Remote sensing for Marine Spatial Planning and Integrated Coastal Areas Management: Achievements, challenges, opportunities and future prospects

Author’s Accepted Manuscript Remote sensing for Marine Spatial Planning and Integrated Coastal Areas Management: Achievements, challenges, opportunities and future prospects William Ouellette, Wondifraw Getinet www.elsevier.com/locate/rsase

PII: DOI: Reference:

S2352-9385(16)30072-6 http://dx.doi.org/10.1016/j.rsase.2016.07.003 RSASE34

To appear in: Remote Sensing Applications: Society and Environment Received date: 19 February 2016 Revised date: 14 July 2016 Accepted date: 25 July 2016 Cite this article as: William Ouellette and Wondifraw Getinet, Remote sensing for Marine Spatial Planning and Integrated Coastal Areas Management: Achievements, challenges, opportunities and future prospects, Remote Sensing Applications: Society and Environment, http://dx.doi.org/10.1016/j.rsase.2016.07.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Remote sensing for Marine Spatial Planning and Integrated Coastal Areas Management: achievements, challenges, opportunities and future prospects William Ouellette, Wondifraw Getinet Marine Policy and Regional Coordination Section of the Intergovernmental Oceanographic Commission of UNESCO, 7 Place Fontenoy, 75007 Paris, France [email protected] [email protected]

Abstract This paper addresses the past and current uses of remote sensing technologies that are supporting Marine Spatial Planning (MSP) and Integrated Coastal Area Management (ICAM). Satellite and airborne remote sensing have a key role to play in studying the marine and coastal environment. The paper introduces the characteristics of remote sensing systems of interest for studying the oceans and coastal ecosystems. Secondly, a conceptual framework is defined which relates all important components of ICAM/MSP: 1) Ecosystem health and pollution, 2) Natural (coastal) hazards, 3) Marine Space and Use, 4) Coastal land cover and use, 5) population (dynamics), with their respective data collection goals and the most appropriate stateof-the-art sensor technologies to study them, summed up in a comprehensive table. A summary of achievements of remote sensing in each component of ICAM and MSP is given, with a particular interest for developing countries where their implementation is made difficult by several technical and governance issues. Opportunities are also presented to nuance those challenges in the form of programs and initiatives to increase capacity and resources to exploit RS in a MSP/ICAM context, but also to facilitate RS data accessibility and usability. Finally, future satellite missions of particular interest for ICAM and MSP are introduced. Overall, the word “Integrated” in ICAM suggests that a multidisciplinary approach is needed to understand the dynamics of marine and coastal environments, and remote sensing is identified as a piece of the puzzle which coastal and ocean managers should not hesitate to integrate in their practices. This paper acknowledges the need for more in-depth understanding of the underlying structures and ecological functioning of ecosystems, their habitats and their species, before RS can become a truly reliable tool in biophysical variable monitoring. Keywords: ICAM, MSP, coastal ecosystems and habitats, ecosystem health and pollution, coastal hazards, marine space and use Introduction The concepts of Integrated Coastal Area Management (ICAM) and Marine Spatial Planning (MSP) are multidisciplinary in their approach, and the management plans and strategies adopted should always be knowledge-based. In ecological terms, knowledge stands for information about species and ecosystem structures, as well as their interactions and their intrinsic functioning. Data however, concerns the so-called raw information collected about the environment, but which in themselves do not tell us what to do from a planning and management point of view, unless carefully studied and interpreted. The problems that ICAM

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and MSP are currently facing is the lack of knowledge about the risks incurred when carrying out a certain type of activity, be it socio-economic (e.g. shipping) or environmental (e.g. conservation efforts), in coastal areas, but also the lack of follow-up assessment to demonstrate that activities implemented through an ICAM/MSP process are indeed fulfilling their environmental objectives. These knowledge gaps cannot be solved through data and observation networks alone. Halpern et al. (2008, 2015) suggests a cumulative impacts assessment of stressors on coastal and marine ecosystems. The quantification and mapping of cumulative impacts of human activities on ecosystems has gained new interest and relevance as ICAM and MSP practices move towards Ecosystem-Based Management (EBM) approaches that require such assessments (Halpern & Fujita, 2013). Policy and management decisions regarding which stressors require attention are particularly vulnerable to this knowledge gap. Cumulative impact mapping shows great potential to support decision making processes, given the interactions between the stressors involved are well understood. The datasets currently used for cumulative impact mapping hardly consider the use of remote sensing datasets, even though coarse spatial and temporal resolutions (Halpern & Fujita, 2013) and lack of spatially explicit data (Ban et al., 2010) were identified as limitations of the method. This paper will investigate the use of remote sensing as a tool to contribute to the data availability, coverage and reliability requirements for ICAM and MSP, but also beyond towards the development of suitable integral indicators following the EBM approach as a basis to support marine and coastal policy making and management (De Jonge et al., 2012). Remote sensing is a very effective way of obtaining frequent data on a synoptic scale about the state of the oceans and coasts. The airborne and space-borne sensors applied to ICAM and MSP will be the focus of this paper, thus leaving the local scale of data acquisition (Unmanned Aerial Vehicles UAVS, terrestrial and ship-borne sensors) for another review. These sensors are now delivering a great wealth of data from which environmental and climate variables are derived. Some RS data is free and open-source, thus encouraging its manipulation in a wide range of scientific fields, and offers opportunities to fill in the data gap in developing countries where in-situ observation networks are severally lacking (Bocco et al., 2001). The aims of this paper are threefold. RS has a key role to play in ICAM and MSP, and this will be demonstrated through a conceptual framework showing how RS is currently being used to study oceans and coastal areas, sometimes in an operational and routinely manner. A summary of achievements from the public and private sectors in operational uses of RS in ICAM and MSP practices, but also of the current state of research in this domain is presented according to relevant themes. Potential areas of improvement on ways to best integrate remote sensing practices to the coastal and marine management ‘toolbox’ will be discussed in the Challenges and Opportunities part of the paper. The practical limitations of data accessibility and the mechanisms in place for an effective integration of RS to ICAM and MSP in developing countries, and specifically in Africa where practitioners face many technical, technological and difficulties, are also addressed. Finally, the space industry is constantly developing new earth observation satellite missions, some of them particularly relevant to studying the coasts and oceans. These will be the topic of the future missions section. Overall, the intent of this paper is to clearly delineate the role RS has to play in ICAM and MSP in terms of data requirement and availability, how, and to what extent, this has been achieved so far, and what the future holds for the

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operational use of RS to support knowledge-based policy-making for the sustainable management of the coastal and marine environment. 1.

The Role of Remote Sensing for ICAM and MSP RS studies of coastal areas are abundant, but a lot of the research carried out seems to be disjoint.

The majority of the efforts to use RS for ICAM and MSP are done at national or regional levels (e.g. Junta de Andalucia, 2016, NOAA, 2016), but no review has reconciled the advances and findings of the worldwide scientific community since Green et al. (1996) and Malthus & Mumby (2003). The sensors applicable to the ocean and coastal areas are well documented by the space agencies that develop them, but their specific applications to ICAM and MSP are not obvious. Most RS studies, when studying the ocean and coastal areas, tend to focus on a single component of ICAM/MSP such as pollution and ecosystem health, marine natural hazards, marine space and use (e.g. fisheries, aquaculture), coastal land use (e.g. urbanization and agricultural use) or coastal population dynamics. In order to provide an overview of the applicability of RS to ICAM and MSP, it is therefore necessary to adopt an integrated approach taking into consideration all stakeholders and activities taking place at sea. Challenges of ICAM and MSP lie at national and transnational levels. Satellite RS provides a unique capability for regular and timely observations of coastal areas at different spatial scales (Meaden & AguilarManjarrez, 2013), but fails to provide the very high spatial resolutions which airborne RS can, thus making these suitable for different applications, and in some cases complementary. The wealth of global and regional ocean data as well as its derived information products acquired over the past decades is promising, but the efforts to integrate and apply this data specifically to the management of coastal areas still has a way to go.

2.

Remote sensing for ICAM and MSP: a conceptual framework

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In the past, satellite sensors were designed for the sake of research before their applications had yet been identified. Now, after decades of investigative RS research in many environmental and socio-economic fields, the potential of RS technologies has finally been identified for specific applications. We have transitioned from the technology defining the use, to the use defining the technology. This means to a certain extent that politicians and decision-makers have acknowledged the benefits of RS, and are asking the scientific community to design sensors tailored for specific uses deemed important from an economic, social and environmental perspective. This has recently been proven by the launch of earth observation satellites such as the SENTINEL missions, consisting of the backbone of COPERNICUS, the European earth observation program for security

and environment (ESA, 2014).

Figure 1: Conceptual diagram providing an overview of the concepts introduced in this report. The ICAM/MSP components (rectangles) have remote sensing observation requirements, represented by the data collection goals (purple diamonds), and each data collection goal can be achieved through the use of various remote sensing techniques (red cylinders).

