Working document
Projects
184_1_1 Follow-up ecosystem mapping
Activity
Mapping marine ecosystems
Partners
involved
UAB and UMA
Date
30/09/2014
Prepared
by:
Raquel Ubach, Ana Marín and Dania AbdulMalak
Table of Contents
1.
2.
3.
Delimitation of boundaries ................................................................................ 3
1.1 European Sea Regions ............................................................................... 4
1.2 Coastal and marine area by CLC .................................................................. 5
Target classification ......................................................................................... 5
2.1 Marine habitats ......................................................................................... 5
Mapping approach ........................................................................................... 9
3.1 Common framework .................................................................................. 9
3.2 Marine particularities ................................................................................. 9
3.2.1
Seabed ........................................................................................ 9
3.3
3.4
3.5
3.6
4.
3.2.2
Depth ......................................................................................... 14
3.2.3
Light availability........................................................................... 15
3.2.4
Ice ............................................................................................. 17
3.2.5
Water column .............................................................................. 19
Marine rules ............................................................................................20
Datasets .................................................................................................21
Data workflow..........................................................................................25
Methodology ............................................................................................27
3.6.1
Analysis extent ............................................................................ 27
3.6.2
Bathymetry composite .................................................................. 27
3.6.3
Seabed class homogenisation ........................................................ 28
3.6.4
Sea ice data ................................................................................ 29
3.6.5
Marine and coastal ecosystem rules ................................................ 29
Discussion .....................................................................................................30
References ..........................................................................................................32
Annex 1. Rasterize sub regions ..............................................................................33
Annex 2. Extent of analysis script ...........................................................................33
Annex 3. Bathymetry composite script ....................................................................35
Annex 4. Primary seabed data integration ...............................................................38
Annex 5. Secondary seabed data integration ............................................................39
Annex 6. Sea ice script ..........................................................................................40
Annex 7. Marine and coastal rules script ..................................................................41
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Table of Figures
Figure 1 Conceptual framework including EUNIS and MAES classifications..................... 4
Figure 2 European Marine Sea Regions ..................................................................... 4
Figure 3 EUNIS habitat classification: criteria for marine habitats (A) to level 2 ............. 7
Figure 4 EUSeaMap present data coverage (August 2014) .........................................10
Figure 5 MESHAtlantic present data coverage (August 2014) .....................................10
Figure 6 Available substrate data sources ................................................................11
Figure 7 Global seabed map differentiating hard (greenish, value 2) and soft substrates
(brownish, value 1) ..............................................................................................12
Figure 8 Deviance explained by each of the predictors of a tested model .....................13
Figure 9 Bathymetry available datasets for the European Sea Regions (EMODNET
coloured, GEBCO in grey) ......................................................................................14
Figure 10 Sea Ice Concentration on the 11th of September 2013 (Hemisphere N) .........18
Figure 11 Sea Ice extent plotted along the year .......................................................19
Figure 12 Data workflow process – Preparing datasets ..............................................25
Figure 13 Data workflow process – Applying marine ecosystems rules .........................26
Figure 14 Example of unclassified pixels (in yellow) due to unmatched boundaries
between CLC and sea regions in Svalbard archipelago (left) and Northern Black Sea
(right) .................................................................................................................27
Figure 15 Substrate data homogenisation ................................................................28
Figure 16 Distribution of hectares per each ecosystem major type (compared results from
2013 and 2014 methodological approaches) ............................................................30
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1. DELIMITATION OF BOUNDARIES
The updated extent of the coastal and marine Pan-European map covers the area from
the coastline (as defined by a selection of CLC classes) seawards to the outer limit of the
European Sea Regions.
Marine and coastal ecosystems are usually considered together in EEA’s assessments, as
both are highly interrelated. The coastal environment is a heterogeneous ecosystem,
hosting a wide variety of different habitats associated both to water and land. Coast is
defined by the EEA as a mixed area distinguished by the coming together of land and
sea, delimited by the strip of land 10 km inland from the coastline plus the first 10 km
seaward (EEA, 2006).
For the current assessment, and in accordance to the ecosystems-related tasks within
the ETC-SIA framework, the EUNIS classification is the reference for ecosystems
definitions and typologies. At the same time, this work is also intended to give support to
the general EU Biodiversity strategy 2020 framework (Target 2 - Action 5). For this
reason, the proposed ecosystem typology defined by MAES working group has also been
considered in the present approach, where the broad marine ecosystem is divided into
two environments: coastal and marine. Here, ‘Coastal environment’ considers those
terrestrial habitats that always occur along the coast including marshes, sea cliffs,
intertidal habitats and coastal dunes; and also some aquatic habitats effectively occurring
adjacent to the coast, such as marine inlets and transitional waters. Coastal ecosystems
can be defined and spatially delineated using the following EUNIS habitat classes (Figure
1):

terrestrial coast comprising coastal dunes and sandy shores (B1), coastal
shingle (B2), and rock cliffs, ledges and shores (B3), and

aquatic coast including estuaries (X1) and saline and brackish coastal lagoons
(X2-X3).
This represents a different approach to the MAES definition of ‘coastal areas’ which refers
to coastal, shallow, marine systems that experience significant land-based influences,
with diurnal fluctuations in temperature, salinity and turbidity, and also affected by wave
disturbance (MAES, 2013). This is why we slightly modified the name to 'coastal littoral',
so a clear differentiation is made with the terrestrial stripe of coast, widely used in other
assessments, e.g. like the SOER.
On the other hand, 'Marine environment' is characterised by marine waters, and
composed of habitats directly connected to the oceans below the high tide limit (as
defined by EUNIS). Marine ecosystems are a complex of habitats defined by the wide
range of physical, chemical, and geological variations that are found in the sea. Habitats
range from highly productive near-shore regions to the deep sea floor inhabited only by
highly specialised organisms (EEA, 2010 1).
In this section, there are described the methods and approaches to map the ‘marine wet
ecosystems’ as described in Figure 1, which correspond to EUNIS marine habitats (A) and
habitat complexes (X01, X02 and X03).
1
EU 2010 biodiversity baseline
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Figure 1 Conceptual framework including EUNIS and MAES classifications
Source: ETC-SIA
1.1 European Sea Regions
The European Sea Regions are defined by the MSFD provisional dataset on sea regions
and sub-regions - EEA internal version1, Sep. 2013. This draft version of the regions
boundaries at sea are defined to be used for the MSFD reporting. Although, this dataset
has not yet been approved by Member States, it is a good reference for the mapping
boundaries.
Figure 2 European Marine Sea Regions
Source: MSFD provisional dataset on sea regions and sub-regions (EEA)
1
available at: ftps://sdi.eea.europa.eu/data/continental/europe/water/msfd/eea_v_4258_1_mio_msfd-searegions_2013/Regional_seas_extended_version_ETRS89_20130925.shp
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1.2 Coastal and marine area by CLC
Considering that the reference dataset for this task is CLC, the coastal and marine area
defining the landward boundaries of the extent are defined by several classes of CLC:
–
‘Intertidal flats’ (CLC==423) - Generally un-vegetated expenses of mud, sand or rock
lying between high and low water mark.
–
‘Coastal lagoons’ (CLC==521) - Stretches of salt or brackish water in coastal areas,
which are separated from the sea by a tongue of land or other similar topography.
These water bodies can be connected to the sea at limited points, either permanently
or for parts of the year only.
–
‘Estuaries’ (CLC==522) - The mouth of a river, within which the tide ebbs and flows.
–
‘Sea and Ocean’ (CLC==523) - Zones seaward of the lowest tide limit.
2. TARGET CLASSIFICATION
The target classification is a combination between the EUNIS classification at level 3
(from http://eunis.eea.europa.eu/habitats-code-browser.jsp?expand=A#level_A1) and a
selection of CLC classes at level 3 (Annex 1 - Crosswalks between Marine and Coastal
EUNIS habitat types and CLC classes).
The EUNIS is a consolidated common classification scheme in Europe for habitat types
with the object to help harmonising existing information on habitats at this wide scale,
which started developing in the mid-90s and published its last major revision in 2004
(Evans & Royo-Gelabert, 2013). The EUNIS habitat types are distributed in a hierarchical
classification with 10 categories in the highest level 1. Marine habitats are described in 4
levels, while terrestrial and freshwater habitats in 3. However, Marine habitats at level 1
can be considered equivalent to terrestrial and freshwater habitats at level 2 (Davies et
al., 2004). Level 2 and 3 divisions are based on physical parameters such as depth
related to light penetration, substrate composition and energy; while species composition
are used to discriminate divisions at level 4 (Evans & Royo-Gelabert, 2013).
The CLC is a Pan European wide database on land cover that was initiated on 1985 by
the EEA. It is a rather terrestrial classification of land use / land cover in Europe, but it
will be used to complement EUNIS on the coastal delimitation and mapping.
2.1 Marine habitats
EUNIS defined marine habitats as follows: “Marine habitats are directly connected to the
oceans, i.e. part of the continuous body of water which covers the greater part of the
earth’s surface and which surrounds its land masses. Marine waters may be fully saline,
brackish or almost fresh. Marine habitats include those below spring high tide limit (or
below mean water level in non-tidal waters) and enclosed coastal saline or brackish
waters, without a permanent surface connection to the sea but either with intermittent
surface or sub-surface connections (as in lagoons). Rockpools in the supralittoral zone
are considered as enclaves of the marine zone. It includes marine littoral habitats which
are subject to wet and dry periods on a tidal cycle as well as tidal saltmarshes; marine
littoral habitats which are normally water-covered but intermittently exposed due to the
action of wind or atmospheric pressure changes; freshly deposited marine strandlines
characterised by marine invertebrates. Waterlogged littoral saltmarshes and associated
saline or brackish pools above the mean water level in non-tidal waters or above the
spring high tide limit in tidal waters are included with marine habitats. It also includes
constructed marine saline habitats below water level as defined above (such as in
marinas, harbours, etc) which support a semi-natural community of both plants and
animals. The marine water column includes bodies of ice.” (Davies et al., 2004).
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At EUNIS level 2, 8 habitat types are described:

A1 : Littoral rock and other hard substrata
Littoral rock includes habitats of bedrock, boulders and cobbles which occur in the
intertidal zone (the area of the shore between high and low tides) and the splash
zone. The upper limit is marked by the top of the lichen zone and the lower limit
by the top of the laminarian kelp zone. There are many physical variables
affecting rocky shore communities - wave exposure, salinity, temperature and the
diurnal emersion and immersion of the shore.

A2 : Littoral sediment
Littoral sediment includes habitats of shingle (mobile cobbles and pebbles),
gravel, sand and mud or any combination of these which occur in the intertidal
zone. Littoral sediments support communities tolerant to some degree of drainage
at low tide and often subject to variation in air temperature and reduced salinity
in estuarine situations. Littoral sediments are found across the entire intertidal
zone, including the strandline. Sediment biotopes can extend further landwards
(dune systems, marshes) and further seawards (sublittoral sediments). Sediment
shores are generally found along relatively more sheltered stretches of coast
compared to rocky shores. Muddy shores or muddy sand shores occur mainly in
very sheltered inlets and along estuaries, where wave exposure is low enough to
allow fine sediments to settle. Sandy shores and coarser sediment (gravel,
pebbles, cobbles) shores are found in areas subject to higher wave exposures.

