222_5_1 Developing conceptual framework for ecosystem mapping and ecosystem status indicator

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1 Working document Projects Activity 222_5_1 Developing conceptual framework for ecosystem mapping and ecosystem status indicator Mapping marine ecosystems Partners involved UAB and UMA Date 30/06/2013 Prepared by: Raquel Ubach and Dania AbdulMalak Contents 1. Content description Delimitation of boundaries Exclusive Economic Zone (EEZ) Coastal area Target classification Marine habitats Mapping approaches Common framework Marine particularities Seabed Depth Light availability Ice Water column Marine rules Datasets Data workflow Results Recommendations for future References Annex 1.Crosswalk table between Marine and Coastal EUNIS habitat types and CLC classes Annex 2.Marine rules script Annex 3. Extent Annex 4. Sea ice script Annex 5. Sea Ice available datasets Marine Mapping report 1/30

2 Figure 1. Conceptual framework for mapping marine habitats Source: ETCSIA Marine Mapping report 2/30

3 1. CONTENT DESCRIPTION This document presents the method and datasets proposed to develop the conceptual framework and the tools to define the coastal and marine ecosystem mapping at Pan- European level, considering the available datasets. This activity is part of a wider task (222_5_2) within the ETC-SIA 2013 IP that will define both terrestrial and marine ecosystems at Pan-European level. Also, this task is in close collaboration with ETC-BD. 2. DELIMITATION OF BOUNDARIES The extent of the coastal and marine Pan-European mapcovers the area from the coastline (check 2.2. for more details) to the exclusive economic zone (EEZ zones) including the area beyond and adjacent to the territorial European Seas. Figure 2. Extent of analysis Source: EEZ v7 (VLIZ, 2012) and European coastline. 2.1 Exclusive Economic Zone (EEZ) The EEZ is the seazone over which a state has the right to manage and use the marine resources. The general ruledescribed by the UN Convention on the Law of the Sea stretches 200 nautical miles (370.4 km) from the coast baseline, though there are some exceptions when overlapping occurs. Marine Mapping report 3/30

4 As this delimitation is widely accepted among countries, UN bodies, etc., it will be used as the outer boundary in this analysis. The dataset used is the World EEZ v7 ( ), developed by the Flanders Marine Institute (VLIZ, 2012), as it is the first available dataset on global public domain cover. 2.2 Coastal area Considering that the reference dataset for this task is CLC, the coastal area defined by CLC is the terrestrial boundary for the extent of the present analysis. Accordingly, the coastal area (10 km stripe) based on CLC v16 is used as the inner boundary. For the marine ecosystems, all pixels defined in CLC as sea and ocean (class 523) and as intertidal flats (class 423) are added to the extent of analysis. 3. TARGET CLASSIFICATION The target classification is a combination between the EUNIS classification at level 3 (from 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. 3.1 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). At EUNIS level 2, 8 habitat types are described: Marine Mapping report 4/30

5 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. Marine Mapping report 5/30

6 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 Littoral periodically inundated shores of marine water o Infralittoral shallow subtidal o Circalittoral moderately deep subtidal o Offshore circalittoral offshore water, depth <200m o Bathyal depth >200m Depth zones, more detailed than altitude zones, are divided as follows: o Upper shore o Mid-shore o Low shore o 0-5m o 5-10m o 10-20m o 20-30m o 30-50m o m o >200m Substrate can be: o Mobile o Non-mobile Salinity values are comprised between: o Fully saline ppt o Variable salinity ppt fluctuating on a regular basis o Reduced salinity ppt o Low salinity - <18 ppt According to wetness/dryness criteria: o Aquatic open or free-standing fresh or saline water Marine Mapping report 6/30

