논문
Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach
학술지
Remote Sensing
저자
신재기
발표일
20220406
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.
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.