D1.1 Requirements Baseline for Biodiversity of the Coastal Ocean: Monitoring with Earth Observation (BiCOME)
Abstract
Linking Remote sensing data with Essential Biodiversity Variables in coastal areas
Full text
Earth Observation Science for Society” element of FutureEO-1 BIODIVERSITY+ PRECURSORS Biodiversity of the Coastal Ocean: Monitoring with Earth Observation D1.1 Requirements Baseline Issue 5.0 Date 9 September 2022 Ref.: BiCOME-DEL-1.1 ESA contract no. 4000135756/21/I-EF
ESA contract no. 4000135756/21/I-EF Page 2 of 56 EUROPEAN SPACE AGENCY CONTRACT REPORT The work described in this report was done under ESA contract. Responsibility for the contents resides in the author or organisation that prepared it.
ESA contract no. 4000135756/21/I-EF Page 3 of 56 Project name: Biodiversity of the Coastal Ocean: Monitoring with Earth Observation (BiCOME) Document title: D1.1: Baseline requirements Reference no.: BiCOME-DEL-1.1 Issue date: 9 September 2022 Issue and revision: 5.0; related to SPTM 5.0 ESA contract no.: 4000135756/21/I-EF Organization: Plymouth Marine Laboratory, Prospect Place, PL1 3DH, Plymouth Name Company or Institute Signature Prepared by Victor Martinez Vicente (VMV); Stefanie Broszeit (SB); Shubha Sathyendranath (SS); Kim Hockley (KH) PML Dimos Traganos (DT); Avi Putri Pertiwi (AP) DLR Pierre Gernez (PG); Laurent Barillé (LB); Bede Davies (BD); Simon Oiry (SO) UN Reviewed by Maria Helene-Rio (MHR) ESA 26th October 2022, Javier Concha (JC) ESA Distribution list Maria Helene-Rio ESA/ESRIN
ESA contract no. 4000135756/21/I-EF Page 4 of 56 Version Date Authored Modifications 1.0 18-Nov-2021 VMV Initial draft 2.0 11-Jan-2022 SB, VMV Incorporating Early adopter contributions from interviews and SAG feedback 3.0 16-Feb-2022 VMV, SO, PG, LB, BD, AP, DT Major review taking into account feedback from the other Biodiversity + projects 3.1 07-March-22 SS Additional inputs 3.2 21-March-22 VMV Post-KO+1 incorporating comments. Version sent to ESA for final comments and SAG 3.3 18-May-22 VMV, KH, DT Incorporating comments from SAG. Final version. 4.0 25-July-22 VMV, KH, DT, LB, PG Incorporating final comments from ESA MHR, JC 5.0 09-Sept-22 VMV,KH,DT,LB,PG Incorporating Comments from ESA MHR, JC
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 5 of 56 Acronyms AC Atmospheric Correction ATBD Algorithm Theoretical Basis Document CBD Convention on Biological Diversity CMEMS Copernicus Marine Services EBV Essential Biodiversity Variable ECV Essential Climate Variable ESA European Space Agency EOV Essential Ocean Variable EUMETSAT European Organisation for the Exploitation of Meteorological Satellites CERTO Copernicus Evolution - Research for harmonised and Transitional water Observation DESIS DLR Earth Sensing Imaging Spectrometer EnMAP Environmental Monitoring and Analysis Program GOOS Global Ocean Observing System IPBES Intergovernmental Science Policy Platform on Biodiversity and Ecosystem Services DESIS DLR Earth Sens-ing Imag-ing Spec-trom-e-ter OCCCI Ocean Colour Climate Change Initiative PRISMA Hyperspectral Precursor and Application Mission PVR Product Validation Report SDG UN Sustainable Development Goals EnMAP Environmental Monitoring and Analysis Program SPTM Science and Policy Traceability Matrix VENµS Vegetation and Environment monitoring on a New MicroSatellite PVR Product Validation Report
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 6 of 56 Table of Contents 1 Introduction ................................................................................................................................. 10 1.1 Purpose and scope .............................................................................................................. 10 2 Framework and format of the revised SPTM .............................................................................. 11 2.1 Aligning broad scientific questions with policy drivers ....................................................... 12 2.2 Connecting scientific questions and policy drivers with remote sensing through Essential Ocean and Biodiversity variables .................................................................................................... 17 3 Intertidal ecosystems .................................................................................................................. 22 3.1 Science questions ................................................................................................................ 22 3.2 Policy questions ................................................................................................................... 23 3.3 Intertidal Early Adopters specific requirements ................................................................. 24 3.4 Algorithms available ............................................................................................................ 27 3.5 Datasets available ................................................................................................................ 28 3.6 Potential limitations of the approach ................................................................................. 29 3.7 Conclusions .......................................................................................................................... 29 4 Subtidal ecosystems .................................................................................................................... 31 4.1 Science questions ................................................................................................................ 31 4.2 Policy questions ................................................................................................................... 32 4.3 Subtidal Early Adopters specific requirements ................................................................... 32 4.4 Algorithms ........................................................................................................................... 34 4.5 Datasets ............................................................................................................................... 34 4.6 Potential limitations from the approach ............................................................................. 35 4.7 Conclusions .......................................................................................................................... 36 5 Pelagic ecosystems ...................................................................................................................... 37 5.1 Science questions ................................................................................................................ 37 5.2 Policy questions ................................................................................................................... 39 5.3 Pelagic Early Adopters Specific Requirements .................................................................... 40 5.4 Algorithms ........................................................................................................................... 42 5.5 Datasets ............................................................................................................................... 46 5.5.1 Phytoplankton blooms and seascapes ........................................................................ 46 5.5.2 Floating vegetation ...................................................................................................... 46 5.6 Challenges and potential limitations from the approach ................................................... 47 5.7 Conclusions .......................................................................................................................... 47 6 General conclusions .................................................................................................................... 48 7 References ................................................................................................................................... 49 Appendix 1: Glossary ........................................................................................................................... 55
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 7 of 56 List of figures Figure 1: Sequential process for the construction of the Science and Policy Traceability Matrix (SPTM) in the spreadsheet. The numbered items correspond to the tabs in the spreadsheet. 11 Figure 2: Expanding the relationship between Essential Ocean Variables (EOV) and Essential Biodiversity Variables (EBV). From Muller-Karger MBON (2021) slide, citing Pereira et al. (2017) and Framework for Ocean observing (GOOS, 2012). 18 List of tables Table 1 List of Essential Ocean Variables from Miloslavich et al. (2018) with the emerging EOV proposed in this report. Codes are defined for the Science and Policy Traceability Matrix. Some EOV are not included in BiCOME but on the associated ESA biodiversity project BOOMS: Biodiversity in the Open Ocean: Mapping, Monitoring and Modelling. .................................. 12 Table 2: Selected sectors and Knowledge gaps relevant to coastal ecosystems and to remote sensing from the non-exhaustive list of Knowledge Gaps in Appendix 4 of the IPBES report (IPBES, 2019) ................................................................................................................................................... 13 Table 3: Selection of the scientific questions and direct drivers related the Essential Ocean Variable: Macroalgal canopy cover and composition in the intertidal environment. The macroalgal canopy cover will not be addressed in BiCOME explicitly, but as a secondary part to seagrass canopy cover, therefore there is no explicit SPTM for intertidal macroalgal canopy cover. The scientific questions are derived from Miloslavich et al. (2018) review. Also included in this table is the link to knowledge gaps identified through IPBES and IPBES Nature’s Contribution to people (NCP). ............................................................................................................................ 14 Table 4 Selection of the scientific questions and direct drivers related the Essential Ocean Variable: Seagrass canopy cover and composition in the intertidal environment and addressed in BiCOME. A broader list of questions is given in spreadsheet sptm_intertidal_seagrass_v3.5, tab 1_Scientific questions, but these are not included in the BiCOME study. The scientific questions are derived from Miloslavich et al. (2018) review. Also included in this table is the link to knowledge gaps identified through IPBES and IPBES Nature’s Contribution to people (NCP).15 Table 5: Selection of the scientific questions and direct drivers related the Essential Ocean Variable: Invertebrate abundance and composition in the intertidal environment and addressed in BiCOME. A broader list of questions is given in spreadsheet sptm_intertidal_invertebrates_v3.5, tab 1_Scientific questions, but these are not included in the BiCOME study. The scientific questions are derived from Miloslavich et al. (2018) review. Also included in this table is the link to knowledge gaps identified through IPBES and IPBES Nature’s Contribution to people (NCP). .................................................................................... 15 Table 6: Selection of the scientific questions and direct drivers related the Essential Ocean Variable: seagrass cover in the sub-tidal environment and addressed in BiCOME. A broader list of questions is given in 0_Scientific questions, but these are not included in the BiCOME study. The scientific questions are derived from Miloslavich et al. (2018) review. Also included in this table is the link to knowledge gaps identified through IPBES and IPBES Nature’s Contribution to people (NCP). ....................................................................................................................... 16