7 o Frequently submerged predominantly aquatic (saline or brackish) but subject to occasional but regular emersion 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 A7 A8 Altitude zones Depth zones Substrate Salinity levels Bathyal Offshore circalittoral x x Circalittoral x x x Infralittoral 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 30 50m x x x m x x >200m x x Mobile x x x Non mobile x x x x Water Ice Fully saline x x x x x x x Reduced salinity x x x x x x x Low salinity x x x x x x Variable salinity x x x x x x Aquatic x x x x x x x Wetness/dryness Frequently submerged x x Source: Adapted from EUNIS habitat description. 1 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 discriminate those ecosystems characterised by mobile sediments from those of rocky and hard substrates. x x x 1 Marine Mapping report 7/30

8 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. 4. MAPPING APPROACHES 4.1 Common framework As part of a wider work, a common framework is set to provide a pan-european map of terrestrial, coastal, and marineecosystems. 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. 4.2 Marine particularities 5components 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, so the energy parameters will not be considered in this study Seabed The physical nature of the seabedsubstratum 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. After the experience and results from previous projects, EUSeaMap produced broad-scale modelled habitat maps for the Baltic, Celtic, North and western Mediterraneanseas under the EUNIS classification (see figure 4). Right now there are available more than 2 million square kilometres of European seabed, but not the whole analysis extent is covered. The work is still in progress, and expected to be published under the EMODNET project by the end of 2014(Evans &Royo-Gelabert, 2013). Unfortunately, this dataset cannot be used for the present analysis. Marine Mapping report 8/30

9 Figure 4.EUSeaMap present data coverage (June 2013) Source: EUSeaMap project (Mapping European seabed habitats, 2013). 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 dbseabed 1 project, this database compiles benthic substrate point samples data on 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 maps 2, 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 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 Marine Mapping report 9/30

10 - 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 5. Global seabed map differentiating hard (greenish, value 2) and soft substrates (brownish, value 1) Source: derived from dbseabed Depth The bathymetry can be used to discriminate the major divisions of coastal, shelf and open ocean (figure 5).The shelf break occurs at variable depth; however a general rule can be applied considering 200m the average lower limit for the edge of the shelf (Davies et al., 2004). For the depth, GEBCO 1 (General Bathymetric Chart of the Oceans) provides global bathymetry data sets for the world's oceans. The GEBCO 08 Grid is a global 30 arcsecond 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. 1 Marine Mapping report 10/30

11 Figure 6. The major oceanic divisions Source: Wikimedia commons The definition of depth ranges must be done according the ecosystem type definitions as summarised in Table Light availability 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,k d (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, Z eu, 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. Additionally, EUSeaMap presents some regional corrections, as summarised in the following table. 1 K d (λ,e%): Spectral diffuse attenuation coefficient for downwelling irradiance between E d (λ,0) and % of E d (λ,0) Marine Mapping report 11/30

12 Table 2. Regional corrections for thresholds to define the different biological zones Zones Regional Seas Celtic and North Seas Baltic Sea Western Mediterranean Infralittoral Upper circalittoral Circalittoral 0m 1% light reaches the seabed 1% light reaches the seabed Wave base Deep circalittoral Wave base 200m Upper slope 200m 750m Upper bathyal 750m 1,100m Mid bathyal 1,100m 1,800m Bathyal Lower bathyal 1,800m 2,700m Abyssal 2,700m and deeper Source: EUSeaMap, m 1.6 ratio of depth:secchi depth for oligohaline OR 2.5 ratio of depth:secchi depth for mesohaline 1.6 ratio of depth:secchi depth and deeper for oligohaline (deepest zone) OR 2.5 ratio of depth:secchi depth Position of deep halocline for mesohaline Position of deep halocline and deeper for mesohaline (deepest zone) 0m 1% light reaches the seabed 1% light reaches the seabed 0.01% light reaches the seabed 0.01% light reaches the seabed Shelf edge (manual delineation) Shelf edge (manual delineation) Slope change (manual interpretation) Slope change (manual interpretation) 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 (K d490 ) 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 (K d490 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 Z eu map using SeaWiFs composite processed data (Morel et al., 2007). And more recently, satellite derived k dpar and Z eu 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. Marine Mapping report 12/30

