The essence of acquisition time of airborne hyperspectral and on-ground reference data for classification of highly invasive annual vine Echinocystis lobata (Michx.) Torr. & A. Gray

Airborne Hyperspectral Images and Machine Learning Algorithms for the Identification of Lupine Invasive Species in Natura 2000 Meadows

“…The campaign during the flowering period of the identified plants (August) was also optimal for classifying small-reed wood (F1: 0.90) and blackberry (0.98), whereas goldenrod was well identified in every campaign (F1 from 0.96 to 0.99) [58]. Similar conclusions were reached during the classification of Echinocystis lobata in the Bzura River valley in central Poland using the RF algorithm and HySpex images [9]. The F1 score for the species was the highest in summer (0.87) and was lower in spring (F1: 0.64) and autumn (F1: 0.75).…”

“…Remote sensing and machine learning methods have been increasingly used for monitoring the spread of invasive species [8,9]. Currently, various types of remote sensing imagery are available, and their selection involves a compromise between spatial, spectral, and temporal resolution and the spatial extent of the image [8,10].…”

“…Hyperspectral data are useful for morphologically similar plants [15][16][17] due to the identification of unique spectral signatures of plant species [18]. The use of HySpex hyperspectral data has enabled the accurate identification of wild cucumber (Echinocystis lobata, OA RF : 97%, F1: 0.87) [9], steeplebush (Spiraea tomentosa, OA RF : 99%, F1: 0.83) [19], purple moor-grass (Molinia caerulea, F1 RF : 0.86), and wood small-reed (Calamagrostis epigejos, F1 RF : 0.72) [20]. The Cubert S185 imaging spectrometer was successfully used to identify bitter vine (Mikania micrantha Kunth, OA RF : 88%, OA SVM : 84%) [21], and the Cubert UHD-185 hyperspectral camera was used for mapping common milkweed (Asclepias syriaca, OA SVM : 92%, OA ANN : 99%) [22].…”

“…For example, using nine MNF bands to classify herbaceous plant species in Hortobágy National Park provided higher overall accuracy for the support vector machine (SVM; OA: 82%) and random forest (RF; OA: 79%) classifications than the 128 original AISA spectral bands (OA: 73% for both algorithms) [30]. Many researchers have observed that the date of data acquisition also has a significant impact on the accuracy of invasive species identification [9,31,32].…”