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 8 of 56 Table 7: Selection of the scientific questions and direct drivers related the Essential Ocean Variable: Macroalgal cover and composition in the epipelagic environment and addressed in BiCOME. A broader list of questions is given in 0_Scientific questions, but these are not included in the BiCOME study. The scientific questions are derived from Miloslavich et al. (2018) review. Also included in this table is the link to knowledge gaps identified through IPBES and CBD Nature’s Contribution to people (NCP). .................................................................................................. 16 Table 8: Selection of the scientific questions and direct drivers related the Essential Ocean Variable: Phytoplankton biomass and diversity in the epipelagic environment and addressed in BiCOME. A broader list of questions is given in 0_Scientific questions, but these are not included in the BiCOME study. The scientific questions are derived from Miloslavich et al. (2018) review. Also included in this table is the link to IPBES Gaps and IPBES Nature’s Contribution to people (NCP). ................................................................................................................................................... 17 Table 9 Essential Biodiversity Variables (EBV) classes and EBV names from https://geobon.org/ebvs/what-are-ebvs/ (March 2022). Explanation of EBV class is from Skidmore et al. (2021). .............................................................................................................. 19 Table 10: Relationship between the generic questions (Section 2.1) for intertidal seagrass canopy cover sptm_intertidal_seagrass_v3.5.1, tab 01_Scientific questions. References supporting questions are in the SPTM. ....................................................................................................... 23 Table 11: Relationship between the generic questions (Section 2.1) for intertidal seagrass canopy cover sptm_intertidal_invertebrates_v3.5, tab 01_Scientific questions. References supporting questions are in the SPTM. ....................................................................................................... 23 Table 12: Summary of Early Adopters requirements, research focus and reason to join BiCOME .... 24 Table 13: Stakeholder requirements of the Maison Baie, Mont Saint-Michel ................................... 25 Table 14: Intertidal Early Adopter SMBB current versus ideal survey scales ...................................... 26 Table 15: Intertidal Early Adopter Bio-Littoral current versus ideal survey scales ............................. 27 Table 16: Characteristics of direct detection of intertidal EOV from remote sensing. NA is not available in BiCOME ................................................................................................................................. 28 Table 17: Summary of requirements for intertidal seagrass from scientific and Early adopters ....... 30 Table 18: Summary of requirements for intertidal invertebrates from scientific and Early adopters 30 Table 19: Relationship between the generic questions (Section 2.1) for subtidal seagrass composition sptm_subtidal_seagrass_v3.5, tab 01_Scientific questions. ..................................................... 32 Table 20: BANP Early Adopters research focus, reason for monitoring and reason for joining BiCOME ................................................................................................................................................... 33 Table 21: Subtidal Early Adopter current versus ideal survey scales (both stakeholders represented in this table) .................................................................................................................................. 33 Table 22 Algorithms corresponding to the subtidal seagrass ............................................................. 34 Table 23 Summary of requirements for subtidal seagrass from scientific and Early adopters .......... 36 Table 24: Relationship between the generic questions (Section 2.1) for phytoplankton blooms and biogeography. ........................................................................................................................... 38 Table 25: Relationship between the generic questions (Section 2.1) for floating vegetation. ........... 39 Table 26: Pelagic Early Adopters research focus, reason for monitoring and reason for joining BiCOME ................................................................................................................................................... 41 Table 27: Pelagic Early Adopter NERCI at Lake Vembanad current versus ideal survey scales .......... 41 Table 28: Pelagic Early Adopter Martinique current versus ideal survey scales ................................ 42 Table 29: Characteristics of direct detection of floating vegetation and phytoplankton blooms from ocean colour remote sensing. ................................................................................................... 45
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 9 of 56 Table 30 Summary of requirements for epipelagic floating vegetation from scientific and Early adopters .................................................................................................................................... 48
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 16 of 56 community composition) and on nature’s contributions to people Table 6: Selection of the scientific questions and direct drivers related the Essential Ocean Variable: seagrass cover in the sub-tidal environment and addressed in BiCOME. A broader list of questions is given in spreadsheet sptm_subtidal_seagrass_v3.5, tab 1_Scientific questions policy, but these are not included in the BiCOME study. The scientific questions are derived from Miloslavich et al. (2018) review. Also included in this table is the link to knowledge gaps identified through IPBES and IPBES Nature’s Contribution to people (NCP). Generic Questions Direct Drivers IPBES Knowledge Gaps addressed IPBES Nature's contribution to people 1.- What is the status of seagrass cover in subtidal areas? 2.- What are the changes of seagrass cover in subtidal areas? 3.- How is seagrass cover affected by severe events? 4.- How is seagrass cover affected by anthropogenic impacts? Climate Changeanthropogenic pressures Data from monitoring of ecosystem condition in coastal waters More comprehensive understanding of how humancaused changes to any Essential Biodiversity Variable class (e.g., ecosystem structure) have impacts on others (e.g., community composition) and on nature’s contributions to people 7 Regulation of coastal water quality 10 Regulation of detrimental organisms and biological processes Table 7: Selection of the scientific questions and direct drivers related the Essential Ocean Variable: Macroalgal cover and composition in the epipelagic environment and addressed in BiCOME. A broader list of questions is given in spreadsheet sptm_floating_epipelagic_v3.42, tab 1_Scientific questions policy, but these are not included in the BiCOME study. The scientific questions are derived from Miloslavich et al. (2018) review. Also included in this table is the link to knowledge gaps identified through IPBES and CBD Nature’s Contribution to people (NCP). Generic Questions Direct Drivers IPBES Knowledge Gaps addressed IPBES Nature's contribution to people 1.- What is the status of macroalgal cover? 2.- What is the status of macroalgal diversity? 3.- What is the change in macroalgal cover? 4.- What is the change of macroalgal diversity in the (coastal) ocean? 5.- How are macroalgal communities affected by severe events? 6.- Are changes in macroalgal extent and condition affecting fishes or other important species? Changes in land use; Climate change Data from monitoring of ecosystem condition in coastal waters More comprehensive understanding of how humancaused changes to any Essential Biodiversity Variable class (e.g., ecosystem structure) have impacts on others (e.g., community composition) and on nature’s contributions to people 7 Regulation of coastal water quality 10 Regulation of detrimental organisms and biological processes
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 17 of 56 Table 8: Selection of the scientific questions and direct drivers related the Essential Ocean Variable: Phytoplankton biomass and diversity in the epipelagic environment and addressed in BiCOME. A broader list of questions is given in spreadsheet sptm_phytoplankton_v3.4, tab 1_Scientific questions policy, but these are not included in the BiCOME study. The scientific questions are derived from Miloslavich et al. (2018) review. Also included in this table is the link to IPBES Gaps and IPBES Nature’s Contribution to people (NCP). Generic Questions Direct Drivers IPBES Knowledge Gaps addressed IPBES Nature's contribution to people 1.-What is the status of phytoplankton diversity in the (coastal) ocean? 2.-What is the change of phytoplankton diversity in the (coastal) ocean? 3.-What is the biogeography in phytoplankton communities? 4.-Have there been biogeographical shifts in phytoplankton communities? 5.-Has there been a change in extent and location of algal blooms? 6-How do we relate phytoplankton diversity change to pollution and nutrients? 7.-How do we relate phytoplankton diversity change to climate change? Changes in land use; changes in pollution and water quality; Climate change Data from monitoring of ecosystem condition in coastal waters More comprehensive understanding of how humancaused changes to any Essential Biodiversity Variable class (e.g., ecosystem structure) have impacts on others (e.g., community composition) and on nature’s contributions to people 4 Regulation of climate 5 Regulation of ocean acidification 7 Regulation of coastal water quality 10 Regulation of detrimental organisms and biological processes 2.2 Connecting scientific questions and policy drivers with remote sensing through Essential Ocean and Biodiversity variables To translate the questions of the biodiversity research and policy communities into requirements for the remote sensing community, it is necessary to first introduce a common language through some definitions and choice of terminology. The first set of definitions and concepts concern the variables used. We have adopted the Essential Biodiversity Variables (EBV) and Essential Ocean Variables (EOV) concepts (Figure 2) as recommended by GOOS.
