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# <strong>Name</strong> <strong>Surname</strong> <strong>Company</strong> <strong>Abstract</strong> <strong>Title</strong> <strong>Topic</strong> <strong>ID</strong><br />
1 1 T. Haeusler GAF<br />
2 2 M. Schardt<br />
JOANNEUM<br />
RESEARCH<br />
3 3 J. Hill University of Trier<br />
4 4 FM Seifert ESA<br />
5 5 H. Poilve<br />
6 6 I. Jonckheere FAO<br />
7 7 M. Molinier<br />
8 8 I. Ozdemir<br />
9 9 A. Rappl<br />
10 10 A. Elatawneh<br />
Astrium GEO-<br />
Information Services<br />
VTT Technical<br />
Research Centre of<br />
Finland<br />
Suleyman Demirel<br />
University, Forestry<br />
Faculty<br />
Bavarian State Institute<br />
of Forestry (LWF)<br />
Technische Universität<br />
München<br />
11 11 F. Fassnacht University of Freiburg<br />
12 12 G. Galidaki<br />
Aristotle University of<br />
Thessaloniki<br />
The Operationalisation of GMES Forest Monitoring Services- Preparation for Sentinel 2<br />
Applications -<br />
EUFODOS - European Forest Downstream Services, Improved Information on Forest<br />
Structure and Damage<br />
Perspectives of integrating Multitemporal and Multisensor Remote Sensing Data for<br />
providing Improved Forest Inventory Parameter<br />
Systematic Observations of our Forests - Achievements and Strategy of CEOS to<br />
support GEO FCT and GFOI<br />
Forestry<br />
Forestry<br />
Forestry<br />
Forestry<br />
Use of Sentinel-2 Data for Global Forest Monitoring and the REDD Programme Forestry<br />
The Role of Satellite Data for the National Forest Monitoring Systems in the Context of<br />
REDD+<br />
Combining Cellular Phone Images and Wall-to-Wall Satellite Imagery in Forest<br />
Management Planning<br />
Forestry<br />
Forestry<br />
Mapping Forest Stand Structural Diversity Using RapidEye Satellite Data Forestry<br />
Reduction of Forest Inventory Plots by applying RapidEye Data Stratification Forestry<br />
A Comparison between monotemporal Hyperspectral and multiseasonal Multispectral<br />
Image Analysis for Forest Parameters Extraction<br />
A New Type of Vegetation Index for Forest Damage Detection from Imaging<br />
Spectroscopy Data<br />
Forestry<br />
Forestry<br />
Forest Species Differentiation and Mapping using EO-1 Hyperion Imagery Forestry<br />
13 13 M. Dees University of Freiburg Fast Mapping of large Scale biotic and Weather caused Forest Damages Forestry<br />
14 14 A. K. Gjertsen<br />
Norwegian Forest and<br />
Landscape Institute<br />
A new Forest Map of Norway based on Satellite Images Forestry<br />
<strong>Name</strong> <strong>Surname</strong> <strong>Company</strong> <strong>Abstract</strong> <strong>Title</strong> <strong>Topic</strong> <strong>ID</strong><br />
15 1 M. Bachmann DLR Requirements for and potential of Sentinel-2 for Regional Applications Land Cover<br />
16 2 Tim Buchholz ESA<br />
17 3 J. Reiche Wageningen University<br />
18 4 S. Bontemps<br />
19 5 G. Kirches<br />
20 6 E. Järvenpää<br />
Université catholique<br />
de Louvain<br />
Brockmann Consult<br />
GmbH<br />
Finnish Environment<br />
Institute<br />
On the Effectiveness of Sentinel-2 Data for Land-cover Mapping: Comparison with<br />
Landsat and SPOT Imagery<br />
Concepts and processing Techniques for a global Sentinel 1- 3 Land Cover Dynamics<br />
and Change (LCDC) Product<br />
Building a validation Database for global Land Cover Products - The Land Cover Project<br />
of the ESA Climate Change Initiative<br />
Land Cover<br />
Land Cover<br />
Land Cover<br />
LandCover CCI Pre-Processing Land Cover<br />
Integration of GIS Datasets and EO Data in Land Cover Production Land Cover<br />
21 7 M. Paganini ESA Sentinel 2, an essential Tool of the International Environmental Conventions Land Cover<br />
22 8 C. Fonte<br />
23 9 J. Inglada CESBIO<br />
