Slope algorithm to map algal blooms in inland waters for Landsat 8/Operational Land Imager images

Abstract

Abstract. Monitoring algal blooms using traditional methods is expensive and labor intensive. The use of satellite technology can attenuate such limitations. A common problem associated with the application of such technology is the need to eliminate the effects of atmosphere, which can be, at least, a time-consuming task. Thus, a remote sensed algal bloom monitoring system needs a simple algorithm which is nonsensitive to atmospheric correction and that could be applied to small aquatic systems. A slope algorithm (SA red−NIR ) was developed to detect and map the extension of algal blooms using the Landsat 8/Operational Land Imager. SA red−NIR was shown to have advantages over other commonly used indices to monitor algal blooms, such as normalized difference vegetation index (NDVI), normalized difference water index, and floating algae index. SA red−NIR was shown to be less sensitive to different atmospheric corrections, less sensitive to thin clouds, and less susceptible to confusion when classifying water and moderate bloom conditions. Based on ground truth data from Eagle Creek Reservoir, Indiana, SA red−NIR showed an accuracy of 88.46% while NDVI only showed a 46.15% accuracy. Finally, based on qualitative and quantitative results, SA red−NIR can be used as a tool to improve the governance of small size water resources.

Using Synergy between Water Limnology and Satellite Imagery to Identify Algal Blooms Extent in a Brazilian Amazonian Reservoir

“…To assess the extent of algal blooms in the THR, we used a simple slope algorithm approach (referred as SA red-NIR ) developed by [32] for inland waters, using OLI and TM sensors of the Landsat imagery and Equation (2):…”

“…Moderated bloom is probable to occur when the results of the SA red-NIR is in the range of −0.05 to 0.15 and Chl-a 5 to 10 (µg L −1 ). Additionally, severe bloom is characterized by positive slope between band 4 (665 nm) and 5 (865 nm), and it is more neutral for moderated bloom and negative for non-bloom conditions according to [32]. Table 2.…”

Extending Landsat 8: Retrieval of an Orange contra-Band for Inland Water Quality Applications

“…The Operational Land Imager (OLI) on Landsat 8 resolves four visible bands covering the deep-blue (435-451 nm), blue (452-512 nm), green (533-590 nm) and red (636-673 nm) and has found successful application for remote sensing of water quality (e.g., transparency, dissolved organic carbon and algal biomass [10][11][12]). This waveband configuration, however, does not allow retrieval of direct information related to phycocyanin (PC) abundance, a diagnostic pigment for the presence of cyanobacteria which are a major concern in eutrophic inland waters.…”

Automatic Detection of Algal Blooms Using Sentinel-2 MSI and Landsat OLI Images

“…alteration from dam construction [4], and climate change [1,6]. Algal blooms disrupt ecological equilibrium and food webs [1,3,7,[9][10][11], and threaten freshwater systems for drinking, irrigation, fishing and recreation [2,[12][13][14].…”

“…Algal blooms are generally characterized by very complex temporal variability due to their capacity to replicate quickly and migrate vertically within the water column [3,11,12]. Field monitoring with few observations, including conventional ship and station-based investigations, are unable to adequately sample the occurrence, frequency, spatial extent and magnitude of algal blooms in inland waters [1,5,12,14,17,18]. However, satellite imagery with diverse spatial and temporal resolutions has great potential for timely and accurate algal bloom monitoring at large spatial extents, frequency, drifting rates and occurrence duration [6,19,20].…”