April 2019, May perhaps 2019, August August 2019 and ber 2019. (B ) (B ) represents zoom of
April 2019, May well 2019, August August 2019 and ber 2019. (B ) (B ) represents zoom of square in plot A. November 2019. represents a zoomaof the redthe red square in plot A.4. Discussion 4. Discussion 4.1. Wetlands Monitoring by UAVs four.1. Coastal Wetlands Monitoring by UAVs The results of this study demonstrate that vegetation The results of this study demonstrate that vegetation mapping, obtained from highresolution multispectral UAV observations to get a 400 by 400-m wetland, are in excellent agreeresolution multispectral UAV observations for a 400 by 400-m ment with direct observations and field measurements. Indeed, UAVs combined with ment with direct observations and field measurements. Certainly, UAVs combined with field field measurements, deliver really high-resolution vegetation of vegetation in restored measurements, give incredibly high-resolution vegetation maps maps of vegetation in reand engineered marshes, allowing for the for the identification of IQP-0528 In Vitro wetland vegetation dystored and engineered marshes, enabling identification of wetland vegetation dynamics, nearly in real-time. Seasonality and vegetation species migration are recognizable in the namics, almost in real-time. Seasonality and vegetation species migration are recognizable maps the maps made analysis utilizing high employing highUAV imagery, thereby introducing from produced from MSI from MSI evaluation resolution resolution UAV imagery, thereby this new methodologymethodology for monitoring spatial and temporal modifications at higher introducing this new for monitoring spatial and temporal adjustments at higher resolution for coastal wetland management. Determined by the ability to conduct UAV surveys often and resolution for coastal wetland management. Depending on the capability to conduct UAV surveys on quick notice, on short notice, UAV monitoring may be implemented ahead of intense regularly and UAV monitoring could be implemented ahead of extreme events, which include severe storms as extreme stormsmonitor and quantify their impacts on coastal wetlands. on events, such or hurricanes to or hurricanes to monitor and quantify their impacts coastal wetlands. 4.2. Vegetation Species CharacterizationOur study showed how high-resolution data might help to determine and to estimate the 4.2. Vegetation Species Characterization vegetation density in the two dominant marsh vegetationto recognize and to estimate the Our study showed how high-resolution data can help species, S. alterniflora and S. pumilus. Such final results have also been obtained by Doughty and Cavanaugh (2019) [31] who vegetation density from the two dominant marsh vegetation species, S. alterniflora and S. mapped the biomass in coastal wetlands making use of high-resolution multispectral UAV imagery. pumilus. Such results have also been obtained by Doughty and Cavanaugh (2019) [31] whoRemote Sens. 2021, 13,14 ofIn the present study, the high-resolution multispectral photos have allowed us to monitor the annual development and improvement of two species: S. alterniflora and S. pumilus. Furthermore, this study demonstrates that the NDVI values from the multispectral camera might be applied to assess and monitor the growth stage of vegetation, as already observed by other research [25,51,52]. Certainly, the results show an excellent correlation Guretolimod References between the NDVI (a commonly applied proxy for vegetation overall health and productivity) and the measured vegetation traits in the field (permitting us to assess the growth stage in the vegetation). Just before new growth begins in early spring, stand.