A3 : Infralittoral rock and other hard substrata
Infralittoral rock includes habitats of bedrock, boulders and cobbles which occur in
the shallow subtidal zone and typically support seaweed communities. The upper
limit is marked by the top of the kelp zone whilst the lower limit is marked by the
lower limit of kelp growth or the lower limit of dense seaweed growth.

A4 : Circalittoral rock and other hard substrata
Circalittoral rock is characterised by animal dominated communities (a departure
from the algae dominated communities in the infralittoral zone). The circalittoral
zone can itself be split into two sub-zones; upper circalittoral (foliose red algae
present but not dominant) and lower circalittoral (foliose red algae absent). The
depth at which the circalittoral zone begins is directly dependent on the intensity
of light reaching the seabed; in highly turbid conditions, the circalittoral zone may
begin just below water level at mean low water springs (MLWS).

A5 : Sublittoral sediment
Sediment habitats in the sublittoral near shore zone (i.e. covering the infralittoral
and circalittoral zones), typically extending from the extreme lower shore down to
the edge of the bathyal zone (200 m). Sediment ranges from boulders and
cobbles, through pebbles and shingle, coarse sands, sands, fine sands, muds, and
mixed sediments. Those communities found in or on sediment are described
within this broad habitat type.

A6 : Deep-sea bed
The sea bed beyond the continental shelf break. The shelf break occurs at variable
depth, but is generally over 200 m. The upper limit of the deep-sea zone is
marked by the edge of the shelf. Includes areas of the Mediterranean Sea which
are deeper than 200 m but not of the Baltic Sea which is a shelf sea. Excludes
caves in the deep sea which are classified in A4.71 irrespective of depth.

A7 : Pelagic water column
The water column of shallow or deep sea, or enclosed coastal waters.

A8 : Ice-associated marine habitats
Sea ice, icebergs and other ice-associated marine habitats.
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Figure 3 EUNIS habitat classification: criteria for marine habitats (A) to level 2
Source: EUNIS habitat classification revised 2004 (Davies et al., 2004)
Several criteria are used to discriminate the different habitat types. First division criterion
is altitude:
o
o
o
o
o
Littoral – periodically inundated shores of marine water
Infralittoral – shallow subtidal
Circalittoral – moderately deep subtidal
Offshore circalittoral – offshore water, depth <200m
Bathyal – depth >200m
Depth zones, more detailed than altitude zones, are divided as follows:
o
o
o
o
o
o
o
o
o
o
Upper shore
Mid-shore
Low shore
0 - 5m
5 -10m
10 - 20m
20 - 30m
30 - 50m
50 - 200m
>200m
Substrate can be:
o
o
Mobile
Non-mobile
Salinity values are comprised between:
o
o
o
o
Fully saline – 30-40 ppt
Variable salinity – 18-40 ppt fluctuating on a regular basis
Reduced salinity – 18-30 ppt
Low salinity - <18 ppt
According to wetness/dryness criteria:
o
o
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Aquatic – open or free-standing fresh or saline water
Frequently submerged – predominantly aquatic (saline or brackish) but
subject to occasional but regular emersion
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Considering the description of habitat types and the different discriminating parameters,
the marine typology is summarised in Table1, where the main parameters used to define
the ecosystem mapping are highlighted.
Table 1 Description parameters for EUNIS level 2 marine habitats
Parameter
Value
A1
A2
A3
A4
A5
A6
Bathyal
Circalittoral
x
Infralittoral
Depth zones
x
x
x
x
x
x
x
Littoral
x
x
?
Upper shore
x
x
?
Mid-shore
x
x
?
Low shore
x
x
?
0 - 5m
x
x
x
5 -10m
x
x
x
x
10 - 20m
x
x
x
x
20 - 30m
x
x
x
x
x
x
x
x
x
30 - 50m
50 - 200m
>200m
x
Mobile
Substrate
Non-mobile
x
x
x
x
x
x
x
x
Water
x
Ice
Salinity levels
Wetness/dryness
A8
x
Offshore circalittoral
Altitude zones
A7
x
Fully saline
x
x
x
x
x
x
x
Reduced salinity
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Low salinity
Variable salinity
x
x
x
x
x
Aquatic
x
x
x
x
x
Frequently submerged
x
x
x
x
Source: Adapted from EUNIS habitat description.
The altitude and the depth zones provide similar information to define the different
marine habitats. The altitude being rather descriptive and the depth zones are defined
based on quantitative measures. For this task, depth zones can be used to discriminate
different groups of typologies (A1 and A2; A3, A4 and A5; A6). The substrate can
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discriminate those ecosystems characterised by mobile sediments from those of rocky
and hard substrates.
Salinity is not useful to separate habitats at this classification level. Finally, wetness
provides the same guidance than depth, but only for A1 and A2, and therefore its use is
facultative.
Based on this analysis, the discrimination of the marine habitat typology needs to be
based on a multi-criteria approach including depth, substrate and light penetration. The
rationale of this approach is this physical variables summary in Table1 together with the
wider description of the different habitat types presented at the beginning of this chapter.
3. MAPPING APPROACH
3.1 Common framework
As part of a wider work, a common framework is set to provide a pan-European map of
terrestrial, coastal, and marine ecosystems. The main rule is that one pixel corresponds
uniquely to 1 ecosystem type (1 pixel = 1 ecosystem type).
As marine and terrestrial ecosystems are approached separately in the computing
process, the final wall to wall map that will join all ecosystems together needs to consider
the spatial continuity of the ecosystems, providing a solution for any spatial gaps that
may be encountered in the transitional areas between one ecosystem and the other. In
addition the coastal line used is not adjusted to the CLC layer and therefore these
boundaries between coastal and terrestrial delimitations need to be consolidates.
While joining the datasets together, the existent gaps should be considered and treated
between terrestrial, coastal and marine ecosystems. This will be especially important in
the terrestrial-coastal/marine interface. This issue must be solved by setting the inner
marine boundary by using CLC layer.
3.2 Marine particularities
5 components are set to help to define the marine habitats particularities:

Seabed substrate

Depth

Light penetration

Water column

Ice
In EUSeaMap project, energy at both wave and at seabed level is used to classify EUNIS
level 3 habitats discriminating infralittoral and circalittoral rock habitats into high,
moderate and low energy environments (McBreen et al., 2011a). The present analysis
will discriminate EUNIS level 2 classes for the marine environment, consequently energy
parameters (wave and currents at seafloor) will not be considered in this study.
3.2.1 Seabed
The physical nature of the seabed substratum influences the community types that
develop above (McBreen et al., 2011 b). For this reason, it is extremely important to
acquire reliable and accurate data on it. In the last decade, many projects have
addressed this issue like Balance, MESH, HabMap, Infomar, amongst others. The most
relevant results for the current analysis are further described in this section.
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EUSeaMap
After the experience and results from previous projects, EUSeaMap produced broad-scale
modelled habitat maps for the Baltic, Celtic, North and western Mediterranean seas
following the EUNIS classification with some slight modifications (see figure 4). Right now
there are available more than 2 million square kilometres of European seabed,
unfortunately this dataset does not cover the whole extent of the present analysis. By
now, where it is available it will be used as a primary data source for seabed
characterisation.
Figure 4 EUSeaMap present data coverage (August 2014)
Source: EUSeaMap project (Mapping European seabed habitats, 2013).
MESHAtlantic
Complementarily, there is another project that is mapping some areas of the Atlantic
seabed. MESH Atlantic continues to gather existing maps and conduct new mapping
survey and will produce (expected by the end of 2014) a broad-scale modelled map to
continue the modelling work started by MESH and continued by the EUSeaMap project
(as part of the European Marine Observation Data Network, EMODnet).
Figure 5 MESHAtlantic present data coverage (August 2014)
Source: Mapping European Seabed Habitats web portal (http://www.searchmesh.net/)
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EUROSION
The geology map from EUROSION shows the geological patterns of the European coast,
classifying the coastline into several classes (rocks and hard cliffs, small beaches, muddy
sediments, embankments, vegetated strands, soft strands with ‘rocky platforms’, etc.).
Romania, Bulgaria, Cyprus and ultra-peripheral regions are only partially covered.
However, this dataset can be used to discriminate hard from soft substrates where no
primary seabed substrate is available.
Figure 6 Available substrate data sources
Source: EUSEAMAP, MESHAtlantic and EUROSION datasets
MEDINA
MEDINA aims at enhancing monitoring capacity of coastal and marine ecosystems in the
Mediterranean Northern African Countries (Morocco, Algeria, Tunisia, Libya and Egypt).
The project contributes to the assessment and implementation of 80 indicators (37
DPSIR indicators and Ecological objectives, 10 indicators of Earth Observation and 33
indicators from Modelling), accessible throught the project geoportal 1. One of the project
products is the coastal typology describing the morpho-sedimentological typology of
Mediterranean North African (NA) coastline. The division of the NA coastline is a
succession of contiguous segments according to main typologies derived by visual
discrimination using satellite imagery, which makes possible to distinguish between 5
principal classes: Rocky coast, Beaches, Interdial wetlands, Mouths, and Artificial.
Global map of human impacts to marine ecosystems
The NCEAS (National Center for Ecologial Analysis and Synthesis, Santa Barbara – USA)
published a global map of human impacts to marine ecosystems (Halpern et al., 2008).
To undertake this analysis several maps were created to identify distinctive ecosystem
types, including global maps on hard and soft bottoms. These maps are extracted from
the dbSEABED2 project, this database compiles benthic substrate point samples data on
1
2
http://medinageoportal.eu
http://instaar.colorado.edu/~jenkinsc/dbseabed
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the rock composition of particular locations around the world. The dbSEABED data has
been compiled by the Institute of Arctic & Alpine Research in the University of Colorado
at Boulder. Although the distribution of sampling is geographically uneven, areas around
developed nations like in Europe, North America or Australia, present major number of
samples and so have less errors derived from statistical interpolation (kriging). Moreover,
it has to be noted that in general shallow and shelf areas are better sampled than the
continental slope and the deep seafloor (Halpern et al., 2008).
Data from the database was extracted to generate binary maps1, where each cell was
assigned a value of hard or soft substrate depending on the presence of hard substrate in
each sample (samples with greater than 50% hard substrate were counted as hard; and
all others were counted as soft). Grid cells were sampled at 2 arc-minutes (~3.7 km, or
13.69 km2 per cell, depending on latitude) and assigned an ecosystem type depending
on substrate (hard or soft) and bathymetry (shallow, shelf, slope and deep). A
combination of these maps can produce a unique map with the dominant substrate in
each cell. The purpose of the use of this datasets is to discriminate between hard and
soft seabeds, for this reason the the datasets used to create the combination are:
-
Hard shelf
Hard slope
Deep hard bottom
Intertidal mud
Rocky intertidal
Subtidal soft bottom
Soft shelf
Soft slope
Deep soft benthic
All these maps have the same value (1). In order to mosaic all them in on single dataset,
the hard substrate maps have been reclassified with value 2. Afterwards, all the maps
have been put together in one sigle dataset by mosaicing where value 1 corresponds to
soft bottoms and 2 for hard substrates.
Figure 7 Global seabed map differentiating hard (greenish, value 2) and soft
substrates (brownish, value 1)
Source: derived from dbSEABED
1
http://www.nceas.ucsb.edu/globalmarine/ecosystems
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Highlights
In comparison to previous ecosystem typology map methodology developed last year
(2013), the inclusion of more accurate thematic datasets like the outcomes of EUSeaMap
and MESH projects in a first instance, and from MEDINA and EUROSION projects based
on visual digitalisation of coastal substrate, has notably increased the overall quality of
resulting ecosystems map. Nevertheless, this improvement has not been scaled.
Shortcomings
This dataset can be used as a secondary data source for seabed substrate filling the gaps
where no other datasets are available, however it is not useful along the 1st km seaward
from the coast as it considers that rocky intertidal, beach, intertidal mud, suspensionfeeding reefs, and salt marsh ecosystems exist in all cells within the 1st km of shore, not
discriminating their presence. As a result, the presence of areas with no substrate data
have been reduced but not completely eliminated. Therefore, still unclassified pixels are
present in the present version of marine ecosystems map.
Next actions
EUSeaMap phase II work is still in progress, and expected to be published under the
EMODNET project by the end of 2014 (Evans & Royo-Gelabert, 2013). The EUSeaMap
phase II presents some improvements:



