Mapping wetland functions using Earth observation data and multi-criteria analysis Sébastien Rapinel, Laurence HubertMoy, Bernard Clément & Edward Maltby
Environmental Monitoring and Assessment An International Journal Devoted to Progress in the Use of Monitoring Data in Assessing Environmental Risks to Man and the Environment ISSN 0167-6369 Volume 188 Number 11 Environ Monit Assess (2016) 188:1-17 DOI 10.1007/s10661-016-5644-1
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Author's personal copy Environ Monit Assess (2016) 188:641 DOI 10.1007/s10661-016-5644-1
Mapping wetland functions using Earth observation data and multi-criteria analysis Sébastien Rapinel & Laurence Hubert-Moy & Bernard Clément & Edward Maltby
Received: 25 July 2016 / Accepted: 10 October 2016 # Springer International Publishing Switzerland 2016
Abstract Wetland functional assessment is commonly conducted based on field observations, and thus, is generally limited to small areas. However, there is often a need for wetland managers to obtain information on wetland functional performance over larger areas. For this purpose, we are proposing a new field-based functional assessment procedure in which wetland functions are evaluated and classified into hydrogeomorphic units according to a multi-criteria analysis approach. Wetland-related geographic information system layers derived from Earth observation data (LiDAR, multispectral and radar data) are used in this study for a large-scale functional evaluation. These include maps of a hydrogeomorphic units, ditches, vegetation, annual flood duration, biomass, meadows management, and wetland boundaries. To demonstrate the feasibility of this approach, a 132 km2 international long-term S. Rapinel (*) : L. Hubert-Moy LETG-RENNES COSTEL UMR CNRS 6554, Université Rennes 2, Place du recteur Henri Le Moal, 35043 Rennes, France e-mail:
[email protected] L. Hubert-Moy e-mail:
[email protected] B. Clément ECOBIO UMR CNRS 6553, Université Rennes 1, Avenue du Général Leclerc, 35000 Rennes, France e-mail:
[email protected] E. Maltby School of Environmental Science, University of Liverpool, Liverpool, UK e-mail:
[email protected]
ecological research site located in the west of France was assessed. Four wetland functions were evaluated: flood peak attenuation, low water attenuation, denitrification, and habitat. A spatial distribution map of the individual wetland functions was generated, and the intensity levels of the functions were highlighted. Antagonisms between functions within individual hydrogeomorphic units were also identified. Mapping of hydrological, biogeochemical, and ecological wetland functions over large areas can provide an efficient tool for policy makers and other stakeholders including water authorities, nature conservation agencies, and farmers. Specifically, this tool has the potential to provide a mapping of ecosystem services, conservation management priorities, and possible improvements in water resources management. Keywords Wetland conservation . Ecosystem management . EU habitats directive . EU nitrates directive . LiDAR, remote sensing
Introduction Wetlands have hydrological, biogeochemical, and ecological functions that are widely recognised both by the scientific community as well as by land and water managers (Edward Maltby and Acreman 2011). However, conflicts regarding wetland usage often arise among stakeholders, which may include diverse nature conservation interests, farmers, land, and water planners and engineers. In order to better manage these
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ecosystems, wetland managers can benefit from better spatial knowledge of the level of performance of the processes and resulting functions that characterise their wetland area. The Habitats Directive of the European Union (1992/43/EEC) advocates the integration of intensity of functions as one of the criteria for assessing the state of conservation of natural habitats and associated ecosystem services. Previously, Brinson and Rheinhardt (1996) highlighted the finding that compensatory mitigations for damage to wetlands occurred largely without explicit analysis and replacement of wetland functions. While many studies have investigated the delineation of existing wetlands, mapping of wetland functions remains an ongoing issue (Mérot et al. 2006). However, it represents a key factor in the maintenance and preservation of wetland functions. Currently, there are numerous operational approaches to the functional assessment of wetlands. In the USA, rapid assessment methods (RAMs) are used to evaluate the functions of wetlands on the basis of field observations (Fennessy et al. 2007). Similarly, in Europe, functional assessment procedures (FAPs) have recently been developed under various European research programs. In general, processes and function have been calibrated using field data (E. Maltby 2009). This approach allows managers to assess indirectly ecosystem functions, and to characterise resulting ecosystem services, for a region based on the observation of simple field indicators such as vegetation, soil type, or micro-topography. Thus, functional assessment approaches have essentially been based on in situ observations, and therefore, have been limited to sites that are a few hectares in area (Janssen et al. 2005). However, some processes, such as the flow of nutrients that influence water quality, need to be considered at a catchment scale and this requires an assessment of much larger areas. Recent technological advances in space observation have facilitated the collection of very detailed images that can characterise large areas of wetlands with high spatial resolution. For example, light detection and ranging (LiDAR)-type data can finely describe the microtopography of sites that have an area of several tens of km2 with an elevation precision of 15 cm, including under canopy cover (Rapinel et al. 2015b). In addition, LiDAR data can provide an automatic topography of hydrographic networks (Sofia et al. 2014). Optical multispectral images provide maps of natural habitats (Stenzel et al. 2014), to estimate biomass (Santi et al.
