THE MACROPHYTE FLORA AND VEGETATION OF THE PAUČKO LAKE (MT. KONJUH)

UDK: 581.9:574.5 (497.6) (285) The study presents the first data on biodiversity of macrophyte flora and vegetation of Paučko Lake, which is recognized as an area of great natural, landscape and hydrological value in the Protected Landscape “Konjuh”. Paučko Lake has a small surface and it’s located at 711 m a.s.l. in the catchment area of the Drinjača River. The aquatic and marsh vegetation were studied during spring and summer in 2018 using the traditional Zürich-Montpellier approach. The vegetation of Paučko Lake is comprised of aquatic and marsh associations of the classes Potamogetonetea Klika in Klika et Novák 1941 and Phragmito-Magnocaricetea Klika in Klika et Novák 1941. The following aquatic and marsh plant associations were identified: Myriophyllo-Potametum Soó 1934, Scirpo-Phragmitetum australis W. Koch 1926, Thelypterido palustris-Phragmitetum australis Kuiperex van Donselaar et al. 1961, Schoenoplectetum lacustris Chouard 1924, Typhetum latipholiae Lang. 1973 and Scirpetum silvatici Ht et H-ić prov. (in Ht et al.1974). Rare vulnerable taxa Thelypteris palustris Schott and Menyanthes trifoliata L. were recorded in emerged littoral communities, whose habitats are under successional changes caused by excessive macrophyte overgrowth by competitor species. Restoration measures are necessary to be taken to preserve the habitats of endangered species of the Paučko Lake.

Estimating Ground Elevation and Vegetation Characteristics in Coastal Salt Marshes Using UAV-Based LiDAR and Digital Aerial Photogrammetry

This study evaluates the skills of two types of drone-based point clouds, derived from LiDAR and photogrammetric techniques, in estimating ground elevation, vegetation height, and vegetation density on a highly vegetated salt marsh. The proposed formulation is calibrated and tested using data measured on a Spartina alterniflora-dominated salt marsh in Little Sapelo Island, USA. The method produces high-resolution (ground sampling distance = 0.40 m) maps of ground elevation and vegetation characteristics and captures the large gradients in the proximity of tidal creeks. Our results show that LiDAR-based techniques provide more accurate reconstructions of marsh vegetation (height: MAEVH = 12.6 cm and RMSEVH = 17.5 cm; density: MAEVD = 6.9 stems m−2 and RMSEVD = 9.4 stems m−2) and morphology (MAEM = 4.2 cm; RMSEM = 5.9 cm) than Digital Aerial Photogrammetry (DAP) (MAEVH = 31.1 cm; RMSEVH = 38.1 cm; MAEVD = 12.7 stems m−2; RMSEVD = 16.6 stems m−2; MAEM = 11.3 cm; RMSEM = 17.2 cm). The accuracy of the classification procedure for vegetation calculation negligibly improves when RGB images are used as input parameters together with the LiDAR-UAV point cloud (MAEVH = 6.9 cm; RMSEVH = 9.4 cm; MAEVD = 10.0 stems m−2; RMSEVD = 14.0 stems m−2). However, it improves when used together with the DAP-UAV point cloud (MAEVH = 21.7 cm; RMSEVH = 25.8 cm; MAEVD = 15.2 stems m−2; RMSEVD = 18.7 stems m−2). Thus, we discourage using DAP-UAV-derived point clouds for high-resolution vegetation mapping of coastal areas, if not coupled with other data sources.

