Abstract
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 Chiloé Island (south-central Chile), based on ultra-high-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 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.
Highlights
Invasive species pose a severe threat to ecosystems across the world (Huang & Asner, 2009)
We found that the Sentinel-2 acquisition from November was important in distinguishing Ulex europaeus from other plant species
The overall accuracies (OA%) of the land cover maps for the different tested feature groups (RGB bands [RGB], VIvis [VI], 2D texture param eters [gray-level co-occurrence matrix (GLCM)]) combinations and unmanned aerial vehicle (UAV) flights vary between 0.55% and 0.94%
Summary
Invasive species pose a severe threat to ecosystems across the world (Huang & Asner, 2009). Central Chile has been declared a global biodiversity hotspot with a high fraction of endemic species and is listed in six out of the nine common templates for global conservation priority regions (Brooks et al, 2006). This status is contrasted with an ongoing history of dramatic land-use changes, i.e. the replacement of native forests with plantations of exotic tree species (Echeverria et al, 2008) and the presence of a large number of alien and invasive species (Fuentes et al, 2013). In conse quence, the region has been reported to be one of the most threatened biodiverse ecosystems in the world (Dinerstein et al, 1995).
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