extend the coverage to Canary Islands, the remaining Mediterranean areas
(Adriatic, Ionian and Aegean Seas, and the Black Sea)
increase thematic reliability of resulting maps by the improvement of intermediate
data (hydrodynamics models, seabed substrate layers, bathymetry, etc.)
refine working scale to 100 m pixel size in pilot areas
On the other hand, in the areas where no substrate data is available (in particular, in the
1st km seaward) an alternative approach to predict the sediment nature of shores must
be done. A proposal could be based on a combination of different spatial variables to
predict rocky shore communities. A model should be based on predictor variables easily
available at the scale of analysis. A proposal that should be further developed could use
depth, slope, terrain curvature, and a measure of coastal exposure (Burrows et al.,
2008). The combination of these variables performed well with a high degree of certainty
(ROC-values > 0.8) in a model used to predict rocky shores in Norway (Bekkby et al.,
2009).
Figure 8 Deviance explained by each of the predictors of a tested model
Source: Bekkby et al., 2009
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3.2.2
Depth
The bathymetry can be used to discriminate the major divisions of coastal, shelf and
open ocean. The shelf break occurs at variable depth; however a general rule can be
applied considering 200 m the average lower limit for the edge of the shelf (Davies et al.,
2004).
The EMODnet Bathymetry data products are Digital Terrain Models (DTM) for selected
maritime basins in Europe that have been produced from collated bathymetric data sets
and that are integrated into a central DTM. For each region bathymetric survey data and
aggregated bathymetry data sets are collated from public and private organizations.
These are processed and quality controlled. A further refinement is underway, also by
gathering additional survey data sets. The DTM’s have been based, where possible and
available, upon high resolution survey data sets, presenting a final resolution of 1/4 arcminutes (15 arc-seconds ~ roughly 500 m).
Figure 9 Bathymetry available datasets
(EMODNET coloured, GEBCO in grey)
for
the
European
Sea
Regions
Source: EMODNET and GEBCO
For those areas where EMODNET bathymetry data is not available, GEBCO1 (General
Bathymetric Chart of the Oceans), which provides global bathymetry data sets for the
world's oceans, can be used. The GEBCO 08 Grid is a global 30 arc-second grid largely
generated by combining quality-controlled ship depth soundings with interpolation
between sounding points guided by satellite-derived gravity data. However, in areas
where they improve on the existing grid, data sets generated by other methods have
been included. Land data are largely based on the Shuttle Radar Topography Mission
(SRTM30) gridded digital elevation model.
The definition of depth ranges must be done according the ecosystem type definitions as
summarised in Table 1.
1
http://www.gebco.net/data_and_products/gridded_bathymetry_data/
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Highlights
Similarly to the case of seabed, the inclusion of more accurate datasets by means of
EMODNET bathymetry data has notably increased the overall quality of the resulting
ecosystems typology map. Again, due to short resources available, this improvement has
not been calculated.
Next actions
A new release of EMODNET bathymetry is expected by mid-December 2014, which will be
enlarged incorporating:

