Abstract
Algal bloom in an inland lake is characterized by significant spatial and temporal dynamics. Accurate assessment of algal bloom distribution and dynamics is highly required for tracing the causes of and creating countermeasures for algal bloom. Satellite remote sensing provides a fast and efficient way to capture algal bloom distribution at a large scale, but it is difficult to directly derive accurate and quantitative assessment based on satellite images. In this study, the Gini coefficient and Lorenz asymmetry coefficient were introduced to examine the spatio-temporal algal bloom distribution of Chaohu Lake, the fifth largest inland lake in China. A total of 61 remote sensing images from three satellite sensors, Landsat, Gaofen, and Sentinel were selected to obtain algal bloom distributions. By dividing remote sensing images into 0.01°*0.01° grid cells, the normalized difference vegetation index (NDVI) for each grid cell was derived, forming a spatial and time series database for quantitative analysis. Two coefficients, Gini coefficient and Lorenz asymmetry coefficient, were used to evaluate the overall intensity, unevenness, and attribution of algal bloom in Chaohu Lake from 2011 to 2020. The Gini coefficient results show a large variety of algal bloom in the spatial and temporal scales of Chaohu lake. The lake edge and northwestern part had longer lasting and more severe algal bloom than the lake center, which was mainly due to nutrient import, especially from three northwestern tributaries that flow through the upstream city. The Lorenz asymmetry coefficient revealed the exact source of the unevenness. Spatial uncertainties were mostly caused by the tiny areas with high NDVI values, accounting for 53 cases out of 61 cases. Temporal unevenness in northwestern and northeastern parts of the lake was due to the most severe breakout occurrences, while unevenness in the lake center was mainly due to the large number of light occurrences. Finally, the advantage of Gini coefficient and Lorenz asymmetry coefficient are discussed by comparison with traditional statistical coefficients. By incorporating the two coefficients, this paper provides a quantitative and comprehensive assessment method for the spatial and temporal distribution of algal bloom.
Highlights
Algal bloom in water areas has been a critical worldwide environment issue for the past several decades (Haag, 2007)
This paper examined the characteristics of algal bloom distribution between 2011 and 2020 using mean normalized difference vegetation index (NDVI), Gini coefficient, and Lorenz asymmetry coefficient of Chaohu lake, China
By dividing 61 remote sensing images into equidistant grid cells, statistical analysis can be carried out based on grid cell data to explore spatial and temporal distribution and trend in a quantitative way
Summary
Algal bloom in water areas has been a critical worldwide environment issue for the past several decades (Haag, 2007). Algal bloom indicators derived from satellite remote sensing bands include normalized difference vegetation index (NDVI) (Van Der Wal et al, 2010; Lin et al, 2016), FAI (Hu, 2009; Zhang et al, 2014; Page et al, 2018), Chla (Hu, 2009; Zhang et al, 2014; Page et al, 2018; Guan et al, 2020; Pompeo et al, 2021), etc. In 2019, a global spatiotemporal algal blooms analysis covering 71 large lakes from 33 countries based on Landsat five satellite images (Ho et al, 2019) revealed that algal bloom in over 2/3 of lakes had been increasing during the last 30 years These studies show that the application of satellite remote sensing is a useful and efficient way to observe, track, and evaluate long-term and large-scale algal bloom distribution
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