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2014) or to monitor agricultural practises, such as the mowing or grazing of grasslands (Dusseux et al. 2014); while RaDAR data, which are not sensitive to cloud cover, make it possible to monitor the hydrodynamics of wetlands (Marechal et al. 2012). Thus, Earth observation data represent a potentially practical and economically feasible tool for extracting functional descriptors of wetlands. Micro-topography influences the hydrodynamics of a wetland and affects the level of performance of processes and functions. In the present study, it is proposed that a FAP can be based on a multi-criteria analysis (MCA) which presents many advantages. First, it allows layers of heterogeneous information to be combined and recorded spatially in a geographic information system (GIS). Second, modelling with the use of a MCA can be applied over larger areas on the basis of remote sensing data. As an example, the vulnerability of coastal wetlands has been assessed and mapped by using a MCA (Marotta et al. 2011; OmoIrabor et al. 2011). The objective of the present study was to use a spatially explicit approach to assess the ecological processes and functions of a wetland area at a catchment scale by using remote sensing data. To achieve this, we adapted the FAP by characterizing wetlands using Earth observation data (e.g., optical images, as well as RaDAR and LiDAR data). A MCA was used to evaluate four main wetland functions of a 132 km2 international long-term ecological research (ILTER) site located in the western region of France.
Materials and methods Conceptual framework of the functional assessment procedure The development of sufficiently robust, science-based tools for wetland functional assessment has been impeded by the lack of integration of the wide range of specialised scientific necessary. However, the sequence of European Community research projects investigating wetland functioning in Europe was targeted largely to overcome this impediment and enable the production of the functional assessment procedure (E. Maltby 2009). The field and laboratory-based studies carried out in the framework of three major research projects (FAEWE, PROTOWET, and EVALUWET) resulted in the identification and measure of key processes operating in river marginal wetland ecosystems.
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The fundamental functional unit In this procedure, the fundamental unit of land on which functional assessment is performed is a sub-area of the wetland called a hydrogeomorphic unit (HGMU). HGMUs are defined as areas of homogeneous geomorphology, hydrology, and soil (E. Maltby et al. 1996). Assessment of processes and functions Determination of process performance is based upon the degree of operation of controlling variables, and determination of function performance is based upon the performance of one or more process underlying them. Assessment for each HGMU therefore has two main steps: determination of the occurrence and performance of relevant process within each function, and the combination of groups of process outputs to give an estimate of functioning (E. Maltby 2009). Controlling variables and process performances are described through descriptors and indicators, respectively. Descriptors and indicators should be sensitive enough to represent functional performance and relatively easy to measure (Morgan and Short 2002). For these reasons, in this study, FAP was applied using descriptors and indicators derived from Earth observation data and MCA (Fig. 1). Study site The area investigated is part of a long-term ecological research (LTER) site entitled zone atelier armorique (ZAA), located on the southern portion of the Bay of Mont-Saint-Michel, France, and referenced in the ILTER networks (www.lternet.edu ) (Fig. 2). Multidisciplinary research has examined the effects of landscape and climate change on biodiversity and abiotic fluxes in this agricultural site (Baudry et al. 2003). This 132 km2 site contains a broad river floodplain that is a Natura 2000 and RAMSAR protected area (https://osur. univ-rennes1.fr/za-armorique/page.php?107). At this location, the wetlands represent highly diversified and mosaic environments. For example, there is a small area of wetlands in low-lying areas near the large marshes of Couesnon that are dominated by meadows that are intended for grazing and mowing. In Sougeal and Mesnil marshes (Fig. 2), flood is retained for waterbirds and pike reproduction. The flood management of these natural areas must meet both the challenges of maintaining biodiversity (Habitats
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Directive of the European Union, 1992/43/EEC) and achieving a qualitative and quantitative improvement of water resources (Water Framework Directive of the European Union, 2000/60/EEC).
GIS layers derived from Earth observation data The data collected for the present study included seven GIS layers at a 1:10,000 scale that were derived from several types of remote sensing data: (i) LiDAR data acquired in 2010 with a 4 points pts/m2 density from which a digital terrain model was derived; (ii) a temporal series of 15 RADARSAT-2 images that were acquired between February 2010 and February 2011 in polarimetric mode with a spatial resolution of 7 m; (iii) two multispectral optical images at very high spatial resolution: a Kompsat-2 image acquired in September 2008 with 4 m resolution and a Quickbird image acquired in January 2008 with 2.4 m resolution. The GIS layers included (Fig. 3): (1). a map of the hydrogeomorphic units (HGMUs) used. The HGMUs were delineated using a digital terrain model (DTM) derived from LiDAR data. HGMU were delineated using contrast filter segmentation which isolates groups of pixels based on their elevation differences relative to their neighbours. This map included 1929 HGMUs with surface areas that comprised between 0.02 and 40 ha. (2). A hydrographic network map derived from LiDAR data (Rapinel et al. 2015b) which was automatically extracted by applying a linear filter. A total of 1920 ditches or streams were characterised according to their width and depth. (3). A vegetation map which was obtained by coupling LiDAR data with two multispectral optical satellite images (Rapinel et al. 2015a). This map characterised the natural habitats based on their physiognomy (i.e., plant formations). Image classification was performed by combining an objectbased approach and decision-tree modelling, in merging similar adjacency pixels into objects characterised by spectral, shape, and contextual criteria (Blaschke 2010). The classification subsequently integrated summer and winter multispectral image data and three layers derived from LiDAR data: vegetation height, micro-topography, and intensity return.
