EGU23-6092, updated on 08 Jan 2024
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Historical habitat mapping based on black-and-white aerial photography

Nica Huber1, Bronwyn Price1, Christian Ginzler1, Rolf Holderegger2, and Matthias Bürgi3
Nica Huber et al.
  • 1Remote Sensing, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
  • 2Biodiversity and Conservation Biology, Swiss Federal Research Institute WSL, Switzerland
  • 3Land Change Science, Swiss Federal Research Institute WSL, Switzerland

Information regarding the spatial arrangement and extent of habitats in the past is highly important for understanding present biodiversity patterns, assessing restoration potential and fighting extinction-debt effects. Due to increasing intensity of land use, European landscapes have changed profoundly over recent decades, with the trend accelerating following World War 2. Here, we explore the feasibility of deriving a 1946 habitat map for Switzerland compatible and hence comparable with the present-day area-wide habitat map. We take advantage of the newly available orthorectified composite of aerial photographs taken in summer 1946 by the US-Army and provided by swisstopo. The ortho imagery (1 m resolution) is segmented into image objects based on spectral and shape homogeneity for 7 case study areas (320 -508 km2), which represent the main biogeographical regions of Switzerland. Initial training data is derived by manual aerial orthoimage interpretation differentiating 16 habitat classes including wetland, grassland, arable land, hedges, orchard meadows and open forest. A random forest model is trained to classify the segments using variables describing spectral information, image texture, segment shape, topography and climate. To increase the accuracy of the classification, an iterative and semi-automated active learning technique is applied. This technique complements the initial training data with new data for segments with high classification uncertainty. With this contribution, we demonstrate the potential and challenges of object-based image analysis, machine learning and active learning to derive habitat maps from historical black-and-white aerial photography.

How to cite: Huber, N., Price, B., Ginzler, C., Holderegger, R., and Bürgi, M.: Historical habitat mapping based on black-and-white aerial photography, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6092,, 2023.