Starting point / motivation
Mountain forests provide a wide range of ecosystem services in terms of protective, productive, social and economic functions. Additionally they are a major factor in the CO2 balance and their change needs to be considered in international reporting. Therefore, a comprehensive knowledge of forest dynamics is required to monitor and better understand interactions between forests, human activities, and the atmosphere.
Monitoring of forests is therefore of very high importance. To quantify the services provided, detailed knowledge of e.g. forest gaps, tree height, and crown coverage are required with high spatial and temporal resolution.
Contents and goals
Current monitoring approaches are commonly based on statistical analyses of National Forest Inventory (NFI) data, which are based on high precision field measurements for sample plots and are therefore not able to provide the required spatial resolution. While airborne laser scanning and digital imaging can provide the necessary geometric resolution, they cannot provide an Alpine coverage within a vegetation period.
National update cycles of aerial imagery are in the order of 3 years and more. Above that, no homogeneous datasets exists for the entire Alpine region, and thus also no homogeneous forest parameters. Above that, no homogeneous datasets exists for the entire Alpine region, and thus also no homogeneous forest parameters.
The lack of detailed, cross-border, up-to-date forest parameters for assessing their services builds the motivation for the exploratory project PleiAlps.
Pleiádes images show a great potential for meeting those demands, because of the agility of the sensor, its high sub-meter resolution, and the large area coverage in one swath.
The guiding idea is to establish an Alpine forest map showing at high resolution the important parameters. This shall be enabled by high resolution analysis, i.e. based on each individual tree crown in the uppermost crown layer. The specific aim of this exploratory project is to explore the possibility for generating such a map with satellite imaging technology.
The knowledge gap to cross emerges because of the large area, roughly 200.000 km², which necessitates reliable, while still highly accurate, automated processing of large data sets. Satellite image orientation over large areas needs to be investigated, especially because ground control points cannot be assumed to be available.
Requirements on imaging geometry that lead to reliable matching are unknown. Thus, balancing matching precision and fast acquisition of large areas, is not possible yet. This will be investigated in PleiAlps. An entirely new aspect of our research is to propagate the quality of matching to the derived forest parameters.
PleiAlps will lead to an deeper understanding of the requirements for generating an Alpine wide high detail forest map and its quality. In the long run, this shall be embedded in change detection and the realization of forest information on ecosystem services overarching the entire Alps. Additionally, results from PleiAlps are expected to be scalable, with potential usage by the Copernicus Land Information System.
University of Technology Vienna - Department for Geodesy and Geoinformation
Swiss Federal Research Institute WSL
University of Technology Vienna
Department for Geodesy and Geoinformation
Univ.Prof. Dipl.Ing. Dr.techn. Norbert Pfeifer