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 18 of 56 Figure 2: Expanding the relationship between Essential Ocean Variables (EOV) and Essential Biodiversity Variables (EBV). From Muller-Karger MBON (2021) slide, citing Pereira et al. (2017) and Framework for Ocean observing (GOOS, 2012). Generic Essential Biodiversity Variables (EBV) characterising biodiversity at different levels of the ecosystem (defined by Pereira et al. (2013) and updated online (https://geobon.org/ebvs/what-areebvs/ ) are summarised in Table 9.
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 19 of 56 Table 9 Essential Biodiversity Variables (EBV) classes and EBV names from https://geobon.org/ebvs/what-are-ebvs/ (March 2022). Explanation of EBV class is from Skidmore et al. (2021). EBV class EBV names Examples of Observable EBV 1 Species population (a local species population) 1 Species distribution Species presence-absence Occurrence probability Number of individuals of single taxa 2 Species (population) abundance Biomass per taxonomic group Density of individuals of a single taxonomic group 3 Species (population) structure (Size/vertical distribution) NA? 2 Species traits (trait of an organism of known species that can be monitored at a local level) 4 Phenology Timing, such as day of year of flowering, leaf sprouting, egg laying, etc. of a particular species 5 Natal dispersion distance NA 6 Movement NA 7 Demographic traits NA 8 Morphology traits (Skidmore et al.,2021) Variation in physical attributes of organisms as body mass, of the same species 9 Physiological traits (Skidmore et al.,2021) Chemical concentrations, such as leaf nitrogen or phosphorus concentrations. 3 Community Composition (composition of a community that can be monitored at a global level) 10 Taxonomic and phylogenetic diversity The diversity of species identities, and/or phylogenetic positions, of organisms in ecological assemblages. 11 Trait diversity The diversity of functional traits of organisms in ecological assemblages. 12 Community abundance The abundance of organisms in ecological assemblages 13 Species interactions/interaction diversity The diversity and structure of multi-trophic interactions between organisms in ecological assemblages. 4 Ecosystem structure (an ecological structure that can be monitored at a 15 Live cover fraction The horizontal (or projected) fraction of area covered by living organisms, such as vegetation, macroalgae or live hard coral. 16 Ecosystem extent and fragmentation
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 20 of 56 global level) 17 Ecosystem distribution horizontal distribution of discrete ecosystem units 18 Ecosystem vertical profile The vertical distribution of biomass in ecosystems, above and below the sea surface. 5 Ecosystem functioning (an ecological function monitored over time at a global level) 19 Ecosystem phenology NA? 20 Primary Productivity NA? 21 Net ecosystem production (Muller-Karger et al., 2018) NA? 22 Ecosystem disturbances NA?
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 21 of 56 For each EOV and environment combination, the EBV are defined and matched to the closest remote sensing product and algorithms. For clarity, in our work we will use status as a synonym for condition, thus resilience or risks to the ecosystem are derived from our status variables (Smit et al., 2021) and not inherently a characteristic of the ecosystem component. One important assumption of our framework is that the observable EBV are related to the status of the ecosystem, and, although some of them imply some changes with time (e.g. phenology, production), these changes are at shorter time scales than the phenomena of interest. The following step is to link the observable EBV to tractable remote sensing variables. For this step we have followed the proposal by Skidmore et al. (2021), linking “typical remote sensing enabled biodiversity variable names” to “remote sensing biodiversity products”. In the SPTM, we have attributed them to the combination EBV-EOV-Ecosystem and given them a development level (“routine”; “demonstrated in some limited cases”; “not yet proven”; “obtained from combining satellite with models”). This combination results in a unique code (Remote sensing product number) linking questions to EBV-EOV-Ecosystem-satellite products, which guarantees traceability, for the questions addressed by this project and for future projects addressing remaining questions. Furthermore, each Remote sensing product number is mapped to the relevant international initiatives and policies on biodiversity: • Drivers (from Miloslavich et al., 2018; CBD, MSFD) • Policy relevant (CBD post 2020) • Policy relevant (MSFD, WFD) • CBD Aichi Biodiversity targets • Sustainable Development Goals The Remote sensing product number is linked to the general observational product requirements (threshold/ideal): spatial coverage; spatial resolution, ground sampling distance, m/pixel; temporal coverage; temporal frequency, frequency of observations (e.g. daily, weekly, biweekly, monthly), accuracy. The Remote sensing product number is also linked to specific requirements of the algorithms that will be used in this study. To note here, different algorithms can be used for identifying the same remote sensing product in two ways: 1) different algorithms can be available to obtain the same remote sensing product (e.g. % dominance of Diatoms in one pixel); 2) different algorithms can be used for different species for instance in the EBV species distributions. The remote sensing product specifications for algorithms sheet also contains the description of the remote sensing input data needed, the minimum and ideal spectral resolutions required, as well as the nonremote sensing input data required (e.g. Look up tables), the characteristics of the output provided by the algorithm, and the validation dataset types and sources. This information will feed into D2.1 Biodiversity + Development Database and D2.2 Algorithm Theoretical Baseline Document. Finally, the remote sensing products selected for status EBV for each EOV-EBV is linked to key global questions of trends/changes and pressures guided by Miloslavich et al. (2018) for general questions. In addition, key questions around trends/change and pressures are formulated for each case study to ensure applicability for the end users.
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 22 of 56 3 Intertidal ecosystems Intertidal areas consisting of soft sediment and emerging at low tide cover more than 10000 km2 worldwide along the 35000 km2 of the tidal coastline (Murray et al. 2019). The main soft-sedimentary intertidal habitats (seagrass, microphytobenthos 1 , macroalgae, oyster reefs, and polychaetes reefs e.g. the honeycomb worm Sabellaria alveolata) are impacted by human activities, biotic interactions, and climate change. Seagrass are significantly affected by many anthropogenic activities (McKenzie et al. 2020), microphytobenthos is impacted by the global reduction of tidal flats (Murray et al. 2019), while the Pacific oyster is an invasive species with a poleward expansion (Thomas et al. 2016) which can impact local habitats such as polychaete reefs in Western Europe (Bajjouk et al. 2020). With the adapted spatial, spectral, temporal characteristics, EO can assess several EBVs for these habitats. In combination with relevant anthropogenic drivers and climate variables, EO-derived information can help answer the main scientific questions related to the intertidal habitats as summarised in the SPTM. By definition, the intertidal zone is the dynamic frontier area between the ocean and the coastline and is alternatively submerged (high tide) and emerged (low tide). In comparison with submerged aquatic vegetation (SAV, Vahtmäe et al., 2006; Klemas, 2013; Hossain et al., 2015; Traganos et al., 2018; Traganos, & Reinartz 2018; Kutser et al., 2020) and coral reefs EO studies (Hochberg et al., 2003; Mumby et al., 2004; Hedley et al., 2016), the remote sensing of emerged benthic communities has received much less attention, despite being very productive and diverse ecosystems. The aim of this section is three-fold. Firstly, it is to refine the scientific and policy questions for the pelagic ecosystems, secondly to summarise the current remote sensing approaches to measure pelagic biodiversity from remote sensing, and thirdly to define the characteristics of the datasets required to improve the contribution of remote sensing to addressing those questions. 3.1 Science questions For the intertidal habitats, there are two existing Essential Ocean Variables (EOVs): seagrass and macroalgae, and two emerging EOVs: microbial diversity and benthic invertebrates (Miloslavich et al. 2018). Microphytobenthos belongs to the EOV microbial diversity while oyster and polychaete reefs correspond to the EOV benthic invertebrates. For these four EOV, the overarching scientific questions are related to 1) the description of status and changes of these habitats, 2) the identification and quantification of anthropogenic pressures impacting these habitats, 3) the identification and quantification of climate change on these habitats, 4) the deconvolution of anthropogenic and climate change effects. For the two macrophyte EOV, the specific questions are about the status and changes of seagrass/macroalgae cover and associated diversity, the detection of ecological shifts in response to climate change, the impact of anthropogenic pressures and severe events (i.e. storms, heatwaves), and the carbon sequestration in response to disturbances. The two emerging EOVs share the same questions about their status/changes and associated diversity. For oyster reefs the main question is about biogeographical shifts in response to climate change and the impact of this invasive species on receiving ecosystems. For polychaete reefs the questions are mainly related to anthropogenic impacts (epibionts from aquaculture) and severe events. In the framework of BiCOME, 1 Unicellular microalgae and cyanobacteria colonizing superficial sediments and forming large biofilms during low tide.
