Sentinel-2 - A Valuable Data Source for the Romanian National ...

Sentinel-2 - A Valuable Data Source for the
Romanian National Land Cover Database
Iulia DANA, Alexandru BADEA, Cristian MOISE, Vlad OLTEANU
Romanian Space Agency
Sentinel-2 Preparatory Symposium, 23 – 27 April 2012, ESA-ESRIN, Frascati, Italy

GMES - Global Monitoring for Environment and Security
Land Marine Atmosphere
Climate change Emergency Security

(Past and) Current projects using Earth Observation data
Land monitoring
Land cover
LCCS (Land Cover Classification System)
CLC (Corine Land Cover)
MUTER (Applications and Services for Land Use Monitoring Using Geospatial
Data and Technologies)
Agriculture
MARS (Monitoring of Agriculture with Remote Sensing)
IACS-LPIS and CwRS (Integrated Administration and Control System - Land Parcel
Identification System in Romania and Control with Remote Sensing)
SIAT (Integrated System of Early Warning, Monitoring and Drought Risk Analysis)
METAGRO (Assessment of the Balance of Metals in Romanian Agro-systems )
SIRIUS (Sustainable Irrigation Water Management and River-basin Governance)

(Past and) Current projects using Earth Observation data
Land monitoring
Environment
IMAGIS (Complex system for the application of the RS techniques for
environmental quality monitoring and Romanian ICZM implementation support )
GEOLAND2 (Supporting the Monitoring, Protection and Sustainable
Management of our Environment)
Emergency response
RISCASAT (Development of New Satellite-Derived Products Adapted to Users'
Requirements for Hydro-Meteorological Risk Management)
SIGUR (Satellite Based Emergency Response Services)
SAFER (GMES Emergency Response service) – Point of Contact
UN-SPIDER RSO (United Nations Platform for Space-based Information for
Disaster Management and Emergency Response – Regional Support Office)

LCCS – Land Cover Classification System – in Romania
LCCS 2000 – Landsat ETM LCCS 2003 - Landsat TM LCCS 2007 – SPOT 5 HRG
"Land Cover / Land Use Inventory
by Remote Sensing for the Agricultural
Reform" – UN-FAO ↓
compatible with Corine Land Cover
"Hybrid Method for Thematic Update of the Land
Use Inventory by Remote Sensing / GIS Technology,
Support for the Implementation of the European
Agriculture and Environment Programmes" - PNCDI

Objectives:
LCCS – Land Cover Classification System – in Romania
production of land cover maps for the entire country (total mapped area: 238,380 sq.
km), as well as detailed land use/land cover maps for a few areas of particular interest
production of a comprehensive database included also information on soil types and
land degradation (erosion)
Input data:
Products:
Results:
Landsat ETM+ (2000), Landsat TM (2003) - update, SPOT HRG (2006-2007) - update
additional data: topographical maps, thematic maps, in-situ data, HR and VHR data
730 maps (map scale 1 : 50,000) – based on Landsat data
provided the Ministry of Agriculture with an objective and accurate database
strengthened the capacity of the national remote sensing experts on land cover/land
use mapping by satellite remote sensing and GIS technologies
Credits: LCCS07 project (ROSA, ICPA, IGAR, INCDIF-ISPIF, GEOSYSTEMS ROMANIA, TRADSYM)

LCCS – Land Cover Classification System – in Romania
Mosaic of Landsat ETM+ images acquired in 2000

LCCS – Land Cover Classification System – in Romania
Dynamics of the landscape

LCCS – Land Cover Classification System – in Romania
Dynamics of the landscape

LCCS – Land Cover Classification System – in Romania
Dynamics of the landscape

LCCS – Land Cover Classification System – in Romania
Dynamics of the landscape: 1990-2000
Land cover is (according to FAO):
one of the most important elements for description and study of the environment
the easiest detectable indicator of human interventions on the land
a critical parameter for environmental databases
changing quickly over time
the basic geographic feature that environmental applications can use as reference