Moreover, the synoptic and mesoscale nature of RS data facilitates the EBM approach, making it an essential tool in measuring and monitoring the changing states of ecosystems, and indirectly to convince

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decision-makers to take remedial actions towards the recovery and sustainability of degraded goods and services (Pernetta et al., 1993). The remote sensing methods that best support ICAM and MSP are the ones that provide timely information (high temporal resolution) over the area of interest, be it national or transnational, as they provide constant evidence and support to decision-makers. Concerns have been raised from RS data users for the need of higher spatial resolution and changes in earth observation satellite constellations, which systematically causes disagreement amongst different scientific communities due to the temporal/spatial resolution and coverage trade-offs involved (Wulder et al., 2015). Coastal research is suffering from limited spatial resolution due to the spatially heterogeneous and complex nature of the coastal environment. Airborne surveys can be carried out to obtain the spatial resolution required, but acquiring data over large swaths of the coast is expensive, and does not guarantee a temporal continuity. On the contrary, satellite RS data is cost-efficient (considering its long operational time in orbit) and provides information at regular time intervals, thus complementing in-situ data networks for cross-validation and consistency. Moreover, the gradual democratization of RS data and products, as well as the increasing user-friendliness of the tools to exploit them are bringing the information within the reach of more and more stakeholders. Figure 1 illustrates the conceptual framework behind this report by relating the different concepts and components relevant for RS as a tool to support ICAM and MSP. It offers a “bird’s eye” view of the RS techniques which can be applied to ICAM and MSP, given the specific observation requirements of each ICAM/MSP component. In order to pinpoint exactly which RS technologies can be instrumental for ICAM and MSP, it is necessary to identify the thematic components for which RS data can be collected. All these components combine environmental and socio-economic impacts, underlining the need for an integrated and EBM approach to coastal management. For this framework, we have identified the following components of ICAM/MSP: Ecosystem health and pollution Ecosystem health takes into consideration biological organization, vigor and geological, physical and chemical properties of ecosystems (IOC-UNESCO, 2006). Biological organization is defined as the variety of living forms at the community, species, population and genetic levels, the ecological roles that these perform and the diversity they contain (Wilcox, 1984). The vigor is concerned with the productivity of the ecosystem, and relates to the energy flows within it and the interaction of organizational components. The geological, physical and chemical properties of the ecosystem are abiotic properties that have an important influence on biological organization and vigor, and vice versa (Wilcox, 1984). If these environmental properties are directly or indirectly altered by anthropogenic means, the quality of the ecosystem’s environment (e.g. water turbidity, nutrient levels, habitat quality) may find itself affected, and subsequently the ecosystem health itself. The concept of ecosystem health thus provides a straightforward ecological framework for an EBM approach to ICAM. Natural (coastal) hazards This component is defined as phenomena which represent a danger to coastal terrestrial environments and their populations in the form of physical destruction, human loss, or morphological alteration of the coastline. They can either occur with rapid onset, such as tsunamis, storm surges and extreme wind-forced waves, or be

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more cumulative or progressive over longer timescales such as sea level rise and coastal erosion, also referred to as creeping hazards (IOC-UNESCO, 2009). Marine space and use This component concerns the marine segment of ICAM and is often an area of contention amongst stakeholders. Marine resources provide a wealth of ecosystem services for coastal societies such as fishing, transport, energy and hydrocarbon extraction, to name a few. They are central to the concept of ‘blue economy’, which is high on the agenda of decision-makers (Blue Economy, 2015). Coastal land use This component refers to the terrestrial segment of ICAM constituting an interface with the marine component of the coast. The boundaries between land and sea are usually ambiguous and unclear, but land use can clearly be distinguished from marine use, and the methods required to monitor them are also fundamentally different (Cicin-Sain et al., 1995). The mutual impacts that land and ocean may have on each other is something that needs to be understood in order to carry out effective ICAM. The geographic extent of the coastal land that should be considered in ICAM is not exactly clear, and it is important to consider elements such as watersheds and catchments, which are oriented towards a marine economy, however far they may find themselves from the coast. Population In 2010, 44 % of the world's population lived within 150 kilometers of the coast, and this number is likely to increase in the years to come due to the wealth of coastal resources providing transport, food and ecological and economic benefits to its residents (UNEP, 2001). Eight of the ten largest cities in the world are located on the coast, and this coastal population is steadily rising, especially in developing countries where valuable ecosystems vulnerable to anthropogenic threats remain. ICAM cannot be achieved without putting population at the forefront of planning and decision-making. The larger the population grows, and the greater the pressure on coastal and marine environments, leading to alteration and destruction of natural habitats and pollution of coastal waters, inevitably compromising the valuable ecosystem services that the marine environment provides. To support ICAM and MSP processes, data collection is primordial as it constitutes the social and scientific knowledge about coastal areas upon which decisions are taken and strategies based. Approaching ICAM and MSP from the ‘eye’ of RS, three data collection goals have been identified: Monitoring It suggests the tracking of a phenomenon over time. It typically aims at characterizing environmental processes in space and time, in order to gain better understanding of its evolution and its drivers. Monitoring is most relevant for slowly occurring and synoptic phenomena, as it provides information with high temporal resolution and provides continuous empirical support for dynamic decision-making. Prevention/Mitigation Tackling the symptoms of hazard occurrences, as well as reduce their impact in a post-disaster scenario. It may involve monitoring in order to detect the imminence of a hazard, but once this hazard has been detected, RS

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data becomes an active part in the intervention efforts to either completely prevent its occurrence, or minimize its effects by mitigating the risks. Assessment Collecting information after the occurrence of an event and gaining insight on the spatial and temporal extent of its impact. RS enables quick assessments of post-disaster conditions, allowing decision makers to scale the size of their intervention accordingly, as well as target it in space. These paradigms of data collection are important when deciding how and where to collect RS data for effective ICAM and MSP. The last data collection goal targets the a posteriori conditions of an event, the prevention and mitigation are focused on the a priori conditions of an event, while monitoring is a continuous observation of the conditions. Table 1 provides an exhaustive list of (mostly) recent and operational sensors in all categories relevant to ICAM and MSP, along with their specifications and how to access their data.

3.

Achievements in remote sensing for ICAM and MSP

In this section, the academic and professional achievements made in each respective ICAM/MSP component described in figure 1 are synthetized. This summary does not try to cover all the research carried out in each of the thematic fields, but tries to be comprehensive in covering all of the applications respective to each category.

a.

Ecosystem health and pollution

For ecosystem health and pollution, figure 2 sums up the different elements of the coastal and marine environment that can be observed through RS. The ecological definition of habitat, a natural environment in which species or group of species live, is somewhat fuzzy in RS terms. The underlying process for habitat mapping therefore does not involve RS observations alone, but also surveying, collating, analyzing and modelling data to derive habitat distribution and then designing the layout of habitat maps that are clear and fit their intended purpose (MESH, 2008). Habitat mapping can be performed in various ways using spaceborne optical (multispectral and hyperspectral) sensors and LiDAR for terrestrial (Lengyel et al., 2008, Chust et al., 2008, Klemas, 2014), shallow sub-surface (Zavalas et al., 2014) and intertidal habitats (Hennig et al., 2007), while SAR can be very useful for surface and terrestrial habitats only (radar signal does not penetrate water), and particularly for flooded ecosystems such as coastal wetlands due to its ability to penetrate canopies and thus fully characterize the structures of coastal habitats, especially when operated in polarimetric modes (Cornforth et al., 2013, Niculescu et al., 2015). All three types of RS variables (optical, SAR and LiDAR) can also be combined to further increase habitat classification accuracies. Detailed airborne LiDAR and SAR surveys come at a cost, but have proven to be complementary to targeted ecological studies (van Beijma, 2015). Deeper-water habitats in margins of continental shelves can, to a certain extent, be mapped using ship-borne sonars (NOC-NERC, 2015). From the RS measurement of biophysical variables such as primary productivity, SST, currents and front patterns structuring the spatiotemporal distribution of marine biodiversity (Valavanis et al., 2008, Hardman-Mountford et al., 2008), a habitat classification can be derived (Fraschetti et al., 2008) which can serve as the basis for the effective implementation of MPAs (Kachelriess et

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al., 2014, Klemas, 2011a). Particular efforts have been recorded for the mapping and monitoring of mangrove forests (Kuenzer et al., 2011), seagrass meadows (Godet et al., 2009), coral reefs and their health (Rowlands et al., 2012, Goodman et al., 2013), as well as their mortality through coral bleaching (Maynard et al., 2008). A concrete example of a habitat-mapping project is the study from Ferreira et al. (2012). This example is taken in the African context to illustrate the extreme case in which close to no ancillary and field data are available to assist the ICAM/MSP process. The project consists in coastal habitat mapping of the Trans-boundary networks of marine protected areas (MPAs) for integrated conservation and sustainable development: biophysical, socio-economic

and

governance

assessment

in

East

Africa

(TRANSMAP

project

http://www.transmap.fc.ul.pt/index.asp?01pu ). The study uses RS techniques to classify and map coastal habitat types (coral reefs, coastal dunes, mangroves, seagrasses, tidal flats, and coastal lagoons) along the Tanzanian/Mozambique trans-boundary zone. The area is large with limited financing, manpower, and a quasi-absence of field data about different habitats to be sampled due to accessibility problems of the study area, as well as limited time and money. For these reasons, RS was selected as a cost effective method for synoptic sampling and mapping of resources. Landsat TM5 with a spatial resolution of 30x30m was used. The overall map accuracy was measured with a visually assessed training sample and reached 77%. Although far from perfect, it was considered fit for distribution to different stakeholders in the region and was positively received. Such a habitat map provides a general inventory of coastal resources as background to a management plan, and any accuracy reached can be of support (Mumby & Green, 2000). The authors concluded an RS approach is a cost effective way to tackle large area habitat mapping for conservation projects. The costs involved in the project were minimal as it relied on free Landsat data. The result showed that using RS methods for larger areas has a high impact for scientific and management applications. For smaller scale and more local mapping endeavors, very-high resolution (VHR) data is rarely available for free (i.e. DigitalGlobe VHR Worldview suite), which makes RS far less suitable for local management and planning efforts in Africa at this point in time. In terms of biodiversity monitoring, spaceborne RS was shown to be useful in measuring the different indicators of biodiversity health/loss defined by the Convention on Biodiversity (CBD), either through direct measurement or through proxy from habitat mapping data or other variables interacting or influencing species living in the observed ecosystems (Secades et al., 2014, Strand et al., 2007). The study of water’s biophysical characteristics is done through sea surface salinity (SSS) monitoring using the SMOS (soil and moisture ocean salinity) passive MW (microwave) radiometer satellite mission since 2011 (Font et al., 2008, Land et al., 2015), but also through other commonly measured variables such as sea surface temperature (SST), which can be measured using both thermal infrared and passive microwave radiometers such as AVHRR (Maurer, 2002), as well as the blending of both to take advantage of their respective strengths (Donlon et al., 2007, 2008). SST data processing methods have evolved since the acquisition of data in the past decades, and they can therefore be reprocessed using improved algorithms to obtain more sound climatological trends (Merchant et al., 2012). Oil spills are also of concern when studying ecosystem health and biodiversity, as its impacts on the environment are very pronounced, both for benthic and coastal habitats. For this purpose, spaceborne SAR

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has proved to be the most efficient, detecting the molecular tension differences between the oil surface and the surrounding water surface (Drozdowski et al., 2011, Solberg, 2012, Fingas & Brown, 2014, Warren et al., 2014). CleanSeaNet is an oil spill monitoring service offered by EMSA, guaranteeing a rapid response (European Maritime Safety Agency, 2015). Ocean color is also suitable for oil spill monitoring as it can distinguish between man-made spills and algal blooms, but its use is limited to daytime and cloud-free monitoring (Hu et al., 2003). In spite of this, the complementarity of both optical and radar systems benefit the routine use of oil spill monitoring by using large swath SAR such as Sentinel-1 as a first detection and warning, while multispectral or hyperspectral airborne systems can be called into action to identify the polluter, the extent and the type of spill (Brekke & Solberg, 2005). Tanker oil spills and deep sea drilling spills have a large impact on marine ecosystems, but the oil spills generated from the conventional freight fleets turns out to be larger, and that is why a temporally high resolution and an almost global coverage is instrumental in tracking these “micro-spills” (DUJS, 2012). Eutrophication is the ecosystem’s response to the addition of artificial or natural nutrients, through detergents, fertilizers, or sewage, whether from diffuse or point sources, to an aquatic system, enhancing the blooming of phytoplankton, in turn increasing the turbidity and depriving the water column from light. It poses the most serious threat to the long-term health and function of estuarine and coastal environments (Kennish and de Jonge, 2011). The most acute eutrophic conditions generate harmful algal blooms (HABs), a phenomenon which trickles down from a large scale fostering of photosynthetically active organisms in high concentrations discoloring the water surface, thus making ocean color techniques on synoptic scales highly suitable for their monitoring.

