Remote sensing of macrophyte morphological traits: Implications for the management of shallow lakes

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Paolo Villa *
Monica Pinardi
Viktor R. Tóth
Peter D. Hunter
Rossano Bolpagni
Mariano Bresciani
(*) Corresponding Author:
Paolo Villa |


Macrophytes are important elements of freshwater ecosystems, fulfilling a pivotal role in biogeochemical cycles. The synoptic capabilities provided by remote sensing make it a powerful tool for monitoring aquatic vegetation characteristics and the functional status of shallow lake systems in which they occur. The latest generation of airborne and spaceborne imaging sensors can be effectively exploited for mapping morphologically – and physiologically – relevant vegetation features based on their canopy spectral response. The objectives of this study were to calibrate semi-empirical models for mapping macrophyte morphological traits (i.e., fractional cover, leaf area index and above-water biomass) from hyperspectral data, and to investigate the capabilities of remote sensing in supporting macrophyte monitoring and management. We calibrated spectral models using in situ reflectance and morphological trait measures and applied them to airborne hyperspectral imaging data, acquired over two shallow European water bodies (Lake Hídvégi, in Hungary, and Mantua lakes system, in Italy) in two key phenological phases. Maps of morphological traits were produced covering a broad range of aquatic plant types (submerged, floating, and emergent), common to temperate and continental regions, with an error level of 5.4% for fractional cover, 0.10 m2 m-2 for leaf area index, and 0.06 kg m-2 for above-water biomass. Based on these maps, we discuss how remote sensing could support monitoring strategies and shallow lake management with reference to our two case studies: i.e., by providing insight into spatial and species-wise variability, by assessing nutrient uptake by aquatic plants, and by identifying hotspot areas where invasive species could become a threat to ecosystem functioning and service provision.

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