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 23 of 56 we will focus on scientific questions related to the diversity of intertidal seagrass meadows (Table 4) and polychaete reefs (i.e., reefs of the honeycomb worm Sabellaria alveolata, Table 5). The link is made more explicit in Tables 10 and 11 below. Table 10: Relationship between the generic questions (Section 2.1) for intertidal seagrass canopy cover sptm_intertidal_seagrass_v5.0, tab 1_Scientific questions. References supporting questions are in the SPTM. Generic Questions Specific Questions for BiCOME 1 What is the status of seagrass cover in intertidal areas? What is the extent and condition of seagrass cover? More specifically, is it possible o discriminate seagrass from green macroalgae, and what is the density and extent of intertidal seagrass? 2 What are the changes of seagrass cover in intertidal areas? How did the extent and condition of seagrass cover change over the last decades? (Interannual variability of seagrass health index) 4.- How is seagrass cover affected by anthropogenic impacts? Is there an impact of shellfishing on seagrass cover? 5.- Are changes in seagrass cover affecting other important species? Do changes in seagrass cover affect herbivorous birds? In particular, are changes in seagrass extent and condition affecting Brent goose Branta b. bernicla population, a protected migratory waterbird wintering in seagrass meadows along the European Atlantic coasts? Table 11: Relationship between the generic questions (Section 2.1) for intertidal invertebrate abundance sptm_intertidal_invertebrates_v5.0, tab 1_Scientific questions. References supporting questions are in the SPTM. Generic Questions Specific Questions for BiCOME 1 What is the status of benthic invertebrate abundance? What is the extent and condition of polychaete reefs in intertidal areas? In particular, what is the colonization of polychaete reefs by epibionts? 2 What is the change in benthic invertebrate abundance? How did the extent and condition of polychaete reefs change over the last decades? (Interannual variability) 3 How are benthic invertebrates abundance affected by severe events? How will polychaete reefs change with climate and anthropogenic pressures? In particular, does aquaculture affect the status of polychaetes reefs? 3.2 Policy questions All the intertidal sites are covered by national (e.g. National Nature Reserve in France), European (e.g. Natura 2000 network), and international designations (e.g. RAMSAR convention on Wetlands) translated into regional/national management policies. As an example, one of the case-study sites in France, the Gulf of Morbihan, has designations as a Nature Reserve, Natural areas of ecological, faunistic and floristic interest (ZNIEFF), a Biotope Protection Order, a hunting reserve, a Natura 2000 perimeter (SPA, SCI), and is designated as Ramsar site 517 (https://rsis.ramsar.org/ris/517). Zostera seagrass beds, Sabellaria reefs and intertidal mudflats are also OSPAR habitats identified by the Oslo and Paris conventions (OSPAR) strategy for the protection and conservation of ecosystems and biological diversity (https://www.ospar.org/work-areas/bdc/species-habitats/list-of-threateneddeclining-species-habitats/habitats). Seagrass, macroalgae and polychaete reefs are bioindicators (Biological Quality Element, BQE) of the water quality for the Water Framework Directive (WFD, 2000/60/EC) and the Marine Strategy Framework Directive (MSFD; 2008/56/EC). They are used to monitor and evaluate the ecological status of transitional (estuaries) and coastal water bodies throughout Europe (see in particular Table
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 24 of 56 4: question 7). Microphytobenthos is not yet a biological indicator but could be an alternative to seagrass and macroalgae in turbid areas (Trobajo and Sullivan, 2010). For seagrass and macroalgae, the metrics included in the calculation of an Ecological Quality Ratio (EQR) used to determine a water body’s quality status are the taxonomic composition (species richness), the areal extent (total surface occupied by the seagrass meadow or macroalgal belts), and density or biomass. The EQR should respond to an identified pressure (e.g., eutrophication for all marine plants). However, satellite data are not recognised for the WFD statutory monitoring and reporting (Papathanasopoulou et al., 2019) even though their improved spatial and temporal coverage is a strong argument to complement conventional sampling (Zoffoli et al., 2021; Oiry and Barillé, 2021). Satellite observation can also help fill the gaps with regards to large-sized water bodies (Papathanasopoulou et al., 2019). The policies are increasingly requesting a baseline for these BQE and the identification of a reference status against which to detect anthropogenic impacts. In many cases, the Directives’ requested temporal measurement frequency of 6 years does not allow a clear identification of the trends for bioindicators and pressures (Zoffoli et al., 2021). Policy relevant specific indicators are linked to EBV in the SPTM (tab 2.1_EOV_EBV_STATUS ). 3.3 Intertidal Early Adopters specific requirements Three intertidal Early Adopters are working in BiCOME. They are located around Brittany and Normandy in France and each have slightly different questions they would like to address using Earth Observation tools (see next Tables). Table 12: Summary of Early Adopters requirements, research focus and reason to join BiCOME Research focus Reason for monitoring Reason for joining BiCOME Maison de la Baie (Mont Saint Michel Bay) Measure epibionts of Sabellaria reefs, anthropogenic pressures nearby Monitoring the Natural Heritage of the Bay, restoring a traditional fishery To make better use of RS data SMBB (Bourgneuf Bay) Assess the impact of recreational and commercial clam fishing in seagrass beds, interactions between habitats and waterfowl and map Sabellaria reefs in relation to anthropogenic pressures To manage Natura 2000 sites in the Bay, for the Water Framework Directive To be able to get more and more regular data than through fieldwork (both spatial and temporal extent) Bio-littoral (South of Brittany) Differentiate between seagrass and macroalgae, assess anthropogenic pressures (eg. anchoring) To fulfil requirements of monitoring for the Water Framework Directive To co-develop a methodology to differentiate between macroalgae and seagrass
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 25 of 56 Maison de la Baie The Maison de la Baie is an association to promote the natural and human heritage of the bay of Mont Saint-Michel. While one objective of their mission is environmental education for schools and the general public, they also monitor the natural heritage of the bay and are involved in the restoration of a traditional fishery. In addition, they work closely with the mussel producers of the Mont Saint-Michel Bay. Their monitoring activities are focused on oyster and mussel epibionts colonising and impacting the largest polychaete reef in Europe. Their current in situ monitoring methods are based on the use of quadrats to assess epibiont percent cover. They also monitor the reef conservation status in several sectors of the reef area which is 220 ha in total (Saint-Anne reef) in addition to a smaller reef (Champeaux, 4 ha). Monitoring takes place seasonally with the aim of understanding the impact of anthropogenic pressures in the vicinity of the reefs and their effects on them. They also want to gain deeper understanding of the functioning and interactions of these reefs. Some of the work has been carried out with IFREMER who are also holding the data. They would like to use remote sensing products to get a better understanding of anthropogenic impacts on the reefs and to assess the conservation status of the reefs (Table 5: questions 1, 2, 5 and 6; RS products 1.1.1.INV.INT, 1.3.14.INV.INT, 1.4.15.INV.INT, and 1.4.16.INV.INT). Currently, they are not using any satellite-derived data and would therefore like to be trained in the use of QGIS and would like to receive products in maps and shapefiles. Ideally, they would like to receive products to assess the usefulness of BiCOME outputs 1-2 times per year. Table 13: Stakeholder requirements of the Maison Baie, Mont Saint-Michel Maison de la Baie Current Best (Ideal) Minimum (Threshold) Survey area Baie of Mont SaintMichel Same Same Spatial resolution 1 m2 (in situ quadrates) 10 cm (drone multispectral) Same Temporal resolution 4 year-1 Same Same Level of detail of measurements Epibionts Epibionts, conservation status of reefs Syndicate Mixte de la Baie de Bourgneuf (SMBB) Another Early Adopter, the Syndicate Mixte de la Baie de Bourgneuf (SMBB), is responsible for the the management of Natura 2000 areas (FR5200653, FR52120009) including a larger intertidal seagrass meadow (Zostera noltei) and the second largest polychaete reef (Sabellaria alveolata) in France after Mont Saint-Michel Bay. They use in situ seagrass data provided by IFREMER to fulfil Water Framework Directive commitments and monitor one polychaete reef in the Bay using in situ