Baltic Sea

Black Sea

Norwegian and Icelandic Seas

Canary Islands
This new release will double the resolution to 1/8 arc-minutes (7’5 arc-seconds ~ roughly
250 m). Therefore, when updating the ecosystems typology map, this new dataset with
notably increased resolution should be incorporated and the methodology adapted
consequently.
3.2.3
Light availability
The euphotic zone provides a measure of the ocean depth below which light available is
insufficient to support significant photosynthetic activity. It is the upper part of the water
column, where most of the primary production occurs. The euphotic layer is the depth at
which the visible light (400 – 700 nm range) reduces to 1% of the light incident at the
ocean surface. It is a measure of water quality, as well as an important variable to
estimate water column primary production.
Light availability in the water column and the seabed is affected by depth and proximity
to the coast, and by latitude and climate (EUSeaMap, 2012). Light intensity decreases
with depth due to the attenuating effects of scattering and absorption in the water
column. This attenuation produced by water molecules, suspended particulate matter,
phytoplankton and coloured dissolved organic matter, tends to be higher in coastal
waters, due to suspended and dissolved matter being washed down rivers, higher
phytoplankton concentrations and suspension of sediment caused by wave action in
shallow waters. So the proportion of surface light reaching the seabed can be derived by
the diffuse attenuation coefficient: Kd(λ,E%)1.
Light attenuation is used to define the infralittoral zone, as below a certain fraction of
surface light macrophytes (like kelp, seaweeds, or seagrass) will struggle to grow. It is
accepted by convention that the bottom of the euphotic zone, Zeu, is 1% of the
proportion of light at the subsurface of water (Ruther, 1956; Morel et al., 2007). Below
the infralittoral, the circalittoral zone extends to the maximum depth where multicellular
photosynthetic forms can exist, characterised by the predominant presence of sciaphilic
algal communities (EUSeaMap, 2012). The circalittoral range is estimated between 1%
and 0,01% of the surface light. Despite of this general rule, some regional adjustments
can be considered, therefore EUSeaMap presented some regional corrections, as
summarised in the following table (Table 2).
1
Kd(λ,E%): Spectral diffuse attenuation coefficient for downwelling irradiance between Ed(λ,0) and % of Ed(λ,0)
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Table 2 Regional corrections for thresholds to define the different biological
zones
Regional Seas
Zones
Celtic
Seas
and
North
Infralittoral
0m - 1.6
depth:Secchi
oligohaline OR
0m - 1% light depth:Secchi
reaches the seabed
mesohaline
Wave base - 200m
Upper slope
200m - 750m
Upper bathyal
750m - 1,100m
Mid bathyal
1,100m - 1,800m
Position of deep halocline 0.01% light reaches the
and deeper for mesohaline seabed - Shelf edge
(deepest zone)
(manual delineation)
Shelf edge (manual
delineation) - Slope
change
(manual
interpretation)
Bathyal
Abyssal
ratio of
depth for
2.5 ratio of
depth for 0m - 1% light reaches
the seabed
1% light reaches the
seabed - Wave base
Deep
circalittoral
Lower bathyal
Western
Mediterranean
1.6 ratio of depth:Secchi
depth and deeper for
oligohaline (deepest zone)
OR
2.5
ratio
of
depth:Secchi
depth
- 1% light reaches the
Position of deep halocline seabed - 0.01% light
for mesohaline
reaches the seabed
Upper
circalittoral
Circalittoral
Baltic Sea
1,800m - 2,700m
2,700m and deeper
Slope change (manual
interpretation)
Source: EUSeaMap, 2012.
Satellite observations are effective for producing maps of light attenuation across very
large areas at relatively high spatial resolution (McBreen et al., 2011b). Different
algorithms are generally used to derive the diffuse attenuation coefficient of the downwelling spectral irradiance at wavelength 490nm (Kd490) from ocean colour satellite
sensors such as the Medium Resolution Imaging Spectrometer instrument (MERIS), the
Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and the Moderate Resolution Imaging
Spectroradiometer (MODIS) instrument. Most of these existing models have been
calibrated on open ocean waters and provide good results in these areas, but tend to
underestimate the attenuation of light in turbid coastal waters (Frost et al., 2010).
UKSeaMap 2010 used 4km resolution light data (Kd490 values) from the MODIS
instrument on NASA‟s Aqua satellite, together with the UKSeaMap 2010 bathymetry to
calculate values for the fraction of surface light reaching the seabed. In 2007, Morel and
colleagues produced a global Zeu map using SeaWiFs composite processed data (Morel et
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al., 2007). And more recently, satellite derived kdPAR and Zeu were calculated for
European waters using high resolution data at 250m with a wide temporal range from
2005 to 2009 under the framework of the EuSeaMap project (Sauquin et al., 2013).
However, these datasets are still not publicly available.
Highlights
Nevertheless, the MERIS Monthly mean Surface productive layer (Euphotic Depth) is
already available at the Environmental Marine Information System (EMIS), which
provides information on marine ecosystems and coastal state, using biological and
physical variables generated by satellite remote sensing. The monthly mean euphotic
depth (in meter) derived from the ocean colour MERIS (Medium Resolution Imaging
Spectrometer) sensor is available at a low resolution of 4km, covering the time period
between May 2002 and September 2011. The product is calculated according to a QuasiAnalytical Algorithm (Lee et al., 2007) in which vertical attenuation coefficient of the subsurface light is modelled by the inherent optical properties of the water. Besides, it has to
be accounted that using field measurements in different part of the world’s ocean, the
average percentage error in the retrieval of the 1% light depth-level was calculated as
ca. 14% (Lee et al., 2007).
Shortcomings
This dataset can already be used, though it should be analysed the time period to be
considered. A similar approach has been applied to derive the water transparency to
define the condition of marine ecosystems using another dataset from EMIS/JRC. The
tools developed for this subtask could be directly used, but the methodology should
define the time frame (months and years) used to compute the average values. Due to a
lack of resources this could not be done in this year task.
Next Actions
In the coming future, when considering the update of marine ecosystems mapping, this
dataset should be included in the methodological process allowing the discrimination
between infra and circa littoral ecosystems (A3 and A4 classes).
3.2.4
Ice
Ice cover affects species distribution in coastal or shallow waters, but it has less influence
than the physical parameters previously described (seabed sediment, depth, light
penetration) when considering the broad extent of analysis (Cameron & Askew, 2011).
Several data sources have been identified so far as:
 Myocean1
 MODIS2
 NASA products:National Snow and Ice Data Centre 3
http://www.myocean.eu/web/69-myocean-interactivecatalogue.php?option=com_csw&task=results&page_int=1&page_ext=3&scope=ext&referenced_area[]=all&oc
ean_variable[]=cf-standard-name%23sea-ice&product_type[]=temporal-scale%23multiyear&product_type[]=temporal-scale%23invariant&text[]=&text[]=&records_per_page=5
1
2
http://modis-snow-ice.gsfc.nasa.gov/?c=sea
3
http://nsidc.org/data/seaice/visible.html
http://n4eil01u.ecs.nasa.gov:22000/WebAccess/drill?attrib=home&nextKey=group
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 Reverb Echo1
 British Atmospheric Data Centre2
Highlights
It is to note that so far EUSeaMap project has been considered as a main reference in our
work, and that ice was not considered in this study (Cameron & Askew, 2011). However,
a test has been undertaken as a proof of concept. To do so, data from MODIS has been
used, as it provides best available spatial (1km) and temporal resolution (from 2000 to
present). The sea ice map produced is based on the sea ice by reflectance identifies
pixels as sea ice, ocean, land, inland water, cloud or other condition. Here, only sea ice
has been considered ignoring the rest of the values.
As sea ice can be considered as a mobile substrate, allowing the development of other
ecosystem types beneath, a mixed class is proposed combining sea ice (EUNIS A8) with
the other ecosystem types (e.g. A18 = A1 + A8).
Shortcomings
Due to resources constrains, data from a single day has been downloaded (corresponding
to 2013-09-11); though mean values covering a wider temporal range would increase
the accuracy of results. The selection of the day has been done on a conservative
approach considering the time of the year with lowest sea ice concentration. The 11th of
September 2013 the sea ice covered 5131 million km 2. In this way, selected pixels are
more likely to be part of a sea-ice associated ecosystem most of the year.
Figure 10 Sea Ice Concentration on the 11th of September 2013 (Hemisphere N)
Global image, Arctic centred. In light grey, land area not considered in the analysis.
Source: National Snow and Ice Data Center (NSIDC)
http://reverb.echo.nasa.gov/reverb/#utf8=%E2%9C%93&spatial_map=satellite&spatial_type=rectangle&key
words=ice%20surface
1
This dataset contains Sea Surface Temperature climatologies (HadISST SST, Version 1.1) and Sea Ice
coverage (HadISST ICE, Version 1.1).
2
http://badc.nerc.ac.uk/view/badc.nerc.ac.uk__ATOM__dataent_hadisst
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It has to be noted that, there is trend decreasing mean values of sea ice coverage due to
climate change; this is already captured by the interactive graph (Figure 11) showing a
shift towards lowest values (see from 2000 onwards, where in general values are found
below average).
Figure 11 Sea Ice extent plotted along the year
Average values for the period 1981-2010 is computed (black thick line, 2 standard
deviations shaded grey area), however 2013 values are also highlighted (brownish thick
line).
Source: National Snow and Ice Data Center (NSIDC), interactive graph at
http://nsidc.org/arcticseaicenews/charctic-interactive-sea-ice-graph/
3.2.5 Water column
Note the strong temporal character of the pelagic environment, for this reason the water
column can be classified differently at different periods of the year (Davies et al., 2004).
Additionally, the water column presents a different spatial dimension, the depth.
Consequently, a range of different ecosystem types can exist at different depths of the
water column. This quality mismatches the approach proposed (1 pixel = 1 ecosystem
type). For these reasons and considering the resources available, the water column
ecosystem types are not included in the present study. However, for a future approach it
is proposed to consider the possibility to combine ecosystem types creating new classes
(e.g. combined ecosystem 1: water column over sublittoral sediment - A1 and A7). But
this proposal has to be further explored and developed.
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3.3 Marine rules
According to all the described particularities and the data availability, the rules to define
marine ecosystems are based on depth, substrate and presence of sea ice. Additionally,
euphotic zone could discriminate infra and circa littoral ecosystems (A3 and A4).
Table 3 Summary of marine ecosystems rules
Source: ETC-SIA
Right now, the discrimination between EUNIS classes A3 and A4 is not possible with the
available datasets, as to do so it is needed the depth zone. Accordingly, it is proposed a
mixed class composed by A3 and A4 (A34: code 134).
As it has been commented in previous section (3.2.4), an approach is proposed to test
the 3D ecosystems associated to sea ice cover. The rationale considers where ice
ecosystems are present other ecosystem types may develop below the ice substrate.
Accordingly, two different ecosystems do develop in one single place (or pixel). This issue
can be solved by a specific coding which can describe a mixed class composed by sea ice
class (A8) and any other one from A1 to A6. Following the same example, where there is
presence of sea ice and below it is developed a littoral rock ecosystem (A1), the final
code for that ecosystem would be A18 as it combines A1 and A8.
A99 corresponds to those pixels where substrate is none of the analysis selected classes,
and thus it is unclassified. In practise, this is occurs in coastal areas where the coastline
of the different input layers do not match.
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Table 4 Grid labels, codes and short description of ecosystem types
Source: ETC-SIA
3.4 Datasets
A summary of datasets used in the current analysis is presented in the following table.
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Table 5 Datasets used as input data to define the marine ecosystems
Source
EEA
EEA
EEA
Spatial
res
Dataset
Short description
CLC 00 v17
The Corine Land Cover (CLC) is an European programme, coordinated by
the European Environment Agency (EEA), providing consistent information
on land cover and land cover changes across Europe. CLC products are
based on the photointerpretation of satellite images by the national teams
of the participating countries - the EEA member or cooperating countries.
The resulting national land cover inventories are further integrated into a
seamless land cover map of Europe based on standard methodology and
nomenclature.
CLC 06 v17
The Corine Land Cover (CLC) is an European programme, coordinated by
the European Environment Agency (EEA), providing consistent information
on land cover and land cover changes across Europe. CLC products are
based on the photointerpretation of satellite images by the national teams
of the participating countries - the EEA member or cooperating countries.
The resulting national land cover inventories are further integrated into a
seamless land cover map of Europe based on standard methodology and
nomenclature.
European
Sea Regions
MSFD provisional dataset on sea regions and sub-regions. Draft version of
the regions boundaries at sea to be used for the MSFD reporting. This
dataset has not been approved by Member States. EEA internal version,
Sep. 2013
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100 m
100 m
Temp
res
2000
2006
Link
/data/continental/europe/nat
ural_areas/corine_land_cover
/land_cover/eea_r_3035_100
_m_clc_2000_rev17/
/data/continental/europe/nat
ural_areas/corine_land_cover
/land_cover/eea_r_3035_100
_m_clc_2000_rev17/
ftps://sdi.eea.europa.eu/data
/continental/europe/water/m
sfd/eea_v_4258_1_mio_msfd
-searegions_2013/Regional_seas_
extended_version_ETRS89_20
130925.shp
Comments
European Topic Centre Spatial Information and Analysis
EUSeaMap
Predicted
habitats
These layes iare predictive EUNIS seabed habitat maps for the analysed
seas. These maps follow the EUNIS 2007-11 classification system. They do
not include the intertidal zone.
MESH
Atlantic
Broad-scale
EUNIS
habitat maps
EUROSION
Coastal
classification
map
MEDINA
Coastal
typology
This layer is a predictive EUNIS seabed habitat map for the Atlantic area.
The layer has been created using three pre-processed input datasets:
substrate, biological zone and energy.
The seabed substrate type layer is a compendium of historical maps. The
biological zones layer was modeled thanks to layers of bathymetry, light
attenuation, and wave wavelength. The layer of energy was prepared
thanks to archived results of numerical models of waves and currents.
The map follows the EUNIS 2007-11 classification system supplemented by
additional categories in deep sea areas (Howell et al., 2010). The map does
not include the intertidal zone.
Baseline information on the different factors influencing coastal erosion
processes and the value of assets at risk. The geology map from EUROSION
shows the geological patterns of the European coast, classifying the
coastline into several classes (rocks and hard cliffs, small beaches, muddy
sediments, embankments, vegetated strands, soft strands with ‘rocky
platforms’, etc.)
Morpho-sedimentological typology of Mediterranean North African (NA)
coastline. The division of the NA coastline is a succession of contiguous
segments according to main typologies derived by visual discrimination
using satellite imagery, which makes possible to distinguish between 5
principal classes: Rocky coast, Beaches, Interdial wetlands, Mouths, and
Artificial.
dbSEABED
Several maps were created to identify distinctive ecosystem types,
including global maps on hard and soft bottoms. Data from the database
was extracted to generate binary maps , where each cell was assigned a
value of hard or soft substrate depending on the presence of hard
substrate in each sample (samples with greater than 50% hard substrate
were counted as hard; and all others were counted as soft).
NCEAS
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http://www.emodnetseabedhabitats.eu
http://www.emodnet-
1:1,000,000
2013 seabedhabitats.eu
1:100,000
2004 abase/
http://www.eurosion.org/dat
2013 http://medinageoportal.eu
2 arcminutes
http://www.nceas.ucsb.edu/
GlobalMarine
More information on
dbSEABED:
http://instaar.colorad
o.edu/~jenkinsc/dbse
abed/
European Topic Centre Spatial Information and Analysis
EMODNET
GEBCO
NASA
Bathymetry
A harmonised EMODnet Digital Terrain Model (DTM) is generated for
European sea regions from selected bathymetric survey data sets and
composite DTMs, while gaps with no data coverage are completed by
integrating the GEBCO Digital Bathymetry.
GEBCO 08
Grid
GEBCO (General Bathymetric Chart of the Oceans) provides global
bathymetry data sets for the world's oceans. The GEBCO 08 Grid is a global
30 arc-second grid largely generated by combining quality-controlled ship
depth soundings with interpolation between sounding points guided by
satellite-derived gravity data.
MODIS_MO
D29
The sea ice map produced based on the sea ice by reflectance in the
algorithm is stored as coded integers in the Sea_Ice_by_Reflectance SDS.
The sea ice algorithm identifies pixels as sea ice, ocean, land, inland water,
cloud or other condition.
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http://portal.emodnetbathymetry.eu
http://www.gebco.net/data_a
nd_products/gridded_bathym
etry_data/
30 arcsecond
1km
2000presen
t
http://nsidc.org/data/modis/
order_data.html
3.5 Data workflow
The data workflow is summarised in the following schemas.
Figure 12 Data workflow process – Preparing datasets
Source: ETC-SIA
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Figure 13 Data workflow process – Applying marine ecosystems rules
Source: ETC-SIA
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The workflow can be divided into three different steps described in python scripts
allowing future reproducibility (see Annexes for further details):

