Using Single- and Multi-Date UAV and Satellite Imagery to Accurately Monitor Invasive Knotweed Species

Abstract

Understanding the spatial dynamics of invasive alien plants is a growing concern for many scientists and land managers hoping to effectively tackle invasions or mitigate their impacts. Consequently, there is an urgent need for the development of efficient tools for large scale mapping of invasive plant populations and the monitoring of colonization fronts. Remote sensing using very high resolution satellite and Unmanned Aerial Vehicle (UAV) imagery is increasingly considered for such purposes. Here, we assessed the potential of several single- and multi-date indices derived from satellite and UAV imagery (i.e., UAV-generated Canopy Height Models—CHMs; and Bi-Temporal Band Ratios—BTBRs) for the detection and mapping of the highly problematic Asian knotweeds (Fallopia japonica; Fallopia × bohemica) in two different landscapes (i.e., open vs. highly heterogeneous areas). The idea was to develop a simple classification procedure using the Random Forest classifier in eCognition, usable in various contexts and requiring little training to be used by non-experts. We also rationalized errors of omission by applying simple “buffer” boundaries around knotweed predictions to know if heterogeneity across multi-date images could lead to unfairly harsh accuracy assessment and, therefore, ill-advised decisions. Although our “crisp” satellite results were rather average, our UAV classifications achieved high detection accuracies. Multi-date spectral indices and CHMs consistently improved classification results of both datasets. To the best of our knowledge, it was the first time that UAV-generated CHMs were used to map invasive plants and their use substantially facilitated knotweed detection in heterogeneous vegetation contexts. Additionally, the “buffer” boundary results showed detection rates often exceeding 90–95% for both satellite and UAV images, suggesting that classical accuracy assessments were overly conservative. Considering these results, it seems that knotweed can be satisfactorily mapped and monitored via remote sensing with moderate time and money investment but that the choice of the most appropriate method will depend on the landscape context and the spatial scale of the invaded area.

Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

“…The importance of using CHM to identify Phragmites, as it reduces both errors of omission and commission, is also clearly demonstrated, especially when CHM is combined with NDVI Oct . Several other studies showed the advantages of CHM [83][84][85]. The reduction of errors of omission and commission for Phragmites was observed in the study of Samiappan et al [33], where the author used the SVM classifier.…”

Mapping Invasive Lupinus polyphyllus Lindl. in Semi-natural Grasslands Using Object-Based Image Analysis of UAV-borne Images

“…Phragmites australis in estuaries by Abeysinghe et al (2019) and Fallopia spp. in floodplains by Martin et al (2018)).…”

“…In grasslands, invasive woody (Mirik et al 2013) and shrubby (Laliberte et al 2004;Ishii and Washitani 2013) species mapping has shown excellent results with satellite and airborne image classification. Meanwhile, using an unmanned aerial vehicle (UAV)-borne remotely sensed (RS) imaging was increasingly applied for invasive species mapping in the recent years in flood plains and coastal regions (de Sá et al 2018;Martin et al 2018;Abeysinghe et al 2019;Jianhui et al 2019). Cost-effectiveness, high spatial and temporal resolution and the increasing availability of various miniature sensors (e.g.…”

“…Object-based image analysis (OBIA) can better be employed when pixels are considerably smaller than the object to be identified (Blaschke 2010). Several studies indicated that OBIA provides substantial advantages for mapping invasive plant species based on UAV-borne RS data (Michez et al 2016;Martin et al 2018;Abeysinghe et al 2019). Typical OBIA employs rules derived from the object's attributes to classify an object, which requires expert knowledge and limits the transferability of the classification model (Belgiu et al 2014).…”

Invasive Japanese knotweed (Reynoutria japonica Houtt.) and related knotweeds as catalysts for streambank erosion

“…Pairing such models with field studies or experiments would provide especially valuable insights into the relationship between these species and erosive processes. Photogrammetric analyses can provide useful insights into general fluvial biogeomorphic dynamics (Hortobágyi et al, 2017), and satellite and unmanned aerial vehicles (UAVs) can accurately map knotweed s.l . (Martin et al, 2018). Utilizing both technologies could generate biogeomorphic insights specific to locations infested with knotweed s.l . Establishing and conducting small field studies in various locations using standardized techniques would permit inter‐regional comparisons of hydrological regimes and soil types. Organizations concerned with improving road/stream crossings could integrate knotweed s.l .‐related questions into infrastructure assessments.…”