Abstract
Expansive species classification with remote sensing techniques offers great support for botanical field works aimed at detection of their distribution within areas of conservation value and assessment of the threat caused to natural habitats. Large number of spectral bands and high spatial resolution allows for identification of particular species. LiDAR (Light Detection and Ranging) data provide information about areas such as vegetation structure. Because the species differ in terms of features during the growing season, it is important to know when their spectral responses are unique in the background of the surrounding vegetation. The aim of the study was to identify two expansive grass species: Molinia caerulea and Calamagrostis epigejos in the Natura 2000 area in Poland depending on the period and dataset used. Field work was carried out during late spring, summer and early autumn, in parallel with remote sensing data acquisition. Airborne 1-m resolution HySpex images and LiDAR data were used. HySpex images were corrected geometrically and atmospherically before Minimum Noise Fraction (MNF) transformation and vegetation indices calculation. Based on a LiDAR point cloud generated Canopy Height Model, vegetation structure from discrete and full-waveform data and topographic indexes were generated. Classifications were performed using a Random Forest algorithm. The results show post-classification maps and their accuracies: Kappa value and F1 score being the harmonic mean of producer (PA) and user (UA) accuracy, calculated iteratively. Based on these accuracies and botanical knowledge, it was possible to assess the best identification date and dataset used for analysing both species. For M. caerulea the highest median Kappa was 0.85 (F1 = 0.89) in August and for C. epigejos 0.65 (F1 = 0.73) in September. For both species, adding discrete or full-waveform LiDAR data improved the results. We conclude that hyperspectral (HS) and LiDAR airborne data could be useful to identify grassland species encroaching into Natura 2000 habitats and for supporting their monitoring.
Highlights
Non-forest communities such as grasslands and meadows are recognized as the most species-rich plant assemblages hosting numerous rare and endangered species
The objective of this study is to investigate the use of HySpex data and LiDAR products to classify the two expansive grass species C. epigejos and M. caerulea in Natura 2000 habitats, which have been recognized as aggressive competitors with the tendency of dominating the plant community, causing negative changes in its structure and species composition
This study investigated the use of HySpex and LiDAR data for mapping the distribution of M. caerulea and C. epigejos—expansive grass species in “Jaworzno Meadows” Natura 2000 site in Poland at their different growth stages
Summary
Non-forest communities such as grasslands and meadows are recognized as the most species-rich plant assemblages hosting numerous rare and endangered species. Increasing degradation of grassland and meadow communities have been reported recently by many authors [1,2,3]. One of the manifestations of the disadvantageous changes in grassland and meadow communities, including the ones important from a point of view of biodiversity conservation, is the entering of expansive species which can dominate the community and considerably limit species diversity. Research on the encroachment of alien invasive species into non-forest habitats is widely used and a large increase of native expansive species has been observed in many patches of non-forest communities without proper management. Preserving the species-rich non-forest communities requires monitoring of the state of their conservation values, in particular to detect any proliferation of undesirable species
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