LCCS – Land Cover Classification System – in Romania
Examples of LCCS maps (1)

LCCS – Land Cover Classification System – in Romania
Examples of LCCS maps (2)

LCCS – Land Cover Classification System – in Romania
Examples of LCCS maps (3)

LCCS – Land Cover Classification System – in Romania
Statistics based on the three LCCS datasets (development of mathematical models for the
study of landscape dynamics) – test area: Valea Lovistei
DATASET LCCS 2000 LCCS 2003 LCCS 2007
Count: 13730 polygons 15268 polygons 37973 polygons
Minimum: > 0,01 sq km > 0,01 sq km > 0,01 sq km
Maximum: 3520.7 sq km 3173,9 sq km 5369.6 sq km
Sum: 41906.9 sq km 41906.9 sq km 41906.9 sq km
Mean: 3 sq km 2,7 sq km 1.1 sq km
Number of classes 52 49 56
Credits: LCCS07 project (ROSA, ICPA, IGAR, INCDIF-ISPIF, GEOSYSTEMS ROMANIA, TRADSYM)

Land monitoring: SIAT
Objective: building an Integrated System of Early Warning, Monitoring and Drought Risk
Analysis for Romania.
Results: fine scale (calibrated and validated) regional climate model, algorithms for HR and
VHR data processing, numeric simulation products (last 30 years), data post-processing for
impact model validation, numeric simulation products (next 60 years), feedback simulations
for impact models on climate change, forecasts (precipitation and temperature) 32 days in
advance (with a frequency of 12 hours). Simulations were performed for 2007 (one of the
most droughty years on the past 50 years) and 2008.
Validation: satellite data, meteorological and climate data, in-situ measurements.
Input data: AVHRR (NOAA), MODIS (TERRA), MERIS (ENVISAT), SPOT VEGETATION.
Products based on MERIS data: LAI, fCover, fAPAR (Fraction of active photosynthetic
absorbed radiation) and on AATSR data: surface albedo, net radiation, land surface
temperature, evaporative fraction, actual evaporation on daily base.
Credits: SIAT project (ROSA, METEO, INCDIF-ISPIF, GEOSYSTEMS ROMANIA, USAMV)

Land monitoring: SIAT

Test area Ocnele Mari
Drought index (light tones: dry areas)
Input data: Landsat ETM+, July 2007
Land monitoring: SIAT
© Google Earth
Credits: Doctoral thesis : "Vegetation Monitoring Using Multi-temporal Radar and Optical Satellite Imagery in the Context of Land Degradation
in Ocnele Mari", Violeta Domnica Poenaru (ROSA)

Land monitoring: METAGRO
Investigated methods: principal component analysis, analysis based on the normalized
differential vegetation index (NDVI), multi-temporal analysis of the spectral signature in the
near infrared band together with histogram interactive stretching for image enhancement,
unsupervised classification, and spectral band arithmetic like subtraction and/or rationing.
Ground truth data: 50 samples were collected in the field in order to determine the
concentration of lead, zinc, cadmium, and copper.
Pilot case study: Copsa Mica (industrial town that represented for many years one of the
most polluted regions in Europe. Air, water, soil and vegetation were highly contaminated
with heavy metals such as lead, cadmium, zinc and copper. In the last two years, the
polluting factories stopped their activity, leading to decreasing levels of contamination).
Satellite imagery: multi-temporal series of Landsat data (2007 and 2011) and SPOT.
Project's web page: http://www.metagro.cesec.ro/
Credits: METAGRO project (UB, ICPA, USAMV, IMNR, ROSA)

Land monitoring: METAGRO
Test area Copsa Mica, Tarnava hydrographic basin (background image: Landsat ETM+)

Land monitoring: METAGRO
Identification of polluted areas using Principal Component Analysis (PCA) – SPOT data
Regional analysis (hydrographic basin-level)
Credits: METAGRO project (UB, ICPA, USAMV, IMNR, ROSA)

