A Review of Secchi’s Contribution to Marine Optics and the Foundation of Secchi Disk Science

Freshwater ecologists have shown increased interest in assessing biotic responses to environmental change using functional community characteristics. With this article, we investigate the potential of using functional traits of the aquatic plants to assess eutrophication in freshwater lakes. To this end we collected macrophyte and physicochemical data from thirteen lakes in Greece and we applied a trait-based analysis to first identify discrete groups of macrophytes that share common functional traits and then to assess preliminary responses of these groups to water quality gradients. We allocated 11 traits that cover mostly growth form and morphological characteristics to a total of 33 macrophyte species. RLQ and fourth corner analysis were employed to explore potential relationships between species, trait composition and environmental gradients. In addition, a hierarchical cluster analysis was conducted to discriminate groups of plants that share common trait characteristics and then the position of the groups along the environmental gradients was assessed. The results showed total phosphorus, chlorophyll-a, conductivity, pH and Secchi disk depth as main drivers of the environmental gradients. Hierarchical cluster analysis showed a clear separation of macrophyte assemblages with discrete functional characteristics that appeared to associate with different environmental drivers. Thus, rooted submerged plants were related with higher Secchi disk depth, conductivity and alkalinity whereas rooted floating-leaved plants showed a preference for enriched waters with phosphorus and nitrogen. In addition, free-floating plants were related positively with nitrogen and increased pH. Although we did not identify specific trait patterns with environmental drivers, our findings indicate a differentiation of macrophytes based on their functional characteristics along water quality gradients. Overall, the presented results are encouraging for conducting future monitoring studies in lakes focused on the functional plant trait composition, as expanding the current approach to additional lakes and using quantifiable functional characteristics will provide more insight about the potential of trait-based approaches as ecological assessment systems.

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Long-Term Change of the Secchi Disk Depth in Lake Maninjau, Indonesia Shown by Landsat TM and ETM+ Data

Remote Sensing ◽

10.3390/rs11232875 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2875

Author(s):

Fajar Setiawan ◽

Bunkei Matsushita ◽

Rossi Hamzah ◽

Dalin Jiang ◽

Takehiko Fukushima

Keyword(s):

Water Quality ◽

Lake Management ◽

Landsat Tm ◽

Quality Data ◽

Atmospheric Effects ◽

Secchi Disk ◽

Secchi Disk Depth ◽

Water Quality Data ◽

Using Data

Most of the lakes in Indonesia are facing environmental problems such as eutrophication, sedimentation, and depletion of dissolved oxygen. The water quality data for supporting lake management in Indonesia are very limited due to financial constraints. To address this issue, satellite data are often used to retrieve water quality data. Here, we developed an empirical model for estimating the Secchi disk depth (SD) from Landsat TM/ETM+ data by using data collected from nine Indonesian lakes/reservoirs (SD values 0.5–18.6 m). We made two efforts to improve the robustness of the developed model. First, we carried out an image preprocessing series of steps (i.e., removing contaminated water pixels, filtering images, and mitigating atmospheric effects) before the Landsat data were used. Second, we selected two band ratios (blue/green and red/green) as SD predictors; these differ from previous studies’ recommendation. The validation results demonstrated that the developed model can retrieve SD values with an R2 of 0.60 and the root mean square error of 1.01 m in Lake Maninjau, Indonesia (SD values ranged from 0.5 to 5.8 m, n = 74). We then applied the developed model to 230 scenes of preprocessed Landsat TM/ETM+ images to generate a long-term SD database for Lake Maninjau during 1987–2018. The visual comparison of the in situ-measured and satellite estimated SD values, as well as several events (e.g., algal bloom, water gate open, and fish culture), showed that the Landsat-based SD estimations well captured the change tendency of water transparency in Lake Maninjau, and these estimations will thus provide useful data for lake managers and policy-makers.

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