GENERATING BIOLOGICALLY RELEVANT ENVIRONMENTAL DATA FROM REMOTE SENSING IMAGERIES AND OCEANOGRAPHIC MODELS TO SUPPORT SPATIAL PRIORITIZATION OF MARINE BIODIVERSITY CONSERVATION AND MANAGEMENT IN INDONESIA

2019 ◽  
Vol 3 ◽  
pp. 965
Author(s):  
Safran Yusri ◽  
Vincentius P. Siregar ◽  
Suharsono Suharsono
Keyword(s):  
Environmental Variables ◽  
Google Earth ◽  
Earth Observation ◽  
Environmental Data ◽  
Marine Biodiversity ◽  
Observation Data ◽  
Distribution Models ◽  
Surface Salinity ◽  
Biologically Relevant ◽  
Earth Observation Data

Long term Earth observation data stored in Google Earth Engine (GEE) can be ingested and derived to biologically relevant environmental variables that can used as the predictors of a species niche. The aim of this research was to create a script using GEE to generate biologically meaningful environmental variables from various Earth observation data and models in Indonesia. Elevation and bathymetry raster data from GEBCO were land masked and benthic terrain modelling were done in order to get the aspect, depth, curvature, and slope. HYCOM and MODIS AQUA dataset were filtered using spatial (Indonesia and surrounding region) and temporal filter (from 2002–2017), and reduced to biologically meaningful variables, the maximum, minimum, and mean. Water speed vector (northward and eastward) data were also converted in to scalar unit. In order to fill data gaps, kriging was done using Bayesian slope. Results shows the water depth in Indonesia ranges from 0 – 6827 m, with slope ranging from 0 – 34.33°, aspect from 0 – 359.99°, and curvature from 0 – 0.94. Variables representing water energy, mean sea surface elevation ranges from 0 – 0.85 m, and mean scalar water velocity 0 – 4 m/s. Mean surface salinity ranges from 20.09 – 35.32‰. Variables representing water quality includes mean of particulate organic carbon which ranges from 25.31 – 953.47‰ and mean of clorophyll-A concentration from 0.05 – 13.63‰. These data can be used as the input for species distribution models or spatially explicit decision support systems such as Marxan for spatial planning and zonation in Marine and Coastal Zone Management Plan.

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>

Analyzing large-scale Earth Observation data repositories made simple with OpenEO Platform

2021 ◽  
Author(s):  
Edzer Pebesma ◽  
Patrick Griffiths ◽  
Christian Briese ◽  
Alexander Jacob ◽  
Anze Skerlevaj ◽  
...  
Keyword(s):  
Large Scale ◽  
Virtual Machines ◽  
Google Earth ◽  
Earth Observation ◽  
Data Cube ◽  
Observation Data ◽  
Data Repositories ◽  
Software Ecosystem ◽  
Wide Range ◽  
Earth Observation Data

&lt;p&gt;The OpenEO API allows the analysis of large amounts of Earth Observation data using a high-level abstraction of data and processes. Rather than focusing on the management of virtual machines and millions of imagery files, it allows to create jobs that take a spatio-temporal section of an image collection (such as Sentinel L2A), and treat it as a data cube. Processes iterate or aggregate over pixels, spatial areas, spectral bands, or time series, while working at arbitrary spatial resolution. This pattern, pioneered by Google Earth Engine&amp;#8482; (GEE), lets the user focus on the science rather than on data management.&lt;/p&gt;&lt;p&gt;The openEO H2020 project (2017-2020) has developed the API as well as an ecosystem of software around it, including clients (JavaScript, Python, R, QGIS, browser-based), back-ends that translate API calls into existing image analysis or GIS software or services (for Sentinel Hub, WCPS, Open Data Cube, GRASS GIS, GeoTrellis/GeoPySpark, and GEE) as well as a hub that allows querying and searching openEO providers for their capabilities and datasets. The project demonstrated this software in a number of use cases, where identical processing instructions were sent to different implementations, allowing comparison of returned results.&lt;/p&gt;&lt;p&gt;A follow-up, ESA-funded project &amp;#8220;openEO Platform&amp;#8221; realizes the API and progresses the software ecosystem into operational services and applications that are accessible to everyone, that involve federated deployment (using the clouds managed by EODC, Terrascope, CreoDIAS and EuroDataCube), that will provide payment models (&amp;#8220;pay per compute job&amp;#8221;) conceived and implemented following the user community needs and that will use the EOSC (European Open Science Cloud) marketplace for dissemination and authentication. A wide range of large-scale cases studies will demonstrate the ability of the openEO Platform to scale to large data volumes.&amp;#160; The case studies to be addressed include on-demand ARD generation for SAR and multi-spectral data, agricultural demonstrators like crop type and condition monitoring, forestry services like near real time forest damage assessment as well as canopy cover mapping, environmental hazard monitoring of floods and air pollution as well as security applications in terms of vessel detection in the mediterranean sea.&lt;/p&gt;&lt;p&gt;While the landscape of cloud-based EO platforms and services has matured and diversified over the past decade, we believe there are strong advantages for scientists and government agencies to adopt the openEO approach. Beyond the absence of vendor/platform lock-in or EULA&amp;#8217;s we mention the abilities to (i) run arbitrary user code (e.g. written in R or Python) close to the data, (ii) carry out scientific computations on an entirely open source software stack, (iii) integrate different platforms (e.g., different cloud providers offering different datasets), and (iv) help create and extend this software ecosystem. openEO uses the OpenAPI standard, aligns with modern OGC API standards, and uses the STAC (SpatioTemporal Asset Catalog) to describe image collections and image tiles.&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 GEE TIMESERIES EXPLORER FOR QGIS – INSTANT ACCESS TO PETABYTES OF EARTH OBSERVATION DATA