24 10 G Trianni JRC<br />
25 11 P. Salvador<br />
26 12 M. Törmä<br />
27 13 L. Gonçalves<br />
28 14 S. Shrestha<br />
29 15 P. Mhangara<br />
30 16 H. Reese<br />
31 17 K. Turlej<br />
32 18 F. Santaniello<br />
33 19 C. Gläßer<br />
34 20 M. Kadiri CESBIO<br />
University of Coimbra /<br />
INESC Coimbra<br />
Universidad de<br />
Valladolid<br />
Finnish Environment<br />
Institute SYKE<br />
Polytechnic Institute of<br />
Leiria, School of<br />
Technology and<br />
Management<br />
Institute of Geodesy<br />
and Cartography<br />
South African National<br />
Space Agency<br />
(SANSA)<br />
Swedish University of<br />
Agricultural Sciences<br />
(SLU)<br />
Institute of Geodesy<br />
and Cartography<br />
University of Rome<br />
Sapienza<br />
Martin Luther University<br />
Halle<br />
Establishing a consistent Training Set with available low Resolution Ancillary Data Land Cover<br />
Fusion of Sentinel-2 and Proba-V/Sentinel-3 Images for Multi-Temporal Land-Cover Map<br />
Production<br />
From Landsat to Sentinel for a global urban Mapping using the Normalized Difference<br />
Spectral Index<br />
Land Cover<br />
Land Cover<br />
Reflectance Measurement of main urban Covers in the Sentinel-2 spectral Windows Land Cover<br />
Seasonality of Land Cover Types as Basis for Improved Land Cover Classification within<br />
Pan-European Area Frame Sampling Scheme<br />
A Novel Approach for Land Cover Mapping Based on the Combination of Soft Classifiers<br />
and on Uncertainty Information<br />
A Comparison of supervised classification Techniques for Landcover Classification for<br />
the Warsaw Region using SPOT 4 Imagery<br />
Object based Land Use and Land Cover Classification using Sumbandilasat Imagery in<br />
Pretoria, South Africa.<br />
The Importance of Pixel Size and Amount of training Data for supervised Classification<br />
of alpine Vegetation<br />
Change Detection and Direction of Changes for Europe - Case Study based on<br />
KOMPSAT-2 Satellite Data<br />
Monitoring land cover changes in the Siberian region of Komi. Estimate of the<br />
vegetation trend using remote sensing<br />
Enhanced mulitscale Vegetation Mapping using phenological Data, DEM and<br />
multisource remote sensing Data<br />
Dry and wet Grasslands Characterization: use of multi-temporal spot Images over<br />
several contrasted Climatic Years.<br />
Land Cover<br />
Land Cover<br />
Land Cover<br />
Land Cover<br />
Land Cover<br />
Land Cover<br />
Land Cover<br />
Land Cover<br />
Land Cover<br />
35 21 N. Oppelt Kiel University Integration of Land Cover Data into the open Source Model SWAT Land Cover<br />
36 22 K Wester<br />
37 23 K. Stelzer<br />
Brockmann Geomatics<br />
Sweden AB<br />
Brockmann Consult<br />
GmbH<br />
38 24 A. Dehouck GEO-Transfert<br />
39 25 S. Losmanova<br />
CENIA, Czech<br />
Environmental<br />
Information Agency<br />
Operational Monitoring of the Swedish Wetlands using Satellite Data Land Cover<br />
Application of Remote Sensing Techniques in intertidal Flats - the Step to operational<br />
Applications<br />
High-Resolution Optical and Radar Data for Mapping Intertidal Flats and Coastal Salt-<br />
Marshes<br />
Limits and Perspectives of Satellite hyperspectral Data Utilization for Identification of<br />
contaminated Sites<br />
Land Cover<br />
Land Cover<br />
Land Cover
<strong>Name</strong> <strong>Surname</strong> <strong>Company</strong> <strong>Abstract</strong> <strong>Title</strong> <strong>Topic</strong> <strong>ID</strong><br />
40 1 W. Park Yonsei University<br />
41 2 C. Rogass<br />
42 3 C. Brockmann<br />
43 4 M. Huc CESBIO<br />
44 5 M. Eder DLR<br />
45 6 A Cristo<br />
Helmholtz Centre<br />
Potsdam GFZ German<br />
Research Centre for<br />
Geosciences<br />
Brockmann Consult<br />
