A 30-Year Assessment of Phytoplankton Blooms in Erhai Lake Using Landsat Imagery: 1987 to 2016

Abstract

Long-term information of phytoplankton bloom is critical for assessing the processes driving blooms in lakes. A three-decade survey of the phytoplankton blooms was completed for Erhai Lake from 1987 to 2016 with Landsat imagery. A modified three-band model using Landsat broad bands is developed by comparing reflectance data from Landsat imagery to two field datasets. The model is applied to the archived imagery to predict chlorophyll-a (Chl-a). Predicted ln(Chl-a) and observed ln(Chl-a) measurements are significantly correlated (R 2 = 0.70; RMSE = 0.13 µg/L). Bloom maps are generated by identifying Landsat pixels that have Chl-a concentrations larger than 20 µg/L as bloom area. Bloom extent and magnitude are estimated. Our study reveals that algal blooms first occurred in 1996 with a bloom area of 150 km 2 . Bloom occurred frequently from 2002 to 2016, with extreme blooms in 2003, 2013 and 2016. Algal blooms were mostly distributed in the northern and southern part of the lake. The proposed method uses one model for all Landsat images for Erhai Lake and can predict past blooms and extend the record to early years when field data is not available. The bloom extent and magnitude produced in this study can be used as the basis for the understanding of the processes that control the bloom outbreak.

Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

“…In Figure 8, we can see that the worst water-quality regions are in the north. The result is consistent with conclusions in previous studies [11]. Besides, in [11], the Chl-a concentration map on 12 November 2016 is similar to the water-quality-level distribution on 20 November 2016, shown in Figure 8.…”

“…The result is consistent with conclusions in previous studies [11]. Besides, in [11], the Chl-a concentration map on 12 November 2016 is similar to the water-quality-level distribution on 20 November 2016, shown in Figure 8. Although the water-quality levels of GB3838-2002 are not directly dependent on Chl-a according to Appendix A, nonoptically active parameters such as TN and TP are closely related to Chl-a and other optically active parameters [3,5].…”

“…Many scholars have proposed a variety of methods for water-quality monitoring [4,[6][7][8][9], and some studies were used for Erhai Lake and Chaohu Lake [5,11,39,40]. These methods mainly focus on a single water-quality parameter, such as Chl-a or TP.…”

“…However, the presented regression models are site-, season-, and scene-specific [10]. Therefore, a model obtained at one water body is not usually available for other water bodies.These models also mainly focus on the estimation of a single parameter, but remote-sensing reflectance may be influenced by changes of other water-quality variables [11]. Therefore, this may cause errors.…”

Estimating Environmental Preferences of Freshwater Pelagic Fish Using Hydroacoustics and Satellite Remote Sensing

“…For each satellite image, the corresponding sampling-date data subset was extracted and parameter values for chl-a, temperature, yellow substance (colored dissolved organic matter), green algae, and cyanobacteria were averaged for the euphotic zone. Temperature values were compared directly, but for the rest of the parameters, combinations of the surface reflectance values were calculated and tested for suitability based on algorithms and statistical models referenced in the literature (e.g., [22][23][24][25]). Parameters were log-transformed [22,23] for most of the models, along with the corresponding band surface reflectance values, unless a better fitting model could be calculated using the original values.…”