documents that are available for both human and machine utilization. We will also include our vision for APT within the broader NASA Earth science data system and how this tool may assist in standardizes and easing the ATBD creation and maintenance process.&lt;/p&gt;

scholarly journals Effective, Low-cost Methods of Applying Computer Vision to Public Earth Observation Data

2020 ◽  
Author(s):  
Michael Evans ◽  
Taylor Minich
Keyword(s):  
Wildlife Conservation ◽  
Low Cost ◽  
Ground Truth ◽  
Google Earth ◽  
Earth Observation ◽  
Learning Technologies ◽  
Observation Data ◽  
Solar Arrays ◽  
Observation Systems ◽  
Earth Observation Data

We have an unprecedented ability to analyze and map the Earth&rsquo;s surface, as deep learning technologies are applied to an abundance of Earth observation systems collecting images of the planet daily. In order to realize the potential of these data to improve conservation outcomes, simple, free, and effective methods are needed to enable a wide variety of stakeholders to derive actionable insights from these tools. In this paper we demonstrate simple methods and workflows using free, open computing resources to train well-studied convolutional neural networks and use these to delineate objects of interest in publicly available Earth observation images. With limited training datasets (&lt;1000 observations), we used Google Earth Engine and Tensorflow to process Sentinel-2 and National Agricultural Imaging Program data, and use these to train U-Net and DeepLab models that delineate ground mounted solar arrays and parking lots in satellite imagery. The trained models achieved 81.5% intersection over union between predictions and ground-truth observations in validation images. These images were generated at different times and from different places from those upon which they were trained, indicating the ability of models to generalize outside of data on which they were trained. The two case studies we present illustrate how these methods can be used to inform and improve the development of renewable energy in a manner that is consistent with wildlife conservation.

scholarly journals Design and Development of Interoperable Cloud Sensor Services to Support Citizen Science Projects

2020 ◽  
Author(s):  
Henning Bredel ◽  
SImon Jirka ◽  
Joan Masó Pau ◽  
Jaume Piera
Keyword(s):  
Citizen Science ◽  
Low Cost ◽  
Flow Analysis ◽  
Open Data ◽  
Building Blocks ◽  
It Infrastructure ◽  
Science Data ◽  
Area Of Interest ◽  
Observatory Data ◽  
Set Up

&lt;p&gt;&lt;span&gt;Citizen Observatories are becoming a more and more popular source of input data in many scientific domains. This includes for example research on biodiversity (e.g. counts of specific species in an area of interest), air quality monitoring (e.g. low-cost sensor boxes), or traffic flow analysis (e.g. apps collecting floating car data).&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;For the collection of such data, different approaches exist. Besides frameworks providing re-usable software building blocks (e.g. wq framework, Open Data Kit), many projects rely on custom developments. However, these solutions are mainly focused on providing the necessary software components. Further work is necessary to set-up the necessary IT infrastructure. In addition, aspects such as interoperability are usually less considered which often leads to the creation of isolated information silos.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In our presentation, we will introduce selected activities of the European H2020 project COS4CLOUD (Co-designed citizen observatories for the EOS-Cloud). Among other objectives, COS4CLOUD aims at providing re-usable services for setting up Citizen Observatories based on the European Open Science (EOS) Cloud. We will especially discuss how it will make use of interoperability standards such as the Sensor Observation Service (SOS), SensorThings API as well as Observations and Measurements (O&amp;M) of the Open Geospatial Consortium (OGC).&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;As a result, COS4CLOUD will not only facilitate the collection of Citizen Observatory data by reducing the work necessary to set-up a corresponding IT infrastructure. It will also support the exchange and integration of Citizen Observatory data between different projects as well as the integration with other authoritative data sources. This shall increase the sustainability of data collection efforts as Citizen Science data may be used as input for many data analysis processes beyond the project that originally collected the data.&lt;/span&gt;&lt;/p&gt;

scholarly journals Experiences from the Danish Fungal Atlas: Linking mushrooming, nature conservation and primary biodiversity research 

2021 ◽  
Vol 5 ◽  
Author(s):  
Jacob Heilmann-Clausen ◽  
Tobias Frøslev ◽  
Jens Petersen ◽  
Thomas Læssøe ◽  
Thomas Jeppesen
Keyword(s):  
Nature Conservation ◽  
Dna Sequencing ◽  
Citizen Science ◽  
High Throughput Sequencing ◽  
Low Cost ◽  
Mobile App ◽  
Science Data ◽  
Global Biodiversity Information Facility ◽  
Validation Data ◽  
Species Discovery

The Danish Fungal Atlas is a citizen science project launched in 2009 in collaboration among the University of Copenhagen, Mycokey and the Danish Mycological Society. The associated database now holds almost 1 million fungal records, contributed by more than 3000 recorders. The records represent more than 8000 fungal species, of which several hundred have been recorded as new to Denmark during the project. In addition several species have been described as new to science. Data are syncronized with the Global Biodiversity Information Facility (GBIF) on a weekly basis, and is hence freely available for research and nature conservation. Data have been used for systematic conservation planning in Denmark, and several research papers have used data to explore subjects such as host selection in wood-inhabiting fungi (Heilmann‐Clausen et al. 2016), recording bias in citizen science (Geldmann et al. 2016), fungal traits (Krah et al. 2019), biodiversity patterns (e.g. Andrew et al. 2018), and species discovery (Heilmann-Clausen et al. 2019). The project database is designed to faciliate direct interactions and communication among volunteers. The validation of submitted records is interactive and combines species-specific smart filters, user credibility, and expert tools to secure the highest possible data credibility. In 2019, an AI (artificial intelligence) trained species identification tool was launched along with a new mobile app, enabling users to identify and record species directly in the field (Sulc et al. 2020). At the same time, DNA sequencing was tested as an option to test difficult identifications, and in 2021 a high-throughput sequencing facility was developed to allow DNA sequencing of hundreds of fungal collections at a low cost. The presentation will give details on data validation, data use and how we have worked with cultivation of volunteers to provide a truly coherent model for collaboration on mushroom citizen science.

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