Filtern
Dokumenttyp
- Wissenschaftlicher Artikel (3) (entfernen)
Sprache
- Englisch (3) (entfernen)
Schlagworte
- Luxemburg (3) (entfernen)
Extension of an Open GEOBIA Framework for Spatially Explicit Forest Stratification with Sentinel-2
(2022)
Spatially explicit information about forest cover is fundamental for operational forest management and forest monitoring. Although open-satellite-based earth observation data in a spatially high resolution (i.e., Sentinel-2, ≤10 m) can cover some information needs, spatially very high-resolution imagery (i.e., aerial imagery, ≤2 m) is needed to generate maps at a scale suitable for regional and local applications. In this study, we present the development, implementation, and evaluation of a Geographic Object-Based Image Analysis (GEOBIA) framework to stratify forests (needleleaved, broadleaved, non-forest) in Luxembourg. The framework is exclusively based on open data and free and open-source geospatial software. Although aerial imagery is used to derive image objects with a 0.05 ha minimum size, Sentinel-2 scenes of 2020 are the basis for random forest classifications in different single-date and multi-temporal feature setups. These setups are compared with each other and used to evaluate the framework against classifications based on features derived from aerial imagery. The highest overall accuracies (89.3%) have been achieved with classification on a Sentinel-2-based vegetation index time series (n = 8). Similar accuracies have been achieved with classification based on two (88.9%) or three (89.1%) Sentinel-2 scenes in the greening phase of broadleaved forests. A classification based on color infrared aerial imagery and derived texture measures only achieved an accuracy of 74.5%. The integration of the texture measures into the Sentinel-2-based classification did not improve its accuracy. Our results indicate that high resolution image objects can successfully be stratified based on lower spatial resolution Sentinel-2 single-date and multi-temporal features, and that those setups outperform classifications based on aerial imagery only. The conceptual framework of spatially high-resolution image objects enriched with features from lower resolution imagery facilitates the delivery of frequent and reliable updates due to higher spectral and temporal resolution. The framework additionally holds the potential to derive additional information layers (i.e., forest disturbance) as derivatives of the features attached to the image objects, thus providing up-to-date information on the state of observed forests.
Digital technologies have become central to social interaction and accessing goods and services. Development strategies and approaches to governance have increasingly deployed self-labelled ‘smart’ technologies and systems at various spatial scales, often promoted as rectifying social and geographic inequalities and increasing economic and environmental efficiencies. These have also been accompanied with similarly digitalized commercial and non-profit offers, particularly within the sharing economy. Concern has grown, however, over possible inequalities linked to their introduction. In this paper we critically analyse the role of sharing economies’ contribution to more inclusive, socially equitable
and spatially just transitions. Conceptually, this paper brings together literature on sharing economies, smart urbanism
and just transitions. Drawing on an explorative database of sharing initiatives within the cross-border region of Luxembourg and Germany, we discuss aspects of sustainability as they relate to distributive justice through spatial accessibility, intended benefits, and their operationalization. The regional analysis shows the diversity of sharing models, how they are appropriated in different ways and how intent and operationalization matter in terms of potential benefits.
Results emphasize the need for more fine-grained, qualitative research revealing who is, and is not, participating and
benefitting from sharing economies.
Up-to-date information about the type and spatial distribution of forests is an essential element in both sustainable forest management and environmental monitoring and modelling. The OpenStreetMap (OSM) database contains vast amounts of spatial information on natural features, including forests (landuse=forest). The OSM data model includes describing tags for its contents, i.e., leaf type for forest areas (i.e., leaf_type=broadleaved). Although the leaf type tag is common, the vast majority of forest areas are tagged with the leaf type mixed, amounting to a total area of 87% of landuse=forests from the OSM database. These areas comprise an important information source to derive and update forest type maps. In order to leverage this information content, a methodology for stratification of leaf types inside these areas has been developed using image segmentation on aerial imagery and subsequent classification of leaf types. The presented methodology achieves an overall classification accuracy of 85% for the leaf types needleleaved and broadleaved in the selected forest areas. The resulting stratification demonstrates that through approaches, such as that presented, the derivation of forest type maps from OSM would be feasible with an extended and improved methodology. It also suggests an improved methodology might be able to provide updates of leaf type to the OSM database with contributor participation.