13 4.2.4 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: Myocean 1 MODIS 2 NASA products:national Snow and Ice Data Centre 3 Reverb Echo 4 British Atmospheric Data Centre 5 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. Due to resources constrains, data from a single day has been downloaded (corresponding to ), though if possible mean values covering a wider temporal range would increase the accuracy of results. 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) 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. 1 ean_variable[]=cf-standard-name%23sea-ice&product_type[]=temporal-scale%23multiyear&product_type[]=temporal-scale%23invariant&text[]=&text[]=&records_per_page= words=ice%20surface 5 This dataset contains Sea Surface Temperature climatologies (HadISST SST, Version 1.1) and Sea Ice coverage (HadISST ICE, Version 1.1). ATOM dataent_hadisst Marine Mapping report 13/30

14 4.3 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. Table 3. Summary of marine ecosystems rules bathymetry substrate seai ice cover a1 >= 0 hard no a2 >= 0 soft no a34 < 0 and >= 200 hard no a5 < 0 and >= 200 soft no a6 < 200 both no a18 >= 0 hard yes a28 >= 0 soft yes a38 < 0 and >= 200 hard yes a58 < 0 and >= 200 soft yes a68 < 200 both yes a99 any unclassified both 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 and no datasets are currently public available. Accordingly, it is proposed a mixed class composed by A3 and A4 (A34, code 234). As it has been commented in previous section (4.2.4), 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 an 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. Table 4. Grid labels, codes and short description of ecosystem types label code description a1 210 EUNIS class A1 a2 220 EUNIS class A2 a mixed EUNIS classes A3 and A4 a5 250 EUNIS class A5 a6 260 EUNIS class A6 a mixed EUNIS classes A1 and A8 a mixed EUNIS classes A2 and A8 a mixed EUNIS classes A3 and A4 and A8 a mixed EUNIS classes A5 and A8 a mixed EUNIS classes A6 and A8 a unclassified 4.4 Datasets A summary of datasets used in the current analysis is presented in the following table. Marine Mapping report 14/30

15 Table 5. Datasets used as input data to define the marine ecosystems Marine Mapping report 15/30

16 4.5 Data workflow The data workflow is summarised in the following schema. Figure 7. Data workflow process 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 Marine Mapping report 16/30

17 5. RESULTS Results are composed by a single grid for each ecosystem type, which can be joined in a single mosaic (as presented in the following map). Figure 8. Final marine ecosystems map Source: ETCSIA As it could be expected, the deep-sea bed ecosystem type is the most frequent representing the 68,54% of the total extent. It is followed by the sublittoral sediment, with a 28%. It has to be noted that the dbseabed may overestimate the amount of soft substrate (Halpern et al., 2008). This may occur for a couple of different reasons: from one side, the methodology assumes that samples with greater than 50% hard substrate are counted as hard and all others are counted as soft; from the other, the data may reflect the tendency to avoid sampling hard substrates as they may present more difficult conditions for extraction. Marine Mapping report 17/30

18 Table 6. Distribution of results per marine ecosystem type (ha and %) ecosystem types code ha % littoral rock a ,37% littoral rock and sea ice a ,00% littoral sediment a ,08% littoral sediment and sea ice a ,00% infralittoral circalittoral rock a ,81% infralittoral circalittoral rock and sea ice a ,00% sublittoral sediment a ,03% sublittoral sediment and sea ice a ,01% deep sea bed a ,54% deep sea bed and sea ice a ,07% unclassified a ,08% total cells RECOMMENDATIONS FOR FUTURE Though a methodology has been proposed in this analysis, there are still many sources of uncertainty. In particular, this is the case for the spatial and thematic resolution of substrate data, and the spatial gaps with near 0,08% of the total marine extent of analysis, which still remain unclassified. It is known that better datasets are being prepared by the project EUSeaMap, though still not covering the whole extent of European waters. The use of this dataset is highly recommended when it becomes complete and publicly available. Moreover, this methodology can be notably improved by the use of the light availability in the water column and reaching the seabed. The depth of the euphotic zone has not been computed in the present analysis, so no differentiation between A3 and A4 ecosystems has been possible. However, different approaches can be taken meanwhile the EUSeaMap datasets are not available, as global EO products for K d490, PAR (Photosynthetically active radiation) and [Chl-a] (Chlorophyll-a concentration) are already distributed products. Several equations (Morel et al., 2007) must be then considered to compute the Z eu, depending on the data used as input: K d490 = [Chl-a] [eq 1] K dpar = K d K -1 d490 [eq 2] Z eu = -ln 0.01 / K dpar [eq 3] So using the combination of the remote sense data and the corresponding equations, the derived euphotic range can be obtained. In this way, the differentiation between infralittoral and circalittoral ecosystems (A3 and A4) would be feasible. It has to be accounted that light availability changes through time (seasonal and climatic variability), so data from a wide temporal range should be used (see Annex 4 for available EO datasets). Finally, the presence of sea ice has been tested using EO data from a single day. This test has resulted with a classification of ha (summing A18, A28, A38, A58 and A68) as ice-associated ecosystems. However, the results of this analysis could be more accurate if considering a wider temporal range to compute the mean sea ice coverage for a given period. Marine Mapping report 18/30