GmbH<br />
Universidad de<br />
Extremadura<br />
Radiometric Cross-Calibration of polar orbital Sensors using Geostationary Ocean Color<br />
Imager (GOCI)<br />
Tools and Methods<br />
Striping Reduction for pushbroom Sensors - Applications to the SENTINEL-2 Mission Tools and Methods<br />
Cloud Screening and Pixel Characterisation at Level 1: IdePix Approach and Validation<br />
using PixBox<br />
Multi temporal Methods for Cloud and Shadow Detections for high resolution optical<br />
Images<br />
On-board Cloud Detection and as a Result on-board Rescheduling of the planned Tasks<br />
for optimization of the Satellite Efficiency<br />
Simulation of Sentinel-2 Data Acquisition and Fusion by using the Diffused Matrix<br />
Format.<br />
Tools and Methods<br />
Tools and Methods<br />
Tools and Methods<br />
Tools and Methods<br />
46 7 F. Thonfeld University of Bonn Simulation of Sentinel-2 images from hyperspectral airborne Data Tools and Methods<br />
47 8 A. Damm RSL<br />
48 9 P. D'Odorico RSL<br />
49 10 J Dash<br />
University of<br />
Southampton<br />
50 11 J Jackson ARGANS<br />
51 12 A. Alessandrini INRA<br />
52 13 M. Weiss INRA<br />
53 14 C. Brockmann<br />
54 15 S. Mantovani MEEO<br />
Brockmann Consult<br />
GmbH<br />
The APEX Vegetation Toolbox to assess Sentinel-2 biochemical and -physical Data<br />
Products<br />
The Dilemma of spectral Performance and Continuity:Comparability of Sentinel-2 SRFs<br />
with other Sensors for Vegetation Monitoring<br />
Evaluating the Performance of the S-2 MSI Sensor for the Estimation of biophysical<br />
Variables in Vegetation<br />
Preparation for Sentinel-2 through Landsat: Data Quality Control within the ESA <strong>ID</strong>EAS<br />
Framework<br />
DEMENAGE: A Meta-Analysis of VALERI Database for the Evaluation of retrieval<br />
Algorithms over a large Range of vegetation Types<br />
The On Line Validation Exercise (OLIVE) Tool: a CEOS/LPV Initiative for Validation of<br />
Land Products<br />
Calvalus - Supporting agile Algorithm Development and large scale Data Processing for<br />
Sentinel 2 and other EO Missions<br />
The Multi-sensor Evolution Analysis (MEA) System for Multi-temporal / Multi-field Data<br />
Exploitation<br />
Tools and Methods<br />
Tools and Methods<br />
Tools and Methods<br />
Tools and Methods<br />
Tools and Methods<br />
Tools and Methods<br />
Tools and Methods<br />
Tools and Methods<br />
55 16 R. Ruiloba C-S Using exogenous Data for image Analysis in the Orfeo ToolBox Framework Tools and Methods<br />
56 17 R. Ruiloba C-S<br />
The Orfeo Toolbox, an open source software for the development of the Venus ground<br />
segment L2 and L3 processors.<br />
Tools and Methods<br />
57 18 M. Grizonnet CNES Potential of Orfeo ToolBox for the Sentinel 2 Preparatory Program Tools and Methods<br />
58 19 S Cherchali CNES The National Land Thematic Centre and its Links with S2 Data Tools and Methods<br />
59 20 C. Tinel CNES<br />
60 21 G. Dedieu CESBIO<br />
61 22 O. Hagolle CESBIO<br />
The Kalideos Databases: a Framework for the scientific Analysis and Exploitation of<br />
Sentinel-2 Images<br />
The Potential of long term territorial Experiments and Living lab Approach to develop<br />
Research and Applications<br />
Production And Distribution Of LANDSAT Time Series To Prepare Sentinel-2<br />
Applications<br />
Tools and Methods<br />
Tools and Methods<br />
Tools and Methods<br />
62 24 C. Woodcock Boston University The Importance of Data Policy: Lessons from Landsat and Implications for Sentinel-2 Tools and Methods<br />
63 25 Z. Mitraka ESA<br />
Land Surface Emissivity Estimation from Synergistic Use of Sentinels 2 and 3 based on<br />
Spatial-Spectral Unmixing Techniques<br />
Tools and Methods
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