Science of the Total Environment 527–528 (2015) 493–506

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Application of remote sensing for the optimization of in-situ sampling for monitoring of phytoplankton abundance in a large lake Isabel Kiefer a,⁎, Daniel Odermatt b,c, Orlane Anneville d, Alfred Wüest a,e, Damien Bouffard a a

Physics of Aquatic Systems Laboratory, Margaretha Kamprad Chair, EPFL-ENAC-IEE-APHYS, CH-1015 Lausanne, Switzerland Odermatt & Brockmann GmbH c/o The HUB Zurich Association, Viaduktstrasse 93–95, CH-8005 Zürich, Switzerland Brockmann Consult GmbH, Max-Planck-Str. 2, D-21502 Geesthacht, Germany d INRA — UMR CARRTEL, 75 avenue de Corzent, BP 511, F-74203 Thonon-les-Bains Cedex, France e Eawag, Swiss Federal Institute of Aquatic Science and Technology, Surface Waters — Research and Management, Seestrasse 79, CH-6047 Kastanienbaum, Switzerland b c

H I G H L I G H T S • • • •

The spatio-temporal evolution of chlorophyll-a in Lake Geneva was analysed using MERIS data. The representativeness of Chl-a in-situ measurement locations was examined. A method for determining most representative in-situ locations is proposed. The advantage of combined in-situ and satellite monitoring of lakes is shown.

a r t i c l e

i n f o

Article history: Received 23 January 2015 Received in revised form 30 April 2015 Accepted 4 May 2015 Available online 20 May 2015 Editor: Simon Pollard Keywords: Remote sensing MERIS Chlorophyll-a Water quality monitoring Lake Geneva Phytoplankton Spatial heterogeneity

a b s t r a c t Directives and legislations worldwide aim at representatively and continuously monitoring the ecological status of surface waters. In many countries, chlorophyll-a concentrations (CHL) are used as an indicator of phytoplankton abundance and the trophic level of lakes or reservoirs. In-situ measurements of water quality parameters, however, are time-consuming, costly and of unknown but naturally limited spatial representativeness. In addition, the variety of the involved lab and field measurement methods and instruments complicates comparability and reproducibility. Taking Lake Geneva as an example, 1234 satellite images from the MERIS sensor on the Envisat satellite from 2002 to 2012 are used to quantify the spatial and temporal variations of CHL concentrations. Based on histograms of spring, summer and autumn CHL estimates, the spatial representativeness of two existing in-situ measurement locations is analysed. Appropriate sampling frequencies to capture CHL peaks are examined by means of statistical resampling. The approaches proposed allow determining optimal in-situ sampling locations and frequencies. Their generic nature allows for adaptation to other lakes, especially to establish new survey programmes where no previous records are available. © 2015 Elsevier B.V. All rights reserved.

1. Introduction The anthropogenic pressure on freshwater systems has degraded their quality worldwide (Biodiversity Indicators Partnership, 2010; Brönmark and Hansson, 2002). The abiotic environments of lakes, reservoirs and ponds have been partly altered drastically through eutrophication, acidification or contamination, changing climatic conditions, water chemistry or habitat size and isolation. The high levels of biodiversity characterizing many of these ecosystems are partly threatened. At the same time, the water use and the demand for higher water ⁎ Corresponding author at: EPFL-ENAC-IEE-APHYS; GR A2 412, Station 2, CH-1015 Lausanne, Switzerland. E-mail address: [email protected]fl.ch (I. Kiefer).

http://dx.doi.org/10.1016/j.scitotenv.2015.05.011 0048-9697/© 2015 Elsevier B.V. All rights reserved.

quality have increased, too, for drinking and domestic use, fisheries, agriculture, navigation, cooling of power plants, hydropower generation and recreational activities (Chapman, 1996). The monitoring of the water quality of lakes is a global concern that requires regional action. In 2000, the European Union has adopted the Water Framework Directive, aiming at a “good ecological and chemical status” of surface, coastal and groundwater by 2015 (European Commission on water policies, 2010). This directive applies to all river basins and catchments, including natural and artificial lakes with a surface area N 0.5 km2. It enforces the establishment of “a coherent and comprehensive overview of water status within each river basin district” (European Parliament and Council, 2000). In the United States, the National Lake Assessments in 2007 and 2012 by the US Environmental Protection Agency (USEPA, 2009, 2012) aimed at a uniform

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approach of collecting and evaluating data from water resources nationwide. A statistical sampling method was used for the selection of the water bodies to be sampled, including initially all lakes, ponds or reservoirs with a surface of N0.04 km2 (2007) or N0.01 km2 (2012) and a depth of N1 m (USEPA, 2009, 2012). Of totally over 49,000 lakes that were accessible for assessment, only around 1000 lakes were finally sampled at the deepest point of each lake and ten sampling stations regularly distributed around the lake perimeter. The lakes were sampled on one day in summer 2007 and 2012, and not necessarily during both years. Such practical constraints decrease the representativeness of in-situ monitoring programmes because cost increases almost linearly with the number of sites, lakes and samples to be considered. Remote sensing is recognised as an effective method for the monitoring of water quality and spatio-temporal changes in water characteristics, although it should not completely substitute in-situ measurements (Chen et al., 2004). However, the synergies of combining in-situ monitoring measurements with remote sensing estimates remain largely unexploited. A recent survey by EPA summarizes several concerns that hold monitoring agencies off the integration of remote sensing products, including cost assumptions, insufficient product accuracy, uncertain data continuity and lack of programmatic support (training, software, management commitment) (Schaeffer et al., 2013). Overcoming these concerns, whether applicable or not, is today's major challenge for water quality remote sensing, and requires coordinated and continuing effort. By using satellite remote sensing for the conception of in-situ measurement programmes, we propose a way to partially exploit the added value of remote sensing in ordinary monitoring programmes even before full compliance in the abovementioned terms is achieved. Several studies have examined the representativeness of in-situ sampling locations using remote sensing data. Vos et al. (2003) coupled in-situ data from 1999 and 2000 with SeaWiFS satellite optical data. Based on mean and standard deviation of approximately ten images per year, the representativeness of an in-situ location was discussed for each year. Kallio et al. (2003) have compared chlorophyll-a (CHL) maps from airborne remote sensing with measurements obtained from in-situ stations. AISA (airborne imaging spectrometer for applications) images from August 1996 and 1998 were used to analyse the accuracy improvement using remote sensing data, compared to spatially discrete water samples only. We aim to advance the assessment of representativeness from a few comparative datasets to the robust optimization of spatio-temporal sampling patterns in a lake, using ten years of MERIS satellite images. Suggestions are made how to assess the representativeness of existing in-situ measurement locations, and how to identify the most representative locations for long-term measurements where they are not yet determined. CHL as the principal photosynthetic pigment of phytoplankton and essential factor of the ecological state of lakes (Bresciani et al., 2011; Carvalho et al., 2008; USEPA, 2009) is our parameter of interest, and corresponding monitoring measurements are used for the regression-based absolute calibration of remotely sensed CHLs (s: satellite) (Odermatt et al., 2012a). This demonstration study is carried out for Lake Geneva, the largest lake of Western Europe situated at the border of France and Switzerland, but can be adapted to any other lake. 2. Study area and data 2.1. The study area Lake Geneva is a peri-alpine lake situated at an altitude of 372 m above sea level and of 580 km2 surface area extent. It is composed of two main basins (Fig. 1), the Petit Lac (“small lake”) and the Grand Lac (“large lake”). The small lake has an average depth of 41 m and consists of 4% of the total water volume, while the large lake is on average 172 m deep. The catchment area of the lake comprises alpine valleys in the South and East, whereas the Swiss lakeshore in the