Spatial distribution of field samples
Local analysis: Copsa Mica
Background image: Landsat ETM+
Credits: METAGRO project (UB, ICPA, USAMV, IMNR, ROSA)
Land monitoring: METAGRO

Land monitoring: METAGRO
NDVI mean values and mean values of the spectral response in the near-infrared band
computed based on the Landsat TM multi-temporal series for each Copsa Mica field sample
Credits: METAGRO project (UB, ICPA, USAMV, IMNR, ROSA)

Land monitoring: METAGRO
Analysis of the contamination level for Copsa Mica (darker tones indicate higher
contamination level) – Landsat TM images acquired on July 2009 (up) and June 2011 (down)

Land monitoring: METAGRO
The limitations of this research study are given by the spatial and spectral resolution of the
images that play a decisive role in obtaining accurate results. Moreover, the integration of
ground truth data within the processing chain would improve the accuracy of the results.
In most of the cases, the results show that the discrimination of the polluted agricultural
fields (as a unique class) was very difficult, almost impossible. The spectral signature of the
contaminated elements should be determined in advance using auxiliary data in order to
avoid confusion with the spectral response of similar classes. Also, the validation of the
multi-spectral analysis is mandatory to be performed on the samples collected in the field.
Future research plans involve the use of Sentinel-2 data and/or remote sensing hyper-
spectral data acquired in narrower, more sensitive spectral bands that are recommended for
the identification and monitoring of heavy metal contamination in agricultural fields.
Credits: METAGRO project (UB, ICPA, USAMV, IMNR, ROSA)

Land monitoring: SIRIUS (GMES service)
SIRIUS is developing efficient water resource management services in support of food
production in water-scarce environments. It addresses water governance and management
in accordance with the vision of bridging and integrating sustainable development and
economic competitiveness.
Expected results: NDVI and LAI maps (integrating meteorological data: temperature and
precipitation), maps detailing irrigation water requirements in different areas and crop water
consumption estimates.
Pilot case studies (that represent a sample of the wide range of conditions found in the
world): Spain, Italy, Romania, Turkey, Egypt, Mexico, Brazil, and India.
Satellite imagery: Landsat (archived data), FORMOSAT (planned acquisitions) ← ESA's Data
Warehouse for GMES projects.
Credits: SIRIUS FP7 project

Land monitoring: SIRIUS (GMES service)
Example of NDVI plot for corn (April – October 2010)

Emergency response: RISCASAT, SIGUR, SAFER, UN-SPIDER
Overview of floods in Romania: more than 1,000,000 hectares of floodplain, more than
900,000 people living in areas with high risk of flooding, more than 88,000 households
could be flooded at any time in average, 8 people lose their lives annually
Siret, 2005
at least 15 people have been killed
more than 12,000 people have been
evacuated from their homes
Siret, Prut, 2008
thousands of people were evacuated
at least 4 people died
Danube, 2006
more than 4,700 people had to be evacuated (3,000 in Rast village)
600 buildings were flooded (115 collapsed)
more than 40,000 hectares covered by flooding
Siret, Danube 2010
numerous people were reported dead
hundreds of hectares of farmland were flooded

Emergency response: RISCASAT, SIGUR, SAFER, UN-SPIDER
Work flow for the generation of value-added products
Satellite data
Assessment of the
affected areas for
each land cover class
Integration of land use /
land cover information
(method developed by
ROSA / CRUTA since 2005)
Reprojection into
the national grid
Creation of flood
thematic maps in
a GIS environment
Integration of GIS layers
(boundaries of administrative
units, places, geographical
names, roads, rivers, etc.)
Check of the topology rules
Extraction of the
water mask
Manually → visual
photo interpretation
Semi-automatically
→ image classification
and visual check
Processing of the
water mask
Integration of elevation
data (DEMs, contour lines,
geomorphological maps)
Elimination of artefacts
inherent to automatic
image processing