Figure 2: Scheme illustrating all the components that can be adequately monitored through RS in the framework of ecosystem health and monitoring (Wilson, 2015).

Figure 3: Schematic diagram of the different pathways of nutrient and sediment run-off, the conditions they create, the symptoms that can be studied and analysed, as well as the consequences they have on the environment and society (adapted from Kennish & de Jonge, 2011). The red rectangles are elements which can be, either directly, through proxy or modelling, studied using RS.

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HABs lead to depletion of oxygen levels (hypoxia, or anoxia if total depletion) through excessive respiration or decomposition, leading to fauna, and sometimes human, mortality (Shen et al., 2012, Ahn et al., 2006). Figure 3 illustrates the anthropogenic pathways of nutrient and organic enrichment leading to severe consequences manifested by HABs, but also an array of diverse effects such as hypoxia and anoxia, elevated epiphytic growth, loss of essential habitat (e.g., seagrass and shellfish beds), reduced biodiversity, declining harvestable fisheries, imbalanced trophic food webs, and diminished ecosystem services and resilience (Kennish and de Jonge, 2011). The detection of Chl-a through spaceborne RS, although indicated as possible in Figure 3, is sometimes made difficult by the presence of CDOM (colour dissolved organic matter) in coastal waters. Escalating population growth in estuarine and coastal land areas are at the root of the pathways of nutrient runoffs, generating trajectories towards land uses and covers which are detrimental to water quality. Residential and urban tissues increase the imperviousness of the land cover and thus reduce absorption by pervious surfaces and increases peak rainfall runoff quantities (Verbeiren et al., 2013). The quality of the diffuse runoffs in the urban environment is affected by many factors including traffic, emissions, air pollution and storm water pollution (Shorshani et al., 2013), coupled with the point source pollution from household and industry wastewater (EEA, 2008). An evidence of anthropogenic impact of the environment is demonstrated by the strong relationship between built-up land and degraded water quality (Galbraith and Burns, 2007), putting cities and areas of high population density at the centre of focus for ICAM. Agricultural production systems, including crop production, livestock and aquaculture, are the other major contributors to non-point/diffuse source pollution in coastal and estuarine waters (Turral et al., 2012), which in turn lead to the consequences shown in figure 3. Overall, the runoff water quality is a strong indicator of land use and landscape patterns around watersheds as it reflects the underlying human activities taking place (Wiens, 2002, Gautam et al., 2003, Huang et al., 2014).

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Water quality and turbidity monitoring using RS is a well-established method bearing the name of ocean color, a technique that allows the derivation of different water constituents, which in turn can infer something about the impact of nutrient and pollutant runoff on coastal and marine environments (Schaeffer et al., 2012). In figure 3, the elements framed in red are the variables or phenomena that can be monitored through ocean colour RS. The commonly used multispectral sensors for this are MODIS and MERIS, and soon Sentinel-3. They essentially detect the water hue due to the presence of micro-organisms containing chlorophyll, sediments and colored dissolved organic material (NOAA, 2014). Hyperspectral sensors like HICO and Hyperion have also demonstrated their potential for ocean color (Moses et al., 2014, Ryan et al., 2014), but their high Signal-to-Noise ratio greatly limited their operational use. Future launches of space-borne hyperspectral missions such as EnMap (Guanter et al., 2015), scheduled for 2018, and HyspIRI (Lee et al., 2015), scheduled for 2021, are highly awaited by the scientific community for ocean color mapping, but also shallow water bathymetry. The sum of these constituents can be defined as suspended particulate matter, which encompasses organic and inorganic matter controlling light penetration (Loisel et al. 2013). This penetration depth, or optical thickness, is particularly telling about the biologically active ocean surface layer and can derive chlorophyll-a measurement by proxy, an indicator of marine primary productivity (Wernand et al., 2013, Blondeau-Patissier et al., 2014), but can also indirectly measure other water parameters such as particulate organic carbon content, particle size distributions. Although the conventional way to study coastal water quality is using polar-orbiting satellites, the combined use of geostationary satellites’ high temporal resolution with the former’s high spatial resolution can produce synergistic ocean color products (Vanhellemont et al., 2014). Coastal waters are also victims of Persistent Organic Pollutants (POPs) and plastics discharge (GESAMP, 2015). These cannot be detected using satellite RS, but modelling of the fluxes and cycles of POPs in the atmosphere-ocean interface can be done (Costanzini et al., 2014). Airborne RS has also been used for the detection of abandoned fishing gear and other macroplastics (Mace, 2012, Pichel et al., 2012, Veenstra & Churnside, 2012), and in the future could also be used for microplastics detection using Raman spectroscopy techniques with airborne LiDAR (Driedger et al., 2015). HABs can also be forecasted and modelled, thanks to an improved knowledge of the environmental conditions in which HABs thrive, but also in the detailed knowledge of ocean dynamics that we had thanks to decades of oceanographic studies supported by RS, which allows us to model their movement (Stumpf et al., 2005, Kudela et al., 2015). However, high frequencies of observations are required to track highly dynamic HABs (Glibert et al., 2005). Another basic requirement of HAB monitoring and modelling is that discrimination between HAB and non-HAB species can be made by identifying phytoplankton functional groups (Alvain et al., 2008). NOAA has already developed a “HAB operational forecast system” (NOAA, 2013), while the IOC-IPHAB has its own forecasting system underway (Karlson, 2009). For more hands-on examples of ecosystem assessment and management endeavours worldwide, the book from Yang (2009) can be consulted.

b.

Coastal land cover and use

The implications of LULC for the environment were identified as consequential through the nutrient and pollutant runoff originating from urban areas and agricultural systems, for which RS can assist in

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observing. RS is also able to characterize the impact of coastal populations on land cover change, and vice versa (Freire et al., 2009). The capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and prevent spread of infectious diseases is seriously undermined due to fast conversion of natural ecosystem to more intensive land use and urbanization (Foley et al., 2005). This once again underlines the importance of the urban environment as the most impacting land cover on coastal ecosystems (Creel, 2003, Weng, 2011), while integrating the concept that booming population is the driver behind urbanization and other LULC changes in coastal areas. Studying the linkages between LULC and population dynamics can therefore help us understand the impact of human society on terrestrial and coastal ecosystems, and reciprocally how certain land uses drive population distributions in coastal areas (Horigue et al., 2012, NOAA, 2015). RS is once again fit for the task thanks to the large spatial coverage it offers, allowing the study of entire strips of coasts using both airborne (Nieke et al., 2005) and space-borne sensors (Klemas, 2013a). LULC mapping and change detection is probably the most researched and advanced field of RS. Multispectral, hyperspectral, SAR, LiDAR sensors (Huang & Klemas, 2012), their data fusion (Joshi et al., 2016), as well as a variety of algorithms have been used for the task (Somers et al., 2011, Li et al., 2014). Multispectral sensors, due to their abundance and the relatively small sizes of the images, are routinely used for the task, but it has been found that newer hyperspectral sensors, although only airborne for now until the launch of ENMap and HyspIRI, can provide higher levels of detail in land cover classification, especially in spatially complex and heterogeneous landscapes like coasts where the urban tissue is dense and the population densities high (Adam et al., 2014). The hyperspectral data load is heavy and not particularly user-friendly to process however, although a lot of efforts are being put in to develop workflows to simplify their exploitation (Chang, 2013). LiDAR data can also offer synergies in land cover classification by providing additional 3D information allowing for further discrimination between classes (Alonzo et al., 2014). Satellite SAR applications to LULC mapping are more recent, but have shown their potential for a variety of landscapes like wetlands (Niculescu et al., 2015), but also urban mapping and change detection (Jensen et al., 2007, Corbane et al., 2008, Ban et al., 2015). For multiple dates and multiple data sources, temporal decorrelation due to atmospheric conditions and seasonality, as well as sensor interoperability needs to be addressed (Huang & Klemas, 2012, Miura et al., 2011). Efforts to produce global land cover products for the climate modelling community and the industry have been made, but their production time remains very lengthy due to lack of fully automatic methods to generate them, as well as an extremely time consuming validation processes (Friedl et al., 2002). However, crowd-sourced mapping initiatives like Geo-Wiki are making a leap forward towards the generation of more reliable and more timely global land cover products (Fritz et al., 2012). Currently a few regional and global products are available for different dates ranging from 2000 until 2012 such as the CORINE CLC (Europe), Africover (certain African countries), USGS national land cover data (USA), GlobCover and ESA-CCI global land cover product, to name a few. These products are not regularly updated and have faced the criticism of inconsistencies between the map classes (Congalton et al., 2014). A new ambitious global land cover product revealed during the Copernicus Global Land Services User Workshop in June 2016 is underway that will offer a yearly update at 100 m resolution, but little is known to date apart from the partners involved (Wageningen

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University (WUR), Vlaamse Instituut voor Technologische Onderzoek (VITO) and International Institutes for Applied Systems Analysis (IIASA)).

c.

