摘要
Algae blooms pose a threat to water quality by depleting oxygen during decomposition and also cause other issues with water quality and water use. Algae biomass is traditional monitored through field samples analyzed for chlorophyll-a, a pigment present in all algae. Field sampling can be time- and cost-intensive, especially in areas that are difficult to access and provides only limited spatial coverage. Estimations of algal biomass based on remote sensing data have been explored over the past two decades as a supplement to information obtained from limited field samples. We use Landsat data to develop and demonstrate seasonal remote sensing models, a relatively recent method, to evaluate spatial and temporal algae distributions for the Jordanelle Reservoir, located in north-central Utah. Remote sensing of chlorophyll as a monitoring and analysis method can provide a more spatially complete representation of algae distribution and biomass;information that is difficult to obtain using point samples.
Algae blooms pose a threat to water quality by depleting oxygen during decomposition and also cause other issues with water quality and water use. Algae biomass is traditional monitored through field samples analyzed for chlorophyll-a, a pigment present in all algae. Field sampling can be time- and cost-intensive, especially in areas that are difficult to access and provides only limited spatial coverage. Estimations of algal biomass based on remote sensing data have been explored over the past two decades as a supplement to information obtained from limited field samples. We use Landsat data to develop and demonstrate seasonal remote sensing models, a relatively recent method, to evaluate spatial and temporal algae distributions for the Jordanelle Reservoir, located in north-central Utah. Remote sensing of chlorophyll as a monitoring and analysis method can provide a more spatially complete representation of algae distribution and biomass;information that is difficult to obtain using point samples.
