EO4D_ash – Earth observation data for detection, discrimination & distribution (4D) of volcanic ash

Volcanic eruptions are a natural disaster with significant impact on human activities. The unprecedented European Volcanic Ash Crisis in 2010 demonstrated the vulnerability of the infrastructure and the need for new approaches to enable stakeholders in the aviation sector to obtain fast and accurate information. Currently, there are many data sources available and cutting-edge technology to provide the means to detect and monitor high impact eruptions. However, the information from multiple data sources is not yet efficiently integrated and aviation-specific products incorporating multi-platform datasets is not in place. To this end, the integration of tailored ground-based, satellite, and model data as well as information from volcanic observatories in Europe is essential. The Pilot EO4D_ash &#8211; Earth observation data for detection, discrimination & distribution (4D) of volcanic ash &#8211; of the e-shape project aims to strengthen the Earth Observation and in-situ data exploitation and multi-source (satellite, remotely sensed, and ground-based network) data integration&#160; to derive innovation for ash discrimination and monitoring; to enhance the capability of 4D forecasting volcanic ash dispersal and to foster innovation in the decision making processes and mitigate ash related impact and hazard resilience. The overall pilot structure, tailored products, aerosol lidar profile assimilation and study cases will be presented at the conference.Acknowledgements: This work has been conducted within the framework of the H2020 e-shape (Grant Agreement n. 820852) project.

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Achieving the Full Vision of Earth Observation Data Cubes

Data ◽

10.3390/data4030094 ◽

2019 ◽

Vol 4 (3) ◽

pp. 94 ◽

Cited By ~ 6

Author(s):

Steve Kopp ◽

Peter Becker ◽

Abhijit Doshi ◽

Dawn J. Wright ◽

Kaixi Zhang ◽

...

Keyword(s):

Image Data ◽

Earth Observation ◽

Data Cube ◽

Observation Data ◽

Data Cubes ◽

Multiple Data ◽

Storage Location ◽

Science Community ◽

Earth Observation Data ◽

And Storage

Earth observation imagery have traditionally been expensive, difficult to find and access, and required specialized skills and software to transform imagery into actionable information. This has limited adoption by the broader science community. Changes in cost of imagery and changes in computing technology over the last decade have enabled a new approach for how to organize, analyze, and share Earth observation imagery, broadly referred to as a data cube. The vision and promise of image data cubes is to lower these hurdles and expand the user community by making analysis ready data readily accessible and providing modern approaches to more easily analyze and visualize the data, empowering a larger community of users to improve their knowledge of place and make better informed decisions. Image data cubes are large collections of temporal, multivariate datasets typically consisting of analysis ready multispectral Earth observation data. Several flavors and variations of data cubes have emerged. To simplify access for end users we developed a flexible approach supporting multiple data cube styles, referencing images in their existing structure and storage location, enabling fast access, visualization, and analysis from a wide variety of web and desktop applications. We provide here an overview of that approach and three case studies.

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Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art

Geocarto International ◽

10.1080/10106049.2019.1629647 ◽

2019 ◽

pp. 1-32 ◽

Cited By ~ 13

Author(s):

Prem Chandra Pandey ◽

Nikos Koutsias ◽

George P. Petropoulos ◽

Prashant K. Srivastava ◽

Eyal Ben Dor

Keyword(s):

Land Use ◽

Land Cover ◽

State Of The Art ◽

Earth Observation ◽

The State ◽

Data Sources ◽

Observation Data ◽

Land Use Land Cover ◽

Earth Observation Data

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OpenSearch API for Earth observation DataHub service

10.5194/egusphere-egu2020-21522 ◽

2020 ◽

Author(s):

Jovanka Gulicoska ◽

Koushik Panda ◽

Hervé Caumont

Keyword(s):

User Interface ◽

Development Project ◽

Earth Observation ◽

International Standards ◽

Data Sources ◽

Observation Data ◽

General Description ◽

Search Results ◽

Earth Observation Data ◽

Data Collections

OpenSearch is a de-facto standard specification and a collection of technologies that allow publishing of search results in a format suitable for syndication and aggregation. It is a way for websites and search engines to publish search results in a standard and accessible format.Evolved through extensions within an international standards organisation, the Open Geospatial Consortium, OpenSearch has become a reference to make queries to a repository that contains Earth Observation information, to send and receive structured, standardized search requests and results, and to allow syndication of repositories. It is in this evolved form a shared API used by many applications, tools, portals and sites in the Earth sciences community. The OGC OpenSearch extensions that have been implemented for the NextGEOSS DataHub, following the OGC standards and validated to be fully compatible with the standard.The OGC OpenSearch extensions implemented for CKAN, the open source software solution supporting the NextGEOSS Datahub, add the standardized metadata models and the OpenSearch API endpoints that allow the indexing of distributed EO data sources (currently over 110 data collections), and makes these available to client applications to perform queries and get the results. It allowed to develop a simple user interface as part of the NextGEOSS DataHub Portal, which implements the two-step search mechanism (leveraging data collections metadata and data products metadata) and translates the filtering done by users to an OpenSearch matching query. The user interface can render a general description document, that contains information about the collections available on the NextGEOSS DataHub, and then get a more detailed description document for each collection separately.For generating the structure of the description documents and the result feed, we are using CKAN&#8217;s templates, and on top of that we are using additional files which are responsible for listing all available parameters and their options and perform validation on the query before executing. The search endpoint for getting the results feed, uses already existing CKANs API calls in order to perform the validation and get the filtered results taking into consideration the parameters of the user search.The current NextGEOSS DataHub implementation therefore provides a user interface for users who are not familiar with Earth observation data collections and products, so they can easily create queries and access its results. Moreover, the NextGEOSS project partners are constantly adding additional data connectors and collecting new data sources that will become available through the OGC OpenSearch Extensions API. This will allow NextGEOSS to provide a variety of data for the users and accommodate their needs.&#160;NextGEOSS is a H2020 Research and Development Project from the European Community under grant agreement 730329.

