Coastal salt marshes are valuable and critical components of tidal landscapes, currently threatened by increasing rates of sea level rise, wave-induced lateral erosion, decreasing sediment supply, and human pressure. Halophytic vegetation plays an important role in salt-marsh erosional and depositional patterns and marsh survival. Mapping salt-marsh halophytic vegetation species and their fractional abundance within plant associations can provide important information on marsh vulnerability and coastal management. Remote sensing has often provided valuable methods for salt-marsh vegetation mapping; however, it has seldom been used to assess the fractional abundance of halophytes. In this study, we developed and tested a novel approach to estimate fractional abundance of halophytic species and bare soil that is based on Random Forest (RF) soft classification. This approach can fully use the information contained in the frequency of decision tree “votes” to estimate fractional abundance of each species. Such a method was applied to WorldView-2 (WV-2) data acquired for the Venice lagoon (Italy), where marshes are characterized by a high diversity of vegetation species. The proposed method was successfully tested against field observations derived from ancillary field surveys. Our results show that the new approach allows one to obtain high accuracy (6.7% < root-mean-square error (RMSE) < 18.7% and 0.65 < R2 < 0.96) in estimating the sub-pixel fractional abundance of marsh-vegetation species. Comparing results obtained with the new RF soft-classification approach with those obtained using the traditional RF regression method for fractional abundance estimation, we find a superior performance of the novel RF soft-classification approach with respect to the existing RF regression methods. The distribution of the dominant species obtained from the RF soft classification was compared to the one obtained from an RF hard classification, showing that numerous mixed areas are wrongly labeled as populated by specific species by the hard classifier. As for the effectiveness of using WV-2 for salt-marsh vegetation mapping, feature importance analyses suggest that Yellow (584–632 nm), NIR 1 (near-infrared 1, 765–901 nm) and NIR 2 (near-infrared 2, 856–1043 nm) bands are critical in RF soft classification. Our results bear important consequences for mapping and monitoring vegetation-species fractional abundance within plant associations and their dynamics, which are key aspects in biogeomorphic analyses of salt-marsh landscapes.
Tight interplays between physical and biotic processes in tidal salt marshes lead to self‐organization of halophytic vegetation into recurrent zonation patterns developed across elevation gradients. Despite its importance for marsh ecomorphodynamics, however, the response of vegetation zonation to changing environmental forcings remains difficult to predict, mostly because of lacking long‐term field observations of vegetation evolution in the face of changing rates of sea level rise and marsh vertical accretion. Here we present novel data of coupled marsh elevation‐vegetation distribution collected in the microtidal Venice Lagoon (Italy) over nearly two decades. Our results suggest that: (a) despite increasing absolute marsh elevations (i.e., above a fixed datum), vertical accretion rates across most of the studied marsh were not high enough to compensate for relative sea‐level rise (RSLR), thus leading to a progressive marsh drowning; (b) accretion rates ranging 1.7–4.3 mm/year are overall lower than the measured RSLR rate (4.4 mm/year) and strongly site‐specific. Accretion rates vary largely at sites within distances of a few tens of meters, being controlled by local elevation and sediment availability from eroding marsh edges; (c) vegetation responds species‐specifically to changes in environmental forcings by modifying species‐preferential elevation ranges. For the first time, we observe the consistency of a sequential vegetation‐species zonation with increasing marsh elevations over 20 years. We suggest this is the signature of vegetation resilience to changes in external forcings. Our results highlight a strong coupling between geomorphological and ecological dynamics and call for spatially distributed marsh monitoring and spatially explicit biomorphodynamic models of marsh evolution.
<p>Coastal salt-marshes are important eco-geomorphic features of coastal landscapes providing valuable ecosystem services, but unfortunately, they are among the most vulnerable ecosystems around the world. Their survival is mainly threatened by sea-level rise, wave erosion and human pressure. Halophytic vegetation distribution and dynamics control salt-marsh erosional and depositional patterns, critically determining marsh survival through complex bio-morphodynamic feedbacks. Although a number of studies have proposed species-classification methods and analyzed halophytic vegetation species distribution, our knowledge of the temporal evolution of species composition remains limited. To fill these gaps and better describe vegetation composition changes in time, we developed a novel classification method which is based on the Random Forest soft classification algorithm, and applied the method to two multi-spectral images of the San Felice marsh in the Venice lagoon (Italy) acquired in 2001 and 2019. The Random Forest soft classification achieves high accuracy (0.60 < <em>R</em><sup>2</sup> < 0.96) in the estimation of the fractional abundance of each species in both images. We also determined the local dominant species, i.e. the species with the highest fractional abundance in each pixel. Our observations on the dominant species in 2001 and 2019 show that: 1) the area dominated by <em>Juncus</em> and <em>Spartina</em> decreased dramatically in such period; 2) the area dominated by <em>Limonium </em>almost maintained constant; 3) a noticeable decrease in the bare-soil area occurred due to the encroachment of <em>Salicornia</em> between 2001 and 2019. We also noticed that the probability distribution of the dominant patch area of each species is consistent with a power-law distribution, with different slopes for different vegetation species at different times. We suggest that vegetation composition changes are related to sea-level rise and to the species-specific inundation tolerance.</p>
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