Comparison of Pixel- and Object-Based Classification Methods of Unmanned Aerial Vehicle Data Applied to Coastal Dune Vegetation Communities: Casal Borsetti Case Study

Abstract

Coastal dunes provide the hinterland with natural protection from marine dynamics. The specialized plant species that constitute dune vegetation communities are descriptive of the dune evolution status, which in turn reveals the ongoing coastal dynamics. The aims of this paper were to demonstrate the applicability of a low-cost unmanned aerial system for the classification of dune vegetation, in order to determine the level of detail achievable for the identification of vegetation communities and define the best-performing classification method for the dune environment according to pixel-based and object-based approaches. These goals were pursued by studying the north-Adriatic coastal dunes of Casal Borsetti (Ravenna, Italy). Four classification algorithms were applied to three-band orthoimages (red, green, and near-infrared). All classification maps were validated through ground truthing, and comparisons were performed for the three statistical methods, based on the k coefficient and on correctly and incorrectly classified pixel proportions of two maps. All classifications recognized the five vegetation classes considered, and high spatial resolution maps were produced (0.15 m). For both pixel-based and object-based methods, the support vector machine algorithm demonstrated a better accuracy for class recognition. The comparison revealed that an object approach is the better technique, although the required level of detail determines the final decision.

Spatial Variation in Coastal Dune Evolution in a High Tidal Range Environment

“…This means the time taken for UAV surveys over dunes is much reduced and the point cloud is not compromised with shadow zones [57]. UAVs have been used to measure dune recovery after storms [58], to investigate the impact of anthropogenic interventions on dune evolution [7,8,59], to map plant species [60] and consider the relationship between vegetation and morphology [44,61].…”

Classification of Atlantic Coastal Sand Dune Vegetation Using In Situ, UAV, and Airborne Hyperspectral Data

“…During the last few decades, multispectral and hyperspectral optical imagery showed high potential to characterize spatial and temporal dynamics of coastal vegetation [23][24][25]. When combined with exogenous data, these methods can answer ecological [26] and geomorphological [27] questions across a wide range of scales.…”

“…For instance, based on phenological features derived from multiyear time series of satellite images, phenology-based mapping approaches allow us to address low biomass and highly fragmented systems [24]. Hybrid pixel-based and object-oriented approaches using spectral and textural information at high [28,29] and very-high [25] resolutions demonstrate a high potential for discrimination of plant species and fine scale interspersed coastal plant communities.…”

The Identification of Seasonal Coastline Changes From Landsat 8 Satellite Data Using Artificial Neural Networks and K-Nearest Neighbor

“…Therefore, the selection of which classification method is used in change detection studies is important. There are many image classification methods used in literature such as membership function and, decision tree (DT) (Im and Jensen, 2005), random forest (RF) (Rodriguez-Galiano et al, 2012), support vector machine (SVM) (Duro et al, 2012, Kesikoglu et al, 2019, maximum-likelihood (ML) (De Giglio et al, 2019). In this study, we used two approaches: artificial neural networks (ANNs) and knearest neighbor (KNN).…”