Preliminary optical classification of lakes and coastal waters in Estonia and south Finland

Journal of Sea Research 49 (2003) 357 – 366 www.elsevier.com/locate/seares

Preliminary optical classification of lakes and coastal waters in Estonia and south Finland Anu Reinart a,b,*, Antti Herlevi c, Helgi Arst b, Liis Sipelgas b a Department of Limnology, Uppsala University, Norbyva¨gen 20, S-752 36, Uppsala, Sweden Marine Systems Institute at Tallinn Technical University, Paldiski Road 1, 10137 Tallinn, Estonia c Department of Physics, University of Helsinki, P.O. Box 64 (Gustaf Ha¨llstro¨minkatu 2), FI-00014 Helsinki, Finland b

Received 26 March 2002; accepted 13 September 2002

Abstract A preliminary optical classification of lakes in Estonia and south Finland which can also be used for small bays of the Baltic Sea is elaborated. The classification is based on the optical properties of water (diffuse attenuation coefficient, diffuse reflectance) and parameters that are routinely monitored in water bodies (Secchi depth, concentration of chlorophyll-a, total suspended matter and yellow substance). The data complex used for our classification covers different types of water ecosystems (ranging from oligotrophic to hypertrophic) and the variability of water constituent concentrations in the ice-free period in Estonia and south Finland. Using cluster analysis, we found 5 optical classes of waters: clear (C), moderate (M), turbid (T), very turbid (V) and brown (B). There is satisfactory correspondence between class of water, shape of diffuse attenuation coefficient and diffuse reflectance spectra and trophic state of the lakes. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Water quality; Optical properties; Fresh and brackish water

1. Introduction The optical properties of natural waters are important for many factors such as primary production, species composition of the phytoplankton, the depth distribution of submerged macrophytes, the heat budget of water bodies, and for monitoring of water quality and interpretation of optical remote sensing data. Classification of water helps to clarify relation-

* Corresponding author. Department of Limnology, Uppsala University, Norbyva¨gen 20, S-752 36, Uppsala, Sweden. E-mail address: [email protected] (A. Reinart).

ships between different properties inside a certain class and quantify variations between classes. Based on different parameters, different versions of optical classification have been elaborated by numerous authors (Jerlov, 1976; Morel and Prieur, 1977; Kirk, 1980; Prieur and Sathyendranath, 1981; Baker and Smith, 1982; Vertucci and Likens, 1989; Kaczmarek and Wozniak, 1995). According to the typical classification used in remote sensing studies, Case 1 represents the phytoplankton-dominated waters, and Case 2 represents all other possible water bodies. In Case 1 waters, simple algorithms to retrieve pigment concentrations should give global application, but for Case 2 waters the use of site-specific algorithms is necessary

1385-1101/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S1385-1101(03)00019-4

358

A. Reinart et al. / Journal of Sea Research 49 (2003) 357–366

(Doerffer et al., 1999). Lakes and coastal waters are typically influenced by optically active organic and mineral particles and dissolved organic matter carried to the water by rivers from the surrounding land or resuspended from the lake bottom. However, the specific optical properties of these constituents are not always well known (IOCCG, 2000). Therefore the classifications made for marine waters are not applicable to optically complex waters in general. Instead of looking for a general classification valid over wide ranges of ocean, sea and lake waters, classification algorithms optimised for local conditions have been found (e.g. Eloranta (1978) for Finnish lakes; Austin and Petzold (1986) for oceanic waters; Tshekhin (1987) for Russian lakes; Koenings and Edmundson (1991) for Alaskan lakes). The purpose of our study was to classify the set of Estonian and southern Finnish lakes placed in glacial drift deposits in the Northern Hemisphere (57j –65j N, 22j –28j E) relying on their optical properties (diffuse attenuation coefficient, diffuse reflectance) and parameters, often collected as part of the routine monitoring of lakes (Secchi depth, concentration of chlorophyll-a, total suspended matter and dissolved organic matter). This classification can also be applied to the waters in small shallow bays of the Baltic Sea, which are typically influenced by strong river inflow and sediment resuspension. These waters are enriched

with dissolved organic and suspended matter and optically comparable with lakes. The methodology presented here can be applied to Case 2 water in other locations because it allows automatic and objective classification using data describing the water constituents and underwater light field.