19 REFERENCES Cameron, A. and Askew, N. (eds.) EUSeaMap - Preparatory Action for development and assessment of a European broad-scale seabed habitat map final report. Available at (Accessed on 23/04/2013) Davies, C.E., Moss, D. & Hill, M.O EUNIS habitat classification revised European Topic Centre on Nature Protection and Biodiversity, Paris. EUSeaMap Light data and thresholds Technical appendix N.1. Available at eamap_ _final.pdf (Accessed on 23/07/2013) Evans, D. & Royo-Gelabert, E Crosswalks between European marine habitat typologies: A contribution to the MAES marine pilot. ETCBD report for the EEA. Frost, N. J., & Swift, R.H 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 A global map of human impact on marine ecosystems. Science 319 (5865): McBreen, F., Askew, N. & Cameron, A a. UKSeaMap 2010: Technical Report 4 Energy. JNCC Report McBreen, F., Askew, N., Cameron, A., Connor, D., Ellwood, H. & Carter, A b. UKSeaMap 2010: Predictive mapping of seabed habitats in UK waters. JNCC Report, No Morel, A., Huot, Y., Gentili, B., Werdell, P.J., Hooker, S.B., Franz, B.A 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 (2007), pp Ryther, J.H Photosynthesis in the ocean as a function of light intensity. Limnology and Oceanography, 1 (1956), pp Sauquin, B., Hamdi, A., Gohin, F., Populus, J., Manguin, A., Fantond Andon, O Estimation of the diffuse attenuation coefficient K dpar using MERIS and application to seabed habitat mapping. Remote Sensing of Environment, 128 (2013), pp VLIZ Maritime Boundaries Geodatabase, version 7. Available online at on Marine Mapping report 19/30

20 ANNEX 1.CROSSWALK TABLE BETWEEN MARINE AND COASTAL EUNIS HABITAT TYPES AND CLC CLASSES. EUNIS_Code EUNIS name EUNIS Level Anzahl CLC A3 Infralittoral rock and other hard substrata A3.1 Atlantic and Mediterranean high energy A3.2 Atlantic and Mediterranean moderate A3.3 Atlantic and Mediterranean low energy A3.4 Baltic exposed infralittoral rock A3.5 Baltic moderately exposed infralittoral A3.6 Baltic sheltered infralittoral rock A3.7 Features of infralittoral rock A4 Circalittoral rock and other hard substrata A4.1 Atlantic and Mediterranean high energy A4.2 Atlantic and Mediterranean moderate A4.3 Atlantic and Mediterranean low energy A4.4 Baltic exposed circalittoral rock A4.5 Baltic moderately exposed circalittoral A4.6 Baltic sheltered circalittoral rock A4.7 Features of circalittoral rock A5 Sublittoral sediment A5.1 Sublittoral coarse sediment A5.2 Sublittoral sand A5.3 Sublittoral mud A5.4 Sublittoral mixed sediments A5.5 Sublittoral macrophyte dominated A5.6 Sublittoral biogenic reefs A5.7 Features of sublittoral sediments A6 Deep sea bed A6.1 Deep sea rock and artificial hard substrata A6.2 Deep sea mixed substrata A6.3 Deep sea sand A6.4 Deep sea muddy sand A6.5 Deep sea mud A6.6 Deep sea bioherms A6.7 Raised features of the deep sea bed A6.8 Deep sea trenches and canyons, channels, A6.9 Vents, seeps, hypoxic and anoxic habitats A7 Pelagic water column A7.1 Neuston A7.2 Completely mixed water column with A7.3 Completely mixed water column with full A7.4 Partially mixed water column with A7.5 Unstratified water column with reduced A7.6 Vertically stratified water column with A7.7 Fronts in reduced salinity water column A7.8 Unstratified water column with full A7.9 Vertically stratified water column with full A7.A Fronts in full salinity water column A8 Ice associated marine habitats A8.1 Sea ice A8.2 Freshwater ice A8.3 Brine channels A8.4 Under ice habitat C2 Surface running waters C2.4 Tidal rivers, upstream from the estuary Water courses Water bodies Estuaries Sea and ocean Marine Mapping report 20/30

21 ANNEX 2.MARINE RULES SCRIPT # marine_rules.py # Created on: # Author: Raquel Ubach (ETCSIA - UAB) # Description: Rules to compute the marine ecosystem types # # Import arcpy module import arcpy, os from arcpy import env from arcpy.sa import * # Set the current workspace env.workspace = "D:\\WkSpace\\marine" env.cellsize = 100 env.extent = "D:\\WkSpace\\marine\\extentclc" # Local variables: extent = Raster ("D:\\WkSpace\\marine\\extentclc") bathyextent = Raster ("D:\\WkSpace\\marine\\bathyextent") # bathymetry for the extent of analysis substrate = Raster ("D:\\WkSpace\\marine\\substrate") seaice = Raster ("D:\\WkSpace\\marine\\seaice") bathyextsubs = Raster ("D:\\WkSpace\\marine\\bathyextsubs") # bathymetry for the extent of analysis including substrate mask # Process: Applying marine ecosystem rules # Ecosystem type A1 outrast = "D:\\WkSpace\\marine\\a1" if not os.path.exists(outrast): result = Con(bathyextsubs >= 0, Con(substrate == 2, Con (IsNull(seaice), 210))) result.save (outrast) arcpy.addmessage("process: finished " + str(outrast)) # Ecosystem type A2 outrast = "D:\\WkSpace\\marine\\a2" if not os.path.exists(outrast): result = Con(bathyextsubs >= 0, Con(substrate == 1, Con (IsNull(seaice), 220))) result.save (outrast) arcpy.addmessage("process: finished " + str(outrast)) Marine Mapping report 21/30

22 # Ecosystem type A3 and A4 outrast = "D:\\WkSpace\\marine\\a34" if not os.path.exists(outrast): result = Con(((bathyextsubs < 0)& (bathyextsubs >= -200)), Con(substrate == 2, Con (IsNull(seaice), 234))) result.save (outrast) arcpy.addmessage("process: finished " + str(outrast)) # Ecosystem type A5 outrast = "D:\\WkSpace\\marine\\a5" if not os.path.exists(outrast): result = Con(((bathyextsubs < 0)& (bathyextsubs >= -200)), Con(substrate == 1, Con (IsNull(seaice), 250))) result.save (outrast) arcpy.addmessage("process: finished " + str(outrast)) # Ecosystem type A6 outrast = "D:\\WkSpace\\marine\\a6" if not os.path.exists(outrast): result = Con(bathyextent < -200, Con (IsNull(seaice), 260)) result.save (outrast) arcpy.addmessage("process: finished " + str(outrast)) # Ecosystem type A1 and A8 outrast = "D:\\WkSpace\\marine\\a18" if not os.path.exists(outrast): result = Con (seaice == 1, Con(bathyextsubs >= 0, Con(substrate == 2, 218))) result.save (outrast) arcpy.addmessage("process: finished " + str(outrast)) # Ecosystem type A2 and A8 outrast = "D:\\WkSpace\\marine\\a28" if not os.path.exists(outrast): result = Con (seaice == 1, Con(bathyextsubs >= 0, Con(substrate == 1, 228))) result.save (outrast) arcpy.addmessage("process: finished " + str(outrast)) # Ecosystem type A34 and A8 outrast = "D:\\WkSpace\\marine\\a38" if not os.path.exists(outrast): result = Con (seaice == 1, Con(((bathyextsubs < 0)& (bathyextsubs >= -200)), Con(substrate == 2, 238))) result.save (outrast) arcpy.addmessage("process: finished " + str(outrast)) Marine Mapping report 22/30