Analysis extent – to define the extent of analysis

Pre-processing thematic data – those processes to prepare data for the analysis

Marine rules – algorithms matching the criteria to define the described ecosystem
types
3.6 Methodology
3.6.1 Analysis extent
The final extent of analysis must be enlarged (compared to first version map of
ecosystem types) to include all the European sea regions together with wet coastal and
marine CLC classes (see ch. 1 Delimitation of boundaries). It has to be noted that CLC 00
has been used to fill the gaps present at CLC 06 (e.g. Greece). First of all, a rasterization
of sea regions must be performed (Annex 1. Rasterize sub regions).
Due to the different delineation of both datasets, an overlapping of boundaries produces
the selection of areas that should not be included in the analysis; that is some areas
where CLC is anything different from wet coastal or marine classes but it is artificially
included in the analysis because it is contained in a sea region. In these cases, a mask
made by CLC can be applied using the map algebra. However, this is not useful for those
areas where CLC is not available (e.g. Svalbard archipelago).
Figure 14 Example of unclassified pixels (in yellow) due to unmatched
boundaries between CLC and sea regions in Svalbard archipelago (left) and
Northern Black Sea (right)
Source: Marine Ecosystem Map (2014)
For further technical details, check the Annex 2. Extent of analysis script.
3.6.2 Bathymetry composite
Similarly to the case of CLC, a gap filling of high resolution EMODNET bathymetry is
proposed with GEBCO data.
For further technical details, check the Annex 3. Bathymetry composite script.
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3.6.3 Seabed class homogenisation
The different seabed datasets need to be reclassified according the EUNIS coding scheme
proposed before (see Table 4).
Figure 15 Substrate data homogenisation
Source: ETC-SIA
For the Atlantic North, the following table has been used to aggregate the biological
zones into the target EUNIS classification.
Table 6 Depth ranges for the Biological zones used by the UK EUSeaMap project
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Source: McBreen et al., 2010 (table 8, page 32)
The same occurs with the ancillary substrate datasets that provide information of seabed
nature at the coast. These datasets come from EUROSION project (see Figure 6 for the
extent coverage) and MEDINA project (for North African coast, from Morocco to Egypt).
Table 7 EUROSION geomorphology classes aggregation into hard/soft substrate
Source: Adapted from EUROSION project (2004)
Table 8 MEDINA geomorphology classes aggregation into hard/soft substrate
Source: Adapted from MEDINA project (2014)
Once the homogenisation field has been created, the different sources must be combined
in a single composite raster one for primary seabed sources (EUSeaMap and and another
for secondary sources (EUROSION, MEDINA and Halpern). For further details on the
computation process check the scripts at Annex 4. Primary seabed data integration
and Annex 5. Secondary seabed data integration.
3.6.4 Sea ice data
This dataset was already processed last year; check the script at Annex 6. Sea ice
script.
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3.6.5 Marine and coastal ecosystem rules
Finally, the execution of ecosystem rules can be applied according to defined workflow
(ch. 3.5 Data workflow). For further technical details, check Annex 7. Marine and
coastal rules script.
4. DISCUSSION
First remark goes to the increase in the extent of analysis. An amount of 334.788.466 ha
have been added in the new version (year 2014) of the ecosystems typology map,
enlarging the extent to more than 1.000 million ha of marine ecosystems
(1.128.129.219 ha). This enlargement has caused a notable increase in the number of
unclassified pixels, resulting from a lack of data for at least one of the defining variables.
Figure 16 Distribution of hectares per each ecosystem major type (compared
results from 2013 and 2014 methodological approaches)
Source: ETCSIA
On the other side, the prioritised used of more accurate data has reduced the
thematic uncertainty. This is derived by the rule of using 1) EUSeaMap and MESH data
where it is available; 2) EUROSION and MEDINA substrate data for 1 st km seawards; and
3) NCEAS elsewhere. Though the improvement achieved by the inclusion of these
datasets has not been directly computed, it can be particularly observed by the
distribution of hectares in the littoral ecosystems. In the version of 2014, there is a clear
dominance of littoral sediment ecosystems (a2) over littoral rock ones (a1).
In this line, further room for improvement is available and expected to be achievable as
more accurate datasets will soon be released. Another improvement comes from
the enlargement of extent, as more areas will be covered. This is the case for the
EUSeaMap phase II datasets as hydrodynamics models, seabed substrate layers,
bathymetry, etc., are expected to be publicly available by the end of 2014. In general,
the data sources used so far are of a low level of detail (broad-scale), but as source
layers with higher resolution will be available, enhanced modelled habitat maps will be
produced and released; consequently, the resulting maps will be highly enhanced.
It is important to highlight that the EUSeaMap project does not include the intertidal
zone. Therefore, there is an important gap in seabed data for this ecological range;
consequently, the proposed model is a good approach, with a resulting map
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available and comparable at pan-European level for the littoral ecosystems.
Nevertheless, this map presents some shortcomings due to the lack of spatially explicit
seabed data at this ecological zone. Possible improvements in this area should consider
the modelling of substrate character (hard/soft) including slope, depth, curvature, and
wave exposure.
In the proposed model, it is still missing a general discrimination between infra
and circa littoral ecosystems. A proposed approach using the Euphotic Depth layer
from EMIS/JRC has been presented in this report (see 3.2.3 Light availability); it should
be considered and incorporated in any future update of the model.
Finally, it has also to bear in mind the update of EUNIS classification. In 2014–2015
the European Topic Centre on Biological Diversity plans to update the marine section of
the EUNIS habitat classification. As the present methodology is primarily based on the
EUNIS classification, any update on this reference will imply a modification of the
proposed model.
5. PROPOSED ACTIONS FOR FUTURE
A summary of next actions already described in the different sections of the present
report is proposed as an ending note:

Use enhanced EUSeaMap phase II datasets available by mid-December 2014:
o
extend the coverage to Canary Islands, the remaining Mediterranean areas
(Adriatic, Ionian and Aegean Seas, and the Black Sea)
o
increase thematic reliability of resulting maps by the improvement of
intermediate data (hydrodynamics models, seabed
substrate layers,
bathymetry, etc.).

Propose a model to define seabed substrate at 1 st km from the coast using a
combination of depth, slope, terrain curvature, and a measure of coastal.

Incorporate Euphotic Depth layer from EMIS/JRC.

