Mapping the fractional coverage of the invasive shrub Ulex europaeus with multi-temporal Sentinel-2 imagery utilizing UAV orthoimages and a new spatial optimization approach

Mapping the occurrence patterns of invasive plant species and understanding their invasion dynamics is a crucial
requirement for preventing further spread to so far unaffected regions. An established approach to map invasive
species across large areas is based on the combination of satellite or aerial remote sensing data with ground truth
data from fieldwork. Unmanned aerial vehicles (UAV, also referred to as unmanned aerial systems (UAS)) may
represent an interesting and low-cost alternative to labor-intensive fieldwork. Despite the increasing use of UAVs
in the field of remote sensing in the last years, operational methods to combine UAV and satellite data are still
sparse. Here, we present a new methodological framework to estimate the fractional coverage (FC%) of the
invasive shrub species Ulex europaeus (common gorse) on Chilo´e Island (south-central Chile), based on ultrahigh-
resolution UAV images and a medium resolution intra-annual time-series of Sentinel-2. Our framework is
based on three steps: 1) Land cover classification of the UAV orthoimages, 2) reduce the spatial shift between
UAV-based land cover classification maps and Sentinel-2 imagery and 3) identify optimal satellite acquisition
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dates for estimating the actual distribution of Ulex europaeus.
In Step 2 we translate the challenging co-registration task between two datasets with very different spatial
resolutions into an (machine learning) optimization problem where the UAV-based land cover classification
maps obtained in Step 1 are systematically shifted against the satellite images. Based on several Random Forest
(RF) models, an optimal fit between varying land cover fractions and the spectral information of Sentinel-2 is
identified to correct the spatial offset between both datasets.
Considering the spatial shifts of the UAV orthoimages and using optimally timed Sentinel-2 acquisitions led to
a significant improvement for the estimation of the current distribution of Ulex europaeus. Furthermore, we
found that the Sentinel-2 acquisition from November (flowering time of Ulex europaeus) was particularly
important in distinguishing Ulex europaeus from other plant species. Our mapping results could support local
efforts in controlling Ulex europaeus. Furthermore, the proposed workflow should be transferable to other use
cases where individual target species that are visually detectable in UAV imagery are considered. These findings
confirm and underline the great potential of UAV-based groundtruth data for detecting invasive species.