2. Material and methods We have used the data of 10 Estonian and 5 Finnish lakes collected during ice-free periods (mostly in spring or early summer and in August – September) in 1994 – 1999 (Table 1). The area investigated is shown in Fig. 1. The data used for our classification cover different types of water ecosystems (ranging from oligotrophic to hypertrophic) and the variability in concentrations of water constituents found the region. Water samples were analysed by determining the following parameters:


total suspended particle concentration, Cs (dry weight after filtrating water through cellulose acetate Sartorius filters, pore diameter 0.45 Am), units g m 3.
Chlorophyll-a concentration (together with phaeophytin), Cchl (seston collected on Whatman glass

Table 1 Minimum and maximum values of OAS and Secchi depth (ZSecchi) for the studied Estonian (E) and Finnish (F) lakes (by measurements in 1994 – 99) Number in map (Fig. 1)

Lake

Cchl (mg m 3)

Cs (g m 3)

af(380) (m 1)

ZSecchi (m)

13 2 4 10 6 7 5 11 3 8 14 1 12 15 9

Nohipalu Mustja¨rv* (E) Valkeakotinen (F) Tuusulanja¨rvi (F) Vo˜rtsja¨rv (E) ¨ lemiste (E) U Uljaste (E) Lammi Pa¨a¨ja¨rvi (F) Verevi (E) Vesija¨rvi (F) Kurtna No˜mmja¨rv (E) Nohipalu Valgja¨rv (E) Pa¨ija¨nne (F) Koorku¨la Valgja¨rv (E) Paukja¨rv (E) ¨ ntu Sinija¨rv* (E) A

1.7 – 46.5 7.8 – 8.4 7.8 – 67.2 25.0 – 69.3 13.0 – 73.6 3.1 – 45.8 3.3 – 11.1 4.4 – 28.4 1.7 – 26.0 1.7 – 3.3 1.5 – 17.0 1.3 – 1.7 2.3 – 11.5 2.1 – 5.9 0.3 – 0.8

2.0 – 16.0 3.0 – 10.0 12.0 – 37.5 5.0 – 145* 8.0 – 34.0 3.0 – 16.5 1.5 – 5.2 1.8 – 10.3 1.5 – 5.8 1.5 – 10.0 1.5 – 6.7 0.7 – 1.8 2.8 – 3.4 3.1 – 6.2 2.0 – 7.2

38.4 – 84.8 14.8 – 19.2 6.1 – 17.3 5.5 – 9.3 3.4 – 17.0 5.0 – 10.4 8.2 – 14.2 4.6 – 7.8 2.0 – 4.0 2.3 – 8.0 1.5 – 4.2 4.2 – 5.4 1.5 – 6.0 0.5 – 1.3 0.7 – 2.7

0.4 – 0.8 0.8 – 1.1 0.4 – 0.9 0.2* – 1.0 0.4 – 1.8 1.1 – 3.4 1.6 – 3.0 1.5 – 3.8 1.2 – 3.7 2.5 – 4.5 3.5 – 6.5 3.5 – 5.9 2.9 – 4.8 4.7 – 5.5 >7* m

* Data not used in statistical analysis.

A. Reinart et al. / Journal of Sea Research 49 (2003) 357–366

359

Fig. 1. Location of investigated water bodies in Estonia and south Finland: M- Muuga Bay, P-Pa¨rnu Bay; numbers refer to Table 1: 1-Pa¨ija¨nne, ¨ ntu Sinija¨rv, 10-Vo˜rtsja¨rv, 11¨ lemiste, 7-Uljaste, 8-Kurtna No˜mmja¨rv, 9-A 2-Valkeakotinen, 3-Vesija¨rvi, 4-Tuusulanja¨rvi, 5-Lammi Pa¨a¨ja¨rvi, 6- U Verevi, 12-Koorku¨la Valgja¨rv, 13-Nohipalu Mustja¨rv, 14-Nohipalu Valgja¨rv, 15-Paukja¨rv.

fibre filters (GF/C); pigments extracted with ethanol and later analysed by the standard spectrophotometric Lorenzen method), units mg m  3.
amount of optically active dissolved organic matter (characterised by beam attenuation coefficient of (GF/C) filtered water at 380 nm, measured by Hitachi U1000 spectrophotometer (Oriola oy, Espoo, Finland)), units m 1. Its value is close to the absorption coefficient of dissolved organic

matter, af(380), as the scattering in filtered water is very small. Water samples were taken from different depths and averaged values of concentrations were calculated over the water column where the optical measurements were made. In Lake Verevi only the upper 4 m layer was investigated because there is a strong permanent stratification of water and below 4 m a thick layer of bacteria occurs.

