this reason, the monitoring of lake water quality is ... is a good indicator for bioproduction in inland water .... culation which require adaptations in the software.
MONITORING OF LAKE WATER QUALITY USING HYPERSPECTRAL CHRIS-PROBA DATA
Sandra Mannheim, Karl Segl, Birgit Heim, and Hermann Kaufmann GeoForschungsZentrum Potsdam, Department of Geodesy and Remote Sensing, Telegraphenberg, 14473 Potsdam, Germany
ABSTRACT The CHRIS sensor, mounted on the PROBA satellite, is one of the first space-borne hyperspectral sensors offering high spatial resolution (18 m × 18 m). This, combined with the possibility of multi-temporal coverage, makes CHRIS-PROBA exceptionally well suited for lake water monitoring. MEMAMON is a project to monitor the water quality of lakes in the Mecklenburg (Germany) and Mazurian (Poland) lake districts. Both test sites contain a large number of lakes with high variability and different trophic states. This paper presents a study, which aims to determine the trophic parameter chlorophyll-a using hyperspectral CHRISPROBA data. To investigate the seasonal dynamics of lakes, CHRIS-PROBA data were acquired in spring, summer and autumn 2002 and 2003. A first analysis of the data showed that CHRIS radiance data have strong artefacts in column direction. Standard destriping techniques were not sufficient to correct the data. Therefore, a novel iterative destriping technique was developed and successfully applied to CHRIS-PROBA data. At the same time to the CHRIS-PROBA data recording, spectral field measurements and acquisition of in-situ data took place in several test-sites. Using these data, chlorophyll algorithms were developed and optimised to the spectral characteristics of the CHRIS sensor. Finally, seasonal maps of chlorophyll concentration are presented, derived from the corrected CHRIS-PROBA data.
Arbeitskreis 1998). Especially CHRIS-PROBA, with its unique possibility of multi-temporal coverage, combined with high spectral and spatial resolution, has the potential for fulfilling the requirements of these environmental directives. The trophic state is an important parameter to evaluate the water quality. One of the main parameters to determine the trophic state is chlorophyll-a in accordance to the German water directive (LAWAArbeitskreis 1998). Chlorophyll-a is a phytopigment, which is present in all algae groups in marine and freshwater systems. The chlorophyll-a concentration is a good indicator for bioproduction in inland water bodies (DIN 38 412 - Part 16; Iluz et al. 2003). Characterised on well-defined specific optical properties in the visible range, chlorophyll-a is suitable for the direct observation with optical instruments (Kirk 1994). In oligotrophic aquatic systems, chlorophylla is usually determined by the blue-green reflectance ratio such as the ratio at 440 and 550 nm (Gordon & Morel 1983). Otherwise, studies of productive marine and freshwater systems demonstrated that the back-reflected signal between wavelengths of about 670 and 740 nm allows an estimation of chlorophyll-a concentration (Ruddick et al. 2001; Thiemann 1999; Schalles et al. 1998; Yacobi et al. 1995; Gitelson 1993; Vos et al. 1986). Within this spectra range, chlorophyll information is not effected by dissolved organic matter and by other pigments. The primary aim of this study is the development of chlorophyll-a algorithms, optimised to the spectral characteristics of the CHRIS sensor. Additionally, the characteristics of reflectance spectra during different seasons in the year will be investigated.
Key words: water quality, monitoring, destriping, feature extraction, hyperspectral. 2. 1.
INTRODUCTION
The most common ecological problem of inland water bodies is the anthropogenic eutrophication. For this reason, the monitoring of lake water quality is specified and regulated by European (European Parliament 2000) and German water directives (LAWA____________________________________________________
Proc. of the 2nd CHRIS/Proba Workshop, ESA/ESRIN, Frascati, Italy 28-30 April (ESA SP-578, July 2004)
STUDY SITE
The Rheinsberg lake district is located 70 km northwest of Berlin. The project test site (Fig. 1) is a part of the lake district and covers about 300 km2 with more than 30 lakes. The landscape and its great abundance of inland water bodies is a result of glacial erosion and deposition during the last ice age Weichsel (Mietz 1996). The lakes in the test area
Table 2. Changes of chlorophyll-a concentration in selected lakes of the Rheinsberg lake district; Schwarzer See(1) and (2) are different lakes with the same name.
Figure 1. Study area of Rheinsberg lake district; Topographic map 1 : 50 000; 1: Lake Wummsee; 2: Lake Braminsee. Table 1. Acquired CHRIS-PROBA data sets. Date 2002/10/10 2003/08/09 2003/08/11 2003/04/23 2003/08/10 2003/09/26 2003/09/10 2003/09/11
Test site Mueritz Mueritz Mueritz Rheinsberg Rheinsberg Rheinsberg Mazurian Mazurian
Cloud cover [%] 10 2 0 0 0 0 60 65
have various sizes and depths and are characterised by a wide range of trophic states. For example, the oligotrophic lake Wummsee has a very low chlorophyll content. It varies from 1 to 3 µg/l in summer. The Braminsee is a hypertrophic lake and shows very high chlorophyll-a concentrations, varying from 50 to over 100 µg/l. The other lakes in the test area show intermediate concentrations. 3.