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 32 of 56 Understanding the drivers and extent of biodiversity loss, the % of seagrasses effectively protected, and the “winners” and “losers” across these complex, interconnected seascape configurations enable transparent data-driven insights into: a) suitable sites for seagrass restoration (Target 1); b) the design of MPAs to effectively protect well-connected hotspots of coastal biodiversity (Target 2); and c) more intact seagrass carbon sinks which can be bundled with coastal protection or biodiversity maintenance to streamline innovative seagrass protection financing mechanisms beyond carbon crediting allowing near-future biodiversity resilience. See Table 19Error! Reference source not found. for a summary linking general top level questions to specific questions in BiCOME. Table 19: Relationship between the generic questions (Section 2.1) for subtidal seagrass composition sptm_subtidal_seagrass_v3.5, tab 1_Scientific questions. Generic Questions Specific Questions for BiCOME 1 What is the status of seagrass cover in subtidal areas? What is the extent and condition of seagrass cover? 2 What are the changes of seagrass cover in subtidal areas? How did the extent and condition of seagrass cover change over the last decade? 3 How is seagrass cover affected by severe events? How is the extent and condition of seagrass cover impacted by heatwaves? 4.- How is seagrass cover affected by anthropogenic impacts? How is the extent and condition of seagrass cover impacted by human activities such as coastal developments and hence increased coastal eutrophication and decreased light availability in the local scale? 4.2 Policy questions The IPBES Global Assessment report (IPBES, 2019) identified marine and coastal areas as being undersampled and understudied. Key knowledge gaps include the lack of inventories of understudied ecosystems such as ocean, coastal, seabed, and wetlands. There is also a lack of data from monitoring ecosystem condition (such as health of a seagrass bed or mangrove density). Seagrass beds provide key ecosystem services to coastal communities (such as food provision and bioremediation of waste) as well as global communities (e. g. climate regulation) (Beaumont et al., 2007; Potts et al. 2014, Nordlund et al. 2018). Monitoring their extent, status, and changes to these are key to address the CBD, IPBES and EU requirements. It will help with ecosystem-based management, threat prevention (because seagrass beds are able to reduce coastal erosion), and food security directly through providing fish and invertebrates found in seagrass beds and through provision of nursery areas for juvenile fish and invertebrates (Jackson et al. 2015). Monitoring from space is a useful way to look for deterioration through anthropogenic pressures but also to measure restoration success (Bandeira, S. December 2021, during the EA interview). Restoration is also an important target in the CBD, with a target of an increased extent of natural ecosystems under Goal A of the post2020 framework and Aichi Target 15 dedicated to the restoration of natural habitats, a target which has not been achieved (Secretariat CBD, 2020). 4.3 Subtidal Early Adopters specific requirements The subtidal pilot studies largely overlap with marine sections of the Bazaruto Archipelago National Park (BANP) in Mozambique, and the Komodo National Park, the Kepulauan Wakatobi Marine National Park and the Aru Island in Indonesia. The Indonesian study arear were added after the initial Early adopters survey, so no specific requirements are available. Key questions addressed in this
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 33 of 56 study site are to assess satellite-derived bathymetry estimation as well as to measure the extent of seagrass dominated benthic habitats (Table 20). The BANP covers 1400 km2 around the Bazaruto Archipelago and is an important area for megafauna, chiefly dugongs and turtles both of which depend on healthy seagrass meadows. In addition, artisanal fishing in seagrass meadows is important for local communities. This site needs user-friendly ways to measure the extent of seagrass meadows and to be able to assess changes over time. The stakeholders of BiCOME at this site are the African Parks organisation and a university based marine botanist. Regarding the three Indonesian case studies, those feature an areal extent of more than 15,600 km2 of hot spots of subtidal biodiversity of up to 13 seagrass species, 500 coral species and more than 2500 fish species. Liaison with local seagrass scientists highlighted the importance of mapping the three targeted case studies due to the broader lack of spatially-explicit mapping data at suitable regional spatial scales and accuracies. Further liaison is ongoing to receive suitable multitemporal field data on seagrass and habitat extent, as well as bathymetry. Table 20: BANP Early Adopters research focus, reason for monitoring and reason for joining BiCOME Research focus To extend their monitoring area (currently mostly fieldwork, with some RS) Reason for monitoring To manage seagrass beds sustainably, protection in the marine park and for the sake of dugongs, cyclone damage Reason for joining BiCOME To be able to use RS in the regular monitoring of the research area Currently, they struggle to measure the extent of seagrass meadows in the BANP and monitoring can only be carried out at irregular intervals of up to twice per year. There is also an urgent need to understand the ecological state of the seagrass habitats and to understand the diversity of seagrass species and of the species associated with seagrass beds. The monitoring that they carry out and need to develop further is important to aid the Mozambique Ocean Governance. They do not currently collect data for CBD or SDGs but this will likely be needed in the future. Their methods include small scale assessment of species diversity of seagrass meadows by fieldwork, for example intertidal transects to measure the extent of seagrass. They have tried Google Image Engine, Landsat and - in other areas – drones and made use of the Allen Coral Atlas (Allen Coral Atlas) and UNEP Seagrass publications (Ocean Data Viewer (unep-wcmc.org)). All methods tested so far have been moderately successful. One issue is the scale of the area they need to assess. Dugongs move over long distances and so ideally they would like to monitor 3000 km2, which includes the BANP, to assess all dugong habitats. Regular monitoring about four times a year of seagrass extent and status will help the stakeholders to achieve their monitoring goals because their main interest is seasonal changes. In particular, cyclones occur in one season and it is paramount to understand the impact of these cyclones on the subtidal marine habitats. They need detailed maps of habitat extents to fulfil questions around ocean governance of Mozambique. Currently they see less need to be able to report to SDGs or CBD but this would be a bonus (Table 21). Table 21: Subtidal Early Adopter current versus ideal survey scales (both stakeholders represented in this table)
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 34 of 56 Bazaruto National Park Current Best (Ideal) Minimum (Threshold) Survey area Small areas of BANP only 3000 km2 1400 km2 Spatial resolution 3 m (but fieldwork) 5-10 m 30 m Temporal resolution 1-2 year-1 4 year-1 4 year-1 Level of detail of measurements Species of seagrass, dugong tracks For ease of access, they would like to receive any BiCOME products in form of maps and if possible in an interactive format to allow zooming in and questioning the data. An online tool similar to the Allen Coral Atlas that is updated regularly would be a useful means of accessing the data they need. One stakeholder also expressed his wish for any products to be extended to the entire country or including and south of the BANP. 4.4 Algorithms Subtidal seagrasses are identified from optical satellite images using machine learning algorithms in the Google Earth Engine (GEE) cloud platform. At the moment, the available optical satellite images used for this work are the Sentinel-2 (10-m spatial resolution) and Planetscope (5-m spatial resolution) NICFI archives which are already integrated into the GEE environment. The Planetscope images were recently introduced into our work due to its higher resolution as well as clearer seawater area on the tropical area, where cloud and cloud shadow artefacts are abundant in the Sentinel-2 images due to limited available processed data. As the input for the machine learning image classification, we feed the first 5 bands of Sentinel-2 (coastal aerosol, blue, green, red, and red edge bands), the derived object features (mean, standard deviation, median), Grey Level Co-occurrence Matrix (GLCM) layers, and Principal Component Analysis (PCA) layers together with the training data. The bands available in the Planetscope images are blue, green, red, and near infrared. Therefore, similar to that of Sentinel-2, we processed the object features, GLCM, and PCA layers from these bands. Support Vector Machine (SVM) and Random Forest (RF) are selected as the classification methods (Table 22). Table 22 Algorithms corresponding to the subtidal seagrass Questio n EOV Remote sensing biodiversity products Remote sensing product Algorithm reference s SPTM reference 1 Seagrass (subtidal ) presence/absence : seagrass 1.1.1.SEA.SUB Traganos et al. (2018); Tassi and SPTM_subtidal_seagrass/2.3_Algorithm_req_in_va l