North hosts industry, is intensely farmed and densely populated. The Rhône River is the largest river inflow, contributing around 73% of the total inflowing water volume of 250 m3 s− 1 (Rapin and Gerdeaux, 2013). Lake Geneva is heavily used as drinking water resource and is an attractive site for tourism and professional fishing activities (Rapin and Gerdeaux, 2013). It is regularly monitored by the Commission Internationale pour la Protection des Eaux du Léman (CIPEL). The increased human population and the fast economic growth in the beginning of the 20th century had among others resulted in an abrupt increase in the phosphorus concentration in Lake Geneva. The excessive nutrient loading has strongly impacted the lake by increasing the algae concentration. The water transparency decreased, summer algal blooms became more frequent and certain fish species diminished drastically (Anneville et al., 2002). After the implementation of phosphorus reduction plans in the early 1970's, the phosphorus concentration in Lake Geneva has decreased by almost 80% (BFS, 2013; www.cipel.org), going along with an improvement of the water quality and a slight reduction of the algal biomass towards the 1990's (Rimet, 2013). Thereafter, despite the ongoing decrease of phosphorus in Lake Geneva, phytoplankton biomass started to increase again (Anneville et al., 2002), reaching new record peaks in 2001, 2007 and 2009 (Rimet, 2013; Tapolczai et al., 2015). 2.2. Satellite data A total of 2291 MERIS L1B images with full or partial coverage of Lake Geneva between June 2002 and April 2012 are available through the Calvalus portal, which facilitates the access to and processing of large volumes of MERIS data (Fomferra et al., 2012). The images are georectified using AMORGOS (Bourg and Etanchaud, 2007) and corrected for the smile effect (Bouvet and Ramoino, 2010); improved pixel flags are added using Idepix (Ruescas et al., 2014), and CHLs is retrieved using the FUB/WeW neural network algorithm (Schroeder et al., 2007). A valid pixel expression is defined that excludes all pixels flagged for the properties listed in Table 1. The same processing chain performed best for oligo- to mesotrophic waters in a parallel assessment of more than 50 candidate algorithm combinations (Odermatt et al., in preparation). The FUB/WeW algorithm consists of several neural networks for the retrieval of constituents and reflectance, which are all retrieved from top-of-atmosphere reflectance, i.e. without a separate atmospheric correction prior to the constituent retrieval. According to its training range, the algorithm can handle CHLs concentrations between 0.05 and 50 mg m−3 (Schroeder et al., 2007). 1053 images were found with no valid pixel outcome and not considered. Four images were removed from processing as outliers, with lake average CHLs being unrealistically high and several times the concentration of previous and subsequent acquisitions. This leads to a dataset of 1234 images (Fig. 2), which represents at most 623 valid observations in an individual pixel. Fewest observations are available directly on the shore, where the number of individual counts in a pixel is reduced due to cases of unfavourable geolocation and mixed pixel identification. Such borderline cases are subject to an increased sensitivity to both procedures, and therefore to higher errors in the derived CHLs. Hence, all pixels with less than 300 observations are excluded from further analysis. FUB/WeW CHLs matchup estimates are extracted as 3 × 3 kernels around a centre pixel at the coordinates of the in-situ locations SHL2 and GE3, representing a surface area of ~ 0.66 km2 (pixel size ~ 260 × 290 m, ESA, 2014) each. Note that the kernel's average and standard deviation are calculated. The pixel size between images was not consistent throughout the dataset due to slight changes in the viewing angle and the distance of the sensor from Earth. Thus, first the concentrations at the in-situ locations were extracted from every image using the respective site coordinates. Then, a regularly spaced reference coordinate grid (270 m) was defined on a rectangle containing

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Fig. 1. Map of Lake Geneva with the main river inflows (dark grey), the in-situ measurement locations (black triangles), the major waste water treatment plants at the lakeshore (white circles) and the division in the two geographical units “small lake” (west) and “large lake” (east). All maps are represented in the WGS84 reference system.

Lake Geneva. The grid size corresponds to the average pixel size of all images of the original dataset (1234 valid images). Thereby, an over- or under-sampling of the data is avoided. At each grid point of the reference grid, the data of all images at the respective coordinate is extracted. This procedure led to a data cube of 1234 images, containing all the same number of values at the same coordinate locations. Pixels of the FUB/ WeW output having values higher than the training range of the neural network were excluded (N 50 mg m−3). 2.3. Lake in-situ data The Lake Geneva monitoring program has started in 1957, and is organized by CIPEL since 1963. Several parameters of the lake water are measured once to twice per month, depending on the season. Since 1986, these measurements are limited to two locations in the lake, SHL2 (WGS84 6.58872 °E, 46.4527 °N; Depth: 309 m) and GE3 (WGS84 6.21994 °E, 46.29721 °N; Depth: 72 m) (CIPEL, 2003). In the following, we use only SHL2-data for the scaling of the satellite data because (i) measurements are more frequent, and (ii) as opposed to GE3, in-situ data are not depth integrated (see Appendix A for GE3). On both locations, water samples are filtered on a glass fibre membrane, then the pigments are extracted from the residues, using an acetone–water mixture. Finally, the CHLf (f: field) concentration computation is based on a spectrometric analysis with the method of Strickland and Parsons (1968), using the absorbance spectrum characteristics of chlorophyll-a (Leboulanger, 2003). At SHL2, the CHLf measurement is based on samples taken by the INRA — UMR — CARRTEL, at different depths between 0 and 30 m. One missing value on July, 9, 2012 at a depth of 1 m has been estimated

