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Mapping Riparian Habitats of Natura 2000 Network (91E0, 3240) at Individual Tree Level Using UAV Multi-Temporal and Multi-Spectral Data
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Mapping Riparian Habitats of Natura 2000 Network (91E0, 3240) at Individual Tree Level Using UAV Multi-Temporal and Multi-Spectral Data
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Mapping Riparian Habitats of Natura 2000 Network (91E0, 3240) at Individual Tree Level Using UAV Multi-Temporal and Multi-Spectral Data
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Journal Article
Mapping Riparian Habitats of Natura 2000 Network (91E0, 3240) at Individual Tree Level Using UAV Multi-Temporal and Multi-Spectral Data
2021
Overview
Riparian habitats provide a series of ecological services vital for the balance of the environment, and are niches and resources for a wide variety of species. Monitoring riparian environments at the intra-habitat level is crucial for assessing and preserving their conservation status, although it is challenging due to their landscape complexity. Unmanned aerial vehicles (UAV) and multi-spectral optical sensors can be used for very high resolution (VHR) monitoring in terms of spectral, spatial, and temporal resolutions. In this contribution, the vegetation species of the riparian habitat (91E0*, 3240 of Natura 2000 network) of North-West Italy were mapped at individual tree (ITD) level using machine learning and a multi-temporal phenology-based approach. Three UAV flights were conducted at the phenological-relevant time of the year (epochs). The data were analyzed using a structure from motion (SfM) approach. The resulting orthomosaics were segmented and classified using a random forest (RF) algorithm. The training dataset was composed of field-collected data, and was oversampled to reduce the effects of unbalancing and size. Three-hundred features were computed considering spectral, textural, and geometric information. Finally, the RF model was cross-validated (leave-one-out). This model was applied to eight scenarios that differed in temporal resolution to assess the role of multi-temporality over the UAV’s VHR optical data. Results showed better performances in multi-epoch phenology-based classification than single-epochs ones, with 0.71 overall accuracy compared to 0.61. Some classes, such as Pinus sylvestris and Betula pendula, are remarkably influenced by the phenology-based multi-temporality: the F1-score increased by 0.3 points by considering three epochs instead of two.