360

A. Reinart et al. / Journal of Sea Research 49 (2003) 357–366

The values of Cchl varied from 1.3 to 73 mg m 3, Cs from 0.65 to 37 g m 3 and af(380), from 0.53 to 19.2 m 1 (except Nohipalu Mustja¨rv, where it was much higher). Water transparency by Secchi disk depth (ZSecchi) varied from 0.4 to 7 m (Table 1). Simultaneously with water sampling, solar irradiance was measured. The spectral distribution of underwater irradiance was measured using a LI – 1800 UW spectroradiometer, measuring in the range of 300 –850 nm, with a resolution of 2 nm (W m 2 nm 1). Changes in underwater irradiance due to variation of incident irradiance (caused by cloud cover) were taken into account following the method of Virta and Blanco-Sequeiros (1995), using simultaneously recorded air pyranometer LI-200 SA (range 400 – 1100 nm) data. Downwelling (Ed) irradiance was measured lowering the device to depths of 0.5 m, 1 m, 1.5 m etc., until there was not enough light for reasonable measurements. Upwelling (Eu) irradiance was measured by turning the device face down and measuring at the same depths. Four series at each depth were averaged. Irradiance over photosynthetically active radiation (PAR, 400 – 700 nm) was calculated from spectral irradiance measurements at each depth: Ed;u ðPARÞ ¼

Z

700

Ed;u ðkÞdk

ð1Þ

400

where k is wavelength. Diffuse attenuation coefficient Kd(k) is defined as: Kd ðkÞ ¼ 

1 AEd ðz; kÞ Ed ðz; kÞ Az

Spectral diffuse reflectance (R) was calculated as the ratio of up- and downwelling irradiances: RðkÞ ¼

Eu ðz; kÞ Ed ðz; kÞ

ð3Þ

To obtain the value of diffuse reflectance for the PAR region, RPAR, irradiances were first integrated over PAR (Eq. (1)) and then Eq. (3) was applied. K-means clustering (Statistica, StatSoft, 1995) was applied to distribute lake water into different classes. This technique is described in Appendix A. Altogether 53 complete data series were used. Even though the number of data is not large, they cover the range of optical properties and concentrations of optically active substances in the region investigated (Arst et al., 1999; Herlevi, 2002). The measurement series were not used if any of the parameters were missing and/or irradiance measurements were of doubtful quality (e.g. due to bad weather conditions: varying cloud cover and rough water surface). Two ¨ ntu Sinija¨rv and Lake Nohipalu Mustlakes (Lake A ja¨rv in Table 1) are discussed only as extreme examples and their respective data are not used in statistical ¨ ntu Sinija¨rv the water was so clear analysis. In Lake A that the whitish bottom could be seen. Therefore the estimated diffuse reflectance was influenced by the bottom rather than just the lake water. In sharp contrast, Nohipalu Mustja¨rv is a lake surrounded by bogs and its water is so rich in dissolved organic matter that the values of af(380) are much greater than those of the other lakes.

ð2Þ

From measured irradiances we estimated the depth (z) averaged spectral diffuse attenuation coefficient (Kd(k)) and integrated values for PAR region (Kd,PAR). For these calculations a similar method was used in both cases: irradiance values at any depth are fitted by least-squares to straight line on a semilog plot, the slope of which gives Kd. To make the results from different water bodies most comparable, the values of Kd were calculated using data from the 0.5– 2 m layer and only in case of very high attenuation a thinner layer was used (in lakes number 2, 4, 10 and 13 in Table 1).

3. Results and discussion 3.1. Classification by the apparent optical properties (AOP) in PAR region and by the amount of optically active substances (OAS) The criterion for including a particular type of water in a particular optical class is found by the Kmeans clustering technique. Results of this analysis are presented in Table 2. In the lakes belonging to class C (Clear) the amount of OAS is relatively small. Their waters are transparent and Kd,PAR is the smallest; RPAR is f 2%. The optical properties of the water are influenced mainly by phytoplankton pigments.

A. Reinart et al. / Journal of Sea Research 49 (2003) 357–366 Table 2 Characteristics of the five classes of Estonian and Finnish lakes Name of water type

ZSecchi [m] Kd,PAR [m – 1] RPAR Cs

[g m 3]

Cchl [mg m 3] af(380) [m 1] from all data

C

M

T

V

B

Clear

Moderate

Turbid

Very Turbid

Brown

4.39 (1.33) 0.67 (0.12) 0.017 (0.009) 1.8 (0.6) 4.8 (2.7) 2.3 (1.0) 32%

2.29 (0.63) 1.09 (0.38) 0.018 (0.012) 3.8 (2.8) 10.5 (6.6) 8.5 (3.5) 36%

0.79 (0.11) 2.14 (0.74) 0.084 (0.020) 14.7 (3.9) 30.5 (10.8) 7.6 (2.1) 19%

0.45 (0.07) 4.77 (0.03) 0.081 (0.006) 34.5 (4.2) 66.4 (1.2) 6.4 (0.3) 8%

0.901 (0.40) 3.8 (1.2) 0.001 (0.001) 5.0 (2.8) 11.8 (8.5) 20.3 (2.2) 5%

The mean values and standard deviation of different characteristics are shown for each class.