by linear interpolation between 0 and 2 m. As the penetration depth of the measurement by a satellite sensor depends on the turbidity of the water (IOCCG, 2000), we assumed in this study that it corresponds approximately to the measured Secchi depth. In Lake Geneva the latter varies between 2 and 8 m for the primary production period between March and November (Leboulanger, 2003; Rimet et al., 2008; Rimet, 2013). We used a mean value of 5 m for the penetration depth, therefore averaging SHL2 in-situ CHLf measurements from 0, 1, 2, 3.5 and 5 m in agreement with previous work (Odermatt et al., 2010). CHLf consists of one “extreme” maximum value of 60 mg m− 3 in 2002. All other measurements are below 25 mg m−3, and the highest concentration represented in a matchup data pair is just below 20 mg m− 3. The maximum value of 60 mg m−3 is thus not considered in the analysis, as no satellite image is available for the corresponding date. During the CIPEL measurement campaigns also the concentration of total and bio-available dissolved phosphorus P-PO4 is determined. Water samples are taken at SHL2 at 20 depth levels and with higher vertical resolution at the surface. They are analysed in the chemical laboratory of INRA — UMR — CARRTEL. In this study, the average of the measurements at 0, 2.5 and 5 m depth is used. From the balances of the wastewater treatment plants (WWTP) in the Canton Vaud along the northern lakeshore, we can deduce the

Table 1 Flags of excluded pixels. Level 1

Level 1 p

Level 2

Cosmetic Duplicated Glint_risk Suspect Land_ocean Bright Coastline Invalid

Land Coastline Cloud Cloud_ambiguous Cloud_buffer Cloud_shadow Snow_ice MixedPixel GlintRisk

Chl_out

Fig. 2. Temporal distribution of MERIS satellite data (1234 images containing valid pixels) used in this analysis.

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daily output of total phosphate and orthophosphate (data extracted from yearly WWTP reports at www.vd.ch, Canton de Vaud, 2014). These plants cover by far the largest part of the direct WWTP discharge into the lake. 3. Methods 3.1. Calibration of remotely sensed CHL and reference datasets The remotely sensed, water-leaving signal from different depths is proportional to the first derivative of the depth-dependent round-trip attenuation (Zaneveld et al., 2005; Piskozub et al., 2008). This means that the portion of water-leaving radiance backscattered from individual layers decreases with depth, unless a turbid layer is covered by clearer water. Appropriate weighting can be applied to reference profile measurements if vertically resolved attenuation measurements are available (Odermatt et al., 2012a; Sokoletsky and Yacobi, 2011). Due to the lack of such measurements, we average the reference profile measurements taken at 0–5 m as in earlier studies with comparable reference data (Odermatt et al., 2010). This implies that even for vertically mixed, clear water and Secchi depths higher than 5 m, the water-leaving signal is still predominantly composed of backscattered photons from the upper layers. These theoretical considerations explain why mixed 20 m composite samples as available for GE3 (see Appendix A) are less appropriate in terms of vertical representativeness of the water-leaving signal. An assessment of Secchi depth dependent vertical averaging is provided in Appendix B, also confirming the considerations above. The relationship between pigment absorption and concentration is highly variable. Since the directly retrieved variables are absorption and scattering, remote sensing algorithms require calibration with this relationship in order to retrieve appropriate concentration values. The default calibration of the FUB/WeW algorithm is aCHL = 0.052*CHL0.635, while other algorithms assume an almost linear relationship (Odermatt et al., 2012a). We use the 0–5 m averaged reference profile measurements in SHL2 to recalibrate the FUB/WeW retrieved CHLs. The slope of a forced zero intercept linear regression between SHL2 CHLf and FUB/ WeW retrieved CHLs matchups is used to scale all FUB/WeW CHLs products to the absolute value range of SHL2 reference measurements. Matchups between sampling and satellite overpass are limited to the same day, assuming no significant changes in CHL concentration within a few hours between in-situ measurement and satellite overpass. Note that a calibration including an intercept would result in a background CHLs concentration of ~2 mg m−3 which is not realistic for Lake Geneva. 3.2. Chlorophyll-a evolution per pixel and spatio-temporal variability The spatial differences in the temporal development of phytoplankton abundance are investigated by means of a trend analysis for each pixel. In order to avoid the influence of seasonality on the temporal trends, this method takes into account entire growth cycles and thus full years only, i.e. the data of 2003 to 2011. For each pixel, daily CHLs values are linearly interpolated from the first to the last valid observation of this period. The linear trend in each pixel is calculated from these interpolated time series, giving an indication of the total CHLs change over nine years. The spatial variability regarding concentration ranges is calculated in terms of the mean and standard deviation in each pixel of the lake, using all available observations in the entire dataset. These statistical parameters highlight the spatial differences in the temporal variability throughout the whole lake. Considering the log-normal distribution of CHLs values at each pixel, the parameters were determined after logtransformation of the data (Van der Zande et al., 2011). This simple method allows a rapid detection of zones frequently prone to strong algal abundance (Oliveira et al., 2009).