Multiscale Mapping of the Salt Marshes Using Sentinel-2, Dove and UAV Imagery in the Bay of Mont-Saint-Michel

The salt marshes, lying at the land-sea temperate interface, furnish a plethora of ecosystems services such as biodiversity niche support, ocean-climate change regulation, ornithology recreo-tourism or plant gathering by hand. They undergo significant worldwide losses due to their conversion into crop fields and to their spatial compression between the rising sea-level and the armoring shoreline. Their monitoring however requires to use a suite of remote sensing sensors to embrace the regional scale while capturing the plant details. This research innovatively adopts a multiscale approach using a cascading spaceborne and airborne process, from the 10-m Sentinel-2, through the 3-m Dove, to the 0.03-m unmanned airborne vehicle (UAV) imageries. The high to very high temporal resolution of the Sentinel-2 and Dove enabled to cover twenties and tens of km2 over five and four years, respectively, in the form of normalized difference vegetation index (NDVI) classes, associated with microphytobenthos, low, medium and high salt marsh vegetation, including the opportunistic Elyma genus. The NDVI was then modelled at the UAV scale (a few km2) using a three-layered NN prediction, providing the final near-infrared (NIR), and the intermediate red, green and blue reflectance imageries, calibrated/validated/tested with the Dove reflectance imageries (R2NIR=0.98, R2red=0.88, R2green=0.84, and R2blue=0.90). The 100fold increase in pixel size allowed to detect the decimeter-scale objects of the tidal flats and salt marshes, to enlarge the NDVI class ranges, and hold great promise to model other spectral bands at the UAV scale for further deeply enhancing the salt marsh mapping.

RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System

Coastal tidal marshes are essential ecosystems for both economic and ecological reasons. They necessitate regular monitoring as the effects of climate change begin to be manifested in changes to marsh vegetation healthiness. Small unmanned aerial systems (sUAS) build upon previously established remote sensing techniques to monitor a variety of vegetation health metrics, including biomass, with improved flexibility and affordability of data acquisition. The goal of this study was to establish the use of RGB-based vegetation indices for mapping and monitoring tidal marsh vegetation (i.e., Spartina alterniflora) biomass. Flights over tidal marsh study sites were conducted using a multi-spectral camera on a quadcopter sUAS near vegetation peak growth. A number of RGB indices were extracted to build a non-linear biomass model. A canopy height model was developed using sUAS-derived digital surface models and LiDAR-derived digital terrain models to assess its contribution to the biomass model. Results found that the distance-based RGB indices outperformed the regular radio-based indices in coastal marshes. The best-performing biomass models used the triangular greenness index (TGI; R2 = 0.39) and excess green index (ExG; R2 = 0.376). The estimated biomass revealed high biomass predictions at the fertilized marsh plots in the Long-Term Research in Environmental Biology (LTREB) project at the study site. The sUAS-extracted canopy height was not statistically significant in biomass estimation but showed similar explanatory power to other studies. Due to the lack of biomass samples in the inner estuary, the proposed biomass model in low marsh does not perform as well as the high marsh that is close to shore and accessible for biomass sampling. Further research of low marsh is required to better understand the best conditions for S. alterniflora biomass estimation using sUAS as an on-demand, personal remote sensing tool.

Phytosociological Data in Assessment of Anthropogenic Changes in Vegetation of Rzeszów Reservoir

Phytosociological research on aquatic and marsh vegetation was conducted in Rzeszów Reservoir (SE Poland): 134 relevés according to the Braun-Blanquet method were collected there in 2016 and compared to 91 relevés published in 1994 (225 relevés in total). Changes in vegetation type, diversity measures, species composition, and Ellenberg Indicator Values (EIVs) for light, moisture, reaction, and nitrogen were analysed. Over the 22 years (1994–2016), the greatest changes were noted in communities of the classes Lemnetea and Potametea and the alliance Salicion albae. The long-term observations demonstrated the disappearance of 14 phytocoenoses and the occurrence of 12 new ones. An expansion of marsh communities (Typhetum latifoliae, Typhetum angustifoliae, Glycerietum maximae, Leersietum oryzoidis) was noted, causing a decline of several species and vegetation types. According to canonical correspondence analysis (CCA), four environmental variables (light, moisture, nitrogen, and pH) were related to plant distribution. The strong disturbances reflected in intensive eutrophication were due to human activity, which is the main factor shaping the ecological succession and overgrowing of the reservoir.