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Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

GIS Business ◽

10.26643/gis.v12i3.5187 ◽

2019 ◽

Vol 12 (3) ◽

pp. 12-14

Author(s):

Eicher, A

Keyword(s):

Earth Observation ◽

Observation Data ◽

Business World ◽

The Earth ◽

Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

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Mapping slums and model population density using earth observation data and open source solutions

2019 Joint Urban Remote Sensing Event (JURSE) ◽

10.1109/jurse.2019.8808934 ◽

2019 ◽

Author(s):

Tais Grippa ◽

Stefanos Georganos ◽

Sabine Vanhuysse ◽

Moritz Lennert ◽

Nicholus Mboga ◽

...

Keyword(s):

Population Density ◽

Open Source ◽

Earth Observation ◽

Observation Data ◽

Model Population ◽

Earth Observation Data

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Examining the Economic and Environmental Impacts of COVID-19 Using Earth Observation Data

Remote Sensing ◽

10.3390/rs13010005 ◽

2020 ◽

Vol 13 (1) ◽

pp. 5

Author(s):

William Straka ◽

Shobha Kondragunta ◽

Zigang Wei ◽

Hai Zhang ◽

Steven D. Miller ◽

...

Keyword(s):

Air Quality ◽

Los Angeles ◽

Environmental Impacts ◽

Demographic Data ◽

The United States ◽

Earth Observation ◽

Observation Data ◽

Washington Dc ◽

Mobility Data ◽

Earth Observation Data

The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.

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2nd Edition of Instrumenting Smart City Applications with Big Sensing and Earth Observatory Data: Tools, Methods and Techniques

Remote Sensing ◽

10.3390/rs13071310 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1310

Author(s):

Gabriele Bitelli ◽

Emanuele Mandanici

Keyword(s):

High Resolution ◽

Exponential Growth ◽

Smart City ◽

Earth Observation ◽

Urban Environments ◽

Observation Data ◽

High Resolution Imagery ◽

Observatory Data ◽

Methods And Techniques ◽

Earth Observation Data

The exponential growth in the volume of Earth observation data and the increasing quality and availability of high-resolution imagery are increasingly making more applications possible in urban environments [...]

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Quantifying Tree Cover Loss in Urban Forests within Nairobi City Metropolitan Area from Earth Observation Data

Environmental Sciences Proceedings ◽

10.3390/iecf2020-07952 ◽

2020 ◽

Vol 3 (1) ◽

pp. 78

Author(s):

Francis Oloo ◽

Godwin Murithi ◽

Charlynne Jepkosgei

Keyword(s):

Land Cover ◽

Metropolitan Area ◽

Land Surface ◽

Urban Forest ◽

Urban Forests ◽

Earth Observation ◽

Tree Cover ◽

Observation Data ◽

Vegetation Health ◽

Earth Observation Data

Urban forests contribute significantly to the ecological integrity of urban areas and the quality of life of urban dwellers through air quality control, energy conservation, improving urban hydrology, and regulation of land surface temperatures (LST). However, urban forests are under threat due to human activities, natural calamities, and bioinvasion continually decimating forest cover. Few studies have used fine-scaled Earth observation data to understand the dynamics of tree cover loss in urban forests and the sustainability of such forests in the face of increasing urban population. The aim of this work was to quantify the spatial and temporal changes in urban forest characteristics and to assess the potential drivers of such changes. We used data on tree cover, normalized difference vegetation index (NDVI), and land cover change to quantify tree cover loss and changes in vegetation health in urban forests within the Nairobi metropolitan area in Kenya. We also used land cover data to visualize the potential link between tree cover loss and changes in land use characteristics. From approximately 6600 hectares (ha) of forest land, 720 ha have been lost between 2000 and 2019, representing about 11% loss in 20 years. In six of the urban forests, the trend of loss was positive, indicating a continuing disturbance of urban forests around Nairobi. Conversely, there was a negative trend in the annual mean NDVI values for each of the forests, indicating a potential deterioration of the vegetation health in the forests. A preliminary, visual inspection of high-resolution imagery in sample areas of tree cover loss showed that the main drivers of loss are the conversion of forest lands to residential areas and farmlands, implementation of big infrastructure projects that pass through the forests, and extraction of timber and other resources to support urban developments. The outcome of this study reveals the value of Earth observation data in monitoring urban forest resources.

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