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Fig. 1 Flowchart of the wetland functional assessment methodology employed
HGMU map
Vegetaon map Hydrographic network map Annual flood duraon map
Input data derived from remotely sensed data Meadows management map
Descriptors Peak biomass map
Standardized descriptors (0-255)
Funconal indicators (0-255)
(4). A map of annual flood duration that was derived from a time series of 15 RADARSAT-2 polarimetric images (Marechal et al. 2012). This methodology was based on the segmentation of the Shannon entropy index which has been shown to be a very sensitive parameter for the temporal variability of flooded areas. (5). A peak biomass map estimated from a normalized difference vegetation index (NDVI) that was calculated from the Kompsat-2 multispectral image. (6). A map of the meadows management mode which was obtained based on a visual interpretation of the homogeneity texture of the NDVI derived from the Kompsat-2 image. (7). A map of the external limits of the wetlands obtained by visual interpretation of the LiDAR DTM.
Mul-Criteria Analysis
Funconal processes (0-255)
Large ditches density Large ditches provide an entry or rapid release of water into wetland (Lang et al. 2012). They are also involved in major transfers between rivers and wetlands, and therefore, they play an important role in flood dynamics. They are generally maintained and ensure the connectivity of water flows between wetlands and adjacency streams. This descriptor was obtained based on the total length of the linear elements, divided by the area of the HGMU. Small ditches density Small ditches provide water retention during periods of flood. They are generally poorly maintained and water is often stagnant. They promote denitrification since denitrification is most effective in small pools of stagnant water (Flynn et al. 1999). They also promote biodiversity while serving as part of an ecological corridor (Verdonschot et al. 2011). This descriptor was obtained based on the total length of the linear elements, divided by the area of the HGMU.
Elaboration of descriptors using Earth observation data A set of 19 spatial descriptors, expressing the controlling variables that determine the process performances, has been derived from the remotely sensed data set.
Wooded elements density This descriptor determines biodiversity (Gelling et al. 2007) and was derived from vegetation map. A woodland hedge density value was obtained based on the total length of the linear elements
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Fig. 2 Study site
considered, divided by the area of the HGMUs that contained these elements. Roadways density A network of roads and railways act as an ecological barrier to the movement of
animal species (Rondinini and Doncaster 2002). Consequently, the expansion of roads and railways limits biodiversity. The density of roads and railways was obtained from the total length of the linear elements considered, and then this value was
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Fig. 3 GIS layers derived from Earth observation data that were used to characterise the wetlands examined
divided by the area of the HGMUs that contained these elements. Wooded elements connectivity The connectivity of wooded linear elements is linked to the concept of an ecological corridor. Greater connectivity of wooded hedges results in more efficient corridors and potentially higher animal and plant biodiversity (Gelling et al. 2007). Connectivity was estimated based on a proximity
index that measured the proximity between elements of the same class (Gustafson and Parker 1994). These calculations were performed by using FRAGSTATS software (version 4; University of Massachusetts, Amherst, MA, USA). Roadways connectivity The connectivity of roads and railways restricts the diffusion of species within a landscape and negatively affects biodiversity (Rondinini and
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Doncaster 2002). Connectivity was estimated based on the proximity index (Gustafson and Parker 1994).
Table 1 Type of contact between the wetland and the catchment for the present study in relation to the slow diffusion of water transfers
Wetland length/contact side length This indicator is used to assess the potential level of denitrification that occurs for nitrogen elements that are derived from the catchment, to the extent that a significant contact length between a wetland and a catchment favours the denitrification process (Clément et al. 2003). From the wetland boundaries map, this descriptor was calculated for homogeneous segments that were determined by dividing the centre line (i.e., the length of the wetland) by the contact side length (i.e., the length of contact between the wetland and the catchment).