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 35 of 56 Vizzari (2020) 1 Seagrass (subtidal ) Seagrass species diversity 1.3.11.SEA.SU B Traganos et al. (2018); Tassi and Vizzari (2020) SPTM_subtidal_seagrass/2.3_Algorithm_req_in_va l 1 Seagrass (subtidal ) Seagrass extent 1.4.11.SEA.SU B Traganos et al. (2018); Tassi and Vizzari (2020) SPTM_subtidal_seagrass/2.3_Algorithm_req_in_va l 4.5 Datasets The following datasets will be used: - Input datasets for subtidal seagrasses and satellite-derived bathymetry (SDB): o Sentinel 2 L2A at 10m covering 100% all our selected study sites [2015-2029 (expected)] o PlanetScope surface reflectance images at 5m covering 100% all our selected study sites (2015-ongoing) o ENMAP images at 30m, expected to cover 100% all our selected study sites (when available; September 2022 onwards) o Training data polygons from Allen Coral Atlas existent in and/or in the vicinity of our selected study sites (5m resolution; pan-tropical benthic habitat maps) (2018-2020) • Validation datasets: o Presence/absence data points on seagrasses and in-situ collected bathymetry from WWF (all our selected study sites in Mozambique; 2015-2019 o In-situ collected validation data points from Allen Coral Atlas project existent in and/or in the vicinity of all our selected study sites (5m resolution; pan-tropical benthic habitat maps) (2018-2020) o • Pressures datasets o Sea Surface Temperature (SST) time series (Mozambique and Indonesia) from OCCI o Light availability (kd490) time series (Mozambique and Indonesia) from OCCI o Coastal Eutrophication (Mozambique and Indonesia) at 4 km between 2003-2021 (Maure et al. 2021) 4.6 Potential limitations from the approach Potential limitations here are the usual showstoppers in subtidal remote sensing approaches: clouds, waves, adjacency effect, confusion between similar dark targets like seagrasses, optically deep waters
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 36 of 56 and macroalgae, and impact of benthic habitats on shallow water bathymetry. We will resolve the limitations induced by clouds and waves by employing multi-temporal Sentinel-2 composites instead of single image approaches. By integrating percentile and/or median multispectral image composites we automatically filter out the outliers of clouds and waves, which are common obstacles over tropical shallow systems. The application of POLYMER-corrected images will alleviate the adjacency effect by mapping its spatially explicit impact and reducing it in turn on the multi-temporal composites. Adjacency effect can influence nearshore pixels by increasing the reflectance of subtidal pixels due to mixed pixels situation with above-tide higher-reflectance sand pixels. This can create a positive bias in bathymetry and benthic habitat maps which certainly requires resolution before mapping. The confusion of similarly looking (reflecting) subtidal seagrasses, macroalgae and optically deep waters will be resolved by employing water column correction approaches by HYGEOS which will in turn produce true benthic reflectances of seagrasses and neighboring dark targets. In addition, we will employ a dense training dataset of benthic habitats, a strategy that we are confident it will accurately detect and differentiate seagrasses from neighboring dark targets. Last but not least, highreflecting and low-reflecting habitats like very shallow sand and seagrasses, respectively, can impact accurate satellite-derived bathymetry estimations, especially applying the automated baseline approach. We will tackle this limitation by integrating more feature spaces in the automated bathymetry estimation like texture and HSV which will enhance the differentiation of surface and benthic reflectances, producing more accurate and less biased depth estimates. 4.7 Conclusions Table 23 summarises and compares the scientific and Early adopters needs. Spatial resolution requirements are similar among the two communities. Table 23 Summary of requirements for subtidal seagrass from scientific and Early adopters Scientific needs (Threshold/Ideal) Early adopters needs (Threshold/Ideal) Spatial resolution (GSD, m/pixel) 30m/10m or less 30m /5m Revisit frequency twice a year (summer, winter) / quarterly Quarterly/Quarterly Products characteristics Extent of seagrass coverage/species of seagrass Extent of seagrass coverage/species of seagrass Our approach for the mapping of subtidal seagrasses here will be based on a combination of wellestablished and validated coastal aquatic remote sensing algorithms and simple statistical preprocessing to reduce environmental interference in the satellite-derived products. In addition to the envisaged resolutions of the common showstoppers in remote sensing of underwater targets like seagrasses, we identify one main potential future avenue that goes beyond the scope of the BiCOME project: the potential spatial harmonization and amalgamation of existing big reference data on seagrass extent and satellite-derived bathymetry, especially towards the required scalability of our herein algorithms. We envisage that the development of large-scale automated training data annotations using machine learning and big spaceborne lidar data like ICESat-2 (Thomas et al., 2021)
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 37 of 56 would render similar endeavours more time and cost efficient, increasing the relevant automation in the future. Such a potential development would allow scientists to produce synthetic reference data which will be used in turn to train and validate older remotely sensed products in remote coastal aquatic sites, which usually lack temporally coincident in situ data collections, in contrast to our case studies and endeavours in BiCOME. 5 Pelagic ecosystems Pelagic ecosystems include the entire water column from the sea surface to just above the seabed and can be divided by water depth and distance from the shore (Kaiser et al., 2011). They feature constantly moving water masses in which oceanographic and features such as currents, waves, ocean fronts and surface turbulence occur at a variety of time scales (Dickey-Collas et al., 2017). Many marine organisms depend on the pelagic ecosystem as habitat for some or all of their life stages (Dickey-Collas et al., 2017). According to depth, the pelagic ecosystem can be separated into epipelagic (top 200m), mesopelagic (200 to 1000 m), bathypelagic (1000 to 4000 m) and abyssopelagic (below 4000 m). In BiCOME we will focus on the epipelagic and on areas on the coastal shelf down to a depth of 50 m. This is an operational definition chosen as the baseline of our study. The epipelagic coastal environment is very diverse due to compound effects of bathymetry, shorelines and tidal variations The aim of this section is three-fold. Firstly, it is to refine the scientific and policy questions for the pelagic ecosystems, secondly to summarise the current remote sensing approaches to measure pelagic biodiversity from remote sensing, and thirdly to define the characteristics of the datasets required to improve the contribution of remote sensing to addressing those questions. 5.1 Science questions Biodiversity includes genetic diversity, species diversity, functional diversity, and ecosystem diversity. Genetic diversity goes beyond the scope of remote sensing, so we focus here on the other three types of diversity. Measures of biodiversity include abundance, distribution and structure. Measures of pelagic biodiversity amenable to remote sensing can be characterised for two main communities: floating macro-algal vegetation and phytoplankton. Whereas the context of evaluating the current status, trends, and drivers of trends remains common for pelagic, intertidal, and sub-tidal ecosystems, what sets pelagic ecosystems apart from the other two is that this is a free-floating ecosystem, at the mercy of all physical forcing, whereas the other two coastal ecosystem types are anchored to the ground and are either fully submerged all the time (sub-tidal ecosystems) or submerged some of the time (inter-tidal zones). The dynamic, floating nature of pelagic ecosystems introduces a requirement for higher temporal resolution for biodiversity observations. Whereas seasonal time scales might be considered the dominant mode of intra-annual variability for sub-tidal zones, with an added component of variability associated with tidal cycles for the inter-tidal zone, the pelagic system is subject to variability at a daily scale, and an hourly scale becomes more important the closer we get to the shore. This dynamic aspect imposes an additional requirement of being able to observe not only the mean state of the ecosystem and its biodiversity
