Hyperspectral analysis of algal biomass in northern lakes, Churchill, MB, Canada (original) (raw)
Related papers
Distinguishing between chlorophyll-a and suspended solids in lake water using hyperspectral data
Remote Sensing for Agriculture, Ecosystems, and Hydrology, 1998
Classifying surface water bodies according to trophic status by remote sensing techniques has had limited success in lakes with relatively high nonalgal turbidity levels. Since the trophic status of a lake is typically defined based on its chlorophyll-a concentration, and since relatively high suspended solids concentrations masks chlorophyll absorption and reflectance peaks, determining trophic status remotely is typically only partially successful. Here, we were interested in exploring hyperspectral data analysis for estimating trophic status. Hyperspectral data (10 nm resolution between 262 and 850nm) of light attenuation were measured in Lake Texoma (USA) at the surface, 0.1, 0.5, 1.0 and 1.5 meters in depth, while simultaneously analyzing the water column for chlorophyll-a and suspended solids concentration. Data were collected at five sampling stations, each representative of a major zone in the 36,000 hectare lake, approximately monthly, during 1996/97 hydrologic year. Downwelling and upwelling vertical attenuation coefficients were calculated using Bouger-Lambert's law. First and second order derivatives, as well as higher order derivatives were applied to the spectral data. The results showed a clear correlation between first order derivatives and turbidity, while the second order derivatives were correlated to chlorophyll-a concentrations.
Remote Sensing of Environment, 2006
Quantitative analysis of coastal marine benthic communities enables to adequately estimate the state of coastal marine environment, provide better evidence for environmental changes and describe processes that are conditioned by anthropogenic forces. Remote sensing could provide a tool for mapping bottom vegetation if the substrates are spectrally resolvable. We measured reflectance spectra of green (Cladophora glomerata), red (Furcellaria lumbricalis), and brown (Fucus vesiculosus) macroalgae and used a bio-optical model in estimating whether these algae distinguish optically from each other, from sandy bottom or deep water in turbid water conditions of the Baltic Sea. The simulation was carried out for three different water types: (1) CDOM-rich coastal water, (2) coastal waters not directly impacted by high CDOM discharge from rivers but with high concentration of cyanobacteria, (3) open Baltic waters. Our modelling results indicate that the reflectance spectra of C. glomerata, F. lumbricalis, F. vesiculosus differ from each other and also from sand and deep water reflectance spectra. The differences are detectable by remote sensing instruments at spectral resolution of 10 nm and SNR better than 1000:1. Thus, the lowest depth limits where the studied macroalgae grow do not exceed the depth where such remote sensing instruments could potentially detect the spectral differences between the studied species.
Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters
Remote Sensing of Environment, 2015
Cyanobacterial blooms are increasingly posing a severe threat to inland waters, particularly at the land-sea interface where toxins can be transported downstream with subsequent impacts to both terrestrial and marine organisms. These blooms are relatively easy to detect optically because of the surface concentration of cells, the presence of phycocyanin pigments, and the elevated backscatter associated with cell size and the presence of gas vacuoles. Major challenges limiting the use of remote sensing have been, first, that many of these water bodies are small relative to the spatial resolution of ocean color satellites, and second, even with a bright algal target, the spectral resolution, signal-to-noise ratio, and repeat time for terrestrial satellites is often inadequate. The next generation of multispectral and hyperspectral sensors begin to address these issues with both increased spatial and spectral resolution. Weekly monitoring of Pinto Lake, California has demonstrated that this small water body provides an ideal testbed for development and application of algorithms applicable for legacy and nextgeneration sensors. Pinto Lake experiences seasonal nearly monospecific blooms with a pronounced species succession. Biomass (as chlorophyll) within Pinto Lake seasonally ranges from~1 to 1000 μg/L. Pinto Lake has been within the flight lines for several recent airborne missions, including the HyspIRI Preparatory Flight Campaign, and is often targeted for HICO acquisitions. Using these data we demonstrate that spectral-shape algorithms requiring minimal atmospheric correction can be used across a range of legacy sensors to detect cyanobacterial blooms and that, with the availability of high spectral resolution data and appropriate atmospheric correction, it is possible to separate the cyanobacterial genera Aphanizomenon and Microcystis. In California Aphanizomenon is typically non-toxic and blooms prior to toxin-producing Microcystis, thus leading to the potential for an early warning system based on the identification of algal types.