These waters are often comparable with coastal seawaters. In M (moderate) lakes the water is darkened mainly due to yellow substance, which in this class is the most important factor affecting the light field. In such lakes absorption processes are relatively more important than scattering, the values of RPAR are comparable with C lakes, but Kd,PAR is higher. Altogether 69% of all investigated lakes belonged to classes C and M. In class T (Turbid) belong turbid, but not highly eutrophic lakes. Suspended particles (both organic and mineral) cause high scattering and high RPAR values. Such lakes are shallow and therefore their suspended matter may contain a rather large amount of mineral particles from the bottom. The V (very turbid) lakes are characterised by a large amount of Cchl (>60 mg m 3), which occurs in the water during phytoplankton blooms. Type V is typical of shallow eutrophic lakes, as seen also from published data (Kirk, 1981; Dekker et al., 1995). In class B (brown) belong brownish-water humic lakes. The amount of dissolved organic matter is so high that their water appears deep brown. Kd,PAR is also very high (by our measurements it can be more than 7 m 1), but the RPAR is extremely low (less than 0.2%).

361

The last two classes should be rather typical in Estonia and south Finland, but they were actually not often sampled by us. The reason is that irradiance measurements in optically thick water are characterised by low signal to noise ratio, so that the measurements of Kd and R often contain large errors. In Koenings and Edmundson (1991) using different water-quality parameters and optical characteristics, Alaskan lakes are divided into classes Clear, Stained and Turbid, also using different water-quality parameters and optical characteristics. Among these, only three (Cchl, Kd,PAR and ZSecchi) correspond to our choice of parameters. The Alaskan lakes analysed contained much less chlorophyll (0.1 –5.6 mg m 3) than ours and the upper limit of Secchi depth (14 m) was considerably higher. Also, in their investigation the water colour was measured in Pt units. Comparative laboratory analysis of af(380) and Pt colour (Kallio, pers. comm., 2000) indicates that our lakes are often more coloured (Pt unit even more than 150) than those investigated in Koenings and Edmundson (1991) (their lakes range between 2 and 41 Pt units). They suggest the use of the parameter Kd,PAR  ZSecchi as the index of water quality. It was found that Kd,PAR  ZSecchi is highest for Stained lakes and lowest for Turbid lakes. In principle, we obtained similar results for our C, M, and T classes, but the values of Kd,PAR  ZSecchi differ from those obtained by Koenings and Edmundson (1991). For the C and T classes, Kd,PAR  ZSecchi is higher than was presented by Koenings and Edmundson (1991) (Table 3). This may be due to dissolved organic matter. The remarkable contribution of yellow substance to water transparency has been shown in previous investigations of Estonian lakes (Arst et al., 1996, 1999). The values of Kd,PAR  ZSecchi for Table 3 Lake classes and appropriate Kd,PAR  ZSecchi values determined from investigated lakes and from Koenings and Edmundson (1991) Type by present study

Mean Kd,PAR  ZSecchi

Type by Koenings and Edmundson (1991)

Mean Kd,PAR  ZSecchi

M C B V T

2.74 2.65 1.94 1.90 1.69

Stained Clear

2.70 1.96

Turbid

0.93

362

A. Reinart et al. / Journal of Sea Research 49 (2003) 357–366

class V are rather similar to those of class B, despite the fact that the processes causing high attenuation are different (mainly scattering in class V and absorption in class B). Consequently, the parameter Kd,PAR  ZSecchi is not suitable for describing conditions of low transparency in our lakes. 3.2. Comparison of optical and limnological classification In nature there exists no strict boundary defined by the water properties of different lakes, and water bodies can move from one class to another according to temporal variations in the bio-optical processes. In limnology, lakes are traditionally classified by their trophic state (Wetzel, 1983). The trophic state concept is multidimensional, involving aspects of nutrient loading, faunal and floral quantity and quality, and lake morphometry. Recognising that the optical classification of water in a particular site may vary with the season, we carried out a comparison of limnological and optical classification, relying on all the data that we had collected. The trophic state of the water bodies investigated was estimated by previous long-term investigations from published sources or from personal contacts (Huttula and No˜ges, 1998). The results shown in Table 4 demonstrate that these two classification systems correspond to each other.

