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How space technology can support hazard and risk mapping in Indonesia

Last November, INDRA has joined the GEP Early Adopters Programme, in order to perform EO Data Exploitation activities, with the aim to deliver feedback and improved awareness about the GEP capabilities. Performed as part of the EO4SD DRR 2 project (‘Earth Observation for Sustainable Development – Disaster Risk Reduction’), such activities on GEP correspond to a foreseen period of use at least up to 2021.


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JRC Global Surface Water dataset is available in HEP!

The European Commission’s Joint Research Centre developed the JRC Global Surface Water dataset in the framework of the Copernicus Programme. This maps the location and temporal distribution of water surfaces at the global scale over the past 32 years and provides statistics on the extent and change of those water surfaces. It can be added to Thematic Apps on Hydrology TEP.


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Discriminate Urban and Rural Settlements

One of the relevant topics in the Urban field of the study is what constitutes Rural Settlement, Urban Settlement and Metropolitan Settlements. In the application provided by TEP Urban for an initial 10 countries, you can select different thresholds for what does it mean for a settlement to be Rural, Urban or Metropolitan.

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Post processing tools made available to screen EO data also allow to make nice images using Sentinel-2

The on-demand Band Combination 8 processing service COMBI provides RGB composites from user defined bands of single or multiple data products from a broad range of EO missions. All bands are in their native format e.g. no radiometric correction is applied thus can serve only for fast screening of the data, not for further processing. We are sharing nice results using time series of Sentinel-2 images of the Copernicus programme.

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Sentinel-2 outperformed Landsat 8 in forest variable estimation

In a recent paper, published in Remote Sensing of Environment we compared the performance of Sentinel-2 and Landsat 8 satellites in the estimation of forest variables in Finland. The variables were stem volume (V), stem diameter (D), tree height (H) and basal area (G), and their species-wise components for pine (Pine), spruce (Spr) and broadleaved (BL) trees. We compared the S2 and L8 performances using twelve different test setups including different Sentinel-2 and Landsat 8 band combinations and pixel resolutions, and using two different modelling methods. They also identified the best predictive image bands for each test setup.

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Cookbook for cloud enabled applications now available

The first version of the Cookbook for cloud enabled applications has been published. It provides a series of tips and guidelines for platform and applications developers who wish to efficiently exploit the features of the cloud in the context of Earth Observation services.

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TEP services in a glance

It is now possible to see what all of the TEPs offer in a glance!

The services currently integrated by the seven Thematic Exploitation Platforms - as of January 2019 - are available in a consolidated table.

Estimating coastal bathymetry from both Sentinel-1 and Sentinel-2 using image processing and machine learning techniques

Bathymetry retrieval in coastal waters is a particularly interesting application of satellite Earth Observation. In fact, creating a bathymetry map from traditional data (shipborne sonar or airborne lidar) is both costly and time consuming, while satellite data can provide maps over large areas in a very short time. However the processing involved in bathymetry retrieval and the interpretation of the results is mastered only by a handful of experts today. This may be about to change, thanks to some recent work on the Coastal TEP.

The Coastal TEP is a cloud-based ‘one-stop shop’ that gathers coastal-zone satellite data, processing algorithms and computing power. Obviously coastal bathymetry has been one of the main topics of interest for the Coastal TEP project.

In the frame of the ESA project ECOBAW, Céline Danilo from Univ. of Trento, has implemented in the Coastal TEP an algorithm which retrieves coastal bathymetry using Sentinel-2 images. In collaboration with Prof. Farid Melgani at Univ. of Trento, this project aims to develop applications for estimating coastal bathymetry without any ancillary data (wave period or pre-information of the bathymetry) from both Sentinel-1 and Sentinel-2 using image processing and machine learning techniques.

The algorithm implemented in Coastal TEP, depends on a first estimation of coastal bathymetry with a coarse resolution and on the reflectance of the four spectral bands of the visible and near infrared domain. In the ECOBAW project, this first estimation is provided by a model based on wave propagation and described in an IEEE TGRS publication[1]. Such estimation is successively improved by means of an innovative approach using a Gaussian Process Regression (GPR) model. In particular, it uses these first estimations as target values to train a GPR model which has as input the four spectral bands. The GPR model learned is then able, given the reflectance of the four spectral bands, to estimate the water depth and a related confidence value (estimate variance).  The theoretical principle relies then on the light extinction with water depth. This approach is thus capable to improve the knowledge of the coastal water depth, especially regarding the resolution and the coverage of the training values. Besides, the algorithm can be applied to any cloudless image and is completely unsupervised and automatic.

After validation of her processor, Céline was able to integrate the processor on the Coastal. It is now available to other users as a “contributed” processor in the Coastal TEP catalogue. In this way, all users will be able to reproduce Céline’s results.

The good thing about Coastal TEP is that it provides an easy interface to integrate a processor, so algorithms developers can work autonomously with minimal support from the Coastal TEP team. Within some weeks, Céline has been able to process some Sentinel-2 images on the platform and compute a bathymetry map for the beach of Waimanalo in Hawaii.

This first success has generated many ideas for the future.

We could extend the capabilities of the current processing to be able to handle any geographical zone. Even if the model providing the first estimation is not yet available on the platform, any other kind of first estimations could be exploited as well for training the model. 

We could also provide other bathymetry retrieval algorithms and allow users to choose between different approaches. Indeed each algorithm has its benefits and drawbacks, so having a set of processors can improve the reliability of the EO-based retrieval. A benchmark of algorithms on a given site where reference in-situ measurements are available could make a lot of sense for the future. It would help provide guidelines and recommendations and help promote the use of EO-derived bathymetry maps.


[1] Céline Danilo and Farid Melgani, “Wave Period and Coastal Bathymetry Using Wave Propagation on Optical Images,” IEEE Transactions on Geoscience and Remote Sensing 54, no. 11 (2016): 6307–6319.

Space helps forest regenerate

In Finland, conifers are not only the most common native species of tree, but economically the most important. Once the trees have been felled, seedlings are planted as part of the regeneration process. Read more here

Exploitation Platforms Open Architecture released

The first version of the Exploitation Platforms Open Architecture has been released. You can find it on this website document repository (for registered users, under the "Public/Exploitation Platforms Open Architecture 1.0" folder) or download it directly from this link.

The Exploitation Platforms Open Architecture is a joint effort of all the Thematic Exploitation Platforms to produce an high-level architecture for the building of an Exploitation Platform, based on Open Source Software components and Open Interfaces. The document is released under CC BY-SA license, freely usable for commercial and non-commercial use.

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The Geohazards TEP enables the use of satellite Earth observation data to support the user community.