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Presence of invasive alien plant species in urban areas has become an issue throughout the globe. City administrations are making efforts to avoid invasions, to eradicate and/or control invasive species, or alternatively, try to process them into useful products. To monitor the spread of species over a region, it is important to map invasive species. In the scope of the APPLAUSE project, we have developed a One-Class support vector machine (SVM) approach to detect invasive species from individual aerial and multiple Sentinel-2 satellite images over the City of Ljubljana (Slovenia). In this paper, we focus specifically on the detection of Japanese knotweed, because it produces large stands and is therefore the most detectable invasive species in the studied area. The proposed SVM approach uses red-green-blue (RGB) band composites and infrared (IR) bands as input data for aerial images, while for satellite images additionally normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) are used. In the study, we used ground truth data collected by experts both as training and as validation data. On aerial images, we first perform segmentation, which is followed by two One-Class SVM classifications. On these classification outputs, we use K-means algorithm on the IR band, which groups the samples and removes the ones that were falsely recognized as Japanese knotweed. Merging the results together and masking out small samples and areas where Japanese knotweed is not present, we get the final result. On satellite data the approach is similar; the only difference is the usage of multiple input images from different acquisition dates for SVM classification. For detection of Japanese knotweed from the aerial images the accuracy was 83%, and 90% for stands larger than 100 m on satellite data. The results demonstrate that the applied methodology with the qualitative ground truth data can be used operationally for automatic detection of Japanese knotweed on the municipality level.

Automatic detection of Japanese knotweed in urban areas from aerial and satellite data