Natural hazards

The distinction between rapid onset and creeping hazard is made, yet both are perfectly observable through RS (Finkl, 2013, Chapter 2). The most evident proxy of climate change, sea level rise (SLR), is a creeping hazard which affects the world coasts at a regional and local scale, and the need to monitor sea surface height (SSH) with high accuracy is necessary to provide reliable SLR estimates and generate accurate forecasts (Cazenave, 2013, Stocker et al., 2013). SAR satellite altimetry is the technology par excellence for the task, as it provides global SSH and SSH anomalies products when data from multiple altimetry missions are combined (Bosch et al., 2014). The data series date back to the early 1990s, which enables the derivation of SSH trends over time for the entire globe, at a resolution of a few dozens of kilometers (ESA, 2013). Such data is key to targeting the zones most at risk as well as the most vulnerable communities (Melet et al., 2015). Altimetry in coastal areas is much trickier to resolve than in the open ocean, but the new Sentinel-3 altimetry mission following the legacy of the JASON altimeters, combined with improved algorithms will provide higher resolution and more accurate data of coastal SLR (Roblou et al., 2007, Bouffard et al., 2011). Coastal vulnerability to SLR can also be assessed through the coastal vulnerability index (CVI), which is dependent on a variety of physical and geological variables which can be derived from RS information like DEMs and SSH trends (Thieler et al., 2002, Abuodha and Woodroffe, 2006, Dwarakish et al., 2009, Consejeria del Medio Ambiente, 2011). The rate of shoreline change and its position can be done using the Digital Shoreline Analysis system (DSAS) (Thieler et al., 2009), while other models such as the sea level affecting marshes model (SLAMM) (Linhoss et al., 2014) or the Marsh equilibrium model (MEM) (Schile et al., 2014) can be used to simulate land cover and land use change through wetland migration under various SLR scenarios. For coastal vulnerability assessment using models and indicators in various case studies, the EEA technical papers can be pursued for further reading (Iglesias-Campos et al., 2010, Ramieri et al., 2011). Storm surges and flash flood events are rapid onset events which are difficult to monitor and predict using RS, but that are amplified by SLR, meaning that an area with a strong SLR trend, combined with the vulnerability of its coast, can be supported in the framework of ICAM to prevent and mitigate the occurrence of such disasters (NOAA, 2011). RS is also a key instrument in post-disaster damage assessment however, as it can cost-effectively evaluate, both from an airborne or space-borne platform, the extent of the human and physical losses, but also the flood extent using a combination of SAR during landfall (when cloudy) and satellite multispectral sensors like MODIS after landfall (when skies clear up) in order to provide relief to the areas that need it the most (Klemas, 2015). SAR is particularly useful for the detection of flooded extent due to the extremely weak backscatter of water (Parkinson, 2003, De Groeve, 2010). The Sentinel-1A and B SAR satellite missions recently launched are providing a revisit time of 5 days at a 15 m spatial resolution, unlocking whole new possibilities of flood and change monitoring considering the gratuity of the data, the first of its kind. Prior to these missions, users had to rely on commercial SAR imagery such as Cosmo-Skymed (3m) and TerraSAR-X (5 m resolution), or ALOS-PALSAR (20m) and Radarsat (30m). Airborne LiDARs and hyperspectral sensors such as AVIRIS can also support such tasks and provide very high resolution data of the damages incurred, both in

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terms of surface area affected and structural changes in the landscape (erosion, building destruction, wetland loss, etc.) (Brakenridge & Anderson, 2006). The damage assessment can be done in a spatially-tiered way depending on availability of data, resources and time (Adams & Gillespie, 2006, Friedland, 2009). The damage assessment of natural terrestrial (Yamazaki & Matsuoka, 2007, Adam et al., 2010) and underwater ecosystems (Brodie et al., 2010, Jones, 2015) is also emerging in research. The sea, lake and overland surges from hurricanes (SLOSH) model has been developed by NOAA-NWS to estimate and predict storm surge heights, and incorporates RS data of physical and climate variables to do so (NOAA/NHC, 2014), while the IOC-UNESCO tsunami program has implemented a tsunami early warning system (EWS) in the Indian Ocean (Behrens et al., 2010), as well as educating communities at risk about preparedness measures (IOC-UNESCO, 2015a). Coastal and beach erosion are also consequences of SLR and rapid onset hazards, and therefore also observable using the same RS techniques. Because of the creeping nature of erosion, long time series of satellite multispectral data is necessary to characterize the erosion rate and subsequently the vulnerability of a certain coast (Stockdon, Doran and Sallenger, 2009). Shoreline position is the variable studied for beach and coastal erosion, and is best detected using both panchromatic and multispectral systems (Boak & Turner, 2005, Brock & Purkis, 2009, Klemas, 2011b). Very high resolution (VHR) sensors made available by Airbus (Pléiades constellation) and DigitalGlobe (Worldview constellation, GeoEye-1), although expensive, provide sub-meter panchromatic and below 2 m multispectral resolutions, making them the fittest for the task. Sentinel-2A and B however, with their 290 km wide swath, 10 m resolution (20 m and 60 m for other bands), 6 day revisit time and gratuity will facilitate coastal erosion mapping on a global scale. The DSAS model previously mentioned is routinely used to determine the seasonal and episodic shoreline oscillations responsible for changing shoreline positions, especially in highly vulnerable areas like low lying atolls (Barnett & Adger, 2003, Mann & Westphal, 2014). Modelling of erosion predictions along the coast have also been pursued using historical data and extrapolation techniques (Prukpitikul et al., 2012), but new research is integrating more RS and ancillary data about estuarine and long-shore sediment transport to drive the models and obtain more accurate predictions (Deepika et al., 2013), and these can only benefit from high performance optical instruments such as the ones mentioned above.

d.

Marine space and use

RS can benefit to most economic activities taking place at sea, as well as to the authorities responsible of monitoring these. However, RS can first and foremost help planners and policymakers define eco-regions which are defined by observable mesoscale indicators (Barale, 2009, 2011). RS together with ancillary data, can define ecosystems according to common characteristics and delineate their boundaries following an ecosystem-based approach (i.e. flexible boundaries following seasonal changes in ocean dynamics) rather than a geographic and administrative approach (i.e. fixed boundaries) (Traykovski and Sosik, 2003, Dowell & Platt, 2009), which is far more representative for the purpose of marine space allocation such as MPA allocation or no-fishing zone allocation (Marine Reserves Coalition, 2012, Kachelriess et al., 2014). RS is particularly useful in measuring variables and deriving biodiversity indicators providing for the Essential Biodiversity Variables (EBVs) in phase of being accepted as standard measures of biodiversity (Pereira et al., 2013) to define coastal areas suitable for MPA inclusion (Maina et al., 2015). The concept of particularly sensitive sea areas (PSSAs,

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International Marine Organization, 2015) and ecologically or biologically significant marine areas (EBSAs, Dunn et al., 2014) are other conservation mechanisms which are likely to reinforce the efforts made in the allocation of MPAs. EBSA is a scheme adopted by the CBD in order to provide support for the conservation efforts done and the delineation of MPAs in areas within and beyond national jurisdiction, but remains strictly a scientific and technical exercise that aims to inform MSP practitioners. PSSAs, on the contrary, are associated with protective measures adopted by IMO to prevent, reduce, or eliminate the threat or identified vulnerability. This designation may be useful, if they can be successfully integrated into national and international processes of MPA creation, which is often problematic as the IMO’s focus on maritime traffic is not well integrated with other international marine law such as marine conservation, and can sometimes lead in conflicts of interests within EEZs (Cochrane et al., 2007). Maritime traffic can also benefit from RS, first by integrating knowledge about ocean currents and sea ice in winter to optimize shipping routes (Sawyer et al., 2015), and second by integrating meteorological knowledge to avoid storms and mitigate risks in the high seas, but also in coastal areas where the impact of ship wrecking (Klemas, 2012) and stationary platform malfunction on ecosystems is the highest (Yan et al., 2015). Where RS is most innovative for maritime traffic is for vessel detection and monitoring, an exercise particularly useful for authorities who want to enforce regulations in place in the framework of national or international MSP or to take measures for national security in their EEZ and beyond (Maresca et al., 2010). Shore-based HF radars and the automatic identification system (AIS) are the commonly used tools but due to the large spatial coverage required for the task. Satellite SAR (Greidanus & Kourti, 2006) and multispectral sensors (Corbane, 2008, Mattyus, 2013) are developing fast for vessel detection and monitoring (Alshawaf et al., 2012). With a resolution of a few dozens of meters, they can detect vessels with relatively high certainty. Operational ship monitoring systems have been developed such as SIMONS which provides a processing chain of vessel detection and classification with confidence levels (Margarit et al., 2009, Yang et al., 2013). It is able to merge its results with other input channels such as polls and AIS, thus bridging the gap with traditional information and RS. Data from more recent satellites like ALOS-PALSAR, TerraSAR-X and Sentinel-1 were not yet in operation at the time of publication but were already analyzed as a source of improvement for the system. AIS and SAR remains a great potential synergy for more timeliness and certainty in the detection, given that their satellite constellations would be rearranged to match each other (Renga et al., 2011). Fisheries and aquaculture, constituting one of the largest marine economic sector and playing a key role in biodiversity loss and habitat destruction (FAO, 2014), needs to be closely monitored to guarantee a sustainable management of our coastal and marine resources (Chassot et al., 2011). RS for fisheries is a double-edged blade which can both support the fishing industry in maximizing its yields without any consideration for sustainability and conservation of marine species and habitats (Zainuddin et al., 2006), while also help manage fisheries at sustainable levels by preventing overfishing and allocating zones where the impact of fishing on ecosystems would be reduced (Forget et al., 2008, Klemas, 2013b, Solanki et al., 2005). For the former, RS can reduce searching time for fish schools and detect favorable conditions for fish aggregation such as temperature, phytoplankton presence, etc., as previously introduced using IR/MW radiometers and ocean color, but also using airborne SAR and LiDAR systems that can detect small surface waves generated by

