In shallow lakes, algal biomass is a fundamental indicator of eutrophication status. However, the vertical movement of phytoplankton within the water column can complicate the determination of total phytoplankton biomass using remotely sensed data of surface conditions. In this study, we develop, validate, and apply a new approach to use remotely sensed reflectance to estimate the variability of total algal biomass in shallow eutrophic lakes. Using the baseline normalized difference bloom index together with hydrological and bathymetric data, we determine the spatial and temporal dynamics of the total algal biomass in Lake Chaohu, a large shallow lake in eastern China under the nonalgae bloom conditions. Over an eleven-year period (2003-2013), the total phytoplankton biomass was highly variable, more than doubling between 2006 and 2007, from 19.95t to 39.50t. The seasonal decomposition of biomass dynamics indicated the highest biomass production occurred in June, while the lowest occurred in April. The estimates of total phytoplankton biomass were both consistent with in situ measurements and consistent for observations made on the same day and on consecutive days. The improved stability and reliability of total biomass estimations provided more complete information about lake conditions with respect to surface concentrations. This has implications for both management and modeling.

Li, J., Zhang, Y., Ma, R., Duan, H., Loiselle, S., Xue, K., et al. (2017). Satellite-Based Estimation of Column-Integrated Algal Biomass in Nonalgae Bloom Conditions: A Case Study of Lake Chaohu, China. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 10(2), 450-462.

Satellite-Based Estimation of Column-Integrated Algal Biomass in Nonalgae Bloom Conditions: A Case Study of Lake Chaohu, China

LOISELLE, STEVEN ARTHUR;
2017

Abstract

In shallow lakes, algal biomass is a fundamental indicator of eutrophication status. However, the vertical movement of phytoplankton within the water column can complicate the determination of total phytoplankton biomass using remotely sensed data of surface conditions. In this study, we develop, validate, and apply a new approach to use remotely sensed reflectance to estimate the variability of total algal biomass in shallow eutrophic lakes. Using the baseline normalized difference bloom index together with hydrological and bathymetric data, we determine the spatial and temporal dynamics of the total algal biomass in Lake Chaohu, a large shallow lake in eastern China under the nonalgae bloom conditions. Over an eleven-year period (2003-2013), the total phytoplankton biomass was highly variable, more than doubling between 2006 and 2007, from 19.95t to 39.50t. The seasonal decomposition of biomass dynamics indicated the highest biomass production occurred in June, while the lowest occurred in April. The estimates of total phytoplankton biomass were both consistent with in situ measurements and consistent for observations made on the same day and on consecutive days. The improved stability and reliability of total biomass estimations provided more complete information about lake conditions with respect to surface concentrations. This has implications for both management and modeling.
Li, J., Zhang, Y., Ma, R., Duan, H., Loiselle, S., Xue, K., et al. (2017). Satellite-Based Estimation of Column-Integrated Algal Biomass in Nonalgae Bloom Conditions: A Case Study of Lake Chaohu, China. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 10(2), 450-462.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/1007597
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