2006
Professor Masaki Sawamoto and Professor So Kazama have welcomed me to Tohoku University in many occasions. Their excellent guidance and advises have been essential to develop and to improve my research. Doctor Norio Hayashi, curator at the Chiba Natural History Museum, has played a pivotal role in the algae classification aspect of this research. I have tremendously benefited from his large experience as a pioneer and leading researcher in eco-engineering specialty in Japan. I would like to say many thanks to Dr. Hayashi. My thanks to Dr Ishikawa and to Dr Ariga who have contributed to my understanding of algae culturing in laboratory. I have benefited from JICA training courses and field excursions for 3 years. Besides the knowledge which I have gained, I have met many friends through the JICA training program and I still keep contact with many people from around the world. Finally, I would like to say my thanks to all students in Murakami Lab and in Watanabe Lab for their help and their friendship.
Annals of Glaciology
Ice algae are a key component in polar marine food webs and have an active role in large-scale biogeochemical cycles. They remain extremely under-sampled due to the coarse nature of traditional point sampling methods compounded by the general logistical limitations of surveying in polar regions. This study provides a first assessment of hyperspectral imaging as an under-ice remote-sensing method to capture sea-ice algae biomass spatial variability at the ice/water interface. Ice-algal cultures were inoculated in a unique inverted sea-ice simulation tank at increasing concentrations over designated cylinder enclosures and sparsely across the ice/water interface. Hyperspectral images of the sea ice were acquired with a pushbroom sensor attaining 0.9 mm square pixel spatial resolution for three different spectral resolutions (1.7, 3.4, 6.7 nm). Image analysis revealed biomass distribution matching the inoculated chlorophyll a concentrations within each cylinder. While spectral resoluti...
Assessment of chlorophyll-a and algal blooms in inland water from hyperspectral data
… Workshop, ESRIN, 17- …, 2010
The study comprises a series of hyperspectral data respectively collected from: (i) in situ spectroradiometer, (ii) airborne imaging spectrometry (MIVIS) and (iii) satellite Chris-Proba sensors for investigating algal blooms in productive inland waters. Fieldworks activities were performed in summer 2007 and allowed to calibrate a band ratio algorithm for estimating chlorophyll-a concentrations. The algorithm was applied to MIVIS and Chris-Proba data acquired in July 2007 and June 2008, respectively. For such a purpose, image data were previously corrected for the atmospheric effects. This correction was a major concern of this study but the use of a band-ratio algorithm permitted to reduce radiometry-related errors. Image-derived products were comparable to in situ measurements (relative error of 16% for Chris and 4% for MIVIS) and showed the spatial and temporal variability of phytoplankton. In July 2007 MIVIS data, that showed a typical feature of phycocyanin, allowed to map a cyanobacterial bloom.
Photogrammetric Engineering and Remote Sensing
Remote sensing is an important technology for measuring algal-chlorophyll concentrations in su$xe waters. Our paper provides hyperspectral signatures, in fhe visible and near-infrored, associated with two experiments conducted outdoors in large water tanks; one involving relatively low amounts of chlorophyll over a narrow range and a second involving relatively high amounts over a wide range. The principal finding was that the commonly used near-infraredlred ratio is best for estimating pigment amounts when the concentration of chlorophyll is relatively low, and the first derivative of reflectance around 690 nm is best when the concentration is relatively high.