Analyse the changes derived from new EUNIS habitat classification.
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REFERENCES
Bekkby, T., Moy, F.E., Kroglund, T., Gitmark, J.K., Walday, M., Rinde, E.R. and
Norderhaug, K.M. 2009. Identifying rocky seabed using GIS-modeled predictor variables.
Marine Geodesy, 32: pp. 379-390, DOI: 10.1080/01490410903297816
Burrows, M.T., Harvey, R., Robb, L. 2008. Wave exposure indices from digital coastlines
and the prediction of rocky shore community structure. Mar Ecol Prog Ser, 353:1-12,
DOI: 10.3354/meps07284
Cameron, A. and Askew, N. (eds.). 2011. EUSeaMap - Preparatory Action for
development and assessment of a European broad-scale seabed habitat map final report.
Available at http://jncc.gov.uk/euseamap (Accessed on 23/04/2013)
Davies, C.E., Moss, D. & Hill, M.O. 2004. EUNIS habitat classification revised 2004.
European Topic Centre on Nature Protection and Biodiversity, Paris.
EUSeaMap. 2012. Light data and thresholds – Technical appendix N.1. Available at
https://webgate.ec.europa.eu/maritimeforum/system/files/TechnicalAppendix_Light_EUS
eaMap_20121010_FINAL.pdf (Accessed on 23/07/2013)
Evans, D. & Royo-Gelabert, E. 2013. Crosswalks between European marine habitat
typologies: A contribution to the MAES marine pilot. ETCBD report for the EEA.
Frost, N. J., & Swift, R.H. 2010. Accessing and developing the required biophysical
datasets and datalayers for Marine Protected Areas network planning and wider marine
spatial planning purposes: Report No.11: Task 1C. Assessing the confidence of broad
scale classification maps.
Halpern, B., Walbridge, S., Selkoe, K., Kappel, C., Michelli, F., D’Agrosa, C., Bruno, F.,
Casey, K., Ebert, C., Fox, H., Fujita, R., Heinemann, D., Leninah, H., Madin, E., Perry, M.,
Selig, E., Spalding, M., Steneck, R. & Watson, R. 2008.A global map of human impact on
marine ecosystems. Science 319 (5865): 948-952
Lee, Z. et al. 2007. Euphotic zone depth: Its derivation and implication to ocean-color
remote sensing. J. Geophys. Res. 112, C03009, DOI: 10.1029/2006JC003802
McBreen, F., Askew, N. & Cameron, A. 2011 a. UKSeaMap 2010: Technical Report 4 –
Energy. JNCC Report
McBreen, F., Askew, N., Cameron, A., Connor, D., Ellwood, H. & Carter, A. 2011 b.
UKSeaMap 2010: Predictive mapping of seabed habitats in UK waters. JNCC Report , No.
446.
Morel, A., Huot, Y., Gentili, B., Werdell, P.J., Hooker, S.B., Franz, B.A. 2007. Examining
the consistency of products derived from various ocean color sensors in open ocean
(Case 1) waters in the perspective of a multi-sensor approach. Remote Sensing of
Environment, 111, pp. 69–88
Ryther, J.H.. 1956. Photosynthesis in the ocean as a function of light intensity. Limnology
and Oceanography, 1, pp. 61–70
Sauquin, B., Hamdi, A., Gohin, F., Populus, J., Manguin, A., Fantond’Andon, O.
2013.Estimation of the diffuse attenuation coefficient KdPAR using MERIS and application
to seabed habitat mapping. Remote Sensing of Environment, 128 (2013), pp. 224-233
VLIZ.2012. Maritime Boundaries Geodatabase, version
http://www.marineregions.org/.Consulted on 2013-06-27.
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ANNEX 1. RASTERIZE SUB REGIONS
# ----------------------------------------------------------------------------# rasterize_SubRegions.py
# Created on: 2014-09-15
# Author: Raquel Ubach (ETCSIA / UAB)
# Description: Rasterization of EU sea subregions
# -----------------------------------------------------------------------------
# Import arcpy module
import arcpy, os
from arcpy import env
from arcpy.sa import *
# Local variables:
extent = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\extent"
inpath = 'V:\\Personal\\r_ubach\\2014\\MarineData\\SeaRegions\\SubRegions'
env.workspace = inpath
featureList = arcpy.ListFeatureClasses()
for feature in featureList:
arcpy.AddMessage(feature)
short_name = feature [0:4]
outRaster = inpath + "\\" + short_name + "_200"
if not os.path.exists (outRaster):
# Process: Polygon to Raster
env.snapRaster = extent
env.cartographicCoordinateSystem =
"PROJCS['ETRS_1989_LAEA',GEOGCS['GCS_ETRS_1989',DATUM['D_ETRS_1989',SPHEROID['GRS_1980',6378137.0,298.25722
2101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Azimuthal_Equal_Area'],PARAM
ETER['False_Easting',4321000.0],PARAMETER['False_Northing',3210000.0],PARAMETER['central_meridian',10.0],PARAMETER['l
atitude_of_origin',52.0],UNIT['Meter',1.0]]"
arcpy.PolygonToRaster_conversion(feature, "FID", outRaster, "MAXIMUM_AREA", "", 200)
ANNEX 2. EXTENT OF ANALYSIS SCRIPT
# --------------------------------------------------------------------------# AnalysisExtent.py
# Created on: 2014-07-09
# Author: Raquel Ubach (ETCSIA / UAB)
# Description: Computation of the extent of analysis in two steps
#
1. extraction of clc classes
#
2. integration of European sea regions
# ---------------------------------------------------------------------------
# Import arcpy module
import arcpy, os
from arcpy import env
from arcpy.sa import *
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# Check out any necessary licenses
arcpy.CheckOutExtension("spatial")
# Input data:
## CLC v 17 from sdi.eea.europa.eu
##/data/continental/europe/natural_areas/corine_land_cover/land_cover/eea_r_3035_100_m_clc_2000_rev17/
CLC00 = Raster("D:\\Raquel\\Marine_14\\input\\clc00.tif" )
##/data/continental/europe/natural_areas/corine_land_cover/land_cover/eea_r_3035_100_m_clc_2006_rev17/
CLC06 = Raster("D:\\Raquel\\Marine_14\\input\\clc06.tif")
## Eureopean Sea Regions from sdi.eea.europa.eu
## data/continental/europe/water/msfd/eea_v_4258_1_mio_msfd-searegions_2013/Regional_seas_extended_version_ETRS89_20130925.shp
SeaReg_Pol = "D:\\Raquel\\Marine_14\\input\\SeaReg.shp"
def CLC_Extract (CLC00, CLC06):
arcpy.AddMessage("Starting CLC extraction")
# Process: Gap filling of CLC06 with CLC00 (CLCv17)
# This process refills those pixels with "no data" in clc06 with data provided by clc00, mainly to fill gaps in Greece.
arcpy.AddMessage("Process: Gap filling of CLC06 with CLC00")
clc = "D:\\Raquel\\Marine_14\\Process\\clc"
if not os.path.exists(clc):
result1 = Con(IsNull (CLC06), CLC00, CLC06)
result1.save (clc)
# Process: Select CLC classes related to coastal and marine waters environment (423, 521, 522 and 523;
corresponding to raster values 39, 42, 43 and 44)
arcpy.AddMessage("Process: Select CLC classes related to marine waters environment")
clc_sel = "D:\\Raquel\\Marine_14\\Process\\clc_sel"
if not os.path.exists(clc_sel):
arcpy.gp.ExtractByAttributes_sa(clc, "\"Value\" = 39 OR \"Value\" = 42 OR \"Value\" = 43 OR \"Value\" =
44", clc_sel)
def SeaRegion_int (SeaReg_Pol):
arcpy.AddMessage("Starting SeaRegion integration")
clc_sel = "D:\\Raquel\\Marine_14\\Process\\clc_sel"
env.snapRaster = clc_sel
env.cellSize = 100
# Process: Project EU Sea Regions polygon layer to LAEA
arcpy.AddMessage("Process: Project EU Sea Regions polygon layer to LAEA")
SeaRegLAEA = "D:\\Raquel\\Marine_14\\Process\\SeaRegLAEA.shp"
if not os.path.exists(SeaRegLAEA):
arcpy.Project_management(SeaReg_Pol, SeaRegLAEA,
"PROJCS['ETRS_1989_LAEA',GEOGCS['GCS_ETRS_1989',DATUM['D_ETRS_1989',SPHEROID['GRS_1980',6378137.0,298.25722
2101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Azimuthal_Equal_Area'],PARAM
ETER['False_Easting',4321000.0],PARAMETER['False_Northing',3210000.0],PARAMETER['Central_Meridian',10.0],PARAMETER['L
atitude_Of_Origin',52.0],UNIT['Meter',1.0]]", "",
"GEOGCS['GCS_ETRS_1989',DATUM['D_ETRS_1989',SPHEROID['GRS_1980',6378137.0,298.257222101]],PRIMEM['Greenwich',
0.0],UNIT['Degree',0.0174532925199433]]")
# Process: Rasterize EU Sea Region
SeaReg = "D:\\Raquel\\Marine_14\\Process\\SeaReg"
if not os.path.exists(SeaReg):
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arcpy.AddMessage("Process: Rasterize EU Sea Region " + str(i))
arcpy.PolygonToRaster_conversion(SeaReg_shp, "id", SeaReg, "MAXIMUM_AREA", "", 100)
# Process: Combine selected clc classes with outer Sea Region
marine_ext = "D:\\Raquel\\Marine_14\\Process\\marine_ext"
arcpy.AddMessage("Process: Combine selected clc classes with outer Sea Region")
if not os.path.exists(marine_ext):
result2 = Con(IsNull (clc), SeaReg, clc_sel)
result2.save (marine_ext)
arcpy.AddMessage("Process: Full extent")
# Process: Reclass final extent
arcpy.AddMessage("Process: Reclass final extent")
extent = "D:\\Raquel\\Marine_14\\Process\\marine_ext"
if not os.path.exists(extent):
result3 = Reclassify(marine_ext, "Value", RemapRange([[1, 44, 1]]))
result3.save(extent)
# Process: Remove overlapping clc pixels from sea regions boundaries
arcpy.AddMessage("Process: Remove overlapping clc pixels from sea regions boundaries")
extent = "D:\\Raquel\\Marine_14\\Process\\extent"
clc = "D:\\Raquel\\Marine_14\\Process\\clc"
if not os.path.exists(extent):
result4 = Con(clc, SetNull(clc, marine_ext, ' "VALUE" < 39 OR "VALUE" = 40 OR "VALUE" = 41'),
marine_ext)
result4.save(extent)
# Execute functions
CLC_Extract (CLC00, CLC06)
SeaRegion_int (SeaReg_Pol)
ANNEX 3. BATHYMETRY COMPOSITE SCRIPT
# --------------------------------------------------------------------------# Bathymetry_v2.py
# Created on: 2014-09-11
# Author: Raquel Ubach (ETCSIA / UAB)
# Description: Integration of different bathymetry datasets for the whole
#
extent of analysis
# ---------------------------------------------------------------------------
# Import arcpy module
import arcpy, os
from arcpy import env
from arcpy.sa import *
# Check out any necessary licenses
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arcpy.CheckOutExtension("spatial")
# Input data:
## Emodnet bathymetry
## http://www.emodnet.eu/bathymetry
All_Ascii_folder = "D:\\WkSpace\\Marine\\input\\bathymetry\\AllAscii"
## GEBCO_08 grid 30 arc-second grid
## http://www.gebco.net/data_and_products/gridded_bathymetry_data/
gebco = Raster("R:\\Marine_14\\input\\bathymetry")
### GDAL conversion from nc to tif
### gdal_translate -a_srs EPSG:4326 GEBCO_08.nc GEBCO_08.tif
### gdal_translate -co COMPRESS=LZW -a_srs EPSG:4326 GEBCO_08.nc GEBCO_08.tif
### os.system("gdal_translate -of GTiff " + sourcefile + " " + destinationfile)
def EMODNET_Trans ():
arcpy.AddMessage("Process: bathymetry from EMODNET - transformation processes ")
# Process: Ascii to raster conversion
arcpy.AddMessage("Process: Ascii to raster conversion")
for file in os.listdir(All_Ascii_folder):
file_nm = file [0:4]
outRaster = "D:\\WkSpace\\Marine\\input\\bathymetry\\grids\\" + str(file_nm)
arcpy.ASCIIToRaster_conversion(file, outRaster, "INTEGER")
# Process: Create target raster
arcpy.AddMessage("Process: Create target raster")
TargetRast = "D:\\WkSpace\\Marine\\Process\\bathy0"
out_path = "D:\\WkSpace\\Marine\\Process"
out_name = "bathy0"
if not os.path.exists (TargetRast):
arcpy.CreateRasterDataset_management(out_path, out_name, "", "16_BIT_SIGNED", "", 1)
# Process: Workspace to raster
arcpy.AddMessage("Process: Workspace to raster")
in_workspace = "D:\\WkSpace\\Marine\\input\\bathymetry\\grids"
in_raster_dataset = TargetRast
arcpy.WorkspaceToRasterDataset_management (in_workspace, in_raster_dataset, "", "LAST", "MATCH")
# Process: Project bathymetry datasets to LAEA projection
arcpy.AddMessage("Process: Project bathymetry datasets to LAEA projection")
arcpy.DefineProjection_management(TargetRast, "GCS_WGS_1984")
