UAV-Based Hyperspectral Imaging for River Algae Pigment Estimation

Riley D. Logan, Madison A. Torrey, Rafael Feijó-Lima, Benjamin P. Colman, H. Maurice Valett, Joseph A. Shaw

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Harmful and nuisance algal blooms are becoming a greater concern to public health, riverine ecosystems, and recreational uses of inland waterways. Algal bloom proliferation has increased in the Upper Clark Fork River due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of nitrogen from various sources. To improve understanding of bloom dynamics and how they affect water quality, often measured as algal biomass measured through pigment standing crops, a UAV-based hyperspectral imaging system was deployed to monitor several locations along the Upper Clark Fork River in western Montana. Image data were collected across the spectral range of 400–1000 nm with 2.1 nm spectral resolution during two field sampling campaigns in 2021. Included are methods to estimate chl a and phycocyanin standing crops using regression analysis of salient wavelength bands, before and after separating the pigments according to their growth form. Estimates of chl a and phycocyanin standing crops generated through a linear regression analysis are compared to in situ data, resulting in a maximum R2 of 0.96 for estimating fila/epip chl-a and 0.94 when estimating epiphytic phycocyanin. Estimates of pigment standing crops from total abundance, epiphytic, and the sum of filamentous and epiphytic sources are also included, resulting in a promising method for remotely estimating algal standing crops. This method addresses the shortcomings of current monitoring techniques, which are limited in spatial and temporal scale, by proposing a method for rapid collection of high-spatial-resolution pigment abundance estimates.
Original languageEnglish
Article number3148
Pages (from-to)3148
JournalRemote Sensing
Issue number12
StatePublished - Jun 2023


  • UAVs
  • algal blooms
  • algal pigment estimation
  • drones
  • hyperspectral imaging
  • imaging systems
  • inland waters
  • linear regression
  • remote sensing
  • river algae
  • water quality


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