3.3. Temporal sampling frequency Resolving peak concentrations is an important requirement on CHL monitoring surveys. On one hand they indicate the maximum phytoplankton abundance and thus extreme ecological conditions, on the other hand they are required to achieve consistence in derived statistical quantities. In order to analyse the temporal sampling strategy necessary to detect the peak of phytoplankton concentration, the sensitivity of the obtained maximum CHLs value as a function of the number of samples was assessed. It is thereby assumed that the satellite measurements detected the real peak, which might not always be the case as data-series are not entirely continuous (Van der Zande et al., 2011). Long-term studies of water quality parameters in Lake Geneva as well as our own data basis have shown a distinct peak in CHL concentration in spring, whereas development is less specific during the summer and autumn months, when peaks are often less pronounced (Leboulanger, 2003; Rimet et al., 2008; Rimet, 2013). Remotely sensed CHLs for the phytoplankton spring bloom cycle between March and May is considered for each year. The number of acquisitions available for each year varies considerably due to cloud coverage (Table 2). 2002 and 2012 are not taken into account because MERIS operations do not cover the full spring cycle. Two zones (with presumably high biological activity), one offshore near Thonon-les-Bains, one in the Rhône inflow area (black crosses in Fig. 1), are chosen for the extraction of the CHLs average in an arbitrary 3 × 3 pixel kernel, with N being the number of valid observations for each location. In order to have a minimal temporal offset Δt between each measurement, only the years 2007, 2009 and 2011 are examined. The probability to detect the CHLs peak depending on the in-situ sampling frequency is investigated using the jackknife resampling strategy (Rodgers, 1999). Here, data series are randomly resampled 1000 times without replacement. Thereby, different sample sizes from one up to all values of the dataset of that period are chosen. E.g. in 2007, the vector containing 25 valid data values (N) is resampled with sample sizes from 1 to 25 values, each 1000 times. For each of these 1000 random extractions and for every sample size, the maximum CHLs concentration in the sample is estimated and compared to the real peak. From 1000 maxima for each sample size, the probability to find a certain value as the maximum value of the sample is calculated. In general, the time series vectors contain (in number) more low than high values, as the peaks are often defined by only one or two observations. The probability to underestimate the real peak is therefore quite high. With an increasing size of the sample, the probability to find a higher percentage of the real maximum CHLs increases as well. Although sampling intervals are in reality not random, we found no significant differences when resampling after fixing regular steps between the samples of daily interpolated time series. 3.4. Spatial representativeness of SHL2 and GE3 Due to the above described annually recurrent changes in phytoplankton dynamic, we analysed the representativeness of SHL2 and GE3 separately for spring, summer and autumn. First, the periods of high algal abundance are identified as having higher than yearly average chlorophyll concentrations as well as a higher standard deviation (regarding the whole lake) than during winter. CHLs values within individual images are either normally or log-normally distributed. The mean and standard deviation are computed for each image depending on the type of distribution and the three static seasons are defined based on the temporal distribution of the mean CHLs peaks. The identification of variable, phenology-derived seasons according to Palmer et al. (2015) was considered, but would require an equal distribution of observations, which implies losing temporal resolution. The first algal peak in Lake Geneva occurs regularly between midMarch and mid-May, thereby defined as spring season. Autumn was chosen from mid-September to mid-December, the period where a potential

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Table 2 Example of data availability for the spring periods, for pixel kernel in the Thonon area (top) and the Rhône area (bottom). Year

Start date

End date

Images

Valid images N

2003 2004 2005 2006 2007 2008 2009 2010 2011

10 March 2 March 1 March 1 March 2 March 5 March 7 March 1 March 5 March

31 May 28 May 31 May 30 May 31 May 30 May 31 May 29 May 29 May

19 34 37 31 43 27 35 37 40

11 22 17 9 25 14 24 18 28

2003 2004 2005 2006 2007 2008 2009 2010 2011

10 March 2 March 1 March 1 March 2 March 5 March 7 March 1 March 5 March

31 May 28 May 31 May 30 May 31 May 30 May 31 May 29 May 29 May

19 34 37 31 43 27 35 37 40

9 21 21 9 25 14 21 17 28

Steps n

Real max CHL mg m−3

7.9 4.1 5.4 6.4 3.3 6.3 3.7 5.2 2.9

1:11 1:22 1:17 1:9 1:25 1:14 1:24 1:18 1:28

34.7 29.7 54.9 34.4 17.3 7.4 14.7 42.1 27.0

10.25 4.3 4.3 9.1 3.3 6.3 4.3 5.6 3.0

1:9 1:21 1:21 1:9 1:25 1:14 1:21 1:17 1:28

31.2 19.8 47.2 20.6 33.3 7.3 24.2 45.4 82.0

Avg. nb of days betw. valid images Δt

Bold data designate the years chosen for the sampling frequency analysis.

Fig. 3. Seasonal chlorophyll-a variability for the entire MERIS deployment period from June 2002 to April 2012: Average concentrations of the lake bCHLs, lakeN in mg m−3 with the standard deviation (grey), CHL at SHL2 from satellite (CHLs, SHL2 black) and in-situ measurements (CHLf, SHL2 red). In order to show the intra-annual variations scaling of the y-axis is not uniform.

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third CHLs peak can be observed (Fig. 3). Consequently, the summer period was chosen from mid-May until mid-September. This period includes a clear-water phase with minimum chlorophyll concentrations, often observed in June. Less intensive and frequent algal growth events of variable duration occur in this season (Fig. 3, e.g. 2003, 2004 and 2008). The analysis of the representativeness of SHL2 and GE3 was based on the assumption that individual pixels within the lake can be segregated into coherent zones depending on their average chlorophyll concentration. The histograms of average values, separately for spring, summer and autumn, reflect the extent of variations of the three seasons. A sampling location with an average concentration X will therefore be representative for all locations in the lake having a concentration in the range of X ± SD, SD being the standard deviation of all lake pixel averages. Again, statistical parameters were calculated after logtransformation of the time series at each pixel, standard deviation and mean being computed from the characteristics of the lognormal distribution. The distribution of the pixels' average values for spring, summer and autumn, however, was considered to be normal. The CHLs temporal trend over the nine observed years (as described above for 2003 to 2011) was not taken into account. This method not only provides an estimate of the seasonal representativeness of a given in-situ location but also indicates the potential need for other in-situ sampling locations to cover the full range and variability of phytoplankton in a lake. 4. Results 4.1. Calibration Calibration of the remotely sensed CHLs is according to the linear regression between the 0–5 m average SHL2 in-situ data and FUB/ WeW products of 46 matchup dates (Fig. 4). The coefficient of determination (R2) is 0.62 (p-value ≪ 0.01). The linear regression with a forced zero intercept between in-situ and satellite data is given by CHLSHL2, f = 2.43 ∗ CHLSHL2, s. This factor is used to scale the satellite observed absolute concentrations, but does not alter the correlation coefficient. 4.2. Chlorophyll-a evolution per pixel and spatio-temporal variability The analysis of the temporal evolution showed an overall temporal decrease of CHLs in Lake Geneva (Fig. 5) but also a relatively strong heterogeneity between different parts of the lake. Between January 2003 and December 2011 CHLs decreased on average by 1.5 mg m−3 (0.2 mg m−3 y−1) at the surface of the lake, which corresponds to a decrease of CHLs by ~27% over one decade. We notice that most of the lake is characterized by a rather slight decrease of ~10 to ~20%, whereas some southern zones (the Bay of Thonon-les-Bains and especially the