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 155-158
Author(s):  
P. Rufin ◽  
A. Rabe ◽  
L. Nill ◽  
P. Hostert
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Satellite Image ◽  
Google Earth ◽  
Earth Observation ◽  
Series Data ◽  
Observation Data ◽  
Local Data ◽  
Small Areas ◽  
Earth Observation Data

Abstract. Earth observation analysis workflows commonly require mass processing of time series data, with data volumes easily exceeding terabyte magnitude, even for relatively small areas of interest. Cloud processing platforms such as Google Earth Engine (GEE) leverage accessibility to satellite image archives and thus facilitate time series analysis workflows. Instant visualization of time series data and integration with local data sources is, however, currently not implemented or requires coding customized scripts or applications. Here, we present the GEE Timeseries Explorer plugin which grants instant access to GEE from within QGIS. It seamlessly integrates the QGIS user interface with a compact widget for visualizing time series from any predefined or customized GEE image collection. Users can visualize time series profiles for a given coordinate as an interactive plot or visualize images with customized band rendering within the QGIS map canvas. The plugin is available through the QGIS plugin repository and detailed documentation is available online (https://geetimeseriesexplorer.readthedocs.io/).

Bridging the gap between Big Earth data users and future (cloud-based) data systems - Towards a better understanding of user requirements of cloud-based data systems

2020 ◽  
Author(s):  
Julia Wagemann ◽  
Stephan Siemen ◽  
Jörg Bendix ◽  
Bernhard Seeger
Keyword(s):  
Data Access ◽  
Google Earth ◽  
Earth Observation ◽  
Environmental Data ◽  
User Requirements ◽  
Observation Data ◽  
Data Systems ◽  
Data Services ◽  
Study Results ◽  
The Future

&lt;p&gt;The European Commission&amp;#8217;s Earth Observation programme Copernicus produces an unprecedented amount of openly available multi-dimensional environmental data. However, data &amp;#8216;accessibility&amp;#8217; remains one of the biggest obstacles for users of open Big Earth Data and hinders full data exploitation. Data services have to evolve from pure download services to offer an easier and more on-demand data access. There are currently different concepts explored to make Big Earth Data better accessible for users, e.g. virtual research infrastructures, data cube technologies, standardised web services or cloud processing services, such as the Google Earth Engine or the Copernicus Climate Data Store Toolbox. Each offering provides different types of data, tools and functionalities. Data services are often developed solely satisfying specific user requirements and needs.&lt;/p&gt;&lt;p&gt;For this reason, we conducted a user requirements survey between November 2018 and June 2019 among users of Big Earth Data (including users of Earth Observation data, meteorological and environmental forecasts and other geospatial data) to better understand user requirements of Big Earth Data. To reach an active data user community for this survey, we partnered with ECMWF, which has 40 years of experience in providing data services for weather forecast data and environmental data sets of the Copernicus Programme.&lt;/p&gt;&lt;p&gt;We were interested in which datasets users currently use, which datasets they would like to use in the future and the reasons why they have not yet explored certain datasets. We were interested in the tools and software they use to process the data and what challenges they face in accessing and handling Big Earth Data. Another part focused on future (cloud-based) data services and there, we were interested in the users&amp;#8217; motivation to migrate their data processing tasks to cloud-based data services and asked them what aspects of these services they consider being important.&lt;/p&gt;&lt;p&gt;While preliminary results of the study were released last year, this year the final study results are presented. A specific focus will be put on users&amp;#8217; expectation of future (cloud-based) data services aligned with recommendations for data users and data providers alike to ensure the full exploitation of Big Earth Data in the future.&lt;/p&gt;

scholarly journals New SMOS SSS maps in the framework of the Earth Observation data For Science and Innovation in the Black Sea

