Characterizing the spectral reflectance of algae in lake waters with high suspended sediment concentrations

Svab, E.; Tyler, Andrew N.; Preston, Tom; Présing, Mátyås; Balogh, Katalin

doi:10.1080/0143116042000274087

Cited by 61 publications

(45 citation statements)

References 7 publications

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“…In order to assess the SAV classification accuracy for the other images (i.e., 2013, 2015, and 2016), we identified total 17 SAV stations used for validation (i.e., Station 1,5,6,9,15,18,27,34,36,38,41,42,43,45,48, 49 and 52) out of 52 observation points (Figure 1) based on the available data in Table 1 and the in situ survey (2016). We also used the SAV biomass maps available in the literature for the south basin of Lake Biwa, primarily for the SAV growth period (i.e., mainly September) in 2002, 2007, 2012, and 2014 [63,65].…”

Section: Sav Distribution Mapmentioning

confidence: 99%

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

et al. 2017

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Assessing the abundance of submerged aquatic vegetation (SAV), particularly in shallow lakes, is essential for effective lake management activities. In the present study we applied satellite remote sensing (a Landsat-8 image) in order to evaluate the SAV coverage area and its biomass for the peak growth period, which is mainly in September or October (2013October ( to 2016, in the eutrophic and shallow south basin of Lake Biwa. We developed and validated a satellite-based water transparency retrieval algorithm based on the linear regression approach (R 2 = 0.77) to determine the water clarity (2013)(2014)(2015)(2016), which was later used for SAV classification and biomass estimation. For SAV classification, we used Spectral Mixture Analysis (SMA), a Spectral Angle Mapper (SAM), and a binary decision tree, giving an overall classification accuracy of 86.5% and SAV classification accuracy of 76.5% (SAV kappa coefficient 0.74), based on in situ measurements. For biomass estimation, a new Spectral Decomposition Algorithm was developed. The satellite-derived biomass (R 2 = 0.79) for the SAV classified area gives an overall root-mean-square error (RMSE) of 0.26 kg dry weight (DW) m −2 . The mapped SAV coverage area was 20% and 40% in 2013 and 2016, respectively. Estimated SAV biomass for the mapped area shows an increase in recent years, with values of 3390 t (tons, dry weight) in 2013 as compared to 4550 t in 2016. The maximum biomass density (4.89 kg DW m −2 ) was obtained for a year with high water transparency (September 2014). With the change in water clarity, a slow change in SAV growth was noted from 2013 to 2016. The study shows that water clarity is important for the SAV detection and biomass estimation using satellite remote sensing in shallow eutrophic lakes. The present study also demonstrates the successful application of the developed satellite-based approach for SAV biomass estimation in the shallow eutrophic lake, which can be tested in other lakes.

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Section: Sav Distribution Mapmentioning

confidence: 99%

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

et al. 2017

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“…There is also a well-defined peak reflectance around 700 nm (Letter D), associated to the energy absorbed and back-scattered from water and phytoplankton. This high reflectance value decrease rapidly to about 750 nm (Letter E) and smoothly increases until 800 nm (Letter F) and, finally, decreases to about 900 nm [25][26][27][28].…”

Section: Correlation Between Spectral Data and Chlorophyllmentioning

confidence: 99%

An Alternative Method of Spatial Autocorrelation for Chlorophyll Detection in Water Bodies Using Remote Sensing

et al. 2017

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Additional measures of in situ water quality monitoring in natural environments can be obtained through remote sensing because certain elements in water modify its spectral behavior. One of the indicators of water quality is the presence of algae, and the aim of this study was to propose an alternative method for the quantification of chlorophyll in water by correlating spectral data, infrared images, and limnology data. The object of study was an artificial lake located at Unisinos University, São Leopoldo/RS, Brazil. The area has been mapped with a modified NGB (near infrared (N), green (G) and blue (B)) camera coupled to an unmanned aerial vehicle (UAV). From the orthorectified and georeferenced images, a modified normalized difference vegetation index (NDVImod) image has been generated. Additionally, 20 sampling points have been established on the lake. At these points, in situ spectral analysis with a spectroradiometer has been performed, and water samples have been collected for laboratory determination of chlorophyll concentrations. The correlation resulted in two models. The first model, based on the multivariate analysis of spectral data, and the second model, based on polynomial equations from NDVI, had coefficients of determination (R 2 ) of 0.86 and 0.51, respectively. This study confirmed the applicability of remote sensing for water resource management using UAVs, which can be characterized as a quick and easy methodology.

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“…These three components are also the major factors that control the spectral signatures of water bodies [19,20]. Since the 1960-70s, a number of attempts have been made to quantitatively evaluate the trophic state of lakes using single-variable trophic indices or multi-parameter approaches [21].…”

Section: Introductionmentioning

confidence: 99%

Trophic State Index derivation through the remote sensing of Case-2 water bodies in the Mediterranean region

Papoutsa¹,

Akylas²,

Hadjimitsis³

2014

Open Geosciences

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Abstract:The main goal of this study is the derivation of Carlson's Trophic State Index (TSI) through the remote sensing of four different Case-2 waters in the Mediterranean region such as Cyprus and Greece. TSI SD is derived through extensive field ground campaign of Secchi Disk Depth measurements for the Asprokremmos Dam, located in Paphos District in Cyprus; Alyki Salt Lake, located in Larnaca District in Cyprus; and in Karla Lake, located in Volos District in Greece; and finally to three coastal water areas in the Limassol coastal area. Several regression models have been applied in order to develop the best regression model between the TSI SD and in-band reflectance values for Landsat TM/ETM derived from spectroradiometric measurements using a GER-1500 field spectroradiometer over the main case study area in Asprokremmos Dam in Cyprus. Finally, we apply several regression models for Asprokremmos Dam for retrieving the suitable Landsat TM/ETM band or band combinations (obtained from field spectroradiometric measurements) in which TSI SD can be determined. Indeed, the best regression model has been obtained by correlating 'TSI Versus Band2/Band3', with R 2 =0.89. All field TSI SD and in-band reflectance values from the four different water bodies have been used to develop the best fitted model for the established T SI SD Versus Band2/Band3 model. We find that the exponential regression model provides the best fitted equation over the four different water bodies. Keywords

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Characterizing the spectral reflectance of algae in lake waters with high suspended sediment concentrations

Cited by 61 publications

References 7 publications

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

A Satellite-Based Assessment of the Distribution and Biomass of Submerged Aquatic Vegetation in the Optically Shallow Basin of Lake Biwa

An Alternative Method of Spatial Autocorrelation for Chlorophyll Detection in Water Bodies Using Remote Sensing

Trophic State Index derivation through the remote sensing of Case-2 water bodies in the Mediterranean region

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