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 38 of 56 but also the variability around the mean. The phenology (or the timing of the life-cycle events over the course of a year) of phytoplankton is particularly important for the ecosystem as a whole, and the finer the scale at which we can observe phytoplankton phenology, the better equipped we would be to detect shifts in phenology in response to climate change. Remote sensing, with its high spatial coverage and high temporal revisit frequencies, is particularly important for observation of the ecosystem and its biodiversity at appropriate time and spatial scales, and for understanding the current status and trends in pelagic ecosystems. As a first-order approximation, one might argue that the closer we get to the land, the higher the impact of human activities on marine ecosystems. Therefore, one might be tempted to believe that anthropogenic stresses may be largely ignored in pelagic, coastal ecosystems, as we move away from the coast. But the world-wide presence of plastic pollution has raised the awareness of long-term impacts of pollutants, once they enter the marine ecosystems. So, a key question for the pelagic ecosystems is to identify the temporal and spatial scales at which the impact of pollutants on biodiversity become negligible in the coastal environment. Though many types of pollutants are invisible to remote sensing, satellite observations have been particularly useful for identifying the flow patterns from rivers into coastal waters. Further investigations into sources and sinks of pollutants are best addressed in collaboration between Earth Observation and modelling. Such an effort would be essential to go beyond status and trends to identifying the stresses that coastal ecosystems are facing; however this goes beyond the scope of BiCOME. We have identified the following questions that can be addressed in BiCOME concerning linkages between land and coastal ecosystems: 1How will the diversity of marine biota in coastal zones change with climate? Within this project, this question can be addressed through studying the seasonal and interannual (20002022) variability of pelagic biodiversity in response to climate changeusing time series of key species (diatoms) coverage, blooms and pelagic habitats defined from spectral water quality changes. 2How will the diversity of life in coastal zones change with increased human uses? This can be explored in BiCOME by studying impact of changes in turbidity and nutrient supply from rivers and land drainage, due to land uses on pelagic algae (spectral water quality) between 2000 and 2022. We can also explore the utility of ocean-colour data to investigate the occurrences of nuisance blooms such as floating algal vegetation at user-selected sites. In BiCOME we will test the questions above, in relation to phytoplankton blooms and floating vegetation. Table 24 and Table 25 highlight the links between specific BiCOME questions and the higher level questions in Section 2.1. We will investigate the potential to improve the results by using hyperspectral data (from ASI satellite PRISMA or NASA PACE test data) where available and where feasible. The temporal changes or trends will be examined using machine learning algorithms as well as the relationships to pressures (using riverine turbidity as a proxy, where a link with BIOMONDO project could be further developed). Table 24: Relationship between the generic questions (Section 2.1) for phytoplankton blooms and biogeography in sptm_pythoplankton_v5.0, tab 1_Scientific questions. Generic Questions Specific Questions for BiCOME
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 39 of 56 1.-What is the status of phytoplankton diversity in the (coastal) ocean? What is the diatom abundance and distribution in coastal waters 2.-What is the change of phytoplankton diversity in the (coastal) ocean? What is the change in diatom abundance in coastal waters? 3.-What is the biogeography in phytoplankton communities? What is the spectral quality of the underwater light field and seascapes in coastal waters? 4.-Have there been biogeographical shifts in phytoplankton communities? How will change in diversity affect the optical quality of water? Investigate spectral water quality and associated biodiversity. 5.-Has there been a change in extent and location of algal blooms? What is the change in extent and location of chlorophyll blooms in coastal waters? Can we infer community structure from chlorophyll concentration? 6-How do we relate phytoplankton diversity change to pollution and nutrients? How will the diversity of marine life in coastal zones change with increased human uses? Studying changes in turbidity and nutrients from rivers due to land uses on pelagic algae (Diatoms) and spectral water quality between 2000 and 2022. 7.-How do we relate phytoplankton diversity change to climate change? How will the diversity of life in coastal zones change with climate? Studying the seasonal and interannual (2000-2022) variability of pelagic biodiversity in response to climate change-induced floods and cyclones pelagic habitats defined from spectral water quality changes Table 25: Relationship between the generic questions (Section 2.1) for floating vegetation in sptm_floating_epipelagic_v5.0, tab 1_Scientific questions. Generic Questions Specific Questions for BiCOME 1.- What is the status of macroalgal canopy cover? What is the extent of coverage of macroalgal and floating vegetation in coastal waters? 2.- What is the status of macroalgal diversity? What is the diversity of macroalgal vegetation and floating vegetation in coastal waters? 3.- What is the change in macroalgal canopy cover? What is the change in macroalgal and floating vegetation cover? 4.- What is the change of macroalgal diversity in the (coastal) ocean? How is the proportion of macroalgal vs floating vegetation changing in the coastal environment? 5.- How are macroalgal communities affected by severe events? How will the diversity of life in coastal zones change with climate? Studying the seasonal and interannual (2015-2022) variability of pelagic biodiversity in response to climate change-induced floods and cyclones 6.- Are changes in macroalgal extent and condition affecting fishes or other important species? How will change in diversity affect ecology and biogeochemistry in coastal areas? 5.2 Policy questions Key policy questions in the pelagic that can be addressed through BiCOME revolve around coastal water phytoplankton communities and processes and the occurrence of algal rafts which can be considered as opportunistic species. Several of the scientific questions address also key policy needs such as knowledge gaps listed in IPBES Global Assessment Report, 2019. A key knowledge gap identified is the lack of monitoring ecosystem processes and condition, especially in coastal and marine areas. Monitoring of coastal processes and ecosystem conditions is key to understanding and managing changes due to anthropogenic drivers such as climate change or land use change.
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 40 of 56 Invasive species are one of the five main threats to biodiversity identified in the CBD (CBD post2020, 2021). In the context of the case studies of BiCOME we would like to add opportunistic species to this group because they can be as harmful and in both case study sites can be considered as either invasive or opportunistic. Aichi Target 9 calls for invasive alien species to be prevented and controlled for which data is needed to understand the condition and processes in pelagic ecosystems. Descriptor 2 of the MSFD aims for invasive alien species to be controlled at a level at which they do not adversely alter the ecosystem. Under Sustainable Development Goal (SDG) 14 dedicated to life below water, Target 14.2 is devoted to protecting and restoring ecosystems with the following stated goal: “by 2020, sustainably manage and protect marine and coastal ecosystems to avoid significant adverse impacts, including by strengthening their resilience, and take action for their restoration to achieve healthy and productive oceans.” Though the statement does not explicitly mention biodiversity, it is intimately linked to biodiversity. It might be safe to say that the target for 2020 has not been met and that the condition of many marine ecosystems is still in decline (IPBES, 2019). Thinking of how this policy requirement may be met requires us to understand coastal biodiversity: its status and trends and its stressors. Target 14.1 in fact states: “By 2025, prevent and significantly reduce marine pollution of all kinds, in particular from land-based activities, including marine debris and nutrient pollution.” The UN has recognised two indicators of pollution, which are coastal eutrophication and marine plastic debris density. The measure of coastal eutrophication, adopted by GEO Blue Planet at the Global Scale and by CMEMS at the level of European monitoring, are based on satellite-based (Ocean Colour Climate Change Initiative) data on chlorophyll concentrations. This points to how UN level reporting requirements on coastal pollution are met through the impact of certain pollutants (in this case nutrients) on the marine ecosystems. Though SDG Goal 15 that deals with life on land mentions the importance of biodiversity, SDG Goal 14 on life below water only goes to the level of ecosystems, perhaps in recognition of the huge challenge in addressing questions related to marine biodiversity. This gap in the SDG Policy Statement also points to another important question: why does marine biodiversity matter? In the long-term, answering that question requires that we understand better how the marine ecosystem, and the biodiversity that underpins it, functions. We can see BiCOME as a small step in that direction, contributing to our understanding of status and variations in coastal biodiversity. 5.3 Pelagic Early Adopters Specific Requirements Two Early Adopters are situated in two very different marine environments, one in a coastal lagoon (Lake Vembanad) in India and the other on the Caribbean island of Martinique (Table 26). Forecasting the arrival of large rafts of Sargassum are a key need in the Caribbean. Forecasting can create a time window to organise clean-up crews (EA interview with Martinique case study site, January 2022). For Lake Vembanad EAs, forecasting is also important, as well as being able to find the exact location of the seaweed rafts and their outlines. In both cases, this knowledge will enable the removal or eradication of the opportunistic species rafts. After removal, use of the ocean surface for boat traffic, fisheries and other users can then carry on safely.