23 # Ecosystem type A5 and A8 outrast = "D:\\WkSpace\\marine\\a58" if not os.path.exists(outrast): result = Con (seaice == 1, Con(((bathyextsubs < 0)& (bathyextsubs >= -200)), Con(substrate == 1, 258))) result.save (outrast) arcpy.addmessage("process: finished " + str(outrast)) # Ecosystem type A6 and A8 outrast = "D:\\WkSpace\\marine\\a68" if not os.path.exists(outrast): result = Con (seaice == 1, Con(bathyextent < -200, 268)) result.save (outrast) arcpy.addmessage("process: finished " + str(outrast)) # Ecosystem type A99 outrast = "D:\\WkSpace\\marine\\a99" if not os.path.exists(outrast): #result = Con ( (IsNull(bathyext)), bathyext, Con ( IsNull (substrate), 299)) #result = Con (extent, Con ((IsNull(substrate)), 299)) result = Con ( (IsNull(substrate)), Con((bathyextent >= -200), Con (extent, 299))) result.save (outrast) arcpy.addmessage("process: finished " + str(outrast)) #(Elapsed Time: 1 hours 21 minutes 59 seconds) Marine Mapping report 23/30

24 ANNEX 3. EXTENT # extent.py # Created on: # Author: Raquel Ubach (ETCSIA - UAB) # Description: Rules to compute the marine ecosystem types # # Import arcpy module import arcpy, os from arcpy import env from arcpy.sa import * # Set the current workspace #env.workspace = "D:\\WkSpace\\marine" env.cellsize = 100 env.extent = "MAXOF" # Local variables: eez = Raster ("D:\\WkSpace\\marine\\eez") clc = Raster ("D:\\2013\\262_48_GreenInfrastructure\\DATA\\clc\\clc.tif") bathymetry = Raster ("D:\\WkSpace\\marine\\bathymetry") substrate = Raster ("D:\\WkSpace\\marine\\substrate") # Process: Extract selected clc classes clc_sel = "D:\\WkSpace\\marine\\clc_sel" if not os.path.exists(clc_sel): arcpy.gp.extractbyattributes_sa(clc, '"Value" = 39 OR "Value" = 44', clc_sel) arcpy.addmessage("process: finished " + str(clc_sel)) # Process: Refill extent with clc selected classes where EEZ is null extentclc = "D:\\WkSpace\\marine\\extentclc" if not os.path.exists(extentclc): result2 = Con(IsNull (eez), Con (clc_sel, 1), 1) result2.save (extentclc) arcpy.addmessage("process: finished " + str(extentclc)) # Process: Adjust the bathymetry to the analysis extent bathyextent = "D:\\WkSpace\\marine\\bathyextent" if not os.path.exists(bathyextent): result3 = extentclc * bathymetry result3.save (bathyextent) Marine Mapping report 24/30

25 arcpy.addmessage("process: finished " + str(bathyextent)) # Process: Adjust the substrate to the analysis extent substext = "D:\\WkSpace\\marine\\substext" if not os.path.exists(substext): result4 = Con(extentclc, Con(substrate, 1)) result4.save (substext) arcpy.addmessage("process: finished " + str(substext)) # Process: Adjust the bathymetry to the analysis extent where there is substrate data bathyextsubs = "D:\\WkSpace\\marine\\bathyextsubs" if not os.path.exists(bathyextsubs): result5 = Con(substext, bathyextent) result5.save (bathyextsubs) arcpy.addmessage("process: finished " + str(bathyextsubs)) Marine Mapping report 25/30