DATA BASE
During the last two years eight CHRIS-PROBA scenes were acquired. Table 1 shows the acquisition dates of different test sites and the corresponding cloud coverage. The satellite data of the Rheinsberg lake district reflect very well the seasonal lake dynamics of the water bodies. Additionally, the acquisition dates are similar to the seasonal dates of limnological field work in accordance to the German water directive (LAWA-Arbeitskreis 1998).
Period
Water bodies
spring to summer spring to summer spring to summer summer to autumn summer to autumn summer to autumn
Rheinsberger See Schwarzer See(1) Zechliner See Schwarzer See(2) Zethner See Vilzsee
Chl-a [µg/l] 25 → 16 15 → 7 3→5 14 → 17 21 → 34 17 → 21
Therefore, field expeditions for the acquisition of ground truth data took place at lake Mueritz as well as at the Rheinsberg and Mazurian lake district at the same time. The data include spectral measurements and sea truth data of chlorophyll-a (chl-a), suspended matter (SPM) and dissolved organic carbon (DOC). They show a wide variety of trophic states, from oligotrophic to hypertrophic, and different seasonal conditions (Tab. 2). The above water measurements of the surface reflectance R+ (λ) as well as the up-welling L+ up (λ) and the sky radiance Lsky (λ) were performed with an ASD field spectrometer (FieldSpec Pro). The ASD radiometer has a spectral range between 350 an 2500 nm and a radiometric resolution of about 3 nm. During the spectral measurements the sensor was adjusted with observation angles of 45◦ in zenith and 135◦ in azimuth direction. This setting minimises the effect of reflected skylight (NASA 2000). Parallel to the spectral field measurements, water samples were collected in the upper water layer for the analytical determination of chl-a, SPM and DOC. The chlorophyll concentration and the dissolved organic carbon concentration were analysed at the Institute of Applied Freshwater Ecology Brandenburg(IaGB), in accordance to the German norm standard (DIN 38 412 - Part 16). The suspended matter content was determined at the GeoForschungsZentrum (GFZ) using a gravimetric method. These sets of ground truth data are used in the following to extract the empirical relationships among the spectral measurements and water quality parameters. 4. PRE-PROCESSING For the investigation of spatial and seasonal variations of lake properties, CHRIS-PROBA data has to be geocoded and transformed to reflectance values, allowing the comparison of different scenes. However, a first analysis of the radiance data showed strong stripes in along track direction. This noise
has a magnitude of about 6 to 7 percent of the signal in some of the image bands. This means that the noise variance affects the accuracy of the classification results and therefore has to be corrected. Standard destriping techniques such as the FFT-filtering were not able to produce a reasonable result. Thus, a novel iterative destriping technique was developed which is able to deal with the specific sensor characteristics of CHRIS. The analysis of noisy image columns showed that they can be corrected by a column and band specific gain x1 and offset x0 : g ∗ = x0 + x1 · g
(1)
To calculate those values it is necessary to estimate the real grey value g ∗ for each pixel in the column. This g¯ is calculated as the mean grey value of the adjacent pixels in row direction (Eq. 2). The filter has a size of 11 pixels. 5 X
g¯r =
gr+i
i=−5
11
(2)
However, two problems arise from this mean calculation which require adaptations in the software. First, grey value extrema (Fig. 3(b)) which have no or less similar pixels in their neighbourhood have to be treated separately. This extrema have a very strong effect on the calculation of the gain and offset. Therefore, an algorithm was included which selects only spectrally similar pixels from the window for the mean calculation. This linear function was defined by a empirical investigation analysing neighbouring pixels. Second, columns without similar pixels in the window (Fig. 3(c)) have to be smoothed. Based on the total number of similar pixels within the column, an automatic differentiation takes place between sensor error and small linear objects, which may also be oriented in stripe direction. If the column is detected as a sensor artefact, 75% of the most similar pixels are used for the mean calculation. If not, the previous adaption is processed. Based on the estimated g¯ values, a least square estimation (Eq. 3) is used to calculate the gain and offset for each column and band. 1 gr1 1 gr2 x0 ~g¯ = . .. x .. 1 . 1 grj ~g¯ = A~x x = (AT A)−1 AT ~g¯ (3) Since the new grey values depend on their neighbourhood, this technique is applied iteratively (3-5 iteration) to allow for an overall good equalisation of the data. Figure 2 presents some results of the gain and offset calculation. It can be observed, that both gain
Figure 2. Calculated gain and offset values for each image column and several bands; black: band 1, red: band 2, green: band 3, blue: band 4, orange: band 5.
