Coupling of phytoplankton and detritus in a shallow, eutrophic lake (Lake Loosdrecht, The Netherlands)

Gons, Herman J.; Burger-Wiersma, Tineke; Otten, Jerko H.; Rijkeboer, Machteld

doi:10.1007/bf00016095

Cited by 45 publications

(15 citation statements)

References 34 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…), carried out at the same time with the Hyperion and ALI image acquisition. It was assumed that the concentration of suspended matter is the sum of nonchlorophyllous and organic particles and the relationship by Gons et al (1992), where 1 mg m Ϫ3 chlorophyll is approximately equal to 0.07 mg L Ϫ1 of suspended matter, was used to estimate the C SM . The nonchlorophyllous suspended matter concentration was taken to be 2 mg L Ϫ1 , and a CDOM (400) ϭ 0.71 m Ϫ1 .…”

Section: Methodsmentioning

confidence: 99%

Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing

Kutser

2004

Limnology & Oceanography

333

199

View full text Add to dashboard Cite

The extent of cyanobacterial blooms has been mapped using different satellite sensors from weather satellites to synthetic aperture radars. Quantitative detection of chlorophyll in cyanobacterial blooms by remote sensing, however, has been less successful. The first civilian hyperspectral sensor in space, Hyperion, acquired an image of cyanobacterial bloom in the western part of the Gulf of Finland on 14 July 2002. A chlorophyll concentration map was produced from this image using a spectral library that was created by running a bio-optical model with variable concentrations of chlorophyll. The results show that chlorophyll concentrations in the bloom area were much higher than reported by conventional water-monitoring programs, ships-of-opportunity, and satellite measurements. The reason why both in situ and satellite methods underestimate the amount of phytoplankton during cyanobacterial blooms is vertical and horizontal distribution of cyanobacteria, because cyanobacteria can regulate their buoyancy and are not uniformly distributed within the top mixed layer of water column in calm weather conditions. Aggregations of cyanobacteria form dense subsurface blooms and surface scums during extensive blooms. This study demonstrates that it is difficult to collect representative water samples from research vessels using standard methods because ships and water samplers destroy the natural distribution of cyanobacteria in the sampling process. Flowthrough systems take water samples from the depths at which the concentration of cyanobacteria is not correlated with the amount of phytoplankton that remote sensing instruments detect. The chlorophyll estimation accuracy in cyanobacterial blooms by many satellites is limited because of spatial resolution, as significant changes in chlorophyll concentration occur even at a smaller spatial scale than 30 m.

show abstract

Section: Methodsmentioning

confidence: 99%

Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing

Kutser

2004

Limnology & Oceanography

333

199

View full text Add to dashboard Cite

show abstract

“…A total of nine lines were drawn across the northern basin of Lake Biwa at near-uniform intervals from north to south, and 28 sampling stations were designated, based on three fixed points (eastern shore, center of lake, western shore) located on these lines (except for line 17, which was based on four points). The method used to estimate the NPSS concentration using a monthly factor d (which represents the phytoplanktonic portion of the TSS), which is based on the method of Gons et al (1992), is explained in Appendix A of the Electronic supplementary material (ESM). In this study, we chose April as the beginning of the year, considering the vertical mixing regime and algae bloom outbreak.…”

Section: Methodsmentioning

confidence: 99%

Long-term light environment variability in Lake Biwa and Lake Kasumigaura, Japan: modeling approach