26 ANNEX 4. SEA ICE SCRIPT # SeaIce.py # Created on: # 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) 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 Marine Mapping report 26/30

27 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() Marine Mapping report 27/30

28 ANNEX 5. SEA ICE AVAILABLE DATASETS Source Dataset Short description Detailed description Variables Spatial res myocean Global Ocean OSTIA Sea Surface Temperature and Sea Ice analysis REPROCESSED ( ) For the Global Ocean The OSTIA global Sea Surface Temperature Reanalysis product provides daily gap free maps of sea surface temperature (referred to as an L4 product) at 0.05deg.x 0.05deg. horizontal resolution, using in situ and satellite data from infrared radiometers. The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system is run by the UK Met Office and produces a high resolution (1/20deg. approx. 5km) daily analysis of the sea surface temperature (SST) for the global ocean. The OSTIA reanalysis uses satellite data provided by the Pathfinder AVHRR project and reprocessed (A)ATSR data together with in situ observations from the ICOADS data set, to determine the sea surface temperature. It also uses reprocessed seaice concentration data from the EUMETSAT OSI SAF. sea surface temperature sea ice area fraction 0.05 degree (approx. 5km) Temp res Link myocean interactive catalogue.php/?option=com_csw&view=details&product_id=sst_glo_ myocean MODIS Global Ocean Sea Ice Concentration Time Series REPROCESSED ( ) MOD29 The reprocessed sea ice concentration dataset of the EUMETSAT OSI SAF, covering the period from October 1978 to October 2009 (SMMR and SSM/I). Ice concentration is computed from atmospherically corrected SSM/I brightness temperatures, using a combination of state of the art algorithms and dynamic tie points. It includes errorbars for each grid cell (uncertainties). Version 1 of the dataset was released early This product is generated using the MODIS sensor radiance data product (MOD021KM), the geolocation product (MOD03), and the cloud mask product (MOD35_L2). The output file contains sea ice extent, ice surface temperature (IST) and quality assessment (QA) SDSs, also latitude and longitude SDSs, all with local attributes and global attributes. The sea ice algorithm identifies sea ice covered oceans by reflectance characteristics; it also stimates ice surface temperature (IST) by the splitwindow technique. For complete global coverage the MOD29 sea ice product is generated for all swaths acquired during a day, 24 hours. Swaths that were acquired completely in night mode contain only the temperature based SDSs. Continuous monitoring of sea ice globally on a daily basis started with the launch of SeaSat in June 1978 and October the same year Nimbus 7 both with a SMMR instrument on board. SMMR was the first multi frequency and dual polarization microwave radiometer satellite instrument which was particularly well suited for sea ice monitoring. American satellite microwave radiometer instruments still provide sea ice observations today in an unbroken record since EUMETSAT initiated the reprocessing of both microwave radiometer datasets in 2005 and the OSI SAF team developed a new processing chain and selected new algorithms.. The first version the global sea ice concentration dataset was complete in Updates are planned with current satellites. 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. A color coded image of a sea ice extent map is the result. Ice surface temperature (IST) calculated by the split window technique is stored in this SDS. The IST is expressed in degrees Kelvin and is stored as calibrated data. To retrieve the IST the data must be descaled to degrees Kelvin using the calibration attributes. Other features such as land and clouds are coded with integer values Values 5000 are not valid IST values but represent the occurrence of land, clouds or other features or conditions in the swath. sea ice area fraction Sea Ice by Reflectance Ice Surface Temperature 12.5 km 1 km myocean interactive catalogue.php/?option=com_csw&view=details&product_id=seaice_g 2000 present Marine Mapping report 28/30

