Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data

Abstract

The Arctic is warming twice as fast as the rest of the planet, leading to rapid changes in species composition and plant functional trait variation. Landscape-level maps of vegetation composition and trait distributions are required to expand spatially-limited plot studies, overcome sampling biases associated with the most accessible research areas, and create baselines from which to monitor environmental change. Unmanned aerial vehicles (UAVs) have emerged as a low-cost method to generate high-resolution imagery and bridge the gap between fine-scale field studies and lower resolution satellite analyses. Here we used field spectroscopy data (400–2500 nm) and UAV multispectral imagery to test spectral methods of species identification and plant water and chemistry retrieval near Longyearbyen, Svalbard. Using the field spectroscopy data and Random Forest analysis, we were able to distinguish eight common High Arctic plant tundra species with 74% accuracy. Using partial least squares regression (PLSR), we were able to predict corresponding water, nitrogen, phosphorus and C:N values (r 2 = 0.61–0.88, RMSEmean = 12%–64%). We developed analogous models using UAV imagery (five bands: Blue, Green, Red, Red Edge and Near-Infrared) and scaled up the results across a 450 m long nutrient gradient located underneath a seabird colony. At the UAV level, we were able to map three plant functional groups (mosses, graminoids and dwarf shrubs) at 72% accuracy and generate maps of plant chemistry. Our maps show a clear marine-derived fertility gradient, mediated by geomorphology. We used the UAV results to explore two methods of upscaling plant water content to the wider landscape using Sentinel-2A imagery. Our results are pertinent for high resolution, low-cost mapping of the Arctic.

Identifying the Spectral Signatures of Invasive and Native Plant Species in Two Protected Areas of Pakistan through Field Spectroscopy

“…However, wavebands in NIR region (751-1075 nm) contributed the most (n = 313) in the discrimination in current study (Figure 9). These results agree with previous studies where leaf spectra have shown the greatest variation in the near-infrared and rededge regions [37,90,91]. Significant wavelengths in the red-edge region (680-750 nm) may be due to the variations in chlorophyll concentration, nitrogen concentration, and water content between different species [92,93].…”

Disturbance Mapping in Arctic Tundra Improved by a Planning Workflow for Drone Studies: Advancing Tools for Future Ecosystem Monitoring

“…The overall high classification accuracy with macro-F1 values of over 80% for all three sites suggests that the classification performed well, comparable or better than other dronebased classifications in the high Arctic, such as Fraser et al [89] and Thomson et al [48]. We found no consistent bias in the misclassification of the ground-cover classes.…”

“…They can help transferring from local and detailed knowledge to broadscale environments with more spatial and temporal complexity and can improve the interpretation of satellite imagery [32]. In the Arctic, drones have been used for vegetation mapping [33][34][35], measurements of cryosphere characteristics [36][37][38][39][40][41][42][43][44][45], observations of permafrost thaw [46,47] and to help bridge the gap between field-and satellite-derived data [32,35,48]. Long-term monitoring is stated as a goal in many recently published drone studies (e.g., [49,50]), but as the technology is quite new, few current studies have compared results from drone data between years or even within the same season [32,35].…”

Using leaf functional traits to remotely detect Cytisus scoparius (Linnaeus) Link in endangered savannahs

“…There are currently no civilian satellite programs capable of providing this type of data at the required spectral and spatial resolution, meaning that the imagery must be acquired from airborne sensors. Some studies have demonstrated that imagery collected from drone-based sensors can accurately map shrubland vegetation (Prošek and Šímová 2019) or predict functional traits in the arctic (Thomson et al 2021), but questions remain surrounding the capacity of these methods to differentiate small individuals in species-rich ecosystems (>20 species per 1 m 2 ), such as mixed woodland-grasslands. It may be possible, however, to generate a new nitrogen-index by selecting only bands common in multi-spectral sensors (Heim et al 2019) or correlate pre-existing multispectral remote sensing indices with the measured leaf %N values, eliminating the need for hyperspectral data collection and reducing the cost of both data acquisition and processing.…”