Multiscale Object Feature Library for Habitat Quality Monitoring in Riparian Forests

Riparian forests are well known for their high biodiversity and critical ecological services. Earth observation-derived information can be used to analyze these habitats and their quality and conservation status, as well as change dynamics over time. In this study, object-based image analysis methods were applied to (semi-)automatically delineate forest habitats and to assess habitat quality in the Salzach river floodplain. Multitemporal and multiseasonal satellite imagery, in particular, WorldView-2 images from 2011/2012 as well as a SPOT-5 scene from 2005, was used. To perform validation on different levels of thematic resolution, locations of single trees of dominating species were collected, and tree species dominance and vegetation density were documented. The main pillar of the described workflow is the use of an object feature library (OFL). Conceptually, the OFL is needed to calibrate the method of information extraction and habitat quality estimations based on satellite imagery of different temporal, spectral, and spatial resolutions. This is considered a critical step to increase the robustness and transferability of the rule-based semantic classification approach.