29 MODIS MODIS MOD29P1D MOD29P1N The daily level 3 sea ice product is the result of selecting an observation from the multiple observations mapped to a cell of the MOD29G product as the observation of the day. The daily sea ice product is a tile of data gridded in the Lambert Azimuthal Equal Area map projection. Spatial resolution is approximately 1 km. Tiles are approximately 1200 x 1200 km in area. There are two tile grids, one for the Northern Hemisphere and one for the Southern Hemisphere. Four SDSs along with local and global attributes compose the data product file. This product contains only the ice surface temperature (IST) data processed from MODIS thermal data acquired during night mode operation of the sensor. Two SDSs are stored in the product. The nighttime sea ice product is a tile of data gridded in the Lambert Azimuthal Equal Area map projection. Spatial resolution is approximately 1 km. Tiles are approximately 1200 x 1200 km in area. The sea ice map is the result of selecting the most favorable observation of all the swath level observations mapped into a grid cell for the day. Mapped sea ice extent, land, water, cloud or other condition. The Ice Surface Temperature (IST) map is the result of selecting the most favorable observation of all the swath level observations mapped into a grid cell for the day. Mapped are IST, land, water, cloud or other condition. Sea Ice by Reflectance Ice Surface Temperature Ice Surface Temperature (ºK) 1 km appr 1km 2000 present 2000 present NSIDC MultisensorAn alyzed Sea Ice Extent Northern Hemisphere (MASIE NH) The MultisensorAnalyzed Sea Ice Extent  Northern Hemisphere (MASIE NH) products provide measurements of daily sea ice extent and sea ice edge boundary for the Northern Hemisphere and 16 Arctic regions in a polar stereographic projection. Products include an ASCII text file of sea ice extent values in square km over the entire Northern Hemisphere with 16 separate Arctic regions identified, time series plots of the 16 regions, and image files that visually show where the sea ice is. The MASIE NH imagery are provided at a nominal 4 km resolution. The input data comes from the 4 km Interactive Multisensor Snow and Ice Mapping System (IMS) snow and ice product produced by the National Ice Center (NIC). NIC utilizes visible imagery, passive microwave data, and NIC weekly analysis products to create their data product. The MASIE NH products are distributed in a number of formats including ASCII text, GeoTIFF, PNG, shapefiles, and Google Earth files and are available for the previous four weeks (28 days). The most recent days worth of imagery plus the ASCII text data file are provided via the MASIE Web site. An archive of the previous four weeks of imagery can be obtained via FTP. 4 km ftp://sidads.colorado.edu/datasets/noaa/g02186/ NSIDC IMS Daily Northern Hemisphere Snow and Ice Analysis at 4 km and 24 km Resolution The National Environmental Satellite, Data, and Information Service (NESDIS), part of the National Oceanic and Atmospheric Administration (NOAA), has an extensive history of monitoring snow and ice coverage. Accurate monitoring of global snow and ice cover is a key component in the study of climate and global change as well as daily weather forecasting. By inspecting environmental satellite imagery, analysts from the Satellite Analysis Branch (SAB) at the Office of Satellite Data Processing and Distribution (OSDPD), Satellite Services Division (SSD), created a Northern Hemisphere snow and ice map from November 1966 until the National Ice Center (NIC) took over production in Initially, the product was produced with a nominal spatial resolution of 190 km and a temporal resolution of seven days. In 1997, the Interactive Multisensor Snow and Ice Mapping System (IMS) became operational, giving the satellite analysts improved access to imagery and drawing tools. Since the inception of IMS, the charts have been produced daily at a nominal resolution of 24 km (1024 x 1024 grid). Beginning in February 2004, further improvements in computer speed and imagery resolution allowed for the production of a higher resolution daily product with a nominal resolution of 4 km (6144 x 6144 grid). NSIDC archives and distributes the 24 km and the 4 km IMS product in ASCII text format from February 1997 to present and February 2004 to present, respectively. NSIDC also distributes browse images in GIF format and latitude and longitude grids for these products. In June 2006, NSIDC started distributing 4 km GeoTIFF files for use with GIS applications. 4 km ftp://sidads.colorado.edu/pub/datasets/noaa/g02156/ Marine Mapping report 29/30