OutRast_pr = "D:\\WkSpace\\Marine\\Process\\bathy\\bathy_pr"
arcpy.ProjectRaster_management(TargetRast,
"ETRS_1989_to_WGS_1984")
OutRast_pr,
"ETRS_1989_LAEA",
"BILINEAR",
def GEBCO08 ():
arcpy.AddMessage("Process: bathymetry from GEBCO08 - transformation processes ")
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# Process: Project extent of analysis to GEBCO_08 projection (GCS_WGS_1984)
arcpy.AddMessage("Process: Project extent of analysis to GEBCO_08 projection (GCS_WGS_1984)")
in_Rast = Raster("D:\\WkSpace\\Marine\\Process\\extent")
out_Rast = "D:\\WkSpace\\Marine\\Process\\extentWGS84_3"
out_CRS = Raster('D:\\2014\\184_1_1_EcoMapping\\inputData\\GEBCO_08.tif') ## "GCS_WGS_1984"
transf = "ETRS_1989_to_WGS_1984"
if not os.path.exists (out_Rast):
arcpy.ProjectRaster_management(in_Rast, out_Rast, out_CRS , "", 100, transf)
# Process: Extract by mask
arcpy.AddMessage("Process: Extract by mask")
in_rast = Raster('D:\\2014\\184_1_1_EcoMapping\\inputData\\GEBCO_08.tif')
mask = out_Rast
gebco_extr = "D:\\2014\\184_1_1_EcoMapping\\inputData\\gebco_extr"
if not os.path.exists (gebco_extr):
outExtractByMask = ExtractByMask(in_rast, mask)
outExtractByMask.save (gebco_extr)
# Process: Project bathymetry datasets to LAEA projection
arcpy.AddMessage("Process: Project bathymetry datasets to LAEA projection")
gebco08_LAEA = "D:\\2014\\184_1_1_EcoMapping\\Process\\gebco08_3"
if not os.path.exists(gebco08_LAEA):
env.snapRaster = "D:\\WkSpace\\Marine\\Process\\clc"
LAEA_rast = Raster("D:\\WkSpace\\Marine\\Process\\bathy\\bathy_pr")
transf= "ETRS_1989_to_WGS_1984"
arcpy.ProjectRaster_management(gebco_extr, gebco08_LAEA, LAEA_rast, "BILINEAR", 100, "")
def Bathymetry_composite ():
arcpy.AddMessage("Bathymetry composite filling EMODNET gaps with GEBCO data")
emodnet = Raster("D:\\WkSpace\\Marine\\Process\\bathy\\bathy_pr")
gebco08_LAEA = Raster("D:\\WkSpace\\Marine\\Process\\gebco_ext")
# Process: Full extent - Combine both datasets by fulfilling extent with clc and SeaReg
env.extent = "D:\\WkSpace\\Marine\\Process\\extent"
bathymetry = "D:\\WkSpace\\marine\\Process\\ext\\bathymetry"
if not os.path.exists(bathymetry):
result1 = Con(IsNull (emodnet), gebco08_LAEA, emodnet)
result1.save (bathymetry)
arcpy.AddMessage("Process: Full bathymetry")
# Execute functions
EMODNET_Trans ()
GEBCO08 ()
Bathymetry_composite ()
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ANNEX 4. PRIMARY SEABED DATA INTEGRATION
# --------------------------------------------------------------------------# SeabedInt.py
# Created on: 2014-09-16
# Author: Raquel Ubach (ETCSIA / UAB)
# Description: Integration of different seabed datasets for the whole
#
extent of analysis
# ---------------------------------------------------------------------------
# Import arcpy module
import arcpy, os
from arcpy import env
from arcpy.sa import *
# Check out any necessary licenses
arcpy.CheckOutExtension("spatial")
def Seabed_composite ():
arcpy.AddMessage("Seabed composite")
# Input data:
extent = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\extent"
atl = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\PrimarySeabed\\atl"
atlnc = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\PrimarySeabed\\atlnc"
baltic = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\PrimarySeabed\\baltic"
wmed = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\PrimarySeabed\\wmed"
# Process: Full extent - Combine both datasets by fulfilling extent with clc and SeaReg
env.extent = extent
pr_seabed = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\PrimarySeabed\\prseabed"
if not os.path.exists(pr_seabed):
result1 = Con (extent, (Con(IsNull (wmed), atl, wmed)))
result2 = Con (extent, (Con(IsNull (result1), atlnc, result1)))
result3 = Con (extent, (Con(IsNull (result2), baltic, result2)))
result4 = Con (extent, (Con(IsNull (result3), 1, result3)))
result5 = Con (extent, (Con(result4==0, 1, result4)))
result5.save (pr_seabed)
arcpy.AddMessage("Process: Full primary seabed integration")
# Execute function
Seabed_composite ()
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ANNEX 5. SECONDARY SEABED DATA INTEGRATION
# --------------------------------------------------------------------------# Secondary_seabed.py
# Created on: 2014-09-18
# Author: Raquel Ubach (ETCSIA / UAB)
# Description: Preparation of secondary seabed datasets
# ---------------------------------------------------------------------------
# Import arcpy module
import arcpy, os
from arcpy import env
from arcpy.sa import *
# Check out any necessary licenses
arcpy.CheckOutExtension("spatial")
def Secondary_seabed ():
arcpy.AddMessage("Process: Preparation of secondary seabed datasets")
# Input data:
extent = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\extent"
# Process1: Feature to raster (Eurosion)
arcpy.AddMessage("Process1: Feature to raster (Eurosion)")
eurosion = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\eurosion"
coast_EU = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\euros_rt"
if not os.path.exists (coast_EU):
arcpy.AddMessage("Process1: Feature to raster")
arcpy.env.snapRaster = extent
arcpy.env.outputCoordinateSystem =
"PROJCS['ETRS_1989_LAEA',GEOGCS['GCS_ETRS_1989',DATUM['D_ETRS_1989',SPHEROID['GRS_1980',6378137.0,298.25722
2101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Azimuthal_Equal_Area'],PARAM
ETER['false_easting',4321000.0],PARAMETER['false_northing',3210000.0],PARAMETER['central_meridian',10.0],PARAMETER['la
titude_of_origin',52.0],UNIT['Meter',1.0]]"
arcpy.FeatureToRaster_conversion(coast, "Subs_cd", coast_EU, "100")
# Process2: Feature to raster (MEDINA)
arcpy.AddMessage("Process2: Feature to raster (MEDINA)")
medina = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\medina"
coast_NA = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\na_rt"
if not os.path.exists (coast_EU):
arcpy.AddMessage("Process1: Feature to raster")
arcpy.env.snapRaster = extent
arcpy.env.outputCoordinateSystem =
"PROJCS['ETRS_1989_LAEA',GEOGCS['GCS_ETRS_1989',DATUM['D_ETRS_1989',SPHEROID['GRS_1980',6378137.0,298.25722
2101]],PRIMEM['Greenwich',0.0],UNIT['Degree',0.0174532925199433]],PROJECTION['Lambert_Azimuthal_Equal_Area'],PARAM
ETER['false_easting',4321000.0],PARAMETER['false_northing',3210000.0],PARAMETER['central_meridian',10.0],PARAMETER['la
titude_of_origin',52.0],UNIT['Meter',1.0]]"
arcpy.FeatureToRaster_conversion(medina, "Subs_cd", coast_NA, "100")
# Process3: Integrate both
euros_medi_pre = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\euromedi0"
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# Process4: Euclidean allocation (1km)
euros_medi = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\euromedi"
# Process5: Integration with Halpern substrate
halpern = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\halpern"
substrate = "V:\\Personal\\r_ubach\\2014\\MarineData\\MAPPING\\Process\\SarySeabed\\ssubstrate"
if not os.path.exists (substrate):
result1 = Con (IsNull(euros_medi), Con(beach, 0, halpern), euros_medi)
result1.save(substrate)
# Execute function
Secondary_seabed ()
ANNEX 6. SEA ICE SCRIPT
# SeaIce.py
# Created on: 2013-09-13
# Author: Raquel Ubach (ETCSIA - UAB)
# Description: Script to reclassify all scenes to one single value for the
#
presence of sea ice
# ---------------------------------------------------------------------------
# Import arcpy module
importarcpy, os
fromarcpy import env
from arcpy.sa import *
# Set the current workspace
env.workspace = "V:\\Personal\\r_ubach\\2013\\MarineData\\SeaIce"
# Local variables:
inputTable = "D:\\2013\\222_51_EcosystemMapping\\SEAICETI.dbf"
field = "TILE"
# Process: Iterating through the table where reference of sea ice scenes are stored
scenes = arcpy.SearchCursor(inputTable)
scene = scenes.next ()
arcpy.AddMessage("Process: Iterating through the table")
while scene:
ref = scene.getValue(field)
arcpy.AddMessage("Process: scene " + str(ref))
refEnd = "*" + str(ref) + ".hdf"
rasterList = arcpy.ListRasters(refEnd, "")
for raster in rasterList:
outRast = "V:\\Personal\\r_ubach\\2013\\MarineData\\SeaIce2\\" + str(ref)
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if not os.path.exists(outRast):
# Process: Save to grid
arcpy.AddMessage("Process: Save " + str(ref) + " to grid")
arcpy.CopyRaster_management (raster, outRast)
outRecPath = "V:\\Personal\\r_ubach\\2013\\MarineData\\SeaIce_rec\\rec_" + str(ref)
if not os.path.exists(outRecPath):
# Process: create a mask with sea ice coverage by reclassifying
arcpy.AddMessage("Process: create a mask with sea ice coverage by reclassifying")
reclassField = "Value"
recRange = RemapRange([[ 0, 199, "NODATA"], [200, 1],[201, 254, "NODATA"]])
outRec = Reclassify (outRast, reclassField, recRange, "NODATA")
outRecPath = "V:\\Personal\\r_ubach\\2013\\MarineData\\SeaIce_rec\\rec_" + str(ref)
arcpy.AddMessage("Process: save the reclassifying output " + str(ref))
outRec.save (outRecPath)
scene = scenes.next()
ANNEX 7. MARINE AND COASTAL RULES SCRIPT
# -----------------------------------------------------------------------------# MarineRules14.py
# Created on: 2014-09-22
# Author: Raquel Ubach (ETCSIA / UAB)
# Description: Execution of marine rules to compute major marine ecosystem types
# ------------------------------------------------------------------------------
# Import arcpy module
import arcpy, os
from arcpy import env
from arcpy.sa import *
# Check out any necessary licenses
arcpy.CheckOutExtension("spatial")
def Marine_rules ():
arcpy.AddMessage("Execution of marine rules to compute major marine ecosystem types")
# Input data:
bathymetry = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\bathymetry")
seaice = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\seaice")
pr_seabed = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\prseabed") # Primary seabed -> integration of substrate data
from EUSeabed and MESH projects
s_seabed = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\sseabed") # Secondary seabed -> integration of substrate data
from Eurosion, MEDINA and Halpern projects
extent = "D:\\WkSpace\\Marine\\Process\\extent2"
clc_sel = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\clc_sel") # selection of coastal and marine classes (clc == 39, 42,
43 and 44)
marine_ext = "D:\\WkSpace\\Marine\\Process\\ext\\clc3944_sreg" # extent where primary seabed is null and without clc
== 42 and 43 (only marine clc ==39 and 44 and outer boundaries of sea regions)
trans_wt = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\transwt_1km") # buffer of transitional waters up to 1km
clc = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\clc")
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# Setting workspace
ws_folder = "D:\\WkSpace\\Marine\\Output\\MarineRules"
if not os.path.exists (ws_folder):
os.makedirs (ws_folder)
# Marine ecosystem rules where no primary seabed is available
env.cellSize = 100
env. mask = marine_ext
env.extent = extent
## Ecosystems with no sea ice
# Ecosystem type "Littoral" A12 (nodata available to differenciate between hard and soft substrates (mixed A1 and A2))
outRast = ws_folder + "\\a12"
if not os.path.exists (outRast):