and offset values approach one respectively zero with increasing wavelengths. In the first bands, the modifications by the calculated gain and offset values are stronger than in last bands. The successful radiometric enhancement is exemplarily shown in Figure 3 presenting subsets of the CHRIS-PROBA scene (April 2003, band 1). The striping effect is well eliminated. The magnitude of change is about 1 to 3% for most image columns, but can also reach values up to 35% in case of the strong artefacts. Furthermore, little point objects are well preserved (Fig. 3(e)). The described radiometric correction method was applied to all CHRIS data including all bands using identical thresholds in the algorithm. After the radiometric correction, the CHRIS dataset with the best ground truth data was atmospherically corrected with the empirical line method. This correction was optimised for water. Input spectra were water reflectance spectra, which were resampled using the CHRIS specific spectral response functions. This master scene (CHRIS-PROBA scene 2003-0810) was then used for the correction of all other scenes using corresponding image spectra. The diagram (Fig. 4) shows some of the resulting CHRIS surface reflectance spectra. The water spectra show a high variability, which allows the extraction of different water parameters.
Figure 3. Radiometric enhancement of the CHRIS-PROBA scene, from April 23th 2003, spectral band 1; (a),(b),(c) before and (d),(e),(f ) after the correction 5.
AUTOMATED FEATURE EXTRACTION METHOD
Water reflectance spectra contain information about the concentration, as well as the composition of dissolved and suspended constituents in the water. This study is focused on spectral features of the reflectance spectra influenced by chlorophyll-a.
Figure 4. Selected reflectance spectra of the atmospherically corrected master scene; 1: Zethner See, 2: Vilzsee, 3: Schwarze See, 4: Zechliner See, 5: Zootzener See, 6: Wummsee.
With a set of ground control points (GCPs), taken from the topographic map 1 : 10 000, the master scene was rectified to a Gauss Krueger map projection. To preserve the boundary shape of objects a cubic convolution resampling method was used. For a perfect overlap of the multi-temporal datasets, the remaining satellite images were registered to the master scene using a first degree polynomial transformation. The resulting root mean square errors are less than ± 15 m.
For the extraction of spectral features, which are linked to specific parameters, an automatic processing method was developed. This method correlates spectral information with selected ground truth parameters. For the water application, CHRIS reflectance spectra are correlated with chlorophyll concentration. For this purpose, reflectance spectra were extracted from the CHRIS-PROBA images at the location of the water samples. During the automatic feature extraction, the spectra are gradually scanned and selected features are calculated such as ratios, absorption depths, the position of minima and maxima as well as the area above or below a baseline. This feature values are then correlated with the specific parameter of interest. Finally, the program delivers the best correlation results for each type of feature.
6.
RESULTS AND DISCUSSION
In the productive water bodies of the Rheinsberg lake district, with chlorophyll concentrations from 1 to over 100 µg/l, the reflectance peak in near infrared range (672 - 742 nm) was found to contain the best
Figure 5. Deliniation of the area above baseline for the chlorophyll-a algorithm; Note the seasonal differentiation of Chris-Proba reflectance spectra; Lake Schwarze See in summer (1a; chl-a 14µg/l) and autumn (1b; chl-a 17µg/l), Lake Zethner See in summer (2a; chl-a 21µg/l) and autumn (2b; chl-a 34µg/l). information for the estimation of chlorophyll concentration in CHRIS data. As an result of the automated feature extraction process, the area delimited by the reflectance curve and the baseline from 672 742 nm (CHRIS spectral bands 8 - 12) shows the best correlations results and the maximal sensitivity to changes in chlorophyll concentration (Fig. 5). The described peak in the near infrared is an outcome of an interaction between strong absorption by chlorophyll and water, as well as scattering by algae cells and other sestonic matter (Thiemann 1999; Schalles et al. 1998; Yacobi et al. 1995; Gitelson 1993; Vos et al. 1986). The magnitude of the peak, as well as its position, depends strongly on chlorophyll concentration and scattering by all suspended matter. With increasing biomass, the height of the peak will simultaneously increase (Yacobi et al. 1995; Gitelson et al. 1994). Otherwise, the position of the peak shifts towards longer wavelengths, with increasing chlorophyll concentration (Schalles et al. 1998; Yacobi et al. 1995; Gitelson 1993; Vos et al. 1986). The lakes in the test area are characterised by phytoplankton and its detritus as main suspended matter components. Therfore, the peak in near infrared is correlated with the chlorophyll concentration via the relationship between chlorophyll and the suspended matter of the phytoplankton biomass. Additionally, there is a seasonal trend. The quantitative accuracy of the developed algorithm is limited by varying phytoplankton populations and changes of absorption and scattering processes by water constituents (Gitelson 1993). Figure 5 shows the strong differences between reflectance spectra, which were acquired in August and September. They represent spectra of the same lakes, but show a very different
Figure 6. Seasonally differentiated regression analysis between chlorophyll-a and the best correlated spectral feature (area above baseline from 672 - 742 nm); 1: summer; 2: spring and autumn; R2 is determination coefficient.
