The potential of remote sensing in ecological status assessment of coloured lakes using aquatic plants

Field-based survey methods for aquatic vegetation have been identified as resource-demanding. Recent advances in remote sensing (RS) with sub-decimetre resolution allow for surveying aquatic vegetation at the species level. Coloured lakes, mainly due to high concentrations of humic substances, are globally widespread. However, high colour impedes the identification of submerged vegetation via remote sensing. Here, we evaluate the potential of using only emergent, floating and floating-leaved taxa that are detectable by high-resolution RS (RS-taxa) to assess the ecological status of lakes. In a dataset covering 72 Swedish low alkaline coloured lakes, we identified 31 RS-taxa. The power of RS-taxa assemblages to predict non-RS assemblages was analysed by a combination of ordination (Detrended Correspondence Analysis, DCA, and Redundancy Analysis) and multiple regression analysis. We compared the performance of a trophic metric score based on RS-taxa with that based on field data from all taxa along different environmental gradients. Forty percent of the variability of the first non-RS taxa DCA-axis was predicted by the DCA-results based on RS-taxa. Correlations of the trophic metric score and total nitrogen concentrations were equally strong for the dataset based on RS-taxa compared to the dataset based on all taxa. For total phosphorous concentrations, the correlation was stronger for the dataset based on all taxa, but for a complex water quality gradient (including sulphate, N-species, chlorophyll and percent cover of wetlands in the riparian buffer) the correlation was higher for the RS-taxa dataset. The significant linkage between the two community fractions (remotely-sensible and non-sensible) revealed considerable assemblage concordance, suggesting a notable potential of the use of remote sensing in lake macrophyte monitoring. The established trophic metric score seems most qualified for surveillance monitoring that, in combination with the eased efforts of data acquisition, detects long-term changes of the aquatic environment caused by shifts in climate, land use and (related) eutrophication.