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The aim of this study was to delimit potential rockfall propagation zones based on simulated 2 m resolution rockfall trajectories using Rockyfor3D for block volume scenarios ranging from 0.05–30 m3, with explicit inclusion of the barrier effect of standing trees, for an area of approx. 7200 km2 in Switzerland and Liechtenstein. For the determination of the start cells, as well as the slope surface characteristics, we used the terrain morphology derived from a 1 m resolution digital terrain model, as well as the topographic landscape model geodataset of swisstopo and information from geological maps. The forest structure was defined by individual trees with their coordinates, diameters, and tree type (coniferous or broadleaved). These were generated from detected individual trees combined with generated trees on the basis of statistical relationships between the detected trees, remote sensing-based forest structure type definitions, and stem numbers from field inventory data. From the simulated rockfall propagation zones we delimited rockfall hazard indication zones (HIZ), as called by the practitioners (because they serve as a basis for the Swiss hazard index map), on the basis of the simulated reach probability rasters. As validation, 1554 mapped past rockfall events were used. The results of the more than 89 billion simulated trajectories showed that 94% of the mapped silent witnesses could be reproduced by the simulations and at least 82% are included in the delimited HIZ.

<p>Laser scanning-based tree detection has been used for many years to complement sample data of forest inventories. Local Maxima (LM) detection methods are suitable for individual tree detection in the forest canopy and allow for detection over large areas due to their computational efficiency. However, the performance of LM methods depends on factors such as the resolution of the input data (point density of aerial laser scanning (ALS) and spatial resolution of the derived rasters), the pre-processing of the input data as well as the structure and species of the detected forest. The main objective of our study was to evaluate to what extent LM tree detection can be improved by considering prior knowledge about forest structure using statistical modelling. To achieve this goal, we developed a statistical model for selecting between 10 different crown height model (CHM) pre-processing methods based on forest structure variables derived from remote sensing data. We fitted linear regression models predicting the error between the number of detected trees and the field inventoried number of the trees reaching the canopy in the sample plot. The model used dominant canopy height, the degree of coverage overall and for different forest layers derived from the CHM, the dominant leaf type derived from Sentinel-2 data, and terrain characteristics as explanatory variables. The model performance was evaluated by assessing tree detection errors using all national forest inventory plots in Switzerland using 10-fold cross-validation. The results showed a reduction of the RMSE to 91 stems per ha (respectively 1.3 when normalized by the inventoried stem number) using the model-based pre-processed CHM for detection compared to 205 stems per ha (normalized = 4) when detecting trees using an unprocessed CHM (number of used inventory plots n=5254). Excluding inventory plots with an ALS point density of less than 15 points per square meter (n=3797) improved the RMSE to 89 stems per ha (normalized = 1.25).The RMSE further improves to 85 stems per ha (normalized = 1.2) by additionally excluding plots with more than 6 years between ALS acquisition and inventory (n=2676). Although the results show a clear reduction of the detection error by our model, they also indicate potential for further refinements. Especially the integration of high-quality ALS data (becoming available for the entire area of Switzerland until 2024), detailed tree species data, and additional, more recent inventory data are recommended. In the future, a combination of our method with point cloud-based approaches will probably be able to further reduce detection errors at national scale.</p>

The revised Swiss Spatial Planning Act (RPG 2016) pursues the goal of inward settlement development to slow down urban sprawl and better protect arable land. This needs to be addressed in planning processes, as quality-oriented and sustainable internal densification is required. The Geodesign Framework by Steinitz is suitable for supporting such planning processes with public participation, where models and visualizations help to convey the complex systemic interrelationships to stakeholders. This paper presents a process model based on Geodesign that integrates GIS and Parametric Design, so that effects on internal densification caused by changes to building regulations can be quantified and communicated. In addition to an overview of the process model, selected results from its tests and verification are presented. The results suggest that the model approximates real interrelations well and is a suitable basis for further work.