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fish schools (Santos, 2000, Zainuddin et al., 2006). It can also increase cost-effectiveness by accounting for fuel savings, crew expanses and lower ship maintenance costs (Santos, 2000). For the latter, the delineation of ecoregions where fishing is authorized (or not) is a way to support sustainable practices (Chassot et al., 2011, Dulvy et al., 2009, Klemas, 2013b). The TurtleWatch project is an example of a system that measures the likelihood of turtle presence based on SST isotherms derived from radiometer data to prevent turtle bycatch during fishing operations (Howell et al., 2008). The detection of illegal fleets practicing Illegal, Unreported and Unregulated (IUU) fishing, encouraged by the framework of the Ecosystem Approach to Fisheries Management (EAFM) (Garcia et al., 2003), is still being infringed and leads to fast depletion of fish stocks (NOAA, 2012). This can be done using SAR, as mentioned above, but is also supported via the Integrated Automation System (IAS) signals on vessels, a publically available signal made mandatory by the International Maritime Organization since 2000 and that allows the geo-localization of all registered vessels at sea (IMO, 2016). The Global Fishing Watch project spearheaded by Skytruth, a US non-profit using RS and digital mapping to identify potential IUU vessels, is close to releasing an online platform where the AIS signals can be monitored, thus offering more transparency for fishing practices (Global Ocean Commission, 2013, Global Fishing Watch, 2015). It does not make use of any optical or radar technologies, but these can be complementary to the Global Fishing Watch project, coupled with air patrol surveillance in improving effectiveness of fisheries enforcement controls (Perez et al., 2013). Aquacultures are also a great benefactor from RS because their productivity directly depends on water quality and nutrient availability, which can be derived from ocean color (Longdill, 2008, Meaden & Aguilar-Manjarrez, 2013). The impact of aquaculture sites on its environment, especially in the case of overcrowding, can also be detected using ocean color (Fuchs et al., 1999, Rajitha et al., 2007), and in some cases space-borne SAR (Travaglia et al., 2004), and airborne SAR sensors (Boivin et al., 2005) to support aquaculture mapping and change detection, as well as water roughness, which is an indicator of whether the ponds are active or not. The detection of physical coastal ecosystem degradation to make space for larger aquaculture sites (e.g. wetlands deforestation in Vietnam) is also extensively monitored (Seto et al., 2007, Palmans et al., 2009, Vo et al., 2013). This rapid expansion of aquaculture sites is putting pressure on the production carrying capacity of coastal areas (Thomas et al., 2006) and can lead to many complications such as hypoxia/anoxia (unfortunately not directly detectable through RS), and HAB occurrence (Kapetsky & AguilarManjarrez, 2007). Marine energy infrastructures and installations, although less obvious, is also a sector which can benefit from RS assessments (Cross, 2013). In the sector of hydrocarbon exploration, RS is already an operational tool to find potential new sources under the sea floor (Andersen et al., 2010), thanks to gravimetric sensors like GOCE and GRACE, and the follow-up GRACE-FO (NASA-JPL, 2015). For renewable energies, RS can be instrumental in filling the data gap for oceanographic and ecological data, invaluable for assessing marine energy site suitability (Shields et al., 2011). The wind energy sector can be supported by surveys to determine wind farm location suitability using spaceborne or airborne scatterometers, buoy or insitu LiDARs (Elvander & Hawkes, 2012, Mourre & Alvarez, 2012). Additionally, turbid wakes associated with offshore wind turbine farms impacting sediment transport and downstream turbidity have recently been

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identified using Landsat 8 multispectral data (Vanhellemont & Ruddick, 2014). The study of water quality in watersheds and basins can also support the management of hydro-electric installations and prevent dam clogging (APEM, 2016). Wave and tidal power is an emerging resource of renewable energy, and can benefit from acoustic Doppler profilers (Cross, 2013) and onshore X-band and HF radars (Nieto-Borge et al., 2010) characterizing wave height, period and direction, peak wavelength and wind shear at fine resolution and with high spatial coverage. All these applications are still in their infancy and show tremendous potential for the development of the marine energy economic sector.

e.

Population

RS can derive population and economic data through a technique called night light RS using the spaceborne DMSP-OLS sensor operating with two bands in the VNIR and TIR part of the spectrum (Yager, 2006). Many methodologies exist to derive socio-economic data by proxy through the brightness distribution of night lights, an excellent way to complement and sometimes replace conventional census data (Jensen and Cowen, 1999), which are way more costly and time consuming to carry out (Doll, 2008). Population figures and night light RS data have correlated well, making them a sound technique for such estimates (Elvidge et al., 1997, Sutton, 2003), while providing a spatial distribution of population and urban extent (Doll et al., 2000, Small et al., 2005). The spatially explicit nature of RS socio-economic data permits its combined used with LULC maps, which will allow us to further understand the land/ocean/population interactions in coastal areas (Doll, 2008, Mazor et al., 2013). Night light is also instrumental in monitoring coastal urban areas in the wake of a disaster such as a storm surge, a hurricane (Kohiyama et al., 2004) or fire (Chand et al., 2006). The newest VIIRS DNB night light radiometer launched on-board the SUOMI satellite in 2012, enables the imaging of sites at much higher latitudes (Kyba et al., 2014). However, new night light sensors operating in different bands will need to be developed in order to be able to observe the new LED lights which are fast replacing the old low pressure sodium lights in cities (Elvidge et al., 2010). New fields of applications to night-light RS are opening up such as light pollution (Cinzano & Falchi, 2012), energy consumption (Elvidge et al., 2010), and even epidemiological studies (Kloog et al., 2008). These applications are covered more extensively in Kyba et al., 2014.

4.

Challenges and Opportunities When studying ocean using RS, the quantity, quality and availability of data sets positively impacts the

quality of ocean analyses, forecasts and associated services (Le Traon et al., 2015). Depending on the oceanographic variable studied and the sensor used, observing challenges can arise according to the following categories: 1) continuity, 2) reliability, 3) resolution and coverage, 4) knowledge gap, 5) institutional barriers, along with their respective opportunities. Continuity The interruption of observations may occur, which can be a problem for a number of monitoring applications that rely on extensive time series. Long-term programmatic coordination, commitments and capacity for operational activities as well as faster transition of experimental sensors to operational use are needed (Schaeffer et al., 2013, Le Traon et al., 2015) if the long-term RS data archives are to be consistent.

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International cooperation is required to develop extensive satellite constellations that provide global coverage, deploy buoys and ships, schedule transboundary aircraft missions, and any other efforts which can improve the coverage and redundancy of data in event of an instrument failure (Bonekamp et al., 2008). Reliability Data reliability is first and foremost about processing satellite images to a meaningful unit (e.g. top-of-canopy reflectance for optical systems, backscatter intensity and phase for radar systems). Moreover, in situ data is for most applications necessary to calibrate/validate the satellite data, and thus guarantee its reliability. For optical systems, cloud masking and atmospheric correction algorithms also pose problems, as assumptions made over the open ocean are rarely applicable for highly turbid coastal waters (Loisel et al., 2013), yet most algorithms commonly used are based on open water assumptions. Moreover, the performance of ocean color algorithms (suspended particulate matter, dissolved organic matter, turbidity, Chl-a retrievals) outside of their calibration/validation regions is limited due to their sensibility to particle size and type (Odermatt et al., 2012), essentially making it difficult to derive truly global algorithms (Dogliotti et al., 2015). For radar systems, reliability is less of an issue, since SAR and space-borne altimetry is little to not affected by clouds and atmospheric disturbances, and the signal processing chains are very well established thanks to its long history in military and telecommunications applications (Palmann et al., 2008, Li et al., 2013). Concerning the accuracy and reliability of derived products such as land cover maps, it is dependent on the quality of the training and validation datasets used. Therefore, capacity development on how to guarantee high standards for product quality is necessary. Resolution and coverage Although it may seem like a big leap forward has been made in terms of spatial and temporal resolutions in the past decades, the requirements are ever so high in coastal regions, and the observation capabilities of RS do not yet match that of the current models. The climate modelling community has already addressed the development of new virtual satellite constellations to reach the desired needs in terms of cloud free observations and global coverage at higher temporal resolutions (Wulder et al., 2015), but they have yet to be implemented because of the inherent complexity and costs of current EO programs, national interests and conflicting observation aims between the different scientific communities and industrial sectors. For increased spatial resolution and coverage, one must inevitably turn towards the private RS industry. Up until recently, the two big players were DigitalGlobe and Airbus Defense & Space, who have the capacity to build large archives of very-high resolution satellite images (sub-meter level). Two new start-ups entered the market, Skybox Imaging and Planet Labs, whose objectives are to provide near real-time sub-meter satellite imagery by launching hundreds of micro-satellites in space (Vance, 2014). The former has recently been purchased by Google and has been renamed to BellaTerra. The European Commission Joint Research Center (EC-JRC), in the context of Google’s Skybox4Good project, is currently investigating in integrating Skybox time-series to its practices. The STARS project, a consortium led by ITC Twente to map agricultural land and support capacity building in west Africa, is supported by DigitalGlobe, who supplies imagery for free to the project’s activities. This illustrates the willingness of the private sector to provide philanthropic support to non-profits. International efforts towards achieving more frequent and global data coverage of ocean and coastal

