Abstract
The use of hyperspectral (HS) and LiDAR acquisitions has a great potential to enhance mapping and monitoring practices of endangered grasslands habitats, beyond conventional botanical field surveys. In this study we assess the potentiality of recursive feature elimination (RFE) in combination with random forest (RF) classification in extracting the main HS and LiDAR features needed to map selected Natura 2000 grasslands along Polish lowland river valleys, in particular alluvial meadows 6440, lowland hay meadows 6510, and xeric and calcareous grasslands 6120. We developed an automated RFE-RF system capable to combine the potentials of both techniques and applied it to multiple acquisitions. Several LiDAR-based products and different spectral indices (SI) were computed and used as input in the system, with the aim of shedding light on the best-to-use features. Results showed a remarkable increase in classification accuracy when LiDAR and SI products are added to the HS dataset, strengthening in particular the importance of employing LiDAR in combination with HS. Using only the 24 optimal features selection generalized over the three study areas, strongly linked to the highly heterogeneous characteristics of the habitats and landscapes investigated, it was possible to achieve rather high classification results (K around 0.7–0.77 and habitats F1 accuracy around 0.8–0.85), indicating that the selected Natura 2000 meadows and dry grasslands habitats can be automatically mapped by airborne HS and LiDAR data. Similar approaches might be considered for future monitoring activities in the context of habitats protection and conservation.
Highlights
Protecting and monitoring natural habitats is essential for the mitigation of biodiversity decline, caused by the negative effects of increased human activity [1] and the natural adaptation to climate change
0.T01he(tbyepsict-atloly-u≤se0s.0p1e)citnradliccahtaesnnthealst stehleerceteids abysttahteistRicFaEllytecsihgnniiqfiucaen(tbadsieffderoennctehebeatpwpereonacthhedtewscoribed in Sreecstuioltns,3w.4h)ilwe hoenntchleascsoinfytrianrgy tfhoer 4a3ƿ0-vSaRlubeanhdigsh(eErxtph.an1a0).0a1r,etphleoretteisdninotFaigsutarteis7ti,cfaolrlyeasicghnisftiucadnyt area anddthfiofefreRreFeaEncchree[as9uc6ql]t.us,Iifswwititieohcnsotmcaatpimsatripceaaailllglynrse(isgdunilfftifsei,crwaennettcdacinoffloeobrressne)r.cveTe. hFtheoarrteeisxnuaamlltlspcslaehs,eofsowtrhttehhMeatBNtNhFersepmsruionlstgstlaoycuqutpuseeisrdiftoisorpmne,ctral chatnhneeMlsNbFelorensgulttoswhainvge aspmecetarnalKraapnpgaeos:f 0.72, while the recursive feature elimination (RFE) results (Exp. 1a) have a mean
The main objective of this study was to develop an automated system based on recursive feature elimination and random forest classification (RFE-RF), capable of complementing the potentiality of RF classification and RFE feature selection, for the mapping of selected Natura 2000 meadows and dry grasslands along Polish river valleys by the fusion of airborne Hyperspectral and light detection and ranging (LiDAR) data
Summary
Protecting and monitoring natural habitats is essential for the mitigation of biodiversity decline, caused by the negative effects of increased human activity [1] and the natural adaptation to climate change. Few works have been found in the current literature focusing on the exploitation of HS potentials for heathland habitats mapping and conservation, using different techniques such as, spectral unmixing [37], machine learning classification [38], or object-based image analysis [39,40] In recent years, it has been proven and recognized that light detection and ranging (LiDAR) systems, referred to as airborne laser scanning (ALS), are a good alternative for vegetation mapping and characterization [41,42,43,44,45]. The selected study areas have in common a highly variable topographical micro-relief (as described above and highlighted in Figure 2), a typical condition for the development of the investigated Natura 2000 habitats In such highly variable topographical context, the extraction of LiDAR-based metrics could be very meaningful and is expected to bring a significant contribution to the quality of the classification results
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