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 41 of 56 Table 26: Pelagic Early Adopters research focus, reason for monitoring and reason for joining BiCOME Research focus Reason for monitoring Reason for joining BiCOME Lake Vembanad, India Extent of water hyacinth rafts and phytoplankton, zooplankton blooms linked to Cholera To assess, monitor and manage health threats due to Cholera, and to forecast and manage WH rafts They would like better access to RS data in formats that are easier to access Martinique Consistent data with little errors to allow forecasting of Sargassum rafts, reduced reflectance, sub surface rafts To allow forecasting to manage rafts near and onshore To create and test a script to download and interpret suitable data with little researcher input Nansen Environmental Research Centre (NERCI), India The stakeholders at Vembanad Lake are two scientists at the Nansen Environmental Research Centre (NERCI), India (Table 26). They carry out regular monitoring of the lake and three coastal stations to monitor phytoplankton and water hyacinth development. The monitoring is used to assess climate related and seasonal changes as well as to assess health threats to the local residents due to bacteria such as Vibrio cholera. Currently, they use in-situ measurements of water samples collected by boat and raw satellite data downloaded from Copernicus. The area of research is 100 km2. Table 27: Pelagic Early Adopter NERCI at Lake Vembanad current versus ideal survey scales NERCI Current Best (Ideal) Minimum (Threshold) Survey area 100 km2 same same Spatial resolution 500 m 5 m 20 m Temporal resolution Monthly every 2-3 days every 16 days but reliable Level of detail of measurements Phytoplankton, zooplankton, benthos To make satellite data more accessible to them they would like to have data in a georeferenced raster format or as point shape files. They also pointed out that they are limited by Copernicus policy to make data only available upon request once it is older than 30 days (Table 27). Early Adopter in Martinique The Eastern Caribbean islands, Martinique, Guadalupe and others are negatively affected by large floating Sargassum rafts. These can span in size to 1 km wide and up to 10 km long. Upon reaching the islands they break up into smaller rafts, land on beaches, and tangle up in coral reefs. They have negative effects on the local communities who need to be able to predict when such events take place so that they can organise clean up teams. The Early Adopter works for the French National
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 48 of 56 Table 30 Summary of requirements for epipelagic floating vegetation from scientific and Early adopters Scientific needs (Threshold/Ideal) Early adopters needs (Threshold/Ideal) Spatial resolution (GSD, m/pixel) 20m / greater than 20m 20m/5m Revisit frequency Weekly/daily or greater fortnight/daily Products characteristics Coverage by floating vegetation/separation among floating vegetation Coverage by floating vegetation/separation among floating vegetation and total mass (below surface) 6 General conclusions The challenges facing the detection of coastal biodiversity by remote sensing are huge. Waters are optically-complex (compared with open-ocean waters) which makes it difficult to disentangle the contributions to the satellite signal from biological and abiotic factors. Furthermore, the time and spatial scales at which the observations have to be made to meet scientific requirements are rarely, if ever, met by existing ocean-colour sensors, such that one often tries to make do with sensors designed primarily for land applications, with radiometric specifications that are less than ideal for aquatic applications; admittedly, all the scientific requirements cannot be met uniquely through satellites, necessitating combinations of in situ and satellite observations or development of indirect methods to detect the target through proxy variables. Through the translational effort from this report, we have attempted to match definitions of Essential Biodiversity Variables to those variables observable from remote sensing, to align and contribute to marine ecosystems global observing systems (Satterthwaite et al. 2021). BiCOME is meeting these challenges strategically: we have partitioned the investigations into intertidal, sub-tidal and pelagic environments, allowing us to focus on the problems specific to those environments, and engaging experts specialising in each of those environments; we have targeted aspects of biodiversity observations from space, which have been demonstrated to perform successfully in the past, while at the same time exploring ways in which the boundaries could be pushed further away, using new technologies on the horizon, such as hyperspectral satellite sensors; we have engaged with the user community from the start, with the objective of tailoring the products, to the extent possible, to meet their requirements; and we plan to explore direct as well as indirect approaches to reach our target.
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BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 55 of 56 Appendix 1: Glossary Term Definition Source Community A community of plants and animals characterized by a typical assemblage of species and their abundances. https://www.biodiversityaz.org/content/community-ecological Population A group of individuals of the same species, occupying a defined area, and usually isolated to some degree from other similar groups. Populations can be relatively reproductively isolated and adapted to local environments. https://www.biodiversityaz.org/content/population Ecosystem structure The biophysical architecture of an ecosystem. The composition of species making up the architecture may vary. https://www.biodiversityaz.org/content/ecosystem-structure Ecosystems dynamic complex of plant, animal and micro-organism communities and their non-living environment interacting as a functional unit CBD definition [URL3] Ecosystem functional type groups of ecosystems or patches of the land surface that share similar dynamics of matter and energy exchanges between the biota and the physical environment. The EFT concept is analogous to the Plant Functional Types (PFTs) concept, but defined at a higher level of the biological organization. As plant species can be grouped according to common functional characteristics, ecosystems can be grouped according to their common functional behavior. https://en.wikipedia.org/wiki/Ecosys tem_Functional_Type Biome The largest community unit that is convenient to recognize. In a given biome the lifeform of the climatic climax vegetation is uniform. Odum (1971) as cited in Longhurst (2007) Province Sub-basing scale partition based on physical and biological variables, with geographical constratins (i.e. related to a specific location in the ocean) Longhurst (2007) Seascape Complex ocean spaces, shaped by dynamic and interconnected patterns and processes operating across a range of spatial and temporal scales Pittman et al (2021) Status The instantaneous condition of an ecosystem. Constable et al. (2016) Trend A general tendency or direction of change over long time. Such changes may be in the mean and/or variability of status, such as the frequency of extreme events. Constable et al. (2016) Attribution The process of determining and assigning the cause of a trend. Constable et al. (2016)
BiCOME D1.1 Baseline Review Ref.: v4.0 Date: 30 June 22 ESA contract no. 4000135756/21/I-EF Page 56 of 56 Pressure The anthropogenic stressors on marine biodiversity and ecosystems. Constable et al. (2016) Drivers Societal need that produces a pressure on the ecosystem and biodiversity Constable et al. (2016) Indicator “Quantitative or qualitative metric or indices that provides reliable means to measure a particular phenomenon or attribute - used for measuring change. Often linked to a management/research question.” Smit et al. (2021) Metric “A metric is a calculated quantitative or composite numerical value based on one or more variables and which constitutes the data to inform an indicator.” Smit et al. (2021) Index “An index is a single score/metric made by combining several other metrics, sometimes by straightforward addition but often in more complex ways, to measure some given variable. A quantitative, scaled composite variable that is used to demonstrate the state or impacts of various drivers/pressures on the ecosystem.” Smit et al. (2021)