Type of contact
Contact length between wetland and side by linear type The type of contact that exists between a wetland and a catchment determines the supply of nitrogenous material within a wetland. Thus, hedges that are transversal to the catchment ensure a slow release of water containing nitrogen and they promote denitrification. If the water flows are not or poorly charged with nitrogen due to the presence of woods or grasslands upstream, then the value of this landscape feature in terms of biogeochemical function is limited and will be described as neutral in the evaluation process. In addition, if a transverse ditch recovers nitrogen-containing water flows and is directly connected to a water stream, a wetland will be disconnected from the flow, and therefore, will have no effect in terms of denitrification. Conversely, the presence of a transverse embankment at the bottom of a catchment will substantially slow surface and sub-surface fluxes of water; and this will contribute to denitrification (Viaud et al. 2005). Therefore, five types of contacts were considered (Table 1): (1) the limit between two categories of land use; (2) one physiographic limit (i.e., embankments); (3) another physiographic limit (i.e., roads) ; (4) one fuzzy limit within a meadow or a wood; (5) another fuzzy limit within a crop. Each polyline received a score between 0 (low interest) and 1 (very high interest), depending on the type of linear contact that was observed (Table 1). The weighted average length of the polylines was then applied to each site. Potential water storage The presence of slight depressions that are only a few centimetres deep can lead to
Example
Interest
Score
Land use limit
Grassland/crops
Low
0.25
Physiographic limit
Embankment
Very high
1
Physiographic limit
Ditch/road
Very low
0
Fuzzy limit
Meadow
Medium
0.5
Fuzzy limit
Crops
High
0.75
water storage during floods (Džubáková et al. 2014). This descriptor, which is simulating an ordinary flood event, is calculated from the LiDAR DTM. Elevation values were normalized according to adjacency streams (Alber and Piégay 2011). Cumulative water storage values were assigned to the HGMUs. Compound topographic index The compound topographic index (Beven and Kirkby 1979) is related to micro-topography and provides an estimation of potentially hydromorphic areas where water flow accumulation is important (Martinez et al. 2010). This index was calculated from the LiDAR DTM. Average index values were calculated for the HGMUs. Vegetation roughness This descriptor determines the velocity of water flow during a flood period. Low roughness promotes the rapid return of water flow to a river, while greater roughness slows water flow and increases the time that water resides in a wetland (Cobby et al. 2003). Roughness of vegetation was estimated from the vegetation map. Each polygon was assigned a score between 0 (low interest) and 1 (high interest) depending on its membership class (Table 2). The weighted average from the surface of each polygon was then applied to each HGMU. Vegetation perspiration This descriptor supports lowlevel waters. Woods produce heavy transpiration, whereas grasslands produce more moderate transpiration, each in relation to leaf biomass (Dietrich et al. 2007). Transpiration from vegetation was estimated from the vegetation map. Each polygon was assigned a score between 0 (low interest) and 1 (high interest) based on its membership class (Table 3). The weighted average from the surface of each polygon was then applied to each HGMU.
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Table 2 Assigned interest and roughness scores according to the class of land cover Class of land cover
Interest
Score
Managed grasslands
Low
0
Crops
Low
0
Shrubs
High
1
Woods
High
1
Grasslands
Medium
0.5
Artificialized surfaces
Low
0
Water surfaces
Low
0
Irrigation The irrigation of crops in wetlands contributes to the worsening of low-level waters. Furthermore, a portion of the water applied during irrigation directly evaporates. Irrigated crops have a high density of underground drains and these drains can dry a wetland (Dixon and Wood 2003). Irrigation was estimated from the vegetation map. Each polygon belonging to the class of ‘crops’ was awarded a score of 1 (high interest). Other polygons were assigned a score of 0 (low interest). The weighted average from the surface of each polygon was then applied to each HGMU. Contributive area/site area This descriptor estimates the proportion of catchment-derived water flows that enter a wetland (Beven and Kirkby 1979). This index was calculated using the LiDAR DTM and the wetland boundaries map. Water bodies and bottom valley system density Soil hydromorphy is a significant factor in the denitrification process. In hydromorphic soils, the denitrification process is effective (Flynn et al. 1999). However, while this indicator is relevant in assessing the biogeochemical function of a wetland, it could not be directly evaluated Table 3 Assigned interest and transpiration scores according to the class of land cover Class of land cover
Interest
Score
Woods
High
1
Shrubs
Medium
0.5
Grasslands
Low
0
Crops
Medium
0.5
Artificialized surfaces
Low
0
Water surfaces
Low
0
in the present study. Alternatively, soil hydromorphy can be partially and indirectly assessed by considering vegetation as a descriptor of environmental conditions (Touzard et al. 2002). In the present study, the presence of hydromorphic soils was assessed based on the presence of water bodies, shrubs, poplars, and hygrophilous grasslands. This descriptor was calculated using the vegetation map. Net productivity Vegetation assimilates a quantity of nitrogen that is necessary for its chlorophyll activity, which improves water quality (Wu et al. 2011). The net productivity or chlorophyll biomass can be estimated from the Kompsat-2 image by calculating one vegetation index, the NDVI (Mutanga et al. 2012). In the present study, average NDVI values were calculated for each HGMU. Meadows management This descriptor determines the flow of carbon and of nutriments within a wetland. Mowing corresponds to a net export equivalent of the quantity of nitrogen and phosphorus contained in the aboveground biomass, whereas grazing combines export and internal recycling via faeces and urine (Güsewell et al. 2000). This descriptor was obtained from the map of meadows management. Values with regard to the denitrification process according to grazing and mowing