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observations exist such as the GEOSS portal (http://www.geoportal.org/web/guest/geo_home_stp) focused on international space-borne earth observation networks, the in situ Global Ocean Observing System (GOOS: http://www.ioc-goos.org/), including the ARGO profiling float network, supported by IOC-UNESCO, WMO, UNEP and ICSU and to which many countries are contributing to, the European Marine Observation and Data Network (EMODnet: www.emodnet-seabedhabitats.eu), the follow-up to MESH, and COPERNICUS Marine Services (http://marine.copernicus.eu/), the follow-up to the Global Monitoring for Environment and Security (GMES), amongst others. These platforms are instrumental to the efficient development and implementation of ICAM and MSP strategies as they channel international observation efforts. They remain too disjoint however, and therefore end up duplicating efforts and/or fail to integrate all relevant data sources for smooth interoperability. Knowledge gap ICAM bears the term “integrated”, which suggests that knowledge stemming from various disciplines of natural and social sciences should be combined to achieve the best possible practices. Therefore, the underlying structure and functioning of coastal ecosystems, along with the interactions between habitats and the species they contain must be better understood. Some biogeochemical indicators can already be derived using RS, but may not necessarily be meaningful if calculated at insufficient resolutions or if not validated and calibrated with in-situ data. This deepening of biological and ecological knowledge is necessary to develop entirely new satellite observing capabilities geared towards the monitoring of meaningful ecological and environmental metrics. Another area lacking knowledge is data fusion between different sensors and data sources (Chapron et al., 2010). With the increasing number of satellite sensors operating in more and more spectral domains, data integration and sensor inter-operability is more necessary than ever to emit new hypothesis, which can then be verified in the field and scaled-up regionally or even globally. The remote sensing community must also not lose sight that the building-up of the knowledge base to exploit new earth observing satellite missions should be geared towards bridging the gap between the potential of new generation sensors and the end-users needs for applications with high societal impacts (Benediktsson et al., 2012) such as ICAM and MSP. Having that said, many RS applications for ICAM and MSP are already operationally integrated into systems such as oil spill detection and monitoring, vessel detection for IUU and post-disaster damage assessment, among others. The efforts must continue to achieve operational use in all thematic fields. Institutional barriers Schaeffer et al. (2013) carried out a survey amongst various American agencies susceptible of using RS data to analyse water quality. The conclusion of the survey was that management decisions rarely rely on satellitederived data because of misconceptions from the decision-maker’s part. The first identified misconception was about costs, whereas in fact, much of the RS data used in the fields of science are free and open-source within a (few) week(s) from their acquisition. Product accuracy and reliability depends on the pre-processing steps applied to the raw data (i.e. ocean colour algorithms as mentioned above). However, a top-down support or training on how to properly exploit RS technologies is not common nowadays, and there is therefore a mutual need for the managers to recognize the value of housing RS expertise, and for the scientific RS community to

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promote it in a simple and tangible way to the managers. Challenges of operationalizing RS in developing countries were identified by the lack of local capacity to exploit the information provided (Joseph et al., 2013). With the democratization of RS data, investing in capacity building in developing countries will become more and more cost-effective as all learning material and resources are becoming free of charge. The Ocean Teacher Global Academy (OTGA), a web-based system supporting classroom training, distance learning and online tutoring, and the IOC Capacity Development (IOC-CD) programs, which are more workshop and interventionbased, are setting the example to fill in this capacity gap (IODE, 2016). However, these are not addressing RS applications in their practices, and the last RS-related workshop was held in Hyderabad in April 2014 and focused on ocean colour RS. The African Union (AU), through the current African space policy and strategy and Africa Integrated Maritime strategy 2050 and Agenda 2063 on EBM approaches for marine resources in the exclusive economic Zones and adjacent waters, should support the integration of RS in management and planning processes. This can be achieved by different mechanisms including financial support at regional level and knowledge transfer to national level, creating a network of data sharing platforms through international cooperation and partnership for data access (e.g. GEOSS, COPERNICUS), but also African initiatives. For instance, African regional centers of excellence have fostered like the Mauritius Oceanography Institute, which is in charge of providing the marine and coastal management services of the Monitoring for Environment and Security in Africa (MESA) initiative, which is essentially the African component of COPERNICUS. The project is a joint Africa European Union strategy project to strengthen the use of earth observation data for climate and environmental applications. The MESA project will maintain and upgrade the EUMETCast reception stations, part of a more global GEONETCast network of stations, to tackle the problem of data availability and facilitate decision-making and planning capacity for national, regional and continental institutions by providing developing countries with little to no internet the opportunity to install an antenna and a data center directly downlinking the data from the satellite. The Bilko project from UNESCO has also greatly relied on the data disseminated through GEONETCast to provide training resources in coastal and marine remote sensing in Africa (Byfield et al., 2012). The COPERNICUS marine environment monitoring services (CMEMS) are providing a list of products such as weekly chlorophyll maps, daily water temperature on their online portal. However, there is currently no information about whether these will be made available through EUMETCast, surprising considering EUMETSAT is the main contributor of the CMEMS with the upcoming Jason-3 and Sentinel-3A missions. These products are very lightweight and do not require a large bandwidth to download, making them very suitable for African ICAM/MSP. Other notable projects are the TIGER initiatives supported by ESA, which aims to assist African countries in overcoming problems faced in collection, analysis and use of water related geo-information using earth observation technology (ESA, 2016). The SERVIR project, supported by NASA and USAID, implements the use of RS products for climate risk management and land use management. At the national levels, in spite of the initiatives mentioned above, the data support remains poor and the available capacity is not sufficient to handle and process the quantity of RS data available. Few countries have their own mini-satellite for specific applications (Algeria, Egypt, Nigeria, South Africa), but they are at different stages of integration and application (Asiyanbola, 2014). The main problems for integration of earth

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observation for sustainable development in the coastal and marine sector in Africa is the lack of financial support from governments due to lack of awareness, high costs of VHR data, poor ICT infrastructures, and a weak institutional human resource capacity. More science-policy linkages are therefore needed to remediate the problem of awareness, while other problems need to be accurately assessed and presented to be visible to potential funding sources. The professional associations like African Association of Remote Sensing of the Environment (AASE), EIS-Africa, GOESS Africa and training centers like Regional Centers for Aerospace Surveys (RECTAS), African Regional Centre for Space Science and Technology Education (ARCSSTE-E) should support capacity developing programs in the marine and coastal environment.

5.

Future Missions The future satellite missions and sensors most relevant in the framework of the ICAM/MSP

components presented in this paper deserve a review. At the time of writing, some of the missions presented here may have already been launched, but have not yet made their data available. The goal here is not to provide a comprehensive list of every single future satellite launch, but to give a hint of missions profiting to ICAM and MSP. All SENTINEL missions (both currently operational and scheduled for launch) will be fed into the COPERNICUS Marine Environment Monitoring Service, as well as the Climate and Land services depending on the intended goal of the mission. These missions were designed with specific societal and environmental applications in mind. In a first phase, the A satellites were launched for SENTINEL-1, -2 and -3, followed by their B counterparts (only SENTINEL-1B and -2B have been launched at time of writing). This tandem configuration will provide a 6-day global coverage for SENTINEL-1, 5-day coverage for SENTINEL-2, and a 1-2 day coverage for SENTINEL-3. The C and D missions have already been planned as successors to the A and B missions, and will guarantee continuity in the data stream until 2030. Negotiations between the DG GROW of the European commission and ESA are undergoing to decide about European space strategy beyond 2030. Only missions which have not yet been launched at time of writing are covered here. Sentinel-3A (Ocean Colour & altimetry mission) th

launched on February 15 2016, and finally combines measurements of SST and SSH in one single mission through its Sea and Land Surface Temperature Radiometer (SLSTR) and its topography package consisting of a Ku and C-band SAR altimeter. This will contribute to the study of the onset of El Nino events as part of early warning systems for regional extreme weather events and other hurricanes and cyclones that may impact coastal areas. Together with sentinel-2, the Ocean and Land Colour instrument (OLCI) will provide a more frequent revisit at coarser spatial resolution (300 m), making the two complementary. Geostationary Ocean Colour (METEOSAT FCI) scheduled for launch in 2018, the flexible combined imager (FCI) on-board the geostationary METEOSAT satellite will measure every 10 minutes in 16 channels at a spatial sampling distance of 1 km in VIS/NIR and 2 km in IR. The sensor will build on the already available GOCI and SEVIRI missions. The high latitudes are difficult to observe due to Earth curvature and high sensor zenith angle observations (Ruddick et al., 2014), but the development of algorithms combining from polar-orbit high spatial resolution (sentinel-2) and

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geostationary high temporal resolution data is the topic of the HIGHROC EU-funded project going on until end 2017. Space-borne hyperspectral missions (HyspIRI, ENMap, PACE) Previously, HICO on board the ISS was the only previous attempt to measure hyperspectral data for the coastal ocean. The unreliability of its data will soon vanish with their arrival of new hyperspectral satellites. The PreAerosol, Cloud, and ocean Ecosystem (PACE) sensor scheduled for 2022/2023, the Hyperspectral Infrared Imager (HyspIRI) scheduled for 2022 and the Environmental Mapper (ENMap) scheduled for launch in 2018, present the greatest opportunity to fill in the biogeochemistry knowledge gap of coastal and marine ecosystems. The insufficient radiometric resolution of current multispectral sensors to study ecosystems was brought up by the scientific community, especially in the retrieval of biophysical variables (Hestir et al., 2015), and these missions will hopefully meet their requirements. Landsat-9 and synergies with Sentinel-2 The Landsat 9 satellite, scheduled for launch in 2023, will extend the NASA and USGS Earth Observing Program to half a century, since the launch of Landsat 1 in 1972. Under the 2017 Budget Blueprint, the Obama administration is planning on speeding up the efforts to launch Landsat 9 by 2021, along with an additional 2.2 Million $ in funding to store, distribute and cross-calibrate sentinel-2A data with Landsat 8, and eventually Landsat 9, to provide a combined surface reflectance product in partnership with ESA (Werner & Berger, 2016). The synergy between the two sensors is obvious, as it could provide a global a revisit time of 5 days, and possibly a lot less if data from Landsat 8, 9 and Sentinel-2A, B are seamlessly combined.

6.

Conclusions

The role of RS for ICAM/MSP can be summed up to the provision of (relatively) cost-effective, spatially explicit, continuous, and frequent observations of coastal areas and marine space, thus deriving information on a synoptic scale that the human eye could never grasp. The conceptual framework introduced relates the concepts of RS and earth observation with that of ICAM and MSP. Its principle is simple: ICAM and MSP are concepts that combine multiple disciplines and sectors in the environmental and socio-economic spheres, which can respectively be studied using a set of RS technologies and supporting techniques to turn the collected data into useful information (see figure 1). After presenting all these achievements, challenges, opportunities, and future potential missions, it is obvious that RS has an important role to play in ICAM and MSP. Concrete examples of how RS is operationally and routinely used to prevent disasters and mitigate risks, assess damage assessment in a post-disaster scenario, contribute to vessel detection and monitoring in the context of IUU and oil spill monitoring, as well as provide information about coastal LULC and its linkages with a growing coastal population and more intensive use of the land. Applications related to the monitoring of the coastal environment’s geochemistry, and more specifically the derivation of biophysical variables, are undermined by the lack of knowledge about the structure, functioning and interactions between marine and coastal ecosystems, their habitats and their species. At present, it is thus difficult to determine what needs to be observed, how to make sense of the data, and relate it to ecosystem services in meaningful ways.

