Sentinel-2 versus PlanetScope Images for Goldenrod Invasive Plant Species Mapping

Abstract

A proliferation of invasive species is displacing native species, occupying their habitats and degrading biodiversity. One of these is the invasive goldenrod (Solidago spp.), characterized by aggressive growth that results in habitat disruption as it outcompetes native plants. This invasiveness also leads to altered soil composition through the release of allelopathic chemicals, complicating control efforts and making it challenging to maintain ecological balance in affected areas. The research goal was to develop methods that allow the analysis of changes in heterogeneous habitats with high accuracy and repeatability. For this reason, we used open source classifiers Support Vector Machine (SVM), Random Forest (RF), and satellite images of Sentinel-2 (free) and PlanetScope (commercial) to assess their potential in goldenrod classification. Due to the fact that invasions begin with invasion footholds, created by small patches of invasive, autochthonous plants and different land cover patterns (asphalt, concrete, buildings) forming heterogeneous areas, we based our studies on field-verified polygons, which allowed the selection of randomized pixels for the training and validation of iterative classifications. The results confirmed that the optimal solution is the use of multitemporal Sentinel-2 images and the RF classifier, as this combination gave F1-score accuracy of 0.92–0.95 for polygons dominated by goldenrod and 0.85–0.89 for heterogeneous areas where goldenrod was in the minority (mix class; smaller share of goldenrod in canopy than autochthonous plants). The mean decrease in the accuracy analysis (MDA), indicating an informativeness of individual spectral bands, showed that Sentinel-2 bands coastal aerosol, NIR, green, SWIR, and red were comparably important, while in the case of PlanetScope data, the NIR and red were definitely the most important, and remaining bands were less informative, and yellow (B5) did not contribute significant information even during the flowering period, when the plant was covered with intensely yellow perianth, and red-edge, coastal aerosol, or green II were much more important. The maximum RF classification values of Sentinel-2 and PlanetScope images for goldenrod are similar (F1-score > 0.9), but the medians are lower for PlanetScope data, especially with the SVM algorithm.

Combining Multitemporal Optical and Radar Satellite Data for Mapping the Tatra Mountains Non-Forest Plant Communities

“…It was also used to map the occurrence of individual invasive species such as goldenrod (Solidago spp.) with a high-performance F1-score of 0.85-0.95 [51]. Most relevant is the application of satellite imagery acquired at different dates, which enables the capture of individual unique spectral features of vegetation that vary spectrally over time through discrete phenological characteristics.…”

Machine-Learning Approach for Identifying Arsenic-Contamination Hot Spots: The Search for the Needle in the Haystack

“…The supervised Mask R-CNN model, trained over Sentinel-2 or PlanetScope satellite imagery, has the ability to automatically produce detailed map views of similar geomorphological objects at alluvial-basin scale. Subsequently, the automatically generated map views are combined in a RF classifier (Figure ) with a set of predictor variables meeting the criteria for As hot spot remediation targets: oxbow-lake vegetation intensity and climate setting for the estimation of the yearly addition of organic matter to the lake sediment, essential for the process of reductive dissolution of As, and ArcGIS-generated digital elevation models (DEMs) combined with population density maps in the potential hot spot areas to identify the coincidence of point-bar locations with topographic high grounds and population nuclei. The approach will yield predictive As-risk maps, which serve to pinpoint target areas for the focused application of mitigation measures.…”