Fig. 5. Chlorophyll-a difference 2003 to 2011. Change is given in absolute CHL concentration (mg m−3) (top) and relative change (%) (middle), and example of the trend-line of FUB CHLs at SHL2 (bottom) based on daily interpolation of the originally 593 measurements at that point.

area north of Evian; Fig. 1), the very central part of the large lake and the eastern region of the small lake are characterized by a strong decline of up to 50% of the initial CHLs concentrations. Lastly, coastal areas of the eastern part of the lake have experienced an increase in concentrations of CHLs. The computation of the standard deviation of CHLs per pixel exhibits several zones of high temporal variability (Fig. 6). The strongest variations are found at the eastern part of the lake, between St-Gingolph in the south on the French side and Vevey (Fig. 1) on the northern Swiss shore, surrounding the Rhône River inflow. Standard deviations are here almost twice as high as in other areas of the lake. In spring, it is the lakeshore of this eastern part which shows extraordinarily high standard deviations while in summer the variability tends to be higher

Fig. 4. Linear regression through zero of satellite vs. in-situ data (SHL2). Right: calibrated data. N = 46, RMSE after calibration = 2.3 mg m−3. R2 = 0.62. Error bars show the standard deviation of the 3 × 3 pixel kernel.

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Fig. 6. Chlorophyll-a variability represented as the mean (bCHLN, left) and the standard deviation (SD CHL, right) of the lognormal distribution of each pixel for ten years of data (top), and separately for spring, summer (middle) and autumn (bottom). Consider the different colour scales for the three seasons, which allow a better recognition of the patterns.

offshore. In autumn, the Rhône area is among zones of the lower variability, while the western end of the large lake shows overall the highest CHLs concentrations. The southern part of the lake experiences stronger CHLs variations than the northern part. In summer, however, this phenomenon is less pronounced. The areas west of Thonon-lesBains and east of Evian-les-Bains are generally of increased concentration variability. We found that subtracting the linear trend of the time-series from 2003 to 2011 first, and then estimating the standard deviation per pixel, did slightly reduce the temporal variability of Fig. 6 in zones with negative CHLs trend, while zones with positive trend were slightly pronounced. The delimitation of more or less variable areas, however, remained the same. We thus assume that the standard deviation is a good indicator for the variability, being not significantly influenced by the trend of a data-series. 4.3. Temporal sampling frequency The annual variability of the phytoplankton abundance is high. Depending on the year, one to three peaks can be observed (Fig. 3). The maxima are not always detected by the in-situ measurements

(CHLf). Some maximum concentrations are significantly underestimated, as in spring 2009, others are completely missing, as in July 2008. Comparing in detail the timing of in-situ sampling and satellite observation in spring (Fig. 7), we can directly see that in-situ measurements show a much smoother curve of the CHL development than the more frequent satellite images. Estimations of chlorophyll variability in the lake are today mainly based on in-situ data from SHL2. The remotely sensed data indicate that these in-situ data do not realistically reflect both the real temporal variations and the spatial differences over the lake. Assuming that the satellite observations resolve the true maximum phytoplankton abundances, Fig. 8 shows the sampling frequencies necessary to detect a certain fraction of the CHLs peak in spring. The sampling frequency can thereby be adapted to the priorities of each monitoring program. E.g., finding the real maximum CHLs with a 50% probability necessitates around twelve samplings within three months (one per week). For some programmes, it might be sufficient to detect at least a certain percentage of the real maximum peak. Considering the current in-situ sampling rate of six samples within three months, we have only ~ 30% probability to measure a maximum CHLs value corresponding to at least 80% of the real maximum value.

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Fig. 7. MERIS CHLs time series offshore between Thonon-les-Bains and Buchillon (left) and in the Rhone inflow area (right) from spring 2007, 2009 and 2011 (black) and in-situ CHLf at SHL2 (grey). Both peaks and spatial differences might be missed if CHL estimations are based on one or two in-situ locations only.

4.4. Analysis of the spatial representativeness of SHL2 and GE3 The histogram bins range from the minimum to the maximum average CHLs concentration per pixel per season. In spring, the minimum and maximum CHLs values were 4.4 mg m−3 and 23.9 mg m−3, respectively. The histogram was therefore plotted between 4 and 24 mg m−3, containing 100 equally spaced bins. Both SHL2 and GE3 show slightly smaller average CHLs concentrations compared to the average lake concentration (88 resp. 82% of bCHLlake, sN, Table 3), and both locations were found to represent quite well the range of average and smaller concentrations during spring. The zones with high CHLs values, however, are not represented by either of the locations. Adding two fictive new in-situ locations, situated in the histogram-bin at a distance of 2∙SD and 4∙SD from the highest existent in-situ b CHLlocation, sN (where location refers in this case to SHL2, Fig. 9), the entire lake CHLs variability can be fully covered. These two new locations would be physically located in the eastern part of Lake Geneva (Fig. 13).

Summer average concentrations ranged from 2.3 mg m− 3 to 5.0 mg m− 3. The histogram range was therefore chosen between 2 and 5 mg m− 3, containing 50 equally spaced bins in order to obtain comparable distributions with spring in terms of elements per bin. During this season, SHL2 and GE3 have seen relatively less similar concentrations. With a mean value (bCHLSHL, sN = 102% of b CHLlake, sN) close to the average concentration of the whole lake, SHL2 covers well the concentration range of the large lake, where only a small zone at the Rhône inflow remains on the upper outside of the confidence interval (yellow zone Fig. 10, upper right map). GE3 represents here the small lake and some zones near the shore, covering almost entirely the range below the average lake concentration (bCHLGE3, sN = 92% of bCHL lake, s N). Compared to spring, the zone not covered by any of the two locations is only limited to the Rhône inflow (Fig. 12, central map). By adding one fictive new sampling location, again in the histogram-bin at a distance of 2∙SD from the highest existing bCHL location, s N (here bCHL SHL2, sN), the lake can be fully represented.