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The applications of RS for ecosystem health and water quality are still in their infancy and require further research before being able to actively support ICAM and MSP, and the theoretical foundation to their applicability need be verified and/or reinforced. Limitations related to continuity, reliability, resolution and coverage, although not a problem specific to earth observation of coastal zones, is something that should draw the attention of the ICAM stakeholders and see this as an opportunity to be part of the design of new payloads and the planning of new satellite missions tailor-made for coastal zone management. Although the challenges faced in the sustainable management of our coasts no doubt require an integrated solution, ocean professionals involved in MSP and ICAM should not miss out on the opportunity to reap some of the benefits of new findings in ocean and coastal RS research and integrate them in their planning and management practices. Acknowledgements I wish to thank Alejandro Iglesias-Campos for providing me valuable feedback on the technical level and language of the report. I would also like to thank Prof. Victor de Jonge for providing me valuable feedback on the structure of the document, but also for reminding me not to miss the point of my research goals. He has also helped me nuance my opinions on the true value of remote sensing for ICAM and MSP, calling for me to “look at the other side of the coin” too. Finally, I would like to thank the Government of Flanders and more specifically the Flemish ministry of foreign affairs for funding my position within the IOC-UNESCO, which has allowed me to develop this document.

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Table 1: Exhaustive list of the latest sensors for each sensor type presented in figure 1, along with their respective relevant ICAM/MSP components. The specifications and characteristics of each sensors are laid out, as well as the link to the data download page. All the satellites listed are still in operation at time of writing.

Instru ment type

Orb it conf igur atio Spati n al Sp Star Sce Mai Senso Cove ect Spat ting ne n r rage ra ial date Size mar Satell Tem l resol of /Sw ine ite/Pl pora ra utio ope ath appl atfor l ng n rati widt icati m Cont e on h ons inuit Tem y por al Res oluti on

Data portal

Passiv e Instru ments

33

Multis pectra l Instru ments Releva nt ICAM /MSP compo nents: Polluti on and Ecosys tem Health Natura l Hazard s Marine Space and Use Coasta l land cover and land use Popula tion

Operat ional land imager (OLI) Lands at 8

11 ba nd s bet we en 0.4 312. 51 µ m

30 m

Lau nche d 11th Febr uary 2013

Multis pectral Instru ment (MSI) Sentin el-2A

13 ba nd s bet we en 0.4 4– 2.1 9 µ m

Band Sent inels 2,3,4, 2A 8: 10 laun m ched Band June 23, s 5,6,7, 2015 8a,11 Sent ,12: inel20m 2B Band sche dule s 1,9,1 d for 0: 30 July m 2016

Moder ate Resolu tion Imagi ng Spectr ometer (MOD IS)

36 ba nd s bet we en 0.4 14.

Band Terr a: s 1,2: 250 Lau m nche d Band s 3- 18th Dec 7: 500 emb m er Band 1999

Sunsync hron ous, near polar 183x 170 km 16day revis it time

Sunsync hron ous, nearpolar 290x 300 km 10day revis it time (5 day com bine d with SEN TIN EL2B) Sunsync hron ous, nearpolar 2330 x10 km 1-2

Globa l cover age (const ant acqui sition ) Unint errupt ed betwe en 1973 (Land sat 1)2023 (Land sat 9)

Mari ne/C oasta l habit at map ping

http://earthexplorer.usgs.gov/

Globa l cover age (const ant acqui sition ) Unint errupt ed betwe en mid2015 (Senti nel2A) 2030 (Senti nel2D)

Mari ne/C oasta l habit at map ping

ESA mirror: https://scihub.copernicus.eu/dhus/ CNES mirror: https://peps.cnes.fr/rocket/#/home

Ocea n Colo ur

Raw data: http://ladsweb.nascom.nasa.gov/ Sea surface and temperature products: http://oceancolor.gsfc.nasa.gov/ COPERNICUS MEMS products: http://marine.copernicus.eu/

Globa l cover age (const ant acqui sition ) Unint

34

EOS Terra for descen ding node EOS Aqua for ascend ing node

4 µ m

s 836: 1 km

Ocean Land Colour Instru ment (OLCI ) Sentin el-3A

21 ba nd s bet we en 0.4 1.0 2 µ m

Full resol ution (FR) over land: 300 m Redu ced resol ution (RR) over ocea n: 1.2 km

Spinni ng Enhan ced Visibl e and Infrare d Image r (SEVI RI) Meteo sat-11

12 ba nd s bet we en 0.5 14. 40 µ m

3 km

Aqu a: Lau nche d 4th May 2002

days revis it time

errupt ed since Laun ch (2002 ) Senti nel3A OLCI offers conti nuity Globa l cover Sunage sync (const hron Sent ant ous, inelaquisi near 3A tion) polar laun Unint 1270 ched errupt km 16th ed swat Febr betwe h uary en 3.82016 midday Sent 2016 revis inel(Senti it 3B neltime sche 3A) (1.9dule 2030 day d for (Senti with June nelSEN 2017 3D) TIN Follo ELws-up 3B) on MOD IS Geos Europ tatio ean/A nary frican over Cover Lau Euro age nche pe (const d and ant 15th Afric acqui July a sition 2015 data ) take Unint ever errupt y 15 ed minu betwe

Ocea n Colo ur

https://scihub.copernicus.eu/dhus/ Data not yet available at time of writing

Ocea n Colo ur Syno ptic ocea n obse rvati ons

http://www.eumetsat.int/website/ho me/Data/DataDelivery/OnlineDataA ccess/index.html

35

tes

Pléiad es-1A and 1B

World view-3

5 ba nd s bet we en 0.4 30.9 5 µ m

29 ba nd s bet we en 0.4 2.2 4 µ m

en 2002 (MS G) 2030 (MT G-I and MTG -S)

Sunsync hron Local ous, cover Pléia nearage despolar (onPanc 1A hro Mult dema laun nd mati iple ched c poin acqui 16th sition (0.48 t Dec ) targ emb 0.83 ets: Unint er 20x2 errupt µm): 2011 0.5 m 0 km ed Pléia Mult Stri betwe desen ispec p 1B tral map 2011( laun (0.43 ping Pléia ched des + : 2nd 100x SPOT 0.95 Dec 100 5) µm): emb 2m km 2023 er Dail (SPO 2012 y T6& revis 7) it at nadir Panc Wor Sun- Local hro ldvie sync cover age mati w-4 hron laun ous, (onc (0.45 ched near- dema nd - 0.8 13th polar µm): Aug Mult acqui 0.31 ust iple sition m 2014 poin ) Unint Mult Wor t ispec ldvie targ errupt w-4 ed tral ets: (0.4 - sche 17.7 betwe en 1.04 dule x17. µm): d for 7 km 2007 1.24 Sept Larg (Worl m emb e are dview er SWI colle -1) -

Very High Reso lutio n Mari ne/C oasta l habit at map ping Mari ne spac e surv eilla nce

http://www.intelligenceairbusds.com/geostore/

Very High Reso lutio n Mari ne/C oasta l habit at map ping Mari ne spac e surv

https://browse.digitalglobe.com/imag efinder/main.jsp;jsessionid=29B1304 247E34295186F0AE45BA217B5? Archive data and on-demand data available on purchase

36

R(1. 2016 22.37 µm): 3.7 m CAV IS(0. 42.25 µm): 30 m

Hyper spectr al Instru ments Releva nt ICAM /MSP compo nents: Polluti on and Ecosys tem Health Coasta l land cover and land use

Airbor ne visible infrare d imagin g spectr ometer (AVIR IS)

Airbor ne HyMa p

Airbor ne APEX

22 4 ba nd s bet we en 0.4 2.5 µ m 12 8 ba nd s bet we en 0.4 5 to 2.4 8 µ m 33 4 ba nd s wit hin 0.3 82.5 µ m

420 m, depe Built nding in on 2006 flight altitu de

ct: 112x 111 km long strip : 17.7 x360 km Dail y revis it at nadir

NA

512 x spati al 2resol 10 m, First ution depe versi (appr nding on ox on built 1.24 flight in km altitu 1998 at 2 de m resol ution ) 1000 x spati 1.75 al m at resol 3500 Built ution m in (1.75 flight 2011 km altitu at de 1.75 m resol

2026 (Worl dview -4)

eilla nce

Local cover age (ondema nd acqui sition )

Very High Reso lutio n Mari ne/C oasta l habit at map ping

http://aviris.jpl.nasa.gov/alt_locator/ Archive freely available New acquisitions can be ordered at a cost

Local cover age (ondema nd acqui sition )

Very High Reso lutio n Mari ne/C oasta l habit at map ping

http://www.hyvista.com/technology/ sensors/hymap/ Not freely available - On demand both for archive and new acquisitions

Local cover age (ondema nd acqui sition )

Very High Reso lutio n Mari ne/C oasta l habit at map

http://www.apex-esa.org/ Archive freely available upon request New acquisitions can be ordered at a cost

37

ution )

Hyperi on EO-1

Radio meters Releva nt ICAM /MSP compo nents: Polluti on and Ecosys tem

Soil Moist ure and Ocean Salinit y (SMO S) Proteu s

24 2 ba nd s wit hin 0.4 2.5 µ m

1.4 G Hz

30 m

35 km

Local cover age (ondema nd acqui sition ) Exper iment 2000 al Polar missi 2001 orbit on (pla 7.5 (no nned km conti ) swat nuity 2001 h plann 16ed) Now day Laun (exp revis ch of erim it Hype ental time rspect ) ral ENM ap (2018 ) and HyspI RI (2021 ) missi ons Globa l cover Lau age nche Sun- (const d sync ant 2nd hron acqui Nov ous, sition emb near) er polar No 2009 plann ed seque l

ping

Mari ne/C oasta l habit at map ping

http://earthexplorer.usgs.gov

Sea surfa ce salin ty

https://smos-ds02.eo.esa.int/oads/access/

38

Health Natura l Hazard s Popula tion

Sea and land surfac e temper ature radio meter (SLST R) Sentin el-3A

11 ba nd s bet we en 0.5 6 an d 10. 85 µ m

500 m for VIS+ SWI R band s (0.56 2.25 µm) 1 km for TIRfire band s (3.74 10.85 µm)

Sent inel3A laun ched 16th Febr uary 2016 Sent inel3B sche dule d for June 2017

Sunsync hron ous, nearpolar 1400 km nadir view 740 km dual (obli que) view 3.8day revis it time (1.9day with Senti nel3B)