# Littoral mixed (A12) must agree with following conditions:
# 1. CLC == 44 or seawards up to marine sea regions boundaries (env. mask = marine_ext)
# 2. bathymetry >= 0
# 3. there is no primary seabed data
# 4. there is no secondary seabed data
# 5. there is no seaice
result = Con (bathymetry >= 0, Con (IsNull (s_seabed), Con (IsNull (seaice), 112)))
result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
# Ecosystem type "Littoral rock and other hard substrata" A1
outRast = ws_folder + "\\a1"
if not os.path.exists (outRast):
# Littoral rock and other hard substrata (A1) must agree with following conditions:
# 1. CLC == 44 or seawards up to marine sea regions boundaries
# 2. bathymetry >= 0
# 3. there is no primary seabed data (pr_seabed == 1)
# 4. secondary seabed data == 2 (hard substrate)
# 5. there is no seaice
result = Con(bathymetry >= 0, Con (s_seabed == 2, Con (IsNull (seaice), 101)))
result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
# Ecosystem type "Littoral sediment" A2
outRast = ws_folder + "\\a2"
if not os.path.exists (outRast):
# Littoral sediment (A2) must agree with following conditions:
# 1. CLC == 44 or seawards up to marine sea regions boundaries
# 2. bathymetry >= 0
# 3. there is no primary seabed data (pr_seabed == 1)
# 4. secondary seabed data == 1 (soft substrate)
# 5. there is no seaice
result = Con(bathymetry >= 0, Con (s_seabed == 1, Con (IsNull (seaice), 102)))
result.save (outRast)
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arcpy.AddMessage("Process: finished " + str(outRast))
# Ecosystem type "Infra and circalittoral rock and other hard substrata" A34 (mixed EUNIS classes A3 and A4)
outRast = ws_folder + "\\a34"
if not os.path.exists (outRast):
# Infra and circalittoral rock and other hard substrata (A34) must agree with following conditions:
# 1. CLC == 44 or seawards up to marine sea regions boundaries
# 2. (bathymetry < 0)& (bathymetry >= -200)
# 3. there is no primary seabed data
# 4. secondary seabed data == 2 (hard substrate)
# 5. there is no seaice
result = Con(((bathymetry < 0)& (bathymetry >= -200)), Con (s_seabed == 2, Con (IsNull (seaice), 134)))
result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
# Ecosystem type "Sublittoral sediment" A5
outRast = ws_folder + "\\a5"
if not os.path.exists (outRast):
# Sublittoral sediment (A5) must agree with following conditions:
# 1. CLC == 44 or seawards up to marine sea regions boundaries
# 2. (bathymetry < 0)& (bathymetry >= -200)
# 3. there is no primary seabed data
# 4. secondary seabed data == 1 (soft substrate)
result = Con(((bathymetry < 0)& (bathymetry >= -200)), Con (s_seabed == 1, Con (IsNull (seaice), 105)))
result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
# Ecosystem type "Deep-sea" A6
outRast = ws_folder + "\\a6"
if not os.path.exists (outRast):
# Deep-sea (A6) must agree with following conditions:
# 1. CLC == 44 or seawards up to marine sea regions boundaries
# 2. bathymetry < -200
# 3. there is no seaice
result = Con(bathymetry < -200, Con (IsNull (seaice), 106))
result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
## Ecosystems with sea ice
# Ecosystem type "Littoral and sea ice" A128 (mixed A1 and A2 and A8))
outRast = ws_folder + "\\a128"
if not os.path.exists (outRast):
# Littoral and sea ice (A128) must agree with following conditions:
# 1. CLC == 44 or seawards up to marine sea regions boundaries (env. mask = marine_ext)
# 2. bathymetry >= 0
# 3. there is no primary seabed data
# 4. there is no secondary seabed data
# 5. there is seaice
result = Con (seaice == 1, Con (bathymetry >= 0, Con (IsNull (s_seabed), 128)))
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result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
# Ecosystem type "Littoral rock and other hard substrata and sea ice" A18
outRast = ws_folder + "\\a18"
if not os.path.exists (outRast):
# Littoral rock and other hard substrata (A1) must agree with following conditions:
# 1. CLC == 44 or seawards up to marine sea regions boundaries
# 2. bathymetry >= 0
# 3. there is no primary seabed data (pr_seabed == 1)
# 4. secondary seabed data == 2 (hard substrate)
# 5. there is seaice
result = Con (seaice == 1, Con (bathymetry >= 0, Con (s_seabed == 2, 181)))
result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
# Ecosystem type "Littoral sediment and sea ice" A28
outRast = ws_folder + "\\a28"
if not os.path.exists (outRast):
# Littoral sediment and ice (A28) must agree with following conditions:
# 1. CLC == 44 or seawards up to marine sea regions boundaries
# 2. bathymetry >= 0
# 3. there is no primary seabed data (pr_seabed == 1)
# 4. secondary seabed data == 1 (soft substrate)
# 5. there is seaice
result = Con (seaice == 1, Con (bathymetry >= 0, Con (s_seabed == 1, 182)))
result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
# Ecosystem type "Infra and circalittoral rock and other hard substrata" A34 (mixed EUNIS classes A3 and A4)
outRast = ws_folder + "\\a348"
if not os.path.exists (outRast):
# Infra and circalittoral rock and other hard substrata (A34) must agree with following conditions:
# 1. CLC == 44 or seawards up to marine sea regions boundaries
# 2. (bathymetry < 0)& (bathymetry >= -200)
# 3. there is no primary seabed data
# 4. secondary seabed data == 2 (hard substrate)
# 5. there is seaice
result = Con (seaice == 1, Con(((bathymetry < 0)& (bathymetry >= -200)), Con (s_seabed == 2, 138)))
result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
# Ecosystem type "Sublittoral sediment and sea ice" A58
outRast = ws_folder + "\\a58"
if not os.path.exists (outRast):
# Sublittoral sediment and sea ice(A58) must agree with following conditions:
# 1. CLC == 44 or seawards up to marine sea regions boundaries
# 2. (bathymetry < 0)& (bathymetry >= -200)
# 3. there is no primary seabed data
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# 4. secondary seabed data == 1 (soft substrate)
# 5. there is seaice
result = Con (seaice == 1, Con(((bathymetry < 0)& (bathymetry >= -200)), Con (s_seabed == 1, 185)))
result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
# Ecosystem type "Deep-sea and sea ice" A68
outRast = ws_folder + "\\a68"
if not os.path.exists (outRast):
# Deep-sea (A6) must agree with following conditions:
# 1. CLC == 44 or seawards up to marine sea regions boundaries
# 2. bathymetry < -200
# 3. there is seaice
result = Con (seaice == 1, Con(bathymetry < -200, 186))
result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
# Ecosystem type "Unclassified" A99
# It includes all those pixels where there is no data for substrate
# except for the ones where bathymetry < -200 that is independent of substrate
outRast = ws_folder + "\\a99"
if not os.path.exists(outRast):
result = Con (IsNull(s_seabed), Con((bathymetry < 0)& (bathymetry >= -200), Con(IsNull(trans_wt), 250)))
# remove from unclassified those pixels where clc is terrestrial, due to mismatches bw searegion and extent delineations
whereClause = '"VALUE" <39 OR "VALUE" = 40 OR "VALUE" = 41'
result2 = SetNull(clc, result, whereClause)
result2.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
def Coastal_rules ():
arcpy.AddMessage("Execution of coastal rules to compute major wet coastal ecosystem types")
# Input data:
extent = "D:\\WkSpace\\Marine\\Process\\extent"
clc_sel = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\clc_sel") # selection of coastal and marine classes (clc == 39, 42,
43 and 44)
coastal_ext = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\clc4243") # selection of coastal classes (clc == 42 and 43 )
s_seabed = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\sseabed") # Secondary seabed
bathymetry = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\bathymetry")
trans_wt = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\transwt_1km") # buffer of transitional waters up to 1km
# Setting workspace
ws_folder = "D:\\WkSpace\\Marine\\Output\\CoastalRules"
if not os.path.exists (ws_folder):
os.makedirs (ws_folder)
# Marine ecosystem rules where no primary seabed is available
env.cellSize = 100
env.extent = extent
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## Coastal Ecosystems
# Ecosystem type "Estuaries" X1
outRast = ws_folder + "\\x1"
if not os.path.exists (outRast):
# Estuaries (X1) must agree with following conditions:
# 1. CLC == 43
# 2. those unclassified pixels matching with transitional waters
result = Con (clc_sel == 43, 109, Con (IsNull(s_seabed), Con(bathymetry >= -200, Con(trans_wt, 109))))
result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
# Ecosystem type "Coastal lagoons" X23 (mixed EUNIS classes X2 and X3)
outRast = ws_folder + "\\x23"
if not os.path.exists (outRast):
# Coastal lagoons (X23) must agree with following conditions:
# 1. CLC == 42
result = Con (clc_sel == 42, 110)
result.save (outRast)
arcpy.AddMessage("Process: finished " + str(outRast))
def Integrate_ecosystems ():
arcpy.AddMessage("Integration of major marine ecosystem types into one raster dataset")
# Input data:
pr_seabed = Raster ("D:\\WkSpace\\Marine\\Process\\ext\\prseabed")
ws_folder = "D:\\WkSpace\\Marine\\Output\\MarineRules"
# Integrate all rasters from marine rules into one dataset
arcpy.AddMessage("Integrate all rasters from marine rules into one dataset")
out_path = "D:\\WkSpace\\Marine\\Output\\Out"
if not os.path.exists (out_path):
os.makedirs (out_path)
out_name = "mosaic_ss"
mosaic_ss = str(out_path ) +"\\" + str(out_name)
if not os.path.exists (mosaic_ss):
arcpy.AddMessage("Create target dataset to integrate ecosystems derived from secondary seabed data")
arcpy.CreateRasterDataset_management(out_path, out_name, 100.0, "8_BIT_UNSIGNED", "", 1)
arcpy.WorkspaceToRasterDataset_management(ws_folder, mosaic_ss, "", "FIRST")
# Integrate all rasters from coastal rules into one dataset
arcpy.AddMessage("Integrate all rasters from coastal rules into one dataset")
cs_folder = "D:\\WkSpace\\Marine\\Output\\CoastalRules"
out_name = "mosaic_cs"
mosaic_cs = str(out_path ) + "\\" + str(out_name)
if not os.path.exists (mosaic_cs):
arcpy.AddMessage("Create target dataset to integrate ecosystems derived from coastal data")
arcpy.CreateRasterDataset_management(out_path, out_name, 100.0, "8_BIT_UNSIGNED", "", 1)
arcpy.WorkspaceToRasterDataset_management(cs_folder, mosaic_cs, "", "FIRST")
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# Integrate the primary and secondary seabed derived ecosystem types (marine types) with the coastal ones
arcpy.AddMessage("Integrate the primary and secondary seabed derived ecosystem types")
# mosaic_sb = "D:\\WkSpace\\Marine\\Output\\mosaic_sb"
# if not os.path.exists (mosaic_sb):
# result = Con (pr_seabed == 1, Con(IsNull(mosaic_ss), mosaic_cs, mosaic_ss), pr_seabed)
# result.save (mosaic_sb)
# arcpy.AddMessage("Process: finished " + str(mosaic_sb))
OutRast_name = "cmarine_ec"
marine_ec = str(out_path ) + "\\" + str(OutRast_name)
if not os.path.exists (marine_ec):
#arcpy.CreateRasterDataset_management(out_path, OutRast_name, 100.0, "8_BIT_UNSIGNED", "", 1)
input_rasters = "pr_seabed;mosaic_cs;mosaic_ss"
arcpy.MosaicToNewRaster_management(input_rasters,
"MAXIMUM", "")
out_path,
OutRast_name,
"",
"8_BIT_UNSIGNED",
100,
# Execute functions
Marine_rules ()
Coastal_rules ()
Integrate_ecosystems ()
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1,