Monitoring Spatial Variability and Temporal Dynamics of Phragmites Using Unmanned Aerial Vehicles

Abstract

Littoral zones of freshwater lakes are exposed to environmental impacts from both terrestrial and aquatic sides, while substantial anthropogenic pressure also affects the high spatial, and temporal variability of the ecotone. In this study, the possibility of monitoring seasonal and spatial changes in reed (Phragmites australis) stands using an unmanned aerial vehicle (UAV) based remote sensing technique was examined. Stands in eutrophic and mesotrophic parts of Lake Balaton including not deteriorating (stable) and deteriorating (die-back) patches, were tracked throughout the growing season using a UAV equipped with a Normalized Difference Vegetation Index (NDVI) camera. Photophysiological parameters of P. australis were also measured with amplitude modulated fluorescence. Parameters characterizing the dynamics of seasonal changes in NDVI data were used for phenological comparison of eutrophic and mesotrophic, stable and die-back, terrestrial and aquatic, mowed and not-mowed patches of reed. It was shown that stable Phragmites plants from the eutrophic part of the lake reached specific phenological stages up to 3.5 days earlier than plants from the mesotrophic part of the lake. The phenological changes correlated with trophic (total and nitrate-nitrite nitrogen) and physical (organic C and clay content) properties of the sediment, while only minor relationships with air and water temperature were found. Phenological differences between the stable and die-back stands were even more pronounced, with ~34% higher rates of NDVI increase in stable than die-back patches, while the period of NDVI increase was 16 days longer. Aquatic and terrestrial parts of reed stands showed no phenological differences, although intermediate areas (shallow water parts of stands) were found to be less vigorous. Winter mowing of dried Phragmites sped up sprouting and growth of reed in the spring. This study showed that remote sensing-derived photophysiological and phenological variability within and between reed stands may provide valuable early indicators of environmental stress. The flexibility of the method makes it usable for mapping fine-scale temporal variability and spatial zonation within a stand, revealing ecophysiological hotspots that might require particular attention, and obtaining information vital for conservation and management of plants in the littoral zones.

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

“…UAVs can acquire very high spatial resolution data (~10 cm) with a user defined flight plan and flexible revisit time [30]. In addition, UAVs allow flying at different heights which can be utilized to adjust the spatial resolution of the images [31]. Consequently, very high spatial resolution imagery captured by UAVs became practical in natural resource management to monitor invasive plant species in several different ecosystems [31][32][33].…”

“…Consequently, very high spatial resolution imagery captured by UAVs became practical in natural resource management to monitor invasive plant species in several different ecosystems [31][32][33]. Several recent studies [22,[31][32][33][34][35][36] have proved that the use of UAV-borne remote sensing is an effective method to classify vegetation. Pande-Chhetri [37] used UAV data to classify wetland vegetation with pixel-based and hierarchical object-based classification approaches.…”

Photophysiology and Spectroscopy of Sun and Shade Leaves of Phragmites australis and the Effect on Patches of Different Densities

“…Nevertheless, wetland characteristics are mapped with remote sensing based mainly on multispectral data from a variety of platforms, such as unmanned aerial vehicles (UAVs) (e.g., [76][77][78][79]), or satellite passive sensors, such as the Landsat Thematic Mapper (TM) and Indian Remote Sensing Satellite (IRS) [80], Satellite pour l'Observation de la Terre (SPOT) [81], WorldView-2 [82] and Sentinel-2 [83]). Figure 7 indicates that there are only a few broadband spectral regions correlating with the parameters under study, and specifically the reflectance of the shade leaves with α at the shorter wavelengths.…”

Leaf reflectance can surrogate foliar economics better than physiological traits across macrophyte species

“…Although some interesting case studies in this respect have been recently documented (e.g. Bolpagni et al, 2014;Tóth, 2018;Villa et al, 2018), they were limited to one or few systems and few aquatic vegetation characteristics (functional types, phenology metrics) and wider applications are needed.…”