Remote Sensing, 2015
The emergence of hyperspectral optical satellite sensors for ocean observation provides potential for more detailed information from aquatic ecosystems. The German hyperspectral satellite mission EnMAP (enmap.org) currently in the production phase is supported by a project to explore the capability of using EnMAP data and other future hyperspectral data from space. One task is to identify phytoplankton taxonomic groups. To fulfill this objective, on the basis of laboratory-measured absorption coefficients of phytoplankton cultures (aph(λ)) and corresponding simulated remote sensing reflectance spectra (Rrs(λ)), we examined the performance of spectral fourth-derivative analysis and clustering techniques to differentiate six taxonomic groups. We compared different sources of input data, namely aph(λ), Rrs(λ), and the absorption of water compounds obtained from inversion of the Rrs(λ)) spectra using a quasi-analytical algorithm (QAA). Rrs(λ) was tested as it can be directly obtained from hyperspectral sensors. The last one was tested as expected influences of the spectral features of pure water absorption on Rrs(λ) could be avoided after subtracting it from the inverted total absorption. Results showed that derivative analysis of measured aph(λ) spectra performed best with only a few misclassified cultures. Based on Rrs(λ) spectra, the accuracy of this differentiation decreased but the
Applied Optics, 2006
We applied two numerical methods to in situ hyperspectral measurements of remote sensing reflectance R rs to assess the feasibility of remote detection and monitoring of the toxic dinoflagellate, Karenia brevis, which has been shown to exhibit unique absorption properties. First, an existing quasi-analytical algorithm was used to invert remote sensing reflectance spectra, R rs ͑͒, to derive phytoplankton absorption spectra, a Rrs ͑͒. Second, the fourth derivatives of the a Rrs ͑͒ spectra were compared to the fourth derivative of a reference K. brevis absorption spectrum by means of a similarity index (SI) analysis. Comparison of reflectance-derived a with filter pad measured a found them to agree well ͑R 2 ϭ 0.891; average percentage difference, 22.8%). A strong correlation ͑R 2 ϭ 0.743͒ between surface cell concentration and the SI was observed, showing the potential utility of SI magnitude as an indicator of bloom strength. A sensitivity analysis conducted to investigate the effects of varying levels of cell concentrations and colored dissolved organic matter (CDOM) on the efficacy of the quasi-analytical algorithm and SI found that a Rrs ͑͒ could not be derived for very low cell concentrations and that, although it is possible to derive a Rrs ͑͒ in the presence of high CDOM concentrations, CDOM levels influence the a Rrs ͑͒ amplitude and shape. Results suggest that detection and mapping of K. brevis blooms based on hyperspectral measurements of R rs are feasible.
Ecological Informatics, 2010
Phytoplankton species Hyperspectral reflectance Absorption spectra Bio-optical model Understanding the spectral characteristics of remotely-sensed reflectance by different phytoplankton species can assist in the development of algorithms to identify various algal groups using satellite ocean color remote sensing. One of the main challenges is to separate the effect of species composition on the reflectance spectrum from other factors such as pigment concentration and particle size structure. Measuring the absorption spectra of nine different cultured algae, and estimating the reflectance of the different species, provides a useful approach to study the effects of species composition on the bio-optical properties. The results show that the absorption spectra of different species exhibit different spectral characteristics and that species composition can significantly change the absorption characteristics at four main peaks (438, 536, 600 and 650 nm). A 'distance angle index' was used to compare different phytoplankton species. Results indicate that this index can be used to identify species from the absorption spectra, using a database of standard absorption spectra of known species as reference. By taking into account the role of species composition in the phytoplankton absorption model, the performance of the model can be improved by up to 5%. A reflectance-species model is developed to estimate the remotely-sensed reflectance from the absorption spectra, and the reflectance of different phytoplankton species at the same chlorophyll-a concentration is compared, to understand effects of species composition on the reflectance spectra. Different phytoplankton species can cause up to 33% difference in the modeled reflectance at short wavelengths under the condition of the same chlorophyll-a concentration, and variations in the reflectance spectrum correspond to the colors of the algae. The standard deviation of the reflectance among different species shows that the variations from 400 to 450 nm are sensitive to species composition at low chlorophyll-a concentrations, whereas variations in the 510 to 550 nm range are more sensitive under high chlorophyll-a concentrations. For this reason, the green bands may be more suitable for estimating species composition from hyperspectral satellite data during bloom conditions, whereas the blue bands may be more helpful in detection of species under low chlorophyll-a concentrations. In this theoretical approach, variations in reflectance at the same chlorophyll-a concentration can be used to identify phytoplankton species. Another approach to identify phytoplankton species from remotely-sensed hyperspectral reflectance measurements would be to derive the absorption spectra of phytoplankton from the reflectance measurements, and compare these with a standard database of absorption spectra.