are set to 0.5 (moderate interest) and 1 (high interest), respectively. The average weight of each polygon surface was applied to each HGMU. Patch richness density index This descriptor performs a major role in the evaluation of ecological functions. A landscape that possesses several types of land cover classes has an ecological value that is a priori more important than a homogeneous landscape that is dominated by a single class of land cover (Schindler et al. 2008). Landscape composition was defined according to the patch richness density (PRD). The average index value was calculated based on the HGMUs. Shannon’s diversity index Configuration of land cover is a criterion for the characterization of potential biodiversity (Schindler et al. 2008). Landscape configuration was defined by using the Shannon diversity index (SHDI) which expresses landscape diversity. The average index value was calculated based on the HGMUs. Duration of flooding This descriptor expresses the annual flood duration. It was obtained from a series
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of radar images that were acquired every 24 days over a one-year period (Marechal et al. 2012). Spring flooding promotes the reception of migratory waterbirds and the reproduction of pikes. When alternating flooding and dewatering periods occur, these promote certain elements of biodiversity (Ferreira 1997). To evaluate habitat function, the duration of flooding in the spring period (i.e., between the months of March and June), was calculated according to the HGMUs. Elaboration of functional indicators A set of 14 indicators expressing process performances has been derived from the combination of descriptors. Water storage This indicator corresponds to the volume of water that can be stored in a wetland. This indicator was estimated from two descriptors: (1) depressions and (2) the ratio between contributory zone areas and the site area. Hydrodynamic This indicator characterises the movement of water fluxes within a wetland during a low water period. This indicator was obtained by considering four descriptors: (1) the drainage network density; (2) the water bodies and bottom valley system density, (3) the vegetation perspiration; and (4) irrigation. Water level management This indicator expresses the monthly frequency of flooding that occurs over a 1-year period and was derived from the descriptor Bduration of flooding^. Aerobic/anaerobic interface Denitrification has been found to more efficient at the aerobic/anaerobic interface, with the presence of unmaintained ditches containing standing water promoting the denitrification process (Flynn et al. 1999). Conversely, large ditches and roadside ditches promote a more rapid flow of water, thereby reducing the efficiency of the denitrification process. This indicator was estimated by combining two descriptors: (1) the density of small, roadside ditches, and (2) the density of large ditches and small, roadside ditches. Wetland length/contact side length This indicator assesses the exchange fluxes from the catchment and was obtained by a descriptor of the same name.
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Contact length between wetland and side by linear type This indicator expresses the intensity of diffusion of fluxes emanating from the watershed and was derived from to the descriptor of the same name. Hydromorphic soil This indicator assesses the waterlogged soil intensity and was derived from the descriptor BWater bodies and bottom valley system density^. Productivity indicator This indicator quantifies the chlorophyll productivity and assimilation of nitrogenous material by plants. It was obtained according to the descriptor net productivity. Meadows management This indicator is derived from the descriptor of the same name. Linear elements density The indicator refers to the concept of ecological corridors/barriers and was evaluated based on four descriptors: (1) rivers and large ditches density; (2) temporary flow ditches density; (3) wooded elements density and (4) roadways density. Linear elements connectivity This indicator also refers to the concept of an ecological corridor/barrier and it was evaluated based on a combination of two descriptors: (1) wooded elements connectivity index; and (2) roadways connectivity index. Land cover composition This indicator was derived from the descriptor patch richness density index. Land cover configuration This indicator was derived from the descriptor Shannon’s diversity index. Spring flood duration This indicator considers the spring flood period that occurs between February and June. Assessment of wetland functions Low water attenuation This function defines the ability of an area to achieve progressive restitution of water towards a stream during periods of low water levels. This function is related to groundwater recharge process and responds directly to the Water Framework Directive of the European Union which objective is to limit low water levels. This function is assessed based on a combination of three indicators (Table 4).
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Table 4 Indicators, descriptors, GIS data, and standardization type used to assess the support for low water function; respective weights are indicated in brackets Indicator [weight] Water storage [0.33] Hydrodynamic [0.33]
Water level management [0.33]
Descriptor [weight]
GIS data
Standardization
Potential water storage [0.50]
LiDAR DTM
Increasing
Contributive Area / Site Area [0.50]
LiDAR DTM
Decreasing
Large ditches density [0.25]
LiDAR DTM
Decreasing
Water bodies and bottom valley system density [0.25]
Vegetation map
Increasing
Vegetation perspiration [0.25]
Vegetation map
Decreasing
Irrigation [0.25]
Vegetation map
Decreasing
Duration of flooding [1.00]
Flood duration map
Decreasing
Flood peaks attenuation This function refers to the reduction of stream flow by the discharge and retention of water in a wetland. This function is assessed based on a combination of three indicators (Table 5).
species where spring flood duration indicator is considered the most important process.
Denitrification This function refers to the chemical reaction whereby nitrate is converted initially to nitrous oxide and then to nitrogen gas by bacteria. This function addresses the objectives of the Water Framework Directive of the European Union to limit nitrate levels. Denitrification function is evaluated by combining six indicators (Table 6).
