From UAV to PlanetScope: Upscaling fractional cover of an invasive species Rosa rugosa.

Invasive plant species pose a direct threat to biodiversity and ecosystem services. Among these, Rosa rugosa has had a severe impact on Baltic coastal ecosystems in recent decades. Accurate mapping and monitoring tools are essential to quantify the location and spatial extent of invasive plant species to support eradication programs. In this paper we combined RGB images obtained using an Unoccupied Aerial Vehicle, with multispectral PlanetScope images to map the extent of R. rugosa at seven locations along the Estonian coastline. We used RGB-based vegetation indices and 3D canopy metrics in combination with a random forest algorithm to map R. rugosa thickets, obtaining high mapping accuracies (Sensitivity = 0.92, specificity = 0.96). We then used the R. rugosa presence/absence maps as a training dataset to predict the fractional cover based on multispectral vegetation indices derived from the PlanetScope constellation and an Extreme Gradient Boosting algorithm (XGBoost). The XGBoost algorithm yielded high fractional cover prediction accuracies (RMSE = 0.11, R2 = 0.70). An in-depth accuracy assessment based on site-specific validations revealed notable differences in accuracy between study sites (highest R2 = 0.74, lowest R2 = 0.03). We attribute these differences to the various stages of R. rugosa invasion and the density of thickets. In conclusion, the combination of RGB UAV images and multispectral PlanetScope images is a cost-effective method to map R. rugosa in highly heterogeneous coastal ecosystems. We propose this approach as a valuable tool to extend the highly local geographical scope of UAV assessments into wider areas and regional evaluations.

Upscaling vascular aboveground biomass and topsoil moisture of subarctic fens from Unoccupied Aerial Vehicles (UAVs) to satellite level.

Arctic and subarctic ecosystems are experiencing rapid changes in vegetation composition and productivity due to global warming. Tundra wetlands are especially susceptible to these changes, which may trigger shifts in soil moisture dynamics. It is therefore essential to accurately map plant biomass and topsoil moisture. In this study, we mapped total, wood, and leaf above ground biomass and topsoil moisture in subarctic tundra wetlands located between Norway and Finland by linking models derived from Unoccupied Aerial Vehicles with multiple satellite data sources using the Extreme Gradient Boosting algorithm. The most accurate predictions for topsoil moisture (R2 = 0.73) used a set of red edge-based vegetation indices with a spatial resolution of 20 m per pixel. On the contrary, wood biomass showed the lowest accuracies across all tested models (R2 = 0.38). We found a trade-off between the spatial resolution and the performance of upscaling models, where smaller pixel sizes generally led to lower accuracies. However, we were able to compensate for reduced accuracy at smaller pixel sizes using Copernicus phenology metrics. A modelling uncertainty assessment revealed that the uncertainty of predictions increased with decreasing pixel sizes and increasing number of co-predictors. Our approach could improve efforts to map and monitor changes in vegetation at regional to pan-Arctic scales.

The Arctic Plant Aboveground Biomass Synthesis Dataset.

Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we present The Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m-2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.