Fig. 8. Average fraction (2007, 2009, 2011) of the real maximum CHL concentration found with different sample sizes (for three months) vs. the probability to find a certain fraction of the maximum CHL concentration or higher. From the bottom, black: 2, 4 and 6 samples; blue: 8, 10 and 12 samples; red: 14, 16 and 18 samples; yellow: 20, 22 and 24 samples (left) and 20, 22 samples (right). The transparent areas show the standard deviation of the results between the different years. Example: The third black line from the bottom indicates the results for six samples within three months. E.g. for the area between Thonon-les-Bains and Buchillon (left), we have on average a probability of 0.4 (40%) to find a fraction of 0.6 (60%) or more of the maximum CHL during spring.

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Table 3 10-year average concentrations and standard deviation for the whole lake and the two in-situ sampling locations. Season

bCHLN lake (mg m−3)

SDbCHLNlake (mg m−3)

bCHLN SHL2 (mg m−3)

SD SHL2 (mg m−3)

bCHLN GE3 (mg m−3)

SD GE3 (mg m−3)

Spring Summer Autumn

7.56 3.25 5.79

1.18 0.22 0.53

6.62 3.33 5.39

8.27 3.19 6.99

6.20 2.99 5.91

7.33 3.07 5.66

During autumn, concentrations ranged from 3.3 mg m− 3 to 8.7 mg m − 3 . The histogram was therefore plotted between 3 and 9 mg m − 3, with 50 equally spaced bins. SHL2 is in autumn in the lower middle of the lake concentrations (b CHLSHL, sN = 93% of bCHL lake, s N), while GE3 shows values slightly higher than the lake mean (bCHL GE3, s N = 102% of b CHL lake, s N); this situation is the inverse of what can be observed during summer. The zone at the Rhône river inflow has no longer higher CHLs concentrations than the rest of the lake. The western end of the large lake, northwest of Thonon-les-Bains, is now the only area which is not realistically represented by SHL2 and GE3 (Figs. 11 and 12). Adding one fictive sampling location with an average CHLs concentration of the histogram-bin at a distance of 2∙SD from the highest existing bCHLlocation, sN (here bCHLGE3, sN), the lake is fully represented. Fig. 12 provides a combination of the two existing in-situ sampling locations SHL2 and GE3. This figure clearly shows the spatial representativeness of the two monitoring sites during spring, summer and autumn. In spring, around one quarter of the lake, especially the eastern part but also some areas towards Evian and Thonon-les-Bains, are not

included in the concentration range of SHL2 and GE3. In summer, this area is much more restricted to the immediate surroundings of the Rhône River inflow in the east. In autumn, the Rhône area is not affected anymore and CHLs concentrations out of the range of SHL2 and GE3 are only found in the area north-west of Thonon-les-Bains. Pixels which are contained in the histogram-bin with the average concentration of the fictive locations NewPoint1 and NewPoint2 are possible appropriate additional sampling locations. Fig. 13 shows their respective position for spring (a), summer (b) and autumn (c).

5. Discussion The R2 = 0.62 correlation between in-situ and satellite CHL estimates (Fig. 4) is higher than the range of correlations obtained with several other lakes using the FUB/WeW algorithm with default conversion coefficients (Duan et al., 2012; Odermatt et al., 2012a). This confirms that the algorithm is more suitable for use with oligo- to mesotrophic lakes, as opposed to eutrophic lakes, where bio-optical

Fig. 9. Distribution of average CHL concentration per pixel in spring with black line indicating the average CHL at the existent in-situ sampling locations SHL2 and GE3 (marked as black cross in the maps) as well as two fictive new points (locations in Fig. 13 a)) to cover the higher concentration range. Red zones are in between the confidence interval of ±SD, blue zones are below, yellow zones above the interval.

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Fig. 10. Distribution of average CHL concentration per pixel in summer with black line indicating the average CHL at the existent in-situ sampling locations SHL2 and GE3 (marked as black cross in the maps) as well as one fictive new point (location in Fig. 13 b)) to cover the high concentration range. Red zones are in between the confidence interval of ±SD, blue zones are below, yellow zones above the interval.

properties may increasingly exceed the algorithm's training range (Odermatt et al., 2012b). The validation results in this paper are improved regarding three significant aspects in comparison to the results for Lake Geneva in

Odermatt et al. (2010). First, image pre-processing for the current study is completely automatized using recently developed flagging procedures, as opposed to manual image quality checking before. Second, the improved pre-processing and the extended length of the

Fig. 11. Distribution of average CHL concentration per pixel in autumn with black line indicating the average CHL at the existent in-situ sampling locations SHL2 and GE3 (marked as black cross in the maps) as well as one fictive new point (location in Fig. 13 c)) to cover the high concentration range. Red zones are in between the confidence interval of ±SD, blue zones are below, yellow zones above the interval.

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Fig. 12. Represented zones for SHL2 and GE3 combined. Top: spring, middle: summer, bottom: autumn. All well represented pixels (red) are either due to assignment with SHL2 or GE3 (OR condition). Zones in blue must be below the confidence interval for both points (AND condition), zones in yellow must be above the confidence interval for both points (AND condition).

MERIS time series allowed to reduce matchup tolerance from three days to one day, providing a much better representation of the temporal scale of relevant aquatic processes. Third, and taking into account the two previous improvements, a recent algorithm round robin study (Odermatt et al., in preparation) revealed that FUB-WeW products agree better with in-situ measurements than those derived with the previously applied ICOL-C2R procedure. Blanc et al. (1993) have also examined the representativeness of SHL2, considering spatial and temporal aspects. The study was based on a field campaign of a total of 70 profiles distributed over twelve days