Standardization and weighting of criteria
Habital This function refers to the suitability of living and reproducing wildlife and addresses the objectives of the Habitats Directive of the European Union (1992/43/EEC). It is based on a combination of five functional indicators (Table 7). This function was stated in two variants: (1) habitat-global, which includes most animal species where all indicators were equally weighted and (2) habitat-waterbird which focuses specifically on migratory birds’
Multi-criteria functional assessment
Since the descriptors originate from various thematic layers and are expressed with different units, they were standardized to allow comparison between them. In this case, criteria were represented by continuous data. Raw data were converted with fuzzy membership function that offers flexibility in a standardisation process (Eastman 1999). Sigmoidal fuzzy membership functions, specified for each factor according to their relevance to wetland functional assessment, were used, i.e., monotonically increasing or decreasing. Criteria were standardized between the values of 0 (very low functional interest) and 255 (very high functional interest). The sigmoidal membership function can be specified by four parameters according to expert knowledge (a: membership rises
Table 5 Indicators, descriptors, GIS data, and standardization type used to assess the support for reduction of flood peaks function; respective weights are indicated in brackets Indicator [weight]
Descriptor [weight]
GIS data
Standardization
Water storage [0.33]
Potential water storage [0.50] Contributive area/site area
LiDAR DTM
Increasing
Site area [0.50]
Wetland boundaries map
Decreasing
Hydrodynamic [0.33]
Water level management [0.33]
Compound topographic index [0.25]
LiDAR DTM
Large ditch presence/absence [0.25]
Hydrological network map
Increasing Increasing
Vegetation roughness [0.25]
Vegetation map
Increasing
Water bodies and bottom valley system density [0.25]
Vegetation map
Increasing
Duration of flooding [1.00]
Flood duration map
Increasing
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Table 6 Indicators, descriptors, GIS data, and standardization type used to assess the denitrification function; respective weights are indicated in brackets Indicator [weight] Aerobic/anaerobic interface [0.16]
Descriptor [weight]
GIS data
Standardization
Temporary flow ditches density [0.50]
Hydrological network map
Increasing
Large ditches density [0.50]
Hydrological network map
Decreasing
Wetland length/contact side length [0.16]
Wetland length/contact side length [1.00]
Wetland boundaries map
Decreasing
Contact length between wetland and side by linear type [0.16] Hydromorphic soil [0.16]
Vegetation map
Increasing
Vegetation map
Increasing
Productivity indicator [0.16]
Contact length between wetland and side by linear type [1.00] Water bodies and bottom valley system density [1.00] Net productivity [1.00]
Biomass map
Increasing
Meadows management [0.16]
Meadows management [1.00]
Meadows management map
Increasing
above 0; b: membership becomes 1; c: membership falls below 1; d: membership becomes 0). In the case of a monotonically decreasing function, it is expressed as: ðx−cÞ π μðxÞ ¼ cos2 ðd−cÞ 2 when x < c , μ(x) = 1. In the case of a monotonically increasing function, it is expressed as: 1−ðx−aÞ π 2 μðxÞ ¼ cos ðb−aÞ 2 when x > b , μ(x) = 1. Weighting expresses the importance or preference of each criterion relative to the others: it is usually a subjective process. In an ecological context, weights of individual criteria are usually determined by direct subjective assessment by a group of experts (Chen et al.
2001). In the case of the FAP, criteria were equally weighted, excepted for the habitat-waterbird function for which a prevailing weight was applied to the indicator Bspring flood duration^. The Boolean wetland boundaries map was used as constraint, since it serves to delineate areas that are suitable (index 1) or not suitable (index 0) for consideration. Calculation of indicators and functions In this study, the FAP approach includes a MCA evaluating firstly each indicator based on a combination of descriptors, and then each function on the basis of functional indicators. Accordingly, we successively evaluated hydrological, biogeochemical, and ecological functions of the determined wetlands area. While each function is expressed homogeneously within each HGMU, functional scores are calculated per HGMU. Evaluating wetland function assessment involves
Table 7 Indicators, descriptors, GIS data, and standardization type used to assess the habitat function; respective weights are indicated in brackets. Asterisk indicates weights for the habitat-waterbird function Indicator [weight]
Descriptor [weight]
GIS data
Standardization
Linear elements density [0.20; 0.11*]
Rivers and large ditches density [0.25]
Hydrological network map
Increasing
Temporary flow ditches density [0.25]
Hydrological network map
Increasing
Wooded elements density [0.25]
Vegetation map
Increasing
Roadways density [0.25]
Vegetation map
Decreasing
Wooded elements connectivity index [0.50]
Vegetation map
Increasing
Roadways connectivity index [0.50]
Vegetation map
Decreasing
Land cover composition [0.20; 0.11*]
Patch richness density index [1.00]
Vegetation map
Increasing
Land cover configuration [0.20; 0.11*]
Shannon’s diversity index [1.00]
Vegetation map
Increasing
Spring flood duration [0.20; 0.55*]
Duration of flooding [1.00]
Flood duration map
Increasing
Linear elements connectivity [0.20; 0.11*]
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Table 8 Classification of functional interest according to the scoring system used in the multicriteria analysis
Interest
Score
Very low
000–051
Low
052–102
Medium
103–153
High
154–204
Very high
205–255
combining—in each group of descriptors and indicators—standardized factors by mean of weighted combination. In addition, the results are multiplied by the Boolean constraint (Eastman 1999). The mathematical formulation for the assignment of the wetland functional interest score is: N
K
i¼1
j¼1
IS ¼ ∑ wi xi ∏ b j
where IS is the overall interest score value, wi is the weight of factor i, xi is the criterion score of factor i, bj is the criterion score of constraint j, N is the number of factors, and K is the number of constraining criteria. Here, factors refer either to descriptors or to indicators.