Table 4 Classification of Estonian and Finnish lakes compared with limnological classification Trophic state Lake

May June July Aug Sept Oct

¨ ntu Sinija¨rv Oligotrophic A Pa¨ija¨nne Paukja¨rv Nohipalu Valgja¨rv Mesotrophic Kurtna No˜mmja¨rv Koorku¨la Valgja¨rv Lammi Pa¨a¨ja¨rvi Vesija¨rvi Eutrophic Verevi (upper 4 m) ¨ lemiste U Vo˜rtsja¨rv Hypertrophic Tuusulanja¨rvi Dyseutrophic Uljaste Dystrophic Nohipalu Mustja¨rv Valkeakotinen

C C C C M C,M M,T T T B B

C C C C,M M C M M M T T T M B -

T,V T T -

C C C C,M C,M C,M C,M C,M M,T T,V T,V T,V M B B

M C M M T,V T,V T B -

T T -

Optically clear lakes (class C) are typically oligotrophic or mesotrophic. In such lakes the amount of nutrients is relatively small (total phosphorus < 12 mg m 3) and they are deep enough (average depth >3.5 m) not to have resuspension of bottom sediments resulting from weak wind mixing. In periods when large amounts of dissolved organic matter flow into the lakes (for instance during melting of snow in spring, heavy rains and river inflow or phytoplankton growth), they can turn into moderate (class M) lakes (e.g. Nohipalu Valgja¨rv, Kurtna No˜mmja¨rv). Lake Lammi Pa¨a¨ja¨rvi has a constantly high level of yellow substance; it is a humic mesotrophic lake and its water belongs into class M. Eutrophic and hyper¨ lemiste, Vo˜rtsja¨rv, Tuusulanja¨rvi) are trophic lakes (U usually turbid and during phytoplankton bloom even very turbid (classes T and V). Dystrophic lakes belong typically to class B. Dyseutrophic (eutrophic, containing large amounts of humic substances) and shallow Lake Uljaste can be classified as type T in late summer, when the water level is low (the amount of suspended sediments in the water increases) but available light generates phytoplankton growth. 3.3. Application of water classification for coastal waters The classification presented can also be applied for coastal regions of the sea (Table 5). For this the data from two small bays in Baltic Sea are used: Pa¨rnu Bay in the west coast and Muuga Bay in the north coast of Estonia. Estonia’s biggest river, Pa¨rnu River, flows into Pa¨rnu Bay and brings much dissolved organic matter into the shallow bay. The result is a marked change in optical properties of the water from the river mouth towards the open part of the Gulf of Riga. Thus, water of types C, M, B, and T was found in Pa¨rnu Bay during two field campaigns (5 –6 August 2000 and 4– 5 June 2001). The water near the river mouth was classified as B or M, but close to the open part of the bay type C was found. In a very shallow part of the bay, where wind-derived suspended sediments probably influenced the light field, type T was determined. Muuga Bay is an example of the opposite: relatively deep (10 –70 m) and without a strong inflow. Measurements carried out there on 11– 13 August 2001 showed that all water belonged to type C.

A. Reinart et al. / Journal of Sea Research 49 (2003) 357–366

363

Table 5 Water classes in the Baltic Sea, Pa¨rnu Bay (5 – 6 Aug. 2000 and 4 – 5 June 2001) and Muuga Bay (11 – 13 Aug. 2001) Region and class

Zsecchi (m)

Kd,PAR (m 1)

RPAR %

Cchl (mg m 3)

Cs (g m 3)

af(380) (m 1)

Muuga Bay C Pa¨rnu Bay C Pa¨rnu Bay M Pa¨rnu Bay T Pa¨rnu Bay B

4–5 2.7 – 3.1 1.1 – 2.0 0.8 – 0.9 1 – 1.4

0.7 – 0.8 0.5 – 0.6 1.1 – 1.5 1.6 – 1.7 1.8 – 1.9

1.2 – 1.4 1.6 – 1.9 1.4 – 1.9 4–5 2–4

9.4 – 12.1 6.8 – 7.3 5.6 – 13.2 12 – 14 12 – 14

2.6 – 3.0 3.0 – 3.2 2.0 – 7.1 8 – 12 2–4

2.9 – 3.2 2.0 – 2.1 3.8 – 11.5 7 – 8.5 15 – 21

3.4. Spectral diffuse attenuation coefficient for downward irradiance Any increase in the concentration of optically active substances increases the diffuse attenuation coefficient and may change its spectral composition. The spectral features of investigated optical parameters were not considered in the present paper. A detailed analysis of spectral diffuse attenuation coefficient and its regular shape for the lakes investigated has been published by Reinart and Herlevi (1999). Spectra of Kd averaged over each class are presented in Fig. 2. These data were compared with Jerlov’s classification (Jerlov, 1976), which relied on the values of Kd(k) for the ocean and coastal waters. The results obtained (Fig. 2) show that many of the investigated water bodies have diffuse attenuation coefficients exceeding 2 – 8 times the most turbid coastal water (type 9 by the classification of Jerlov). Only class C waters are comparable with Jerlov’s

