Partitioning the ocean using dense time series of Earth Observation data. Regions and natural boundaries in the Western Iberian Peninsula

2019 ◽  
Vol 103 ◽  
pp. 9-21
Author(s):  
V.M. Mantas ◽  
A.J.S.C. Pereira ◽  
J.C. Marques
Author(s):  
E.-M. Fuchs ◽  
E. Stein ◽  
G. Strunz ◽  
C. Strobl ◽  
C. Frey

This paper introduces fire monitoring works of two different projects, namely TIMELINE (TIMe Series Processing of Medium Resolution Earth Observation Data assessing Long –Term Dynamics In our Natural Environment) and PHAROS (Project on a Multi-Hazard Open Platform for Satellite Based Downstream Services). It describes the evolution from algorithm development from in applied research to the implementation in user driven applications and systems. Concerning TIMELINE, the focus of the work lies on hot spot detection. A detailed description of the choice of a suitable algorithm (round robin approach) will be given. Moreover, strengths and weaknesses of the AVHRR sensor for hot spot detection, a literature review, the study areas and the selected approach will be highlighted. The evaluation showed that the contextual algorithm performed best, and will therefore be used for final implementation. Concerning the PHAROS project, the key aspect is on the use of satellite-based information to provide valuable support to all phases of disaster management. The project focuses on developing a pre-operational sustainable service platform that integrates space-based EO (Earth Observation), terrestrial sensors and communication and navigation assets to enhance the availability of services and products following a multi-hazard approach.


Author(s):  
P. Rufin ◽  
A. Rabe ◽  
L. Nill ◽  
P. Hostert

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/).


Author(s):  
Rasmus Fensholt ◽  
Stephanie Horion ◽  
Torbern Tagesson ◽  
Andrea Ehammer ◽  
Kenneth Grogan ◽  
...  

2021 ◽  
Author(s):  
Anna Iglseder ◽  
Markus Immitzer ◽  
Christoph Bauerhansl ◽  
Hannes Hoffert-Hösl ◽  
Klaus Kramer ◽  
...  

<p><span><span>At the end of the 1980s the Municipal Department for Environmental Protection of Vienna - MA 22 initiated a detailed biotope mapping on the basis of the Viennese nature conservation law. Approximately 40 % of Vienna’s city area were covered, however only 2 % of the densely populated areas. This biotope mapping was the basis for the biotope types mapping (2005-2011) and of </span></span><span><span>the</span></span><span><span> green areas monitoring (2005). An update of these surveys has been planned in order to meet the various requirements of urban nature conservation and the national and international, respectively, legal monitoring and reporting obligations.</span></span></p><p><span><span>Since the 1970s the municipality of Vienna has built up a comprehensive database and uses state-of-the-art methods for collecting geodata carrying out services for surveying, airborne imaging and laser-scanning. Currently systems for mobile mapping, oblique aerial photos and a surveying flight with a single photon LiDAR system are being implemented or prepared. Because of the numerous high-resolution data available within the municipality and limitations mainly in spatial resolution of satellite data, the City of Vienna saw no need or benefit in integrating satellite images until now.</span></span></p><p><span><span>However, satellite data are now available within the European Copernicus program, which have considerable potential for monitoring green spaces and biotope types due to their high temporal resolution and the large number of spectral channels and SAR data. For the first time, the Sentinel-1 mission offers a combination of high spatial resolution in Interferometric Wide Swath (IW) recording mode and high temporal coverage of up to four shots every 12 days in cross-polarization in the C-band. The Sentinel-2 satellites deliver multispectral data in 10 channels every 5 days with spatial resolutions of 10 or 20 m.</span></span></p><p><span><span>Within the SeMoNa22 project, various indicators are derived for the Vienna urban area (2015-2020) and used for object-oriented mapping and classification of biotope types and characterization of the green space:</span></span></p><ul><li> <p><span><span>Sentinel-1 data (→ time series on the annual cycles in the backscattering properties of the vegetation, phenology),</span></span></p> </li> <li> <p><span><span>Sentinel-2 data (→ multispectral time series via parameters for habitat classification / vegetation indices),</span></span></p> </li> <li> <p><span><span>High-resolution earth observation data (airborne laser scanning (ALS), image matching, orthophoto → various parameter describing the horizontal and vertical vegetation structure).</span></span></p> </li> </ul><p><span><span>The main goals of SeMoNa22 is to explore efficient and effective ways of knowing if, how and to what extent the data collected can form the basis and become an integrative part of urban conservation monitoring. For this purpose, combinations of different earth observation data (satellite- and aircraft- supported or terrestrial sensors) and existing structured fieldwork data collections (species mapping, soil parameters, meteorology) are examined by means of pixel- and object-oriented methods of remote sensing and image processing. The study is done for several test sites in Vienna covering different ecosystems. In this contribution the ongoing SeMoNa22 project will be presented and first results will be shown and discussed.</span></span></p>


2021 ◽  
Vol 13 (13) ◽  
pp. 2428
Author(s):  
Rolf Simoes ◽  
Gilberto Camara ◽  
Gilberto Queiroz ◽  
Felipe Souza ◽  
Pedro R. Andrade ◽  
...  

The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the world’s fast moving agricultural frontiers for the year 2018.


2021 ◽  
Author(s):  
Bramha Dutt Vishwakarma ◽  
Yann Ziegler ◽  
Sam Royston ◽  
Jonathan L. Bamber

<p>Geophysical inversions are usually solved with the help of a-priori constraints and several assumptions that simplify the physics of the problem. This is true for all the inversion approaches that estimate GIA signal from contemporary datasets such as GNSS vertical land motion (VLM) time-series and GRACE geopotential time-series. One of the assumptions in these GIA inversions is that the change in VLM due to GIA can be written in terms of surface mass change and average mantle density. Furthermore, the surface density change is obtained from GRACE data using the relations derived in Wahr et al., 1998, which actually is only applicable for surface processes (such as hydrology) and not for sub-surface processes such as GIA. This leaves us with a tricky signal-separation problem. Although many studies try to overcome this by constraining the inversion with the help of constrains from a priori GIA models, the output is not free from influence of GIA models that are known to have huge uncertainties. In this presentation, we discuss this problem in detail, then provide a novel mathematical framework that solves for GIA without any a priori GIA model. We validate our method in a synthetic environment first and then estimate a completely data-driven GIA field from contemporary Earth-observation data.</p>


Sign in / Sign up

Export Citation Format

Share Document