Due to the apparent effects of climate change on the Earth's ecosystems, it is more important than ever to monitor flora and fauna in affected regions, e.g. mountain areas above the tree line. In the alpine ecosystem, and not just there, Vegetation plays a fundamental role and is the subject of this study. The work aims to develop algorithms for recognising small stature alpine plants from close range top view images. Ideally, automated assessment algorithms of the plant cover should objectively help scientists observe and interpret the state of the plant ecosystem over a long time series. Therefore, the aim in this respect was to derive visualisations that accurately describe plant growth and displacement (translocation). Additionally, recording changes in biodiversity was an intent. This work uses multi-temporal data comprising RGB images and multi-label masks to accomplish the aforementioned task. The evaluated methods involve mask comparison, optical flow estimation, detection of individual plants, and descriptive statistical analