hyperspectral remote sensing data from HyMap, acquired during the ..... Heblinski (DLR) for technical support and data processing, and A. Müller and S.
Spectral Discrimination of Submerged Macrophytes in Lakes Using Hyperspectral Remote Sensing Data Nicole Pinnel1, Thomas Heege1,2 and Stefan Zimmermann1 1 Limnological Institut of Technical University of Munich Hofmark 3, 82393 Iffeldorf, Germany 2 DLR-German Aerospace Center, Remote Sensing Technology Institute PO Box 1116, 82230 Wessling, Germany ABSTRACT Submerged macrophytes give important information about a lake´s trophic state and its ecosystem. Aquatic macrophytes can therefore serve as useful indicators of water pollution along the littoral zones. The spectral signatures of various macrophyte species were investigated to determine whether species could be discriminated by remote sensing. The spectral reflectance of macrophytes collected from several habitats at different lakes in South Germany were measured in the field with the RAMSES hyperspectral radiometer. Spectral variations of sunangle effects and seasonal changes were also investigated to determine the intraspecific variability. The collected specific reflectance spectra were used as basis for developing algorithms and applied to airborne hyperspectral remote sensing data from HyMap, acquired during the HyEurope flight campaign in July 2003 and June 2004. The imagery were corrected for atmospheric, airwater interface and water body effects using the physical based Modular Inversion Program (MIP). The various macrophyte taxa were classified to bottom cover classes by linear spectral unmixing combined with spectral derivative analysis. The result contains classes of small growing macrophytes (Characeae), high growing macrophytes (Potamogetae) and bottom sediments. Because of the different growth heights between the two macrophyte groups, Chara and Potamogeton could be successfully identified. Spectral discrimination to species level seems to be possible to a limited extent depending on the amount of epithetic growths, water depths and clarity, as well as the size and homogeneity of the patches. INTRODUCTION Aquatic macrophytes in the littoral zone react slowly and progressively to changes in nutrient conditions and can thus be used as long-term limnological indicators (Melzer 1999). Areas fully mapped of submerged littoral vegetation in high spatial resolution are therefore of prime importance for the ecological evaluation of the entire lake. The abundance and distribution of littoral vegetation have traditionally been measured using diver surveys, which are time consuming and limited in areal and temporal coverage. Recently, remote sensing has been offered as a possible alternative to diver surveys for the large scale inventory of benthic photosynthetic organisms such as macrophytes, seagrasses and corals (Anstee et al. 2001; Anstee et al. 1997; Bajjouk et al. 1996; Heege et al. 2003a; Heege et al. 2003b; Hochberg et al. 2003; Malthus et al. 1997). Heege et al (2003b) tested multispectral airborne scanner Daedalus AADS1268 at Lake Constance, Germany, for multitemporal analysis . Bottom reflectances could be classified to three
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endmembers of specific reflectance spectra by linear unmixing using the Modular Inversion Program (MIP). Specific reflectance spectra of bottom sediments, small growing macrophytes (Charophytes) and tall macrophytes (Potamogetae) could be successfully identified. This work discusses a further development of this method using hyperspectral data from HyMap to investigate whether macrophytes species could be further discriminated by remote sensing. The main aim of this study is to determine whether there is potential to distinguish macrophytes species in lakes by remote sensing. The spectral signatures of various species of Charophytes (stonewords) and Potamogetae were measured in the field and analysed for spectral discrimination. Variations of sunangle effects and seasonal changes were also investigated to determine the intraspecific variability. This study will also lead to the development of algorithms for macrophytes species discrimination based on spectral libraries measured in the field. Several studies have examined the use of derivative spectroscopy to classify coral reef component spectra using first and second (Clark et al. 2000; Hochberg et al. 2000; Holden et al. 2000) or higher derivatives (Hochberg and Atkinson 2000; Wettle et al. 2003). Mean spectral signatures of macrophytes species were described using first and second derivatives to determine major wavelength ranges best suited for species discrimination. The algorithms were implemented in the Modular Inversion Program (MIP), a physical based processing chain for remote sensing data over water targets (Heege et al. 2003a) and applied to hyperspectral remote sensing data from HyMap flown at Lake Constance in July 2003 and June 2004. MATERIAL AND METHODS Reflectance spectra of tall macrophyte species, Potamogetae pectinatus and P.perfoliatus, and of the small growing charophyte species, Chara contraria, C. aspera, C. tomentosa, Naja marina and Nitellopsis optusa were measured over two years 2003 and 2004 at two different Bavarian Lakes. Reflectances from homogeneous macrophytes patches were sampled during the main growing season from June to August to assess the range of spectral variability being found in each species. The selection of macrophytes species were made according to abundance and distribution, size and homogeneity of the patches as well as existence of species type in both lakes. Measurements were carried out with a RAMSES spectroradiometer measuring upwelling radiance Lu and Irradiance Eu as well as downwelling irradiance Ed simultaneously. Subsurface irradiance reflectance R (0-) just below the water surface and bottom reflectances were measured above homogeneous patches of macrophytes and sediment. Spectral averaging of 10–15 individual spectra per sample was performed to produce a single aggregate reflectance and to ensure optimal signal-noise ratio. Water samples were collected at each test site and filtrated on the boat to retrieve concentrations of water optical properties phytoplankton, total suspended matter and coloured dissolved organic matter. First and second derivative analysis was used to analyze the relative pure
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reflectance spectra of different species types indicating distinct wavelength ranges useful for spectral species discrimination and algorithm development. The algorithms for macrophytes species discrimination were tested on the hyperspectral imagery of the airborne sensor HyMap using the Modular Inversion Program (MIP). In MIP, calibrated airborne data were processed, based on an automated physical interpretation of hyperspectral radiances. MIP provides an automated adjustment of the algorithms and the radiative transfer background to the varying flight conditions and sensor specifications. Different modules support transferable algorithms that derive biophysical parameters from the measured radiance signal at the sensor. Inverted parameters include e.g. aerosol concentrations, the concentration of water constituents and the reflectance characteristics of substrates in shallow waters (Heege 2000; Heege et al. 2000; Heege et al. 2004). Here, we used some new modules for the estimation of aerosol concentrations and a water body correction coupled with lake bottom classification. The radiative transfer modules and database system for a coupled, plane-parallel atmosphere-ocean system was implemented in MIP by V. Kisselev and is based on the Finite Element Method (Bugarelli et al. 1999) . Here, it is used for the atmospheric-, water surface- and Q-factor-correction of the underwater light field in the same manner as explained in (Heege et al. 2004) The module for the simultaneous retrieval of aerosol concentrations and water constituents from S. Miksa (see her conference contribution in this issue) was not yet used here. Instead, the aerosol concentrations were calculated by automatic adjustment of atmospheric corrected airborne spectra to subsurface irradiance reflectance measurements in shallow water regions. By knowledge of the aerosol concentrations, we retrieve the subsurface irradiance reflectance R– using the atmospheric- and water surface-correction. The concentrations of water constituents (phytoplankton pigments, suspended matter, yellow substance) must be provided by the user for the water body correction. Currently, this is the sole exception where additional unkown input data must be adjusted by hand for the image processing. Using the inversion algorithm for shallow water reflectances by A. Albert (see his contribution in this issue), in future we can try to couple both : the water constituent, water depth and bottom coverage estimation. Using one set of water constituent concentrations, bottom albedo spectra A are calculated from R– according to (Albert et al. 2003): A=(R–-R–∞) exp(2 k z)+ R–∞ , (simplified representation of Albert´s formula, in this form like the one of (Maritorena et al. 1994). The extinction k and subsurface reflection for deep water R–∞ is calculated using the specific optical properties of Lake Constance (Heege et al. 2004) and the Gaussian expression for the spectral behaviour of yellow substance absorption (Gege 2000). The water depth z is retrieved iteratively in combination with the bottom albedo A, by increasing z until A (700 nm) is equal A(570 nm).
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Using specific spectral features of A, a first guess for the bottom coverage is retrieved: For example the first guess for the discrimination of bottom sediments from tall or small vegetation is performed by the magnitude of the bottom reflectance at 500 nm: If A(500)>12%, the coverage is estimated as 100% sediment. If A(500) is lower or equal to 3%, 100% vegetation is assumed. Values between are interpolated. The first guess for discrimination between tall and small vegetation habit is supported by fixed features of the 1st derivation of A and the calculated water depth z. The final estimation of the bottom coverage is performed by an iterative fitting algorithm to adjust the bottom spectrum A to the modelled bottom spectrum Am= S* c1 + Vsmall* c2+ Vtall* c3 , where c1, c2 and c3 are the coverage degree of bottom sediments, small vegetation and tall vegetation. S*, Vsmall* and Vtall* denote the specific reflectance spectrum of each bottom type. They once were taken from hyperspectral ground truth measurements and are kept constant for different missions. The fit is performed using the whole spectral information of the retrieved bottom spectrum between 470m and 700nm. The unmixing is performed by downhill simplex minimisation with regularization for unphysical coverage values. Reflectance spectra of pure submerged vegetation types of “tall” macrophytes (mainly P. Pectinatus or P. Perfoliatus) and “small” (mainly different Characeae species, close to lake bottom) are finally analyzed by the derivation rules presented before, if the coverage of one type exceeds 70% bottom coverage. The processing system has been tested on data collected from the hyperspectral airborne sensor HyMap (here we used 17 VIS from 465 to 700nm, FOV +/- 30°) at Lake Constance, Germany. RESULTS Although there is an overall qualitative similarity in spectral response curves of green plant species (Schagerl et al. 2000), a small number of physical and physiological parameters , e.g. genetic variation , seasonal cycles , stage of growths, health and environmental condition can vary for species or an individual plant over space and time. Figure 1 shows mean irradiance reflectance spectra of various macrophytes species measured in situ with a submersible RAMSES spectropradiometer . Measurements are bottom reflectances taken just above the aquatic plants . Spectra shown here were selected as homogenous and representative spectra of one species type. Spectral signatures of measured reflectance spectra are determined primarily by pigments contained in all higher plants ; chlorophylls a and b and a range of xanthophylls and carotenoids. Accurate remote sensing of macrophytes to species level will only be possible of the species prove to be spectrally distinct despite variation within each species (Fyfe 2003). The understanding of spectral variation within one species is of prime importance for the application of accurate algorithms derived from in situ measurements. Spectral reflectance measurements were taken during the main growing season from June –
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August 2004 at the same P.pectinatus patch. The mean spectral response curves were plotted and compared for spectral variation (see Figure 2a,b). In can be seen that spectral reflectance peaks are increasing rapidly at the beginning of the growing season from June (26.6.04, 19.6.04) to middle of July (8.7.04) , a wavelength shift to longer wavelength can also be observed. After the reflectance peak in July the curves are decreasing till middle of August ( 12.8.04). Spectral changes might be caused by varying pigment concentration or by increasing epithetic load accumulated during the growing season. Epithetic growths might cumulative add their own response to the spectra with proceeding season. Daily cycles were also measured to investigate whether changes in sunangle might attribute to a different spectral reflectance behaviour at same plants species (e.g. BRDF) . Measurements were taken at the same P. Pectinatus patch at same sensor location in nadir on one cloudfree day every 30-45 minutes from noon to late afternoon (see Figure 3a). Same measurements were carried out on a chara contraria patch at 1m water depth (see Figure 3b). Results show that there is some variation between spectra but they do not show remarkable differences related to sunangle changes. It can also be seen that a homogeneous bottom type such as low growing charophytes show less variation in reflectance than high growing Potamogetae. With high growing plants the water column and moving leaves may rather effect consistency of situ measurements and therefore increase intraspecific variability. Figure 4a, b, c show an example for possible spectral discrimination of the two macrophyte species using first and second derivative analysis. The graphs shown in Figure 4a are mean spectral reflectance measurements of the two high growing species P.pectinatus (green) and P.perfoliatus (blue). Measurements were taken over 2 years (dotted 2003, lined 2004) at different patches. Derivative analysis proved to be a useful tool in analyzing hyperspectral data. Figure 5a and b show the first derivative at 550 and 665 nm for same spectra seen in Figure 4. Results show that intraspecific variability of one species does exist, but at certain wavelength (here 550 and 656 nm) spectral differences can be observed amongst species (figure 5a, 5b). These differences were used as threshold range for algorithms and implemented in the classification process in MIP. There is one green spectra, corresponding to the species group of P.pectinatus, which showed to be a maverick amongst the measured spectra. This mismatching might be explained by the date of the measurement, as it was taken at the beginning of the growing season ( 26.6.04), whereas other spectra were taken 4 weeks later (29.7.04) . This shows that a better separation of the species P.pectinatus and P.perfoliatus might be possible at the peak of growing season and not at the beginning when plants are still small and premature. A classification of macrophytes in shallow waters near Island Reichenau (Lake Constance) can be seen in figure 6. Hymap data collected in July 2003 were processed with MIP. The result contains classes of small growing macrophytes (Characeae) in green, tall macrophytes (here: mainly Potamogeton perfoliatus and P. pectinatus) in red and bottom sediments in blue (see color triangle). Mixed picture elements contain more
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than a single class, e.g. Characeae and bottom sediment. The sum of the bottom coverage in each pixel is always 100 %. Because of the different growth height, the two macrophyte groups Characeae and Potamogeton could be separated very well. The bottom coverage could be mapped down to a depth of 4.5 m, the maximum depth to which plausible reflectance spectra have been derived after water depth correction. A first result of species discrimination is shown in figure 7. The imagery shown in figure 6 were processed one step further and algorithms were applied to the classified HyMap data (figure 6). Algorithms were tested on high growing macrophytes (red class in figure 6) and were able to distinguish between species of Potamogeton pectinatus (pink) and P.perfoliatus (yellow). DISCUSSION Spectral measurements and remote sensing classification showed potential in distinguishing macrophyte species. Potamogeton pectinatus and P. perfoliatus and some of Charopyte species might be distinguishable according to their spectral behaviour. Macrophytes do change significantly over the year and over space and time. The high intraspecific variation in temporal, spatial and seasonal scale has to be considered when comparing measurements from different dates and different lakes. An important issue might be the date and time of the airborne data collection. According to spectral measurements during the growing season it might be misadventures to collect data at the very beginning of the growing season when spectral differences are not yet discernible. At the end of the growing season spectral variation might also complicate species classification as epithetic growths is overlapping with specific spectral reflectance characteristics. However the varying growth behaviour of the aquatic plants and their sensitive adaption to light and weather condition make it difficult to compare data collected over different years. Algorithms for the small growing Charophyte species (green class in figure 6) is currently under development and will be applied to the same data set. Transferability of algorithms has to be investigated and will be further tested on hyperspectral data from HyMap and ROSIS flown at Lake Constance and Lake Starnberg in the years 2003 and 2004. First results of the automated processing chain in MIP show promising results. The processing seems to be robust and comparable, provided the sensor calibration is stable. The HyMap sensor seems to be well suited for littoral vegetation mapping. However spatial resolution of 4x4 meter might be a bottleneck for macrophytes species recognition especially in smaller lakes where size and homogeneity of the patches requires higher spatial information. Results of an automated general approach for the retrieval of the distribution of submersed vegetation in littoral zones has been presented. They show promising potential for further research in the field of airborne remote sensing over shallow water targets. This approach forms the basis for future developments in precise automated methods. Consequently, remote sensing could become an economical monitoring technology for inland waters, with respect to managing nature restoration, rehabilitation and
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conservation. By use of physical based algorithms, a general transferability to data from different water types and seasons is possible. However, further research needs to be done to stabilize and improve the retrieval procedures (e.g. to improvement of algorithms for species recognition in shallow waters or derivation of growth height of aquatic plants from remote sensing data as an indicator for biomass). ACKNOWLEDGMENTS This work was supported by the project HTO „Waging-Tachinger See“, the project EFPLUS BWCF21011 and the DFG (Deutsche Forschungsgemeinschaft) with the Collaborative Research Center no. 454 „Lake Constance Littoral“. Special thanks to Dr. Kisselev (Russ. Acad. of Sciences, St. Petersburg) for support with the radiative transfer model. Thanks also to S. Wolfer (University of Konstanz), Tom Neubert and Rene Bison (TU München) for assistance with the ground truth measurements, A. Albert and J. Heblinski (DLR) for technical support and data processing, and A. Müller and S. Holzwarth for sensor operation. Download the actual version of these ongoing works and other cited literature cited in this paper at: http://www.uni-konstanz.de/sfb454/TP/TPD3/Literatur.html http://www.limno.biologie.tu-muenchen.de/forschung/publika/index.html REFERENCES Albert, A., and Mobley, C. D. (2003). "An analytical model for subsurface irradiance and remote sensing reflectance in deep and shallow case-2 waters." Optics Express, 11(22), 2873 -2890. Anstee, J. M., Dekker, A., Brando, V., et al.(2001) "Hyperspectral imaging for benthic species recognition in shallow coastal waters." IEEE2001 International Geoscience and Remote Sensing Symposium, Sydney, Australia. Anstee, J. M., Jupp, D. L. B., and Byrne, G. T.(1997) "The shallow benthic cover map and optical water quality of Port Philip Bay." 4th International Conference of Remote Sensing for Marine and Coastal Environment, Orlando, Florida, USA, 1-19. Bajjouk, T., Guillaumont, B., and Populus, J. (1996). "Application of airborne imaging spectrometry system data to intertidal seaweed classification and mapping." Hydrobiologia, 327, 463-471. Bugarelli, B., Kisselev, V. B., and Roberti, L. (1999). "Radiative transfer in the atmosphere ocean system: the finite -element method." Applied Optics, 38(9), 1530-1542. Clark, C. D., Mumby, P. J., Chisholm, J. R. M., et al. (2000). "Spectral discrimination of coral mortality states following a severe bleaching event." International Journal of Remote Sensing, 21(11), 2321-2327.