Emergency response: RISCASAT, SIGUR, SAFER, UN-SPIDER
Editing of the automatically derived water mask produced by the service
Results: flood maps published on the website of the Romanian Space Agency in less than
24 hours since the reception of satellite imagery or satellite-based products (water masks).
Permanent water
bodies (lakes)
Local processing team
Before editing
ER product delivered by
the service provider
After editing

Emergency response: RISCASAT, SIGUR, SAFER, UN-SPIDER
Example of flood maps (1)

Emergency response: RISCASAT, SIGUR, SAFER, UN-SPIDER
Example of flood maps (2)

Emergency response: RISCASAT, SIGUR, SAFER, UN-SPIDER
Example of flood maps (3)

Emergency response: RISCASAT, SIGUR, SAFER, UN-SPIDER
Satellite remote sensing data and its derived products (space-based maps) are essential
components in the management of emergency situations.
The integration of ancillary data improves the quality and content of the disaster crisis
maps offering the guarantee of the in situ collected basic information.
The water mask derived by automatic means should be checked. For example, the
geometric effects (especially shadow) of the radar images may be misinterpreted as
flooded areas. The knowledge of the studied area is vital.
The generated products are more accurate if 3D information (DEMs, contour lines,
geomorphological maps) is integrated in the processing chain, for mask editing. Also,
accurate and updated land use/land cover databases are important for the assessment of
the flooded areas.
From the user's point of view:
satellite data should be received as fast as possible
both temporal and spatial resolution are critical, but (at least) in the first phases of
the crisis situation temporal resolution is more important than spatial resolution

Land monitoring
Future projects using Earth Observation data
Agriculture
FARMSTAR (Integrated Precision Farming Advisory System for Irrigated
Perimeters based on Geospatial Technologies)
objectives: use of satellite observations for crop monitoring and sustainable
irrigation water management and farming solutions
EO data: optical data (Landsat, SPOT, FORMOSAT, Sentinel-2)
advantages on using Sentinel-2 data (compared with Landsat and SPOT): larger
number of spectral bands (13: visible, near infrared, short wave infrared),
improved atmospheric corrections, shorter revisiting time (2 ÷ 3 / 5 days),
wider ground swath (290 km), adequate spatial resolution (10/20 m)
expected outcome: application development to build the foundation for a
Precision Agriculture Service (as a GMES downstream project)
funding within National Plan for Research, Development and Innovation

Future projects using Earth Observation data
Emergency response
GEODIM (Platform for GeoInformation in Support of Disaster Management)
objectives: development of a downstream emergency response service for
contributing to current disaster and risk management approach based on EO
data; identification and adaptation of the existing processing flow charts for
each disaster type; development of new algorithms for an improved accuracy
of the generated value-added products
EO data: optical and radar satellite imagery (including Sentinel-1/2)
advantages on using Sentinel-2 data: shorter revisiting time, optimal spatial
resolution for damage assessment, wider ground swath
expected outcome: the GMES downstream service will enable better future
solutions on environment and security policy by providing decision makers
with better, more complete, consistent, timely and reliable information
funding within National Plan for Research, Development and Innovation

Projects to support advanced analysis and fusion
of Earth Observation data for GMES applications
GeoINF (Prototype for an interactive
system for finding geospatial
information) - is aimed to support
research in the EO field by developing a
prototype for an interactive geo-
information retrieval system. Basically,
it is an interactive map, that may serve
the particular needs of its users.
Objectives: developing a search engine
for data obtained from satellite images
and maps and building a web portal
that would ease the citizens’ access to
information from several sources.
GeoINF
© ASRC

Projects to support advanced analysis and fusion
of Earth Observation data for GMES applications
RoKEO (Romanian Knowledge-Centered Earth Observation) - ESA KEO system that would
fully integrate in the ESA SSE. ROKEO also aims at upgrading the existing infrastructure with
new algorithms, processors, and web services. New functionalities: it will create an EO
reference data set for Romania; it will elaborate new algorithms that would provide EO
images extraction; it will develop new methods to enrich EO images with other exogenous
data, aiming at a new generation of KIM (ESA PECS project).
RoKEO
© ASRC