Fig. 2. Mean spectra of diffuse attenuation coefficients measured for all classes presented and Jerlov types 7 and 9 (Jerlov, 1976).

types 7 – 9 (Fig. 2). Thus, for most of our waters additional classes of spectral diffuse attenuation coefficients must be found. One attempt at such a classification is presented in Reinart and Herlevi (1999), where the classes analogous to C, M, and T are determined based on value of Kd at 490 nm. However, waters with a very high content of yellow substance (>20 m 1) and/or phytoplankton pigments (>60 mg m 3) (our classes B and V) cannot be successfully described using the value of Kd(490); a more complicated approach is needed. 3.5. Diffuse reflectance The irradiance reflectance just below the water surface, R, is necessary for elaborating the optical remote sensing models. Our results confirm that lakes and coastal waters are highly reflective in the green and red regions of the spectrum, but the absolute values of reflectance and its spectral shape may differ greatly. These spectra (Fig. 3) were compared with the

Fig. 3. Diffuse reflectance spectra of different types of investigated waters. Curves are marked similarly as in Fig. 2 and additionally type MB is presented. (For types T and V see the right axis.).

364

A. Reinart et al. / Journal of Sea Research 49 (2003) 357–366

classification proposed by Vertucci and Likens (1989). From our database we did not find any spectra corresponding to the extremely clear waters in their classification. However, their type 3 fits our class C spectra perfectly and type 5 can also be recognised (our class T). Our data (53%) often contain spectra that do not match any of the Vertucci and Likens types, and therefore we divided them into several new types described below. The types of reflectance spectra do not correspond exactly to the lake classes presented in chapter 3.1. because 5% of C and M type spectra can be assigned to classes other than lake classes C and M. Type C spectra have the maximum reflectance at the wavelengths between 550– 580 nm (ranging from 0.5 to 4.6%). Reflectance decreases sharply at the wavelengths on both sides of the maximum, so that its value at 500 nm is higher than at 650 nm. At E >735 nm reflectance does not vary much with wavelength, but its value is different for different spectra, being less than 0.3%. Type M spectra also have maxima at 550 –580 nm, but reflectance at 500 nm is less than at 650 nm. Some lakes in class M had a very different type of reflectance spectra, so an additional type called MB had to be introduced. These spectra were measured in Lake Lammi Pa¨a¨ja¨rvi, where the amount of dissolved organic matter is high (8– 14 m 1). They show very low reflectance (less than 1%) and have a wide maximum range of 600– 700 nm. Between 730 and 840 nm reflectance does not exceed 0.15%. Type T spectra have a reflectance maximum between 580 and 600 nm, and its shape is rather irregular. The maximum reflectance is the highest among all measured spectra (up to 11%). Remarkable is the low reflectance at 675 –680 nm that corresponds to the Chl—a absorption peak. Type V spectra are similar to type T spectra, but an additional reflectance maximum is notable at 690– 710 nm. Reflectance in the region of 750 – 850 nm may be relatively high (up to 6%). Similar reflectance spectra with two maxima were found also by Dekker (1993) in turbid Dutch lakes and rivers, and by Schalles et al. (1998) in many eutrophic lakes. Type B spectra are characterised by extremely low values of reflectance ( < 0.3%). The maximum of reflectance is shifted to the red part of the spectrum

(700 –710 nm). Lakes with type B reflectance spectra need more attention, because they are typical of wetland areas, but have so far rarely been investigated.

4. Conclusions Optical properties and water quality in coastal waters and lakes in Estonia and south Finland have been investigated. It was shown that the waters can be classified relying on the concentrations of optically active substances (chlorophyll-a, total suspended matter and dissolved organic matter), and also on the variables describing optical properties of water in the PAR region (diffuse attenuation coefficient, diffuse reflectance and Secchi depth). Five optical classes: clear (C), moderate (M), turbid (T), very turbid (V) and brown (B) have been proposed for describing the lake and coastal waters. There is satisfactory correspondence between limnological and optical classification of the investigated lakes. However, very turbid and brown lakes need more detailed investigations, both for additional field data and for measurements of specific optical properties. The water in small bays of the Baltic Sea is optically comparable with lakes rather than open ocean waters. C, M, T and B type waters were found in two bays of the Baltic Sea. The classification presented needs further extension and validation using coastal data. Clear differences between classes are also observed in the spectra of diffuse attenuation coefficient. A large number of natural waters have Kd values exceeding Jerlov’s most turbid coastal water type 9. The spectral values of Kd provide a possibility to elaborate the classification of clear and moderately turbid lake and inshore waters analogously to Jerlov’s classification of oceanic and coastal waters; however, for turbid and brown water a more complicated approach is needed. It was shown that different types of reflectance spectra could be measured in different class of water. All water types presented here are most reflective in the region of 550 –710 nm. They differ from each other by the shape and absolute values of reflectance spectra (up to 100 times).