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Fyfe, S. K. (2003). "Spatial and temporal variation in spectral reflectance: Are seagrass species spectrally distinct?" Limnology & Oceanography, 48(1, part 2), 464-479. Gege, P.(2000) "Gaussian model for yellow substance absorption spectra." In proceedings of SPIE-Ocean Opitics XV, Monaco [CD-ROM]. Heege, D. T., Bogner, A., Häse, C., et al.(2003a) "Mapping aquatic systems with a physically based processing chain." 3rd EARSel Workshop on Imaging Spectroscopy, Oberpfaffenhofen, 415-422. Heege, D. T., Bogner, A., and Pinnel, N.(2003b) "Mapping of submerged aquatic vegetation with a physically based processing chain." SPIE- The International Society for Optical Engineering, Barcelona. Heege, T. (2000). "Flugzeuggstützte Fernerkundung von Wasserinhaltstoffen im Bodensee," Phd, Deutsches Zentrum für Luft-und Raumfahrt e.V., Oberpfaffenhofen. Heege, T., and Fischer, J.(2000) "Sun glitter correction in remote sensing imaging spectrometry." Ocean optics XV, Monaco, 11. Heege, T., and Fischer, J. (2004). "Mapping of water constitutents in Lake constance using multispectral airborne scanner data and a physically based processing scheme." Can.J.Remote Sensing, 30(1), 77-86. Hochberg, E. J., and Atkinson, M. J. (2000). "Spectral discrimination of coral reef benthic communities." Coral Reefs, 19, 164-171. Hochberg, E. J., and Atkinson, M. J. (2003). "Capabilities of remote sensors to classify coral, algae, and sand as pure and mixed spectra." Remote Sensing of Environment, 85, 174-189. Holden, H., and LeDrew, E. (2000). "Accuracy assessment of hyperspectral classification of coral reef features." Geocarta International, 15, 5-11. Malthus, T. J., and George, D. G. (1997). "Airborne remote sensing of macrophytes in Cefni Reservoir, Anglesey, UK." Aquatic Botany, 58(3-4), 317-332. Maritorena, S., Morel, A., and Gentili, B. (1994). "Diffuse reflectance of oceanic shallow waters: influence of water depth and bottom albedo." Limnol.Oceanogr., 39(7), 16891703. Melzer, A. (1999). "Aquatic macrophytes Hydrobiologica, 395/396, 181-190.
as tools for lake management."
Schagerl, M., and Pichler, C. (2000). "Pigments composition of freshwater charophyceae." Aquatic Botany, 67, 117-129. Wettle, M., Ferrier, G., Lawrence, A. J., et al. (2003). "Fourth derivative analysis of Red Sea coral reflectance spectra." International Journal of Remote Sensing, 24(19), 38673872.
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Figure 1: Mean irradiance reflectance spectra of various macrophytes species measured in situ with a submersible RAMSES spectropradiometer .
Figure 2a: Seasonal changes of same P.pectinatus patch measured during the main growing season from June – August 2004 at Lake Starnberg.
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Figure 2b: Spectral reflectances of P.pectinatus , seen in figure 2a, are now plotted over time at 400nm, 500nm, 600nm and 700 nm. An distinct increase in reflectance can be observed for all wavelength at the beginning of the growing season from June to middle of July.
Figure 3a: Daily variation of same P.pectinatus patch. Subsurface reflectance measurements were carried out on a cloudfree day from noon to late afternoon just above the Potamogetae plants. 10
Figure 3b: Daily variation of a chara contraria patch. Measurements were carried out on a cloudfree day from noon to late afternoon. Measurments were taken at 1 m water depth just above the Charophytes.
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Figure 4a, b, c: Mean spectral response curves (4a) of two different macrophytes species , P.pectinatus (green) and P.perfoliatus (blue) and corresponding first (4b) and second derivatives(4b) .
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Figure 5a: :First derivative analysis calculated from reflectances spectra of P.pectinatus and P.perfoliatus at 550 nm.
Figure 5b: First derivative analysis calculated from reflectances spectra of P.pectinatus and P.perfoliatus at 665 nm.
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Figure 6 : Classified HyMap data from Lake Constance flown on the 19th July 2003. Imagery were corrected for atmospheric and water column effect using the Modular Inversion Program (MIP). Date were classified to bottom cover classes of sediment (blue) , small growing vegetation ( green) and tall vegetation (red)
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Figure 7 : First result of species discrimination. Algorithms for species recognition were applied to the corrected HyMap data shown in figure 6. Algorithms were tested on high growing macrophytes ( red class in figure 6) and were able to distinguish between species of Potamogeton pectinatus (pink) and P.perfoliatus (yellow)
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