Since the FAP approach is designed to assess and not to measure the intensity of each function, the scores of the functional processes were classified in equal intervals into five clusters of functional interest: very low, low, medium, high, and very high (Table 8). Finally, Pearson’s correlation analysis was used to test the spatial correlation between the functions. MCA was processed with Idrisi GIS software (Clark Labs, Atlanta).
Results The evaluation map generated for the four wetland functions assessed in the present study for a 132 km2 ILTER site located in the western region of France is presented in Fig. 4. The map shows the spatial variability that exists within the same site. Regarding the flood reduction function, the majority of the wetlands examined (56.3 %) were classified as medium interest and 35 % were classified as high to very high interest. Only 8.6 % of the wetlands had a low interest with respect to this function. In particular, the map shows a high potential was associated with the marshes of Sougéal, Mesnil, and Aucey, as well as with the downstream region of the tributaries. However,
Fig. 4 Maps that represent the value levels associated with the wetland functions identified at the catchment scale
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Table 9 Spatial relationship as expressed by Spearman’s rank correlation coefficient (p) between pairs of functions (n = 1919; P < 0.01; P < 0.001). Underlined and bold font indicate positive correlations (p = 0.25–0.49) and strong positive correlations (p ≥ 0.50), respectively Function
1
2
Flood peak attenuation
1
1
Low water attenuation
2
0.16***
1
3
−0.06
Denitrification Habitat-global Habitat-waterbird **
4 5
**
3
4
0.25***
1
***
−0.15***
−0.10***
1
***
−0.18
−0.11***
0.98***
0.40 0.42
***
P < 0.01
***
P < 0.001
within these sectors, some HGMUs that often corresponded to riverbanks exhibited a lower flood reduction potential. The potential for support for low water level was generally high across all of the wetlands in the study site, with 94 % of the wetland surface area having a high to very high interest. However, some of the HGMUs had a lower potential, especially on the northern marshes of Mesnil. The potential for denitrification was also generally low to medium across all of the wetlands, with more than half of the surface area of the wetlands (54.8 %) having a low to very low value. None of the sectors were classified as high, while 45.2 % were classified as medium. This situation is explained by the recurrence of ditches within the limits of the wetlands, and these ditches shortcut the fluxes emanating from the catchments. The potential for the wetlands to represent a global habitat was considered medium to high. The setting of cereal crops, the homogeneity of the landscape, and the presence of a road are the main factors that reduce the habitat potential. It is interesting to note that some areas with the highest values were among the small wetlands, were not closely monitored by naturalists and thus not recognized as protected areas. Finally, the habitat potential for water birds was essentially medium (85.5 %). However, it was high for the marshes of Mesnil and Sougéal, which are regularly flooded during the spring and are recognised as of international heritage value. Spearman’s rank correlation analysis showed that habitat-waterbird was the function with the most and strongest (p > 0.40) correlations, in particular related to habitat-global (0.98) and flood peak attenuation (0.42) (Table 9). Conversely, the pairs of
habitats-global and denitrification (−0.15), as well as flood peak attenuation and denitrification (−0.06) showed a negative correlation.
Discussion Fine scale spatial organisation of wetland functions The contribution of Earth observation data considerably improved the scale of the HGMUs (1:10,000) and the spatial extent of the wetland functional assessment performed in comparison with field-based approaches that achieve a fine scale (1:25,000) assessment of restricted areas that are carried out on few hectares (Janssen et al. 2005) or at a coarse scale (1:100,000) of an entire watershed (Namaalwa et al. 2013). Furthermore, most field evaluations have focused on sites with high heritage value, while the functions of ‘ordinary’ wetlands were also assessed over large areas in the present study. The resulting maps illustrate the spatial disparities of the function intensities within the same site that were previously poorly considered. Mapping of the functions of wetlands at the catchment scale is an integral part of establishing functional connections (Verhoeven et al. 2008), and it also facilitates the identification of ‘corridors’ for each function. Furthermore, the ecosystem services of wetlands (e.g., improving water quality, reducing the risk of flooding, and/or reducing access to water resources) are directly related to the intensity of these functional processes (Edward Maltby and Acreman 2011). The results of the present study demonstrate that these services can be represented spatially and scaled according to aggregation of the functional scores of the HGMUs.
