Application of spectral decomposition algorithm for mapping water quality in a turbid lake (Lake Kasumigaura, Japan) from Landsat TM data

Oyama, Youichi; Matsushita, Bunkei; Fukushima, Takehiko; Matsushige, Kazuo; Imai, Akio

doi:10.1016/j.isprsjprs.2008.04.005

Cited by 99 publications

(60 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We determined the remote sensing reflectance (sr −1 ) by calculating respective average reflectance and divided it by pi. The basic reflectance measurement method and R rs transformation followed the method of Oyama et al [34]. Mobley [35] and Tan et al [36] point out the importance of removing sun and sky glint contained in R rs .…”

Section: Study Area and Field Surveymentioning

confidence: 99%

Evaluation of Unified Algorithms for Remote Sensing of Chlorophyll-a and Turbidity in Lake Shinji and Lake Nakaumi of Japan and the Vaal Dam Reservoir of South Africa under Eutrophic and Ultra-Turbid Conditions

Sakuno

Yajima

Yoshioka

et al. 2018

Water

View full text Add to dashboard Cite

Abstract:We evaluated unified algorithms for remote sensing of chlorophyll-a (Chla) and turbidity in eutrophic and ultra-turbid waters such as Japan's Lake Shinji and Lake Nakaumi (SJNU) and the Vaal Dam Reservoir (VDR) in South Africa. To realize this objective, we used 38 remote sensing reflectance (R rs ), Chla and turbidity datasets collected in these waters between July 2016 and March 2017. As a result, we clarified the following items. As a unified Chla model, we obtained strong correlation (R 2 = 0.7, RMSE = 2 mg m −3 ) using a two-band model (2-BM) and three-band model (3-BM), with R rs (687)/R rs (672) and [R rs −1 (687) − R rs −1 (672)] × R rs (832). As a unified turbidity model, we obtained strong correlation (R 2 = 0.7, RMSE = 260 NTU) using 2-BM and 3-BM, with R rs (763)/R rs (821) and R rs (810) − [R rs (730) + R rs (770)]/2. When targeting the Sentinel-2 Multispectral Imager (MSI) frequency band, we focused on MSI Bands 4 and 5 (R rs (740) and R rs (775)) for the Chla algorithm. When optically separating SJNU and VDR data, it is effective to use the slopes of MSI Bands 3 and 4 (R rs (560) and R rs (665)) and the slopes of MSI Bands 7 and 9 (R rs (775) and R rs (865)).

show abstract

Section: Study Area and Field Surveymentioning

confidence: 99%

Evaluation of Unified Algorithms for Remote Sensing of Chlorophyll-a and Turbidity in Lake Shinji and Lake Nakaumi of Japan and the Vaal Dam Reservoir of South Africa under Eutrophic and Ultra-Turbid Conditions

Sakuno

Yajima

Yoshioka

et al. 2018

Water

View full text Add to dashboard Cite

show abstract

“…Therefore, to apply the developed technique to other satellite data, the image normalization was performed for 2013, 2015, and 2016 images (using the September 2014 atmospherically-corrected image as the reference image). The images were normalized using the relative radiometric normalization technique following Elvidge et al (1995) and Oyama et al (2009) [51,67]. The validation of the atmospherically-corrected Landsat-8 image with the in situ data (September 2016) is shown in Figure 3.…”

Section: Remotely Sensed Datamentioning

confidence: 99%

“…For the biomass estimation of the classified SAV pixels, we developed a new model based on the spectral decomposition algorithm [51]. In this algorithm, the mixed reflectance spectra of a given pixels are conceptualized as a linear combination of potential endmembers substantially contributing to the pixel reflectance.…”

Section: Sav Biomass Estimationmentioning

confidence: 99%

“…Therefore, standard reflectance spectra of the endmembers were simulated using the Bio-Optical model, shown in Equation (6). The details of the equation can be found in Lee et al (1998) [53] and Oyama et al (2009) [51].…”

Section: Sav Biomass Estimationmentioning

confidence: 99%

“…We also measured DOC concentration using the Total Organic Carbon (TOC) Analyzer (TOC-5000A, Shimadzu Kyoto, Japan). Furthermore, we calculated non-phytoplankton suspended sediment (NPSS) by subtracting concentration of phytoplankton suspended sediment (PSS) from TSS [51].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2017

View full text Add to dashboard Cite

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.

show abstract

References

2011

Remote Sensing and Global Environmental Change

View full text Add to dashboard Cite

Application of spectral decomposition algorithm for mapping water quality in a turbid lake (Lake Kasumigaura, Japan) from Landsat TM data

Cited by 99 publications

References 46 publications

Evaluation of Unified Algorithms for Remote Sensing of Chlorophyll-a and Turbidity in Lake Shinji and Lake Nakaumi of Japan and the Vaal Dam Reservoir of South Africa under Eutrophic and Ultra-Turbid Conditions

Evaluation of Unified Algorithms for Remote Sensing of Chlorophyll-a and Turbidity in Lake Shinji and Lake Nakaumi of Japan and the Vaal Dam Reservoir of South Africa under Eutrophic and Ultra-Turbid Conditions

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

References

Contact Info

Product

Resources

About