Mapping invasive Lupinus polyphyllus Lindl. in semi-natural grasslands using object-based analysis of UAV-borne images

Abstract

Knowledge on the spatio-temporal distribution of invasive plant species is vital to maintain biodiversity in grasslands which are threatened by the invasion of such plants and to evaluate the effect of control activities conducted. Manual digitising of aerial images with field verification is the standard method to create maps of the invasive Lupinus polyphyllus Lindl. (Lupine) in semi-natural grasslands of the UNESCO biosphere reserve “Rhön”. As the standard method is labour- and time-intensive, a workflow was developed to map lupine coverage using an unmanned aerial vehicle (UAV)-borne remote sensing (RS) along with object-based image analysis (OBIA). UAV-borne red, green, blue (R, G, B) and thermal imaging, as well as photogrammetric canopy height modelling (CHM), were applied. Images were segmented by unsupervised parameter optimisation into image objects representing lupine plants and grass vegetation. Image objects obtained were classified using random forest classification modelling based on objects’ attributes. The developed classification model was employed to create lupine distribution maps of test areas and predicted data were compared with manually digitised lupine coverage maps. The classification models yielded a mean prediction accuracy of 89 %, and 0.78 mean kappa statistics. The maximum difference in lupine area between classified and digitised lupine maps was 5 %. Moreover, the pixel-wise map comparison showed that 88 % of all pixels matched between classified and digitised maps. Our results indicate that lupine coverage mapping using UAV-borne RS data and OBIA provides similar results as the standard manual digitising method and, thus, offers a valuable tool to map invasive lupine on grasslands.

Systematic review and best practices for drone remote sensing of invasive plants

“…Other related foci included testing different vegetation indices or structural layers (i.e. DSM or CHM) to improve classifications (Kedia et al, 2021;Lopatin et al, 2019;Martin et al, 2018;Müllerová et al, 2017;Wijesingha et al, 2020). For example, Kedia et al found the inclusion of canopy structural information from the DSM and CHM improved overall classification accuracy for multiple invasive species by 13%, while Marzialetti et al (2021) found the inclusion of the DSM improved mapping accuracy of golden wreath wattle (Acacia saligna) by more than 3%.…”

“…Since most camera manufacturers do not specify the wavebands comprising each image channel (Mathews, 2015), camera calibrations are highly recommended. Yet just under 6% of the studies we reviewed articles mentioned calibration, and there was typically very little description of how calibration was accomplished (Michez et al, 2016;Wijesingha et al, 2020). Calibration can be achieved with a highly reflective white reflectance panel designed to give near-perfect diffuse reflectance (Singh, 2021) from which image reflectance values can be standardized and adjusted.…”

“…Flight patterns were rarely reported (11%), but most followed a single grid/parallel lines (Perroy et al, 2017), double grid (Wijesingha et al, 2020) or zig-zag (Gonçalves et al, 2022) formation.…”

“…Only 20% of studies reported the details of using GCPs for photogrammetric control although many more were inferred to have used these. GCP materials included plastic discs (Kedia et al, 2021), orange safety cones (Baron & Hill, 2020), black and white checkered boards (Wijesingha et al, 2020), compact discs (Lehmann et al, 2017), white panels and even natural features (Nascente et al, 2022). However, very little justification for the number, type or location was provided, which aligns with other findings (Mesas-Carrascosa et al, 2017).…”

Synergetic use of unmanned aerial vehicle and satellite images for detecting non-native tree species: An insight into Acacia saligna invasion in the Mediterranean coast

“…On the other side, technical constraints of UAVs (e.g., battery capability and surveying restrictions) often prevent the use of this technology to describe ecosystem patterns on a large scale. However, the ability to acquire highly customized data makes UAVs an interesting option to provide accurate maps of local IAS occurrence (e.g., Wijesingha et al, 2020;Marzialetti et al, 2021), supporting satellite detection at wider scales. The contribution of VHR images for aiding IAS satellite detection deserves to be further explored (Elkind et al, 2019;Alvarez-Vanhard et al, 2021).…”