2021 ◽  
Author(s):  
Estrella Olmedo ◽  
Verónica González-Gambau ◽  
Antonio Turiel ◽  
Cristina González-Haro ◽  
Aina García-Espriu ◽  
...  
Keyword(s):  
Black Sea ◽  
European Space Agency ◽  
Earth Observation ◽  
Sea Surface ◽  
The Black Sea ◽  
Observation Data ◽  
Surface Salinity ◽  
Level 3 ◽  
Earth Observation Data ◽  
Level 2

Abstract. In the framework of the European Space Agency (ESA) regional initiative called Earth Observation data For Science and Innovation in the Black Sea (EO4SIBS), a new dedicated Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) product is generated for the Black Sea for the years 2011–2020. Three SMOS SSS fields are retrieved and distributed: a level 2 product consisting of binned SSS in daily maps at 0.25° × 0.25° spatial resolution grid by considering ascending ((Olmedo et al., 2021b), https://doi.org/10.20350/digitalCSIC/13993) and descending ((Olmedo et al., 2021c), https://doi.org/10.20350/digitalCSIC/13995) satellite overpass directions separately; a level 3 product ((Olmedo et al., 2021d), https://doi.org/10.20350/digitalCSIC/13996) consisting of binned SSS in 9-day maps at 0.25° × 0.25° grid by combining as cending and descending satellite overpass directions; and a level 4 product ((Olmedo et al., 2021e), https://doi.org/10.20350/digitalCSIC/13997) consisting of daily maps at 0.05 × 0.0505° that are computed by merging the level 3 SSS product with Sea Surface Temperature (SST) maps. The generation of SMOS SSS fields in the Black Sea requires the use of enhanced data processing algorithms for improving the Brightness Temperatures in the region since this basin is typically strongly affected by Radio Frequency Interference (RFI) sources which hinders the retrieval of salinity. Here, we describe the algorithms introduced to improve the quality of the salinity retrieval in this basin. The validation of the EO4SIBS SMOS SSS products is performed by: i) comparing the EO4SIBS SMOS SSS products with near-to-surface salinity measurements provided by in situ measurements; ii) assessing the geophysical consistency of the products by comparing them with a model and other satellite salinity measurements; iii) computing maps of SSS errors by using Correlated Triple Collocation analysis. The accuracy of the EO4SIBS SMOS SSS products depend on the time period and on the product level. The accuracy in the period 2016–2020 is better than in 2011–2015 and it is as follows for the different products: i) Level 2 ascending: 1.85 / 1.50 psu (in 2011–2015 / 2016–2020); Level 2 descending: 2.95 1.95 psu; ii) Level 3: 0.7 / 0.5 psu; and iii) Level 4: 0.6 / 0.4 psu.

scholarly journals An Overview of Platforms for Big Earth Observation Data Management and Analysis

2020 ◽  
Vol 12 (8) ◽  
pp. 1253 ◽  
Author(s):  
Vitor Gomes ◽  
Gilberto Queiroz ◽  
Karine Ferreira
Keyword(s):  
Data Management ◽  
Data Access ◽  
Database Systems ◽  
Google Earth ◽  
Earth Observation ◽  
Data Cube ◽  
Observation Data ◽  
Novel Technologies ◽  
Community Interest ◽  
Earth Observation Data

In recent years, Earth observation (EO) satellites have generated big amounts of geospatial data that are freely available for society and researchers. This scenario brings challenges for traditional spatial data infrastructures (SDI) to properly store, process, disseminate and analyze these big data sets. To meet these demands, novel technologies have been proposed and developed, based on cloud computing and distributed systems, such as array database systems, MapReduce systems and web services to access and process big Earth observation data. Currently, these technologies have been integrated into cutting edge platforms in order to support a new generation of SDI for big Earth observation data. This paper presents an overview of seven platforms for big Earth observation data management and analysis—Google Earth Engine (GEE), Sentinel Hub, Open Data Cube (ODC), System for Earth Observation Data Access, Processing and Analysis for Land Monitoring (SEPAL), openEO, JEODPP, and pipsCloud. We also provide a comparison of these platforms according to criteria that represent capabilities of the EO community interest.

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 ◽  
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

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

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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