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Contribution of earth observation data The availability of innovative Earth observational data (i.e., LiDAR, RaDAR, and multispectral data) has allowed the descriptors associated with microtopography, hydrology, and vegetation to be derived for an entire catchment. In particular, LiDAR data appear to be critical because they enable HGMUs to be derived, including areas that are wooded and/or difficult to access in the field. In contrast, the delineation of HGMUs based on field observations often represents a time constraint (Cole 2006). Furthermore, LiDAR data can help characterise a hydrographic network, especially under vegetation strata. However, micro-topography cannot be characterised in flooded areas at the time of LiDAR acquisition. To address this limitation, development of a new bathymetric LiDAR sensor is underway (Abdallah et al. 2013). In the present study, multispectral data with very high spatial resolution have been successfully used to derive descriptors associated with micro-topography, vegetation, and ditches. Vegetation was characterized at the level of plant formation (second level of CORINE biotope typology), and this has proven to be sufficient for deriving the majority of descriptors related to hydrological and habitat functions. However, two types of descriptors initially used in the FAP and related to denitrification have not been entirely derived from remote sensing data. The first type of descriptors is soil-related features (e.g., moisture, infiltration, organic and mineral materials proportion, trace elements concentration). Soil properties were broadly and indirectly assessed based on the descriptor BWater bodies and bottom valley system density^ derived from the plant formation map. A more detailed characterisation of vegetation at the level of plant associations (third level of CORINE biotope typology) obtained from hyperspectral data could have led to the development of finer descriptors related to soil properties, such as moisture content, nutrient, or salinity concentrations (Roelofsen et al. 2014; Schmidtlein 2005). The second type of descriptors, which is related to the agricultural inputs, was not considered in this study because monotemporal remote sensing data was shown inadequate to identify such farming practises. However, time series of multispectral images could be relevant to improve the denitrification assessment, a recent
Environ Monit Assess (2016) 188:641
work having demonstrated that such data can help characterise the intensity of grassland use (Franke et al. 2012). Accuracy of functional assessment The accuracy of the results obtained in this functional assessment was dependent on the quality of the input data and the reliability of the model. The GIS layers that were used in the present study were derived automatically from remote sensing data and they exhibited very good overall accuracy. For example, the accuracy of the vegetation map is greater than 90 %, with the 1:10,000 scale being similar to that of field maps (Rapinel et al. 2015a). In addition, the FAP descriptors that are necessary for evaluating flood storage and habitat functions were derived from the remote sensing data. However, evaluations of the denitrification and flood peak attenuation functions were more uncertain, since the descriptors that were associated with agricultural inputs and infiltration could not be directly derived from Earth observation data. Spatial assessment of wetland functions is based on the MCA approach. The major concern of MCA is the lack of justification and objectivity: the choice of values of factor weighting and standardization are based on Bexpert—knowledge^ and can generate different results (Chen et al. 2001). When integration of the wide range of specialised scientific is lacking, a sensitivity analysis is performed to compare results (Store and Kangas 2001). Here, the MCA was applied without performing a sensitivity analysis, because this study is based on the use of the robust FAP. This strategy has been shown to be reproducible, since it has been calibrated in 20 sites across Europe as part of the EVALUWET program (E. Maltby 2009). However, it is important to recognise that the objective of the present study was to provide an assessment—and not a measure—of wetlands functions. In this sense, MCA appears to be a suitable approach for spatialised assessment. Implications for ecological management Currently, the costs associated with obtaining LiDAR data and multispectral images are reasonable for most managers. In particular, new multispectral Sentinel-2 data are provided free of charge in
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Europe; and there are LiDAR data available that cover the entire UK (0.5 pts/m2), the Netherlands (1 pts/m2), and Spain (0.5 pts/m2). GIS treatments can also be reproduced by a remote sensing engineer. The resulting maps that are generated from these data are able to map the performance of ecosystem functions based on the total area of the territories examined, and this was demonstrated in the present study for a 132 km2 ILTER site located in the western region of France. To date, the feedback received from managers has indicated the usefulness of FAPs in mapping and simulating ecosystem functions across different scenarios; this facilitates compromises among the stakeholders (Janssen et al. 2005). Furthermore, these maps have enabled a characterisation of the functional connections that are present at the catchment scale, and this has led to the objective identification of sites that qualify for restoration or compensation measures under the Habitats and Water Framework Directives of the European Union.
Conclusion Here, we propose an operational approach to the assessment of key wetland functions on the basis of remote sensing data. As a result, an area of several hundred square kilometres can be assessed at a catchment scale. The advantages of this approach include its applicability to (i) other climatic areas (e.g., tropical, continental) and (ii) other functional assessment procedures, e.g., RAM (Fennessy et al. 2007). This proposed approach also offers the opportunity to map ecosystem services and it is easily applied by managers since: (i) the necessary data are easily obtained from Earth observation data (e.g., LiDAR data and multispectral images) and (ii) the method of analysis is relatively simple. Thus, it is anticipated that managers can apply this method to meet the challenges of wetland management and conservation, as well as in situations that involve a need for compensation and restoration measures. Acknowledgments This study was supported by the zone atelier armorique program. We are also grateful to Aurélien Bellanger (Communauté de Communes de la Baie du Mont-Saint-Michel) and Jean Nabucet (LETG Rennes COSTEL) for help in the field work. We also thank Dr. Ann Power Smith of Write Science Right for editorial assistance.
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