A. Reinart et al. / Journal of Sea Research 49 (2003) 357–366

Acknowledgements We are indebted to the Estonian Science Foundation (grants 751, 1804 and 3613) and to the Academy of Finland for financial support to this investigation. We thank Ants Erm for helping in the field measurements and carrying out the laboratory analyses.

Appendix A Classification of lakes was made by K-means clustering technique. We assumed that our lake waters can be divided into five optically different classes: C (clear), M (moderate), T (turbid), V (very turbid) and B (brown). Classification was applied separately for AOP and OAS. AOP used by us for classification are diffuse attenuation coefficient, diffuse reflectance and Secchi depth. In the group of OAS belong the concentrations of chlorophyll-a and suspended matter as well as the absorption at 380 nm of filtered (the latter instead of concentration of dissolved organic mater). In analysis of variance, the variance between the groups was compared to the variance within the groups. The significance p>0.0005 for all parameters, but had the highest value for diffuse reflectance, R. This shows that broad band (PAR) value of R is not the best parameter for classification of lakes. The F factor was highest in the AOP group for Secchi depth and in OAS group for Cchl. This combination of parameters has long been in use to classify lakes by its Trophic State Index (Carlson, 1977). However, Carlson’s method does not say

365

Table 7 Cases of correct classification according to classification scores calculated by parameters presented in Table 6 % of total

C

M

T

V

B

For group M C T V B Total

of AOP (optical properties) 87.5 15 1 100 0 19 100 0 0 100 0 0 100 0 0 96.23 15 20

1 0 10 0 0 11

0 0 0 4 0 4

0 0 0 0 3 3

For group M C T V B Total

of OAS (substances) 100 17 100 0 100 0 100 0 100 0 100 17

0 0 10 0 0 10

0 0 0 4 0 4

0 0 0 0 3 3

0 19 0 0 0 21

In the first row is observed class of water, in the first column the predicted class of water.

anything about other optical properties investigated in the present paper. To determine to which class (C, M, T, V or B) a water body is most likely to belong, the classification scores Si need to be computed. Si ¼ ci þ wi1 x1 þ wi2 x2 þ wi3 x3

ðA1Þ

where i denotes the class C, M, T, V, or B; values wi1,2,3 and ci are the parameters when computing Si for each class (values in Table 6); x1,2,3 are the observed values of the variables. Parameters wi1,2,3 and ci are found separately for the apparent optical

Table 6 Parameters to calculate classification scores for lake classification according to Appendix A Water type T

V

B

Parameters wi to calculate classification scores for group of AOP 6.01 4.86 ZSecchi Kd,PAR 17.03 22.67 RPAR 74.89 32.32 Constant ci  19.87  20.86

C

M

4.79 33.37 203.44  46.37

8.78 74.91 171.30  190.72

5.24 41.76  32.36  58.35

Parameters wi to calculate classification scores for group of OAS CS 0.61 1.73 Cchl 0.18 0.50 0.97 3.04 af(380) Constant ci  3.09  20.64

3.73 1.06 4.50  64.70

7.73 1.78 6.89  217.55

3.02 0.79 5.85  70.04

366

A. Reinart et al. / Journal of Sea Research 49 (2003) 357–366

properties (Kd,PAR, RPAR, zSecchi) and for the optically active substances (Cs, af(380) and Cchl). Each case is classified as belonging to the class in which it has the highest classification score. Based on optical properties, 96% of the estimations were correct; only 2 clear lakes (4%) had been classified as moderate or even turbid (Table 7). In mis-classified lakes the amount of dissolved organic matter is large and causes a relatively high attenuation even when there is not much chlorophyll-a-containing phytoplankton. Additional data from waters rich in dissolved organic matter could facilitate the classification of a water body as brown (B). By using the amount of optically active substances all cases were classified as they were found by clustering, so the percentage of correct classification was 100.

