Sub-metric analisis of vegetation structure in bog-heathland mosaics using very high resolution rpas imagery

Abstract

This work presents a methodology based on Remotely Piloted Aircraft Systems (RPAS) RGB imagery for the analysis and characterization of the structure and condition of complex habitat mosaics of high conservation value in mountain wetlands. Structure from Motion (SfM) image reconstruction techniques were applied on a collection of RGB photographs to derive ultra-high resolution (2,5 cm) digital surface models and ortho-mosaics. Geographical object-based image analysis (GEOBIA) was used for the automatic discrimination of vegetation types for habitat condition assessment via multi-scale object-oriented classifications integrating machine-learning classification techniques with other decision rules. In particular, four vegetation classes were assessed, namely woody vegetation (scrub plants higher than 10 cm), bog herbaceous vegetation, non-bog herbaceous vegetation (as part of wet heathland vegetation mosaics) and areas with scarce or no vegetation (rocky habitats and areas of bare ground). The outputs of the classification were validated against field data of detailed vegetation coverage survey. Results allowed us the automatic and accurate discrimination of habitat types with different management demands, e.g. wet heaths against bogs, as well as the diagnosis of structural characteristics critical for their conservation, such as the ratio of cover herbaceous/woody species or the presence of erosion features.