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during spring, summer and autumn 1991 and 1992. They concluded that SHL2 represented well the seasonal variability of physico-chemical parameters. Biological phenomena related to phytoplankton and zooplankton production, however, could have quantitative differences and temporal shifts when compared to other parts of the lake (Bergamino et al., 2010; Blanc et al., 1993). A bimonthly sampling frequency would therefore not necessarily allow the detection of the development peak of phytoplankton, especially in spring, where the evolution of the CHLf concentrations should rather be investigated at a daily scale. Also, during this season, parts of the lake closer to the shore showed higher peaks than the lake centre. Hence, SHL2 might not capture the highest maxima of CHLf concentrations (Blanc et al., 1993). The present study using satellite images confirms and quantifies these findings. The strong spatial variability in spring would be better monitored with two additional in-situ locations, while using a bi-monthly sampling rate may detect the absolute peak with a chance of only ~ 30%. Kaiblinger et al. (2009) showed that four sampling dates per year are, from a statistical point of view, sufficient to roughly characterize the quality status of a perialpine lake, if using the right metric. However, from an ecological point of view, the best results are obtained with a high sampling frequency. Finger et al. (2013) state that model-based integration of measured monthly carbon assimilation provides significant and reliable annual production estimates. Chlorophyll peak detection, however, as shown in this study, seems to need a much narrower temporal sampling frequency. The maps shown (Figs. 5, 6, 9, 10, 11 and 12) have some common pattern concerning the areas of the lake that differ from the average. It strikes that zones with a high standard deviation of CHLs are among those with the strongest decline in CHLs, such as the area between Thonon-les-Bains and the north shore towards Buchillon as well as the zone just north of Evian. At the same time, these areas have an elevated average CHLs concentration. The area around the Rhône inflow shows overall the highest average CHLs concentrations, especially in spring and summer, and also the highest variability. The CHLs evolution over the nine observed years, however, does not indicate a uniform pattern in this region, where the lakeshore is among the only areas tending towards a positive development of CHLs concentration. During autumn, the Rhône has no visible influence anymore. The river intrudes during that season in around 20 m depth (Halder et al., 2013) and is therefore no longer bringing nutrients into the euphotic surface layer. Nutrient concentrations throughout the lake are now lowest regarding the whole year and their input from any source (recirculation, sewage treatment plant discharge, upwelling, etc.) can cause algal growth. It remains to be

Fig. 13. Possible locations for new in-situ sampling (black dots) a) for spring (NewPoint1 and NewPoint2), b) for summer (NewPoint1) and c) for autumn (NewPoint1), with the lake surface in grey.

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investigated why the area north-west of Thonon-les-Bains is highly affected during autumn. Overall, CHLs peaks in autumn are less regular than in spring or summer (Fig. 3). Therefore, a strong peak in a specific year might have a large influence on the obtained result. The river inflows of Venoge, Dranse and Rhône all lie in areas with a high CHLs variability (Fig. 6), but with different effects on the 10-year evolution. This seems plausible given that nutrient loading from river inflows has a high influence on phytoplankton growth. There is, however, no clear scheme, of how the river development (decrease of nutrient concentration, discharge volume, etc.) affects local phytoplankton abundance over time. There are in fact large regions of the lake showing high average concentrations and high variability, despite the absence of river inflows. Analysing the outflowing fluxes from 16 WWTPs (Allamand, Coppet, Cully, Founex, Gland, Lausanne, Lutry, Mies, Montreux, Morges, Nyon, Prangins, Pully, Rolle, St-Prex and Vevey) along the northern lakeshore from 2002 to 2012 (without 2005), we can find an average decrease in total phosphorus of ~20% and in orthophosphates of ~34%. The computation is based on regular-operation outflow and stormwater overflow discharge, as well as total phosphorus and orthophosphate concentrations. Despite the input of nutrients, there is no evidence of higher phytoplankton abundance in pixels around the inflows of WWTP discharge. This may be due to the inflow into deeper layers which limits their influence on local algal growth. It may also be due to the coarse spatial resolution of the MERIS images and the exclusion of the flagged shore pixels. A better resolution especially at the lakeshore is necessary to examine the WWTP impact in more detail. Also the in-situ concentrations of phosphate measured in the top 5 m at SHL2 diminished by ~ 27% from 2002 to 2010, equal to the observed satellite-based CHL trend. Several points encourage confidence in this general CHLs trend, such as the fact that there are about the same number of observations in each year (Fig. 2), i.e. the likeliness to miss seasonal peaks is similar in each year. Even if absolute peaks might be missed, the high number of observations should be sufficient to quantify the average concentration changes (total number: ~ 5000 valid pixels with each ~ 300–600 individual observations). The vast majority of the individual lake pixels show a negative trend, reinforcing the overall negative change. Furthermore, the trend agrees with a decrease in nutrient abundance from 2002 to 2010. The fact that below the surface layer CHLf was increasing (Tadonléké et al., 2009, data until 2005) is consistent with the observation that the productive layer is thinning out and moving down. Also, observing only a short period of time, significant non-linear effects would be difficult to quantify, as seasonal variations are very high. Monod et al. (1984) have argued that a station in the middle of the large part of the lake would be most suitable for studying the trophic evolution of Lake Geneva, as it is not directly influenced by local human activity including nutrient transport from anthropogenic sources. The present study confirms that SHL2 is weakly representative of coastal areas and zones of influence from the rivers and therefore a good indicator of the offshore part of Lake Geneva. However, the lake ecosystem monitoring should also consider the spatial variations of phytoplankton growth in zones which are under anthropogenic influence and where high phytoplankton abundance may also cause problems for recreational activities. We have shown that these zones are among the most variable and productive in terms of CHLs. The methods presented in this article have two major limitations. The first one concerns the analysis of the sampling frequency. Even though the satellite observations are far more frequent than the existing in-situ sampling, the rate is currently not sufficient to capture the real algal dynamics (Van der Zande et al., 2011). The three years with the shortest temporal intervals between measurements (2007, 2009 and 2011) have on average one satellite observation every three days. Phytoplankton concentrations, however, often vary significantly even on a daily scale. In addition, the method is strongly dependent on the total number of observations as well as the sampling frequency just around the peak. We can see in Fig. 7 that the CHLs development curve often

