Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data
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- More information: Publisher Index Page (via DOI)
- Data Release: USGS data release - Chlorophyll-a concentrations and algal bloom condition paired with Sentinel-2 aquatic reflectance values collected for Brownlee Reservoir, ID from 2015 through 2020
- Open Access Version: Publisher Index Page
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Abstract
Algal blooms are pervasive in many freshwater environments and can pose risks to the health and safety of humans and other organisms. However, monitoring and tracking of potentially harmful blooms often relies on in-person observations by the public. Remote sensing has proven useful in augmenting in situ observations of algal concentration, but many hurdles hinder efficient application by end users. First, numerous approaches to estimate aquatic chlorophyll-a are available and can produce inconsistent results. Second, lack of quantitative in situ observations limits opportunities to train models for specific waterbodies, such that models developed for other systems must be used instead. We (1) implement univariate and multivariate logistic regression models to estimate the probability that aquatic chlorophyll-a concentrations exceed an accepted threshold beyond which harmful effects become likely and (2) evaluate the use of visually classified bloom/no-bloom satellite imagery to augment in situ training data. Using a binary classification of aquatic chlorophyll-a exceeding 10 μg / L, we found that (1) logistic regression models were ∼80 % accurate, (2) univariate models trained with visually classified data produce nearly the same accuracy (79%) as models trained with in situ observations (80%), and (3) augmenting in situ chlorophyll-a observations with visual classifications outperformed (82% accuracy) models trained on in situ observations alone (80% accuracy). These results provide a framework for evaluating multiple spectral indices in retrieving algal bloom presence or absence and illustrate that training data derived directly from satellite imagery can be useful in augmenting in situ observations.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Mapping the probability of freshwater algal blooms with various spectral indices and sources of training data |
| Series title | Journal of Applied Remote Sensing |
| DOI | 10.1117/1.JRS.16.044522 |
| Volume | 16 |
| Issue | 4 |
| Year Published | 2022 |
| Language | English |
| Publisher | SPIE Digital Library |
| Contributing office(s) | Idaho Water Science Center, Texas Water Science Center |
| Description | 044522, 22 p. |
| Country | United States |
| State | Idaho, Oregon |
| Google Analytic Metrics | Metrics page |