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

Abstract

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.

Chlorophyll Estimation of Lake Water and Coastal Water Using Landsat-8 and Sentinel-2a Satellite

“…The surface area of the lake is 670 km 2 and a total catchment area of 3838 km 2 . The lake encompasses two basins, the north basin (618 km 2 ) which is mesotrophic and deep (the mean and the maximum depths are 44 m and 104 m, respectively), and the south basin (52 km 2 ) which is eutrophic and shallow (the mean and the maximum depths are 3.5 m and 8 m, respectively) (Yadav et al, 2017). Lake Biwa experienced eutrophication (mainly the south basin) during the economic growth period after the 1960s.…”

“…To curb the eutrophication and to restore the lake ecosystem, the Shiga Prefectural Government enacted the ordinance Prevention of Eutrophication of Lake Biwa in 1979 followed by the formulation of the Water Quality Conservation Plan in 1986. Consequently, water quality of the lake improved which led to an upsurge in the growth of submerged Macrophytes in the lake (Ishikawa et al, 2002;Yadav et al, 2017). On the other hand, Wakasa Bay has a wide opening to the Japan Sea (35°31´51.5˝ N and 135°17´29.3˝ E).…”

“…In addition, the reflectance characteristic of the aquatic vegetation species samples obtained by the diver survey were measured in the laboratory ASD FieldSpec TM Pro JR Field Portable Spectroradiometer and Handheld spectroradiometer (Yadav et al, 2017). Samples collected particularly from the south basin of Lake Biwa and the shorelines of the north basin, whereas in the Wakasa Bay samples collected from the two locations near the shallow coast of Yura River (3-5 m).…”

“…Excess sediment and nutrient transport from anthropogenic sources (e.g., agriculture, industries and sewage disposal) in case-2 water often lead to increased turbidity and reduced light * S.Yadav availability, which in turn negatively impacts the biogenic species in the aquatic environment such as fishes and corals in freshwater and coastal water (Silva et al, 2016;Fredston-Hermann et al, 2016;Lintern et al, 2017). Nutrient load to inland waters (Le et al, 2009;Yadav et al, 2017) and coastal zones (Cloern and Jassby, 2010) lead to eutrophication including harmful algal bloom. A most serious threat from eutrophication is hypoxia which is the decrease in the dissolved oxygen (DO) levels in bottom waters, created as planktonic algae decompose.…”

Canopy Reflectance Modeling of Aquatic Vegetation for Algorithm Development: Global Sensitivity Analysis

“…Another major application of wetland remote sensing is parameter inversion, which includes mainly biomass [9][10][11][12] and leaf area index (LAI) [13,14]. The inversion is usually based upon statistical methods that contain no fundamental mechanistic information about the optical properties of the vegetation itself.…”