Projects to support advanced analysis and fusion
of Earth Observation data for GMES applications
New methods for image understanding:
semantic classes extraction based on the spatial relationships between objects
short time Fourier Transform based image descriptors
automatic annotation of targets in satellite data using high-level semantic concepts
"Progress in Spatial Analysis and Fusion of Remote Sensing Satellite Data for Generating
High-Level Semantic Geo-information in Support of GMES Applications" – Anca Popescu (1) ,
Corina Vaduva (1) , Dragos Bratasanu (2) , Ion Nedelcu (2) , Mihai Datcu (3)
(1) - University Politehnica Bucharest
(2) - Romanian Space Agency
(3) - German Aerospace Centre
"Interactive Spectral Band Discovery for Exploratory Visual Analysis of Satellite Images" –
Dragos Bratasanu (1) , Ion Nedelcu (1) , Mihai Datcu (2) , IEEE Journal of Selected Topics in
Applied Earth Observations and Remote Sensing
(1) - Romanian Space Agency
(2) - German Aerospace Centre

Image modeling for information mining
Semantic classes extraction based on the spatial relationships between objects:
pixel-level representations: labels for individual pixels computed in terms of spectral, texture, geometry
and segmentation cluster features
region-level representations: land cover labels for groups of pixels obtained through object segmentation
patch-level representations: interactions of different regions computed in terms of their spatial relations
Spectral reasoning Spatial reasoning
Pixel-level labels Region-level labels
Patch-level labels
Credits: RoKEO & GeoINF (UPB, ROSA, DLR): "Progress in Spatial Analysis and Fusion of Remote Sensing Satellite Data for Generating High-
Level Semantic Geo-information in Support of GMES Applications", Anca Popescu, Corina Vaduva, Dragos Bratasanu, Ion Nedelcu, Mihai Datcu

Image modeling for information mining: spatial relationships
Landsat 7 ETM+
CORINE LAND COVER
Credits: RoKEO & GeoINF (UPB, ROSA, DLR): "Progress in Spatial Analysis and Fusion of Remote Sensing Satellite Data for Generating High-
Level Semantic Geo-information in Support of GMES Applications", Anca Popescu, Corina Vaduva, Dragos Bratasanu, Ion Nedelcu, Mihai Datcu

Image modeling for information mining: spatial relationships
Credits: RoKEO & GeoINF (UPB, ROSA, DLR): "Progress in Spatial Analysis and Fusion of Remote Sensing Satellite Data for Generating High-
Level Semantic Geo-information in Support of GMES Applications", Anca Popescu, Corina Vaduva, Dragos Bratasanu, Ion Nedelcu, Mihai Datcu

Multiple sensors - multiple applications capabilities
IKONOS LANDSAT
QUICKBIRD SPOT
TERRASAR-X GEOEYE
Credits: RoKEO & GeoINF (UPB, ROSA, DLR): "Progress in Spatial Analysis and Fusion of Remote Sensing Satellite Data for Generating High-
Level Semantic Geo-information in Support of GMES Applications", Anca Popescu, Corina Vaduva, Dragos Bratasanu, Ion Nedelcu, Mihai Datcu

Future potential Sentinel-2 data use:
Conclusions
land monitoring applications and services (land cover, agriculture, forestry)
emergency response applications and services (GMES downstream service)
synergy with other types of EO data (for example: SAR)
validation of results using in-situ measurements
Benefits in using Sentinel-2 data for the foreseen applications/services:
enhanced continuity of Landsat and SPOT type data
larger number of spectral bands (improved results on atmospheric correction)
optimal spatial resolution and swath width, shorter revisiting time
Requirements for Sentinel-2 data:
easy and free access to data, fast delivery of requested (ordered) data
Advanced analysis of Earth Observation data for GMES applications:
new patch-based analysis method is very suitable for the rapid mapping needs of GMES
extraction of semantic rules successfully serves automated cartography methods

Thank you very much for your attention!