depends on one or two values defining the peak, while time steps between measurements are not uniform. This leads to the linear evolution of the probability of exceedance for the high fractions on the x-axis in Fig. 8, as intermediate CHLs concentrations are not measured or much less frequently. E.g., in the Rhône delta region the next smaller value after the peak is only at 55% of the maximum concentration for the observed years. Overall, we might assume slightly better results, as the resampling was done randomly which is not the case in reality. In order to answer precisely the question of the necessary sampling frequency for peak detection, however, a sampling campaign on a daily basis in spring for two to three years might be inevitable. The second limitation concerns the estimation of the CHLs content, which is restricted to the penetration depth of the light received by the satellite sensor, corresponding approximately to the Secchi depth. In areas where the phytoplankton development occurs at deeper layers, CHL may not be detected by remote sensing (Kutser, 2004). In fact, although we have found an overall negative trend in phytoplankton concentration at the surface, Lake Geneva shows a positive CHLf development in depths below 10 m (Tadonléké et al., 2009, data until 2005), due to a deepening of phytoplankton growth (Rimet, 2013). This occurs as a response to the modification of the vertical profile of phosphorus concentration and light attenuation during the course of reoligotrophication (Anneville and Leboulanger, 2001; Anneville et al., 2001; Finger et al., 2013). Such dynamics can only be determined by in-situ measurements. Despite these restrictions, our method offers a simple approach for the analysis of the representativeness of present and future sampling locations and frequencies. On a lake without regular in-situ sampling, it indicates where to set the sampling location(s) and how often measurements are necessary in order to capture most of the variations of phytoplankton concentration. 6. Conclusion In-situ sampling of chlorophyll-a (CHL) is costly and timeconsuming. Moreover, algal peaks might be missed either spatially or temporally due to the fast changing algae growth, especially in spring. The careful determination of in-situ locations and necessary sampling frequency is therefore crucial for a coherent and comprehensive overview of the chlorophyll dynamics in a lake. Using MERIS satellite images, the present article showed a convincing method to analyse the representativeness of an in-situ sampling location. Utilizing the advantage of having a full spatial coverage of the lake, we have shown that it is possible to divide a lake based on its CHLs (s: satellite) concentration range and thus delimit zones prone to phytoplankton growth more often or in a stronger way. For Lake Geneva, we can conclude:

(i) The analysis of the histograms for spring, summer and autumn of the Lake Geneva satellite CHLs concentrations has revealed that the existent two in-situ locations are well representing the lake during summer. In spring and autumn, however, additional sampling locations are necessary in order to capture the whole range of CHL concentrations. In spring, this concerns especially the eastern part of the lake. In autumn, the western part of the large lake is underrepresented. (ii) The actual temporal frequency of in-situ measurements at SHL2 detects its maximum CHL peak with a rather small probability. In order to capture at least 70% of the peak's value with a probability of 70%, it would be necessary to implement at least weekly in-situ measurements from March to May, or, of course, use remotely sensed estimates to increase the sampling frequency. (iii) The satellite images clearly indicate that the regions of highest CHLs concentrations and variability are related to large river inflows which carry WWTP inputs and agricultural runoff.

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(iv) Over the last decade (2002 to 2012) the CHLs surface values on Lake Geneva have on average decreased by ~ 27% which is in striking agreement with the phosphates decrease at the lake surface of the same range of ~27% (2002 to 2010). Envisat MERIS full resolution data covers the years 2002–2012 and provides a valuable data archive for any lake large enough to be covered by a substantial number of pixels, i.e. usually a few km2. Stakeholders can make use of this data to either examine existing in-situ monitoring locations concerning their representation of the lake, or to plan a new monitoring program if the lake is not yet surveyed. The two limitations we identified correspond to the temporal resolution and the vertical penetration depth of spaceborne remote sensing. The former will to a large extent be resolved by ESA's Sentinel-3 satellites (ESA, 2012), which will provide daily revisit frequency. Cloud coverage will significantly decrease the number of applicable observations, leaving the assumption that phytoplankton growth peaks during cloud-free conditions. The vertical penetration depth of solar illumination as resolved by passive optical remote sensors on the other hand will remain a natural limitation, and constitutes the ultimate constraint to further elaborate the synergies of remote sensing and in-situ measurements in the near future. Acknowledgements The authors are grateful to all the people who have contributed to the long-term in-situ data set of Lake Geneva and to CIPEL for kindly providing the data; to the “Service de l'Écologie de l'Eau” of the Canton of Geneva for providing the chlorophyll-a data from GE3; and to Devis Tuia for his valuable advice on satellite image analysis. Funding for the first author war provided by the Swiss Federal Institute of Technology (EPFL), Lausanne. Appendix A, B and C. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2015.05.011. References Anneville, O., Leboulanger, C., 2001. Long-term changes in the vertical distribution of phytoplankton in the deep Alpine Lake Geneva: a response to the reoligotrophication. Atti Assoc. Ital. Oceanol. Limnol. 14, 25–35. Anneville, O., Ginot, V., Angeli, N., 2001. Evolution de l'état de santé du Léman évaluée par l'analyse des séries chronologiques du phytoplankton. Campagne 2000. Commission Internationale pour la Protection des Eaux du Léman, pp. 161–189. Anneville, O., Ginot, V., Angeli, N., 2002. Restoration of Lake Geneva: expected versus observed responses of phytoplankton to decreases in phosphorus. Lakes Reserv. Res. Manag. 7 (2), 67–80. Bergamino, N., Horion, S., Stenuite, S., Cornet, Y., Loiselle, S., Plisnier, P.-D., et al., 2010. Spatiotemporal dynamics of phytoplankton and primary production in Lake Tanganyika using a MODIS based bio-optical time series. Remote Sens. Environ. 114 (4), 772–780. BFS, Federal Office of Statistics Switzerland, 2013. Natürliche Ressourcen — Phosphorgehalt im Seewasser. [Online] Available at, http://www.bfs.admin.ch/bfs/ portal/de/index/themen/21/02/ind32.indicator.72003.3212.html?open=703,770#702 (Accessed November 2014). Biodiversity Indicators Partnership, 2010. Biodiversity indicators and the 2010 target: experiences and lessons learnt from the 2010 Biodiversity Indicators Partnership. Secretariat of the Convention on Biological Diversity, Montréal, Canada. Blanc, P., Pelletier, J., Moille, J.-P., 1993. Variabilité spatiale et temporelle des paramètres physico-chimiques et biologiques dans l'eau du Léman. Campagnes 1991 et 1992. Commission Internationale de la protection des Eaux du Léman. Bourg, L., Etanchaud, F., 2007. The AMORGOS MERIS CFI (Accurate MERIS Ortho Rectified Geolocation Operational Software). Software User Manual and Interface Control Document. Tech. Rep. PO-ID-ACR-GS-003, Issue 3, Feb. 2007. Bouvet, M., Ramoino, F., 2010. Radiometric intercomparison of AATSR, MERIS, and Aqua MODIS over Dome Concordia (Antarctica). Can. J. Remote. Sens. 36 (5), 464–473. Bresciani, M., Stroppiana, D., Odermatt, D., Morabito, G., Giardino, C., 2011. Assessing remotely sensed chlorophyll-a for the implementation of the Water Framework Directive in European perialpine lakes. Sci. Total Environ. 409 (17), 3083–3091. Brönmark, C., Hansson, L.-A., 2002. Environmental issues in lakes and ponds: current state and perspectives. Environ. Conserv. 29 (3), 290–306.

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