Landsat TM/ETM+ and machine-learning algorithms for limnological studies and algal bloom management of inland lakes

Abstract

Accumulating remotely sensed and ground-measured data and improvements in data mining such as machine-learning techniques open new opportunities for monitoring and managing algal blooms over large spatial scales. The goal of this study was to test the accuracy of remotely sensed algal biomass determined with machine-learning algorithms and Landsat TM/ETM+ imagery. We used chlorophyll-a concentration data from the 2007 National Lake Assessment (NLA) (lake N = 1157) by the US Environmental Protection Agency to train and test Landsat TM/ETM+ algorithms. Results showed significant improvements in chlorophyll-a retrieval accuracy using machine-learning algorithms compared with traditional empirical models using linear regression. Specifically, the results from boosted regression trees and random forest explained, respectively, 45.8% and 44.5% of chlorophyll-a variation. Multiple linear regression could only explain 39.8% of chlorophyll-a variation. The chlorophyll-a concentration derived from Landsat TM/ETM+ and a simple to use Google Earth Engine application, accurately characterized a 2009 algal bloom in western Lake Erie to show the model worked well for the analysis of temporal changes in algal conditions. Compared with chlorophyll-a data from the NLA, chlorophyll-a measurements with our Landsat TM/ETM+ model had almost the same correlation with lake’s total phosphorus concentrations, especially when using multiple Landsat images. Therefore, Landsat measurements of chlorophyll-a have value for ecological assessments and managing algal problems in lakes.