Table 3. Seasonal regression statistics between chlorophyll-a and the best correlated spectral feature - (area above baseline from 672 - 742 nm); Chl-a = a + b · A; R2 is determination coefficient. Period spring\autumn summer
a 0.98 0.99
b -115.05 -42.60
R2 0.97 0.95
spectral behaviour related to the chlorophyll content. In summer, the magnitude of the entire reflectance spectra is higher than in autumn. This also applies to the peak in the near infrared. The reason is the seasonal lake dynamic, i.e. different phytoplankton compositions and amounts of degradation products (Schwoerbel 1994). Therefore, two specific algorithms for summer as well as for spring and autumn were developed to improve the quality of chlorophyll quantification. Figure 6 shows the seasonal differences in the linear relationship between chlorophyll-a and the evaluated spectral features. The seasonal correlation of corresponding spectral features, against chlorophylla concentrations yielded high correlation coefficients (see Fig. 6 and Tab. 3). In Table 3, the regression coefficients are listed. Note, that the correlation coefficients of both regressions have similarly values. On the basis of these two algorithms, seasonal maps of chlorophyll concentration were produced, derived from the CHRIS-PROBA data of April, August and September. Figure 7 shows subsets of the extracted chlorophyll maps. The calculated chlorophyll-a values are classified in accordance to the limnological classification of the German water directive (LAWAArbeitskreis 1998).
Figure 7. Seasonal chlorophyll-a maps; Subsets of Rheinsberg CHRIS-PROBA data; (a) April, (b) August, (c) September. 7.
CONCLUSIONS
The primary aim of this study is the development of chlorophyll-a algorithms, optimised to the spectral characteristics of the CHRIS sensor. Especially for water applications, CHRIS-PROBA data have to be pre-processed to eliminate the strong stripes in along track direction. To remove these noise variances, a novel iterative destriping technique was developed. The radiometrically corrected CHRIS-PROBA data have been atmospherically corrected with an empirical line method. An automated feature extraction method was used for the development of chlorophyll-a algorithms from CHRIS-PROBA data. This method correlates spectral information with selected ground truth parameters. Reflectance spectra, which have been extracted from CHRIS-PROBA data and corresponding chlorophyll-a concentrations are the input parameters. As an result of the automated feature extraction process, the area delimited by the reflectance curve and the baseline from 672 to 742 nm (CHRIS spectral bands 8 - 12) shows the best correlation results and the maximal sensitivity to changes in chlorophyll concentration. However, the quantitative accuracy is limited by varying phytoplankton compositions and varying amounts of degradation products. Therefore, two specific algorithms for summer as well as for spring and autumn have been developed to improve the quality of chlorophyll quantification. The correlation coefficients of both regressions show similar high values. On the basis of these two algorithms, seasonal maps of chlorophyll concentration have been produced. This data show the spatial and the seasonal variations of chlorophyll. Finally, it can be concluded, that CHRIS-PROBA has the potential for monitoring of the trophic pa-
rameter chlorophyll-a with high accuracy. Particularly, the high spatial resolution of the sensor allows the water monitoring of small lakes, such as in the Rheinsberg lake district. ACKNOWLEDGEMENTS We thank ESA and Sira Electro-optics Ltd. for the status of authorised research user of CHRIS-PROBA data and for the production as well as distribution of the satellite data. Special thanks go to Peter Fletcher for the good and fruitful cooperation. We are grateful to the Institute of Applied Fresh Water Ecology in Brandenburg for the determination of chlorophyll-a and dissolved organic carbon. REFERENCES DIN 38 412 - Part 16, 1986, Testverfahren mit Wasserorganismen (Gruppe L) - Bestimmung des Chlorophyll-a-Gehaltes von Oberfl¨achenwasser European Parliament, 2000, EU Water Framework Directive, Directive 2000/60/EC, Tech. rep., European Parliament and the Council of the European Union Gitelson A., 1993, Optical Engineering and Remote Sensing, SPIE 1971 Gitelson A., Mayo M., Yacobi Y., 1994, In: ISPRS 6th International Symposium Gordon H., Morel A. (eds.) 1983, Remote Assessment of Ocean Color for Interpretation of Satellite Visible Imagery. A Review, Springer-Verlag Iluz D., Yacobi Y., Gitelson A., 2003, Int. J. Remote sensing, 24 Kirk J. (ed.) 1994, Light and Photosynthesis in Aquatic Ecosystems, Cambridge University Press
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