Open-Earth-Monitor Global Workshop 2026

High-resolution information on woody vegetation structure is increasingly required for biodiversity monitoring, urban planning, and environmental assessment, particularly in heterogeneous urban and peri-urban landscapes where trees and shrubs are poorly represented in conventional land-cover products. While Earth observation (EO) data provide new opportunities to capture fine-scale vegetation structure, many workflows remain closely tied to specific data environments and lack transparent, transferable implementation.
This contribution presents an open and reproducible workflow for mapping trees and shrubs across urban areas in Switzerland at national scale. The approach integrates airborne LiDAR-derived canopy height models with authoritative Swiss reference datasets, including cadastral data (AV) and the Topographic Landscape Model (TLM) to extract structurally distinct woody vegetation elements. Object-based segmentation and rule-based classification are implemented using LAStools and R, with explicit processing steps designed for transparency and reproducibility.
The workflow focuses on the delineation of above-ground woody structures, distinguishing individual trees and shrub patches based on canopy height, spatial configuration, and their relationship to reference datasets such as the national tree inventory (TLM). While airborne LiDAR provides detailed vertical information, the methodological logic can be adapted to alternative height sources such as photogrammetric surface models, stereo imagery, or emerging spaceborne products, where LiDAR is unavailable.
Results from the national case study demonstrate how EO-derived above-ground structural information can complement existing cadastral and land-use datasets by providing spatially explicit woody vegetation objects in complex urban landscapes. Beyond the Swiss application, this work discusses key considerations for developing reproducible EO workflows, including data dependency management, scalability, and transferability. The presented workflow aims to support the Open-Earth-Monitor community by providing a transparent and adaptable framework for structural habitat mapping using high-resolution EO data.