Visibl e Infrare d Imagi ng Radio meter Suite (VIIR S) Suomi NPP

22 ba nd s bet we en 0.4 12. 49 µ m

VIS band s: 375 m Othe r band s: 750 m

Lau nche d 28th Octo ber 2011

Sunsync hron ous, nearpolar 3000 km swat h 1day revis it time

DMSP -OLS DMSP -5D3 F20

1 VI S ba nd: 0.5

fine mod e: 550 m smoo

Lau nche d 3rd Apri l

Sunsync hron ous, nearpolar

Globa l cover age (const ant acqui sition ) Unint errupt ed betwe en mid2016 (Senti nel3A) 2030 (Senti nel3D)

Globa l cover age (const ant acqui sition ) Unint errupt ed betwe en 2011 Now No plann ed seque l Globa l cover age (const ant

Sea surfa ce temp eratu re

https://scihub.copernicus.eu/dhus/ Data not yet available at time of writing

Nigh t light remo te sensi ng Ocea n colo ur

http://www.nsof.class.noaa.gov/saa/p roducts/search?datatype_family=VII RS

Nigh t light remo te sensi

https://data.noaa.gov/dataset/dmspols-operational-linescan-system

39

0.9 µ m 1 TI R ba nd: 1 µ m

Advan ced very highresolut ion radio meter (AVH RR) MetO P-B

5 ba nd s bet we en 0.5 8 an d 12. 5 µ m

AMS R-E EOS Aqua

6 ba nd s wit hin 6.9 89 G Hz

2014 th mod e: 2.7 km

glob al area cove rage: 4.4 km Loca l area cove rage (only avail able for certa in parts of the worl d): 1.1 km 5.4 km at 89 GHz to 56 km at 6.9 GHz

3000 km swat h 1day revis it time

acqui sition ) Unint errupt ed betwe en 1982 (DM SP 5D2 F6) 2026 (DM SP 5D3 F21)

Met OPB laun ched 17th Sept emb er 2012 Met OPC sche dule d for Octo ber 2018

Sunsync hron ous, nearpolar 2800 km swat h <1day revis it time

Globa l cover age (const ant acqui sition ) Unint errupt ed betwe en 1978 (TIR OSN) 2033 (Met OPC)

Lau nche d 4th May 2002

Sunsync hron ous, nearpolar 1445 km swat h 8day

Globa l cover age (const ant acqui sition ) No plann ed

ng

Sea surfa ce temp eratu re

http://www.class.ngdc.noaa.gov/saa/ products/search?datatype_family=A VHRR

Sea surfa ce wind Sea surfa ce temp eratu re

https://nsidc.org/data/amsre

40

revis it time

seque l

Active instru ments

Light Detect ion and Rangi ng (LiDA R) Releva nt ICAM /MSP compo nents: Polluti on and Ecosys tem Health Coasta l land cover and land use

Local cover age (ondema nd acqui sition )

Very high resol ution Bath ymet ry Bent hic habit at map ping

http://leicageosystems.com/products/airbornesystems/lidar-sensors/leicachiroptera-ii Not distributed by national authority/research institute Sensor available for purchase

http://leicageosystems.com/products/airbornesystems/lidar-sensors/leica-hawkeyeiii Not distributed by a national authority/research institute Sensor available for purchase

https://nsidc.org/data/icesat/data.html

Gr ee n: 53 2 nm IR : 10 64 nm

Point First densi versi ty: on 1.5 built pts/m in 2 2015

Airbor ne Leica Hawk eye III (Shall ow + deep bathy metric laser)

Gr ee n: 53 2 nm IR : 10 64 nm

Shall ow bath ymet ry point densi ty: 1.5 pts/m 2 Deep bath ymet ry point densi ty: 0.4 pts/m 2

First versi on built in 2015

Local cover age (ondema nd acqui sition )

Very high resol ution Bath ymet ry Bent hic habit at map ping

GLAS ICEsat

Gr ee n: 53 2 nm IR : 10 64 nm

70 m footp rint space d at 170 m inter val

ICEs Sun- Globa at sync l laun hron cover ched ous, age 13th near- (const Janu polar ant ary nadir acqui 2003 meas sition ICEs urem ), but at-2 ent gaps sche of due dule footp to d for rint small

Bath ymet ry

Airbor ne Leica Chirop thera 2 (shallo w bathy metric laser)

NA

NA

41

late 2017

Altime ters Releva nt ICAM /MSP compo nents: Polluti on and Ecosys tem Health Natura l Hazard s

Poseid on-3B Jason3

13. 6 an d 5.2 G Hz

10 km footp rint

SRAL Sentin el-3A

13. 57 5 an d 5.4 1 G Hz

300 m footp rint

size footpr only int 8size 91- Unint day errupt revis ed it betwe time en (plat 2003 form (ICEs spee at) d can 2024 be (ICEs mod at-2) ulate d) Globa l cover age (const Sunant sync acqui hron sition ous, ) near- comb polar ined nadir with Lau meas Senti nche urem neld ent 3A 17th of SRA Janu footp L ary rint Unint 2016 size errupt only ed 10- betwe day en revis 2001 it (Jaso time n) 2030 (Jaso n-3, SRA L) Sent Sun- Globa inel- sync l 3A hron cover laun ous, age ched near- (const 16th polar ant Febr Nadi acqui uary r sition 2016 meas )

Sea surfa ce heig ht

http://www.nodc.noaa.gov/sog/jason/

Sea surfa ce heig ht

https://scihub.copernicus.eu/dhus/ Data not yet available at time of writing

42

Sent urem inelent 3B of sche footp dule rint d for size June only 2017 27day revis it time

Radar (SAR) Instru ments Releva nt ICAM /MSP compo nents: Polluti on and Ecosys tem Health Natura l Hazard s Coasta l land cover and land use Marine space and use

Airbor ne FSAR

Xba nd : 9.6 G Hz Cba nd : 5.3 G Hz Sba nd : 3.2 5 G Hz Lba nd : 1.3 3 G Hz Pba nd :

Xband : 0.2x0 .3 m Cband : 0.3x0 .6 m Sband : 0.35x 0.75 m Lband : 0.4x1 .5 m Pband : 1.25x 2.25 m

First fligh t in Nov emb er 2006

12.5 km rang e widt h

comb ined with Senti nel3A SRA L Unint errupt ed betwe en 2001 (Jaso n) 2030 (Jaso n-3, SRA L)

Local cover age (ondema nd acqui sition )

Oil spill moni torin g Mari ne spac e surv eilla nce Abo vesurfa ce habit at map ping

http://www.dlr.de/hr/en/desktopdefa ult.aspx/tabid-2326/3776_read44169 Need to contact the airborne SAR team at DLR for flight scheduling

43

Popula tion

0.3 5 G Hz

RAD ARSA T-2

CSAR Sentin el-1A and 1B

Cba nd : 5.4 1 G Hz

Spotl ight mod e: 8 m Stan dard : 25 m Narr ow Scan SAR Lau : nche 50m d Wide 14th Scan Dec SAR emb : er 100m 2007 Qua d-pol highresol ution : 12 m Qua d-pol stan dard : 25 m

Cba nd : 5.4 1 G Hz

Strip map mod e: 5x5 m Inter fero metr ic wide

Sent inel1A laun ched 3rd Apri l 2014 Sent inel-

Sunsync hron ous, nearpolar 12.5 km rang e widt h 50 530 km swat h depe ndin g on mod e 530 km swat h for ocea n surv eilla nce 24day revis it time Sunsync hron ous, nearpolar 20 400 km swat h

Globa l cover age (ondema nd acqui sition ) Unint errupt ed betwe en 1995 (Rada rsat1) Now (Rada rsat2) No plann ed seque l

Oil spill moni torin g Mari ne spac e surv eilla nce Abo vesurfa ce habit at map ping

http://www.asccsa.gc.ca/eng/satellites/radarsat2/ord er-contact.asp Archive and on-demand data need be purchased

Globa l cover age (const ant acqui sition ) Unint errupt

Oil spill moni torin g Mari ne spac e surv eilla

https://scihub.copernicus.eu/dhus/

44

swat h mod e: 5x20 m Extr awide swat h mod e: 25x1 00 m Wav emod e: 5x20 m

PALS AR-2 ALOS -2 (Diach i-2)

Lba nd : 1.2 7 G Hz

Spotl ight mod e: 13m Strip map mod e: 310 m Scan SAR mod e: 60100 m Qua d-pol mod e: 610 m

1B laun ched 25th Apri l 2016

Lau nche d 24th May 2014

widt h depe ndin g on mod e 12day revis it time (6day with both 1A and 1B)

Sunsync hron ous, nearpolar 25 490 km swat h depe ndin g on mod e 14day revis it time

ed betwe en mid2014 (Senti nel1A) 2030 (Senti nel1D)

Globa l cover age (ondema nd acqui sition ) Unint errupt ed betwe en 2006 (PAL SAR) Now (PAL SAR2) No plann ed seque l

nce Abo vesurfa ce habit at map ping

Oil spill moni torin g Mari ne spac e surv eilla nce Abo vesurfa ce habit at map ping

http://en.alos-pasco.com/ Archive and on-demand data need be purchased

45

phased array SAR TerraS AR-X

Xba nd : 9.6 5 G Hz

High Reso lutio n Spotl ight mod e: 1m Spotl ight: 2m Strip Map : 3m Scan SAR : 18 m

laun ched in 2007 , still in servi ce

Sunsync hron ous, nearpolar 5150 km swat h depe ndin g on mod e 11day glob al cove rage

Globa l cover age (ondema nd acqui sition ) Unint errupt ed betwe en 2007 Now No plann ed seque l

Oil Spill moni torin g Mari ne spac e surv eilla nce Abo vesurfa ce habit at map ping

http://terrasar-x-archive.infoterra.de/ Archive and on-demand data need be purchased

HIGHLIGHTS     

A conceptual framework to support ICAM and MSP using remote sensing (RS) is proposed. Achievements and state of research in RS for ICAM and MSP are presented. Challenges, knowledge-gaps, and opportunities of RS for ICAM and MSP are highlighted. Future satellite missions, particularly hyperspectral, are promising for coastal zones. RS is entering a new era of progress and open-source profiting to ocean/coastal managers.

46