et al. 2012

View full text Add to dashboard Cite

Light environment variability was investigated in the two Japanese lakes Biwa and Kasumigaura, which offer a broad range of optical conditions in the water bodies due to their diverse morphometries and limnological characteristics. To elucidate their light environments, Secchi depths (SDs) were related to long-term monitored datasets of concentrations of optically active substances (OASs) using two approaches based on statistical and mechanistic models. A good estimate for the nonphytoplanktonic suspended solids (NPSS) concentration gained using a monthly factor d (which represents the phytoplanktonic portion in total suspended solids) from a longterm analysis was utilized to develop robust models. Using the mechanistic model, the contribution of each OAS to the SD can be understood and investigated in more detail than possible with a statistical approach, but the statistical model yields better results in terms of SD prediction. Based on the results of an analysis of the contribution of each OAS to the SD, it was clear that NPSS was the component that exerted the most influence on the light environments in the two lakes; in this respect, this study agrees with other studies that show the importance of suspended inorganic particles as the main contributor to the SD in inland waters. Using ANOVA, we analyzed how specific inherent optical properties may have changed spatially and temporally, and the results indicated that the temporal (monthly) effect was primarily responsible for the loss of accuracy in the models. In addition, the ANOVA analysis suggested that grouping the data improved the predictive performances of the statistical models. Finally, we concluded that combining the two models yields the most reliable results in terms of SD prediction and determining the contribution of each OAS to the SD at present.

show abstract

“…Separating NAP from the TSS with laboratory analysis is impossible [72], so the approach of Gons et al [73] was employed to divide the TSS into NAP and phytoplankton suspended solids (PSS). As a result, it was assumed that the 1.0 mg m −3 Chla concentration approximately equaled 0.0687 g m −3 TSS.…”

Section: Simulating the Remote Sensing Reflectancementioning

confidence: 99%

Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data

et al. 2017

View full text Add to dashboard Cite

Many approaches have been proposed for monitoring the eutrophication of Case 2 waters using remote sensing data. Semi-analytical algorithms and spectrum matching are two major approaches for chlorophyll-a (Chla) retrieval. Semi-analytical algorithms provide indices correlated with phytoplankton characteristics, (e.g., maximum and minimum absorption peaks). Algorithms' indices are correlated with measured Chla through the regression process. The main drawback of the semi-analytical algorithms is that the derived relation is location and data limited. Spectrum matching and the look-up table approach rely on matching the measured reflectance with a large library of simulated references corresponding to wide ranges of water properties. The spectral matching approach taking hyperspectral measured reflectance as an input, leading to difficulties in incorporating data from multispectral satellites. Consequently, multi-algorithm indices and the look-up table (MAIN-LUT) technique is proposed to combine the merits of semi-analytical algorithms and look-up table, which can be applied to multispectral data. Eight combinations of four algorithms (i.e., 2-band, 3-band, maximum chlorophyll index, and normalized difference chlorophyll index) are investigated for the MAIN-LUT technique. In situ measurements and Medium Resolution Imaging Spectrometer (MERIS) sensor data are used to validate MAIN-LUT. In general, the MAIN-LUT provide a comparable retrieval accuracy with locally tuned algorithms. The most accurate of the locally tuned algorithms varied among datasets, revealing the limitation of these algorithms to be applied universally. In contrast, the MAIN-LUT provided relatively high retrieval accuracy for Tokyo Bay (R 2 = 0.692, root mean square error (RMSE) = 21.4 mg m −3 ), Lake Kasumigaura (R 2 = 0.866, RMSE = 11.3 mg m −3 ), and MERIS data over Lake Kasumigaura (R 2 = 0.57, RMSE = 36.5 mg m −3 ). The simulated reflectance library of MAIN-LUT was generated based on inherent optical properties of Tokyo Bay; however, the MAIN-LUT also provided high retrieval accuracy for Lake Kasumigaura. MAIN-LUT could capture the spatial and temporal distribution of Chla concentration for Lake Kasumigaura.

show abstract

Coupling of phytoplankton and detritus in a shallow, eutrophic lake (Lake Loosdrecht, The Netherlands)

Cited by 45 publications

References 34 publications

Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing

Quantitative detection of chlorophyll in cyanobacterial blooms by satellite remote sensing

Long-term light environment variability in Lake Biwa and Lake Kasumigaura, Japan: modeling approach

Multi-Algorithm Indices and Look-Up Table for Chlorophyll-a Retrieval in Highly Turbid Water Bodies Using Multispectral Data

Contact Info

Product

Resources

About