Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 7; p. 1754
• Main Authors: Jang, Wonjin, Park, Yongeun, Pyo, JongCheol, Park, Sanghyun, Kim, Jinuk, Kim, Jin Hwi, Cho, Kyung Hwa, Shin, Jae-Ki, Kim, Seongjoon
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2022
• Subjects: Algae; Algorithms; Aquatic ecosystems; Artificial neural networks; band selection; Chlorophyll; chlorophyll-a; Cyanobacteria; data-driven model; Estimation; Freshwater environments; hyperspectral image; Hyperspectral imaging; Mathematical analysis; Neural networks; Optical properties; Phycocyanin; Pigments; Preempting; Quality management; Reflectance; Remote sensing; Rivers; Sensors; Spatial distribution; Spectra; Spectral bands; Temporal distribution; Water management; Water quality; Water quality management; United States > US; South Korea
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14071754

Understanding the concentration and distribution of cyanobacteria blooms is an important aspect of managing water quality problems and protecting aquatic ecosystems. Airborne hyperspectral imagery (HSI)—which has high temporal, spatial, and spectral resolutions—is widely used to remotely sense cyanobacteria bloom, and it provides the distribution of the bloom over a wide area. In this study, we determined the input spectral bands that were relevant in effectively estimating the main two pigments (PC, Phycocyanin; Chl-a, Chlorophyll-a) of cyanobacteria by applying data-driven algorithms to HSI and then evaluating the change in the spatio-temporal distribution of cyanobacteria. The input variables for the algorithms consisted of reflectance band ratios associated with the optical properties of PC and Chl-a, which were calculated by the selected hyperspectral bands using a feature selection method. The selected input variable was composed of six reflectance bands (465.7–589.6, 603.6–631.8, 641.2–655.35, 664.8–679.0, 698.0–712.3, and 731.4–784.1 nm). The artificial neural network showed the best results for the estimation of the two pigments with average coefficients of determination 0.80 and 0.74. This study proposes relevant input spectral information and an algorithm that can effectively detect the occurrence of cyanobacteria in the weir pool along the Geum river, South Korea. The algorithm is expected to help establish a preemptive response to the formation of cyanobacterial blooms, and to contribute to the preparation of suitable water quality management plans for freshwater environments.