References Austin, R.W., Petzold, T.J., 1986. Spectral dependence of the diffuse attenuation coefficient of light in ocean waters. Optical Engin. 25, 471 – 479. Arst, H., Ma¨ekivi, S., Kutser, T., Reinart, A., Blanco-Sequeiros, A., Virta, J., No˜ges, P., 1996. Optical Investigations of Estonian and Finnish lakes. Lakes and Reservoirs: Research and Management 2, 187 – 198. Arst, H., Erm, A., Kallaste, K., Ma¨ekivi, S., Reinart, A., Herlevi, A., No˜ges, T., No˜ges, P., 1999. Investigation of Estonian and Finnish lakes by optical measurements in 1992 – 1997. Proc. Estonian Acad. Sci. Biol. Ecol. 48, 5 – 24. Baker, K.S., Smith, R.C., 1982. Bio-optical classification and model of natural waters, 2. Limnol. Oceanogr. 27, 500 – 509. Carlson, R.E., 1977. Trophic state index. Limnol. Oceanogr. 22, 361 – 369. Dekker, A.G., 1993. Detection of optical water quality parameters for eutrophic waters by high resolution remote sensing. Ph.D. Thesis, Vrije Universiteit, Amsterdam. Dekker, A.G., Zamurovic-Nenad, Z., Hoogenboom, H.J., Peters, S.W.M., 1995. Remote sensing, ecological water quality modelling and in situ measurements: a case study in shallow lakes. Hydrol. Sci. 41, 531 – 547. Doerffer, R., Sorensen, K., Aiken, J., 1999. MERIS potential for coastal applications. J. Remote Sensing 20, 1809 – 1818. Eloranta, P., 1978. Light penetration in different types of lakes in Central Finland. Holarctic Ecol. 1, 362 – 366.

Herlevi, A., 2002. Inherent and Apparent Optical Properties in Finnish and Estonian lakes. Nordic Hydrol. (In press). Huttula, T., No˜ges, T., 1998. Present state and future fate of lake Vo˜rtsja¨rv. Results from Finnish-Estonian Joint Project in 1993 – 1997. Pirkanmaa Regional Environment Centre, Finland. IOCCG, 2000. Remote Sensing Ocean Colout in Coastal, and Other Optically-Complex Waters. In: Sathyendranath, S. (Ed.), Reports of the International Ocean-Colour Coordinating Croup, No. 3. IOCCG, Dartmouth, Canada. Jerlov, N.G., 1976. Marine Optics. Elsevier, Amsterdam. Kaczmarek, S., Wozniak, B., 1995. The application of the optical classification of the waters in the Baltic Sea (Case2 Waters). Oceanologia 37, 285 – 297. Kirk, J.T.O., 1980. Spectral absorption properties of natural waters: contribution of the soluble and particulate fraction to light absorption in some inland waters of south-eastern Australia. Aust. J. Mar. Freshwat. Res. 31, 287 – 296. Kirk, J.T.O., 1981. Estimation of the scattering coefficients of natural waters using underwater irradiance measurements. Aust. J. Mar. Freshw. Res. 32, 533 – 539. Koenings, J.P., Edmundson, J.A., 1991. Secchi disk and photometer estimates of light regimes in Alaskan lakes: Effects of yellow color and turbidity. Limnol. Oceanogr. 36, 91 – 105. Morel, A., Prieur, L., 1977. Analysis of variations in ocean colour. Limnol. Oceanogr. 22, 709 – 922. Prieur, L., Sathyendranath, S., 1981. An optical classification of coastal and oceanic water based on the specific absorption curves of phytoplankton pigments, dissolved organic matter, and other particulate materials. Limnol. Oceanogr. 26, 671 – 689. Reinart, A., Herlevi, A., 1999. Diffuse attenuation coefficient in some Estonian and Finnish lakes. Proc. Estonian Acad. Sci. Biol. Ecol. 48, 267 – 283. Schalles, J.F., Gitelson, A.A., Yacobi, Y.Z., Kroenke, A.E., 1998. Estimation of chlorophyll a from time series measurements of high spectral resolution reflectance in an eutrophic lake. J. Phycol. 34, 383 – 390. StatSoft, 1995. Statistica Electronic Manual. www.statsoft.com. Tshekhin, L.P., 1987. The Light Climate of Lakes. Petrozavotsk, Russia (In Russian). Vertucci, F.A., Likens, G.E., 1989. Spectral reflectance and water quality of Andirondack mountain region lakes. Limnol. Oceanogr. 34, 1656 – 1672. Virta, J., Blanco-Sequeiros, A., 1995. Correction of measured underwater spectral irradiance to the variation of some external effects with examples. Rep. Ser. Geophys. University of Helsinki 32, 93 – 100. Wetzel, R.G., 1983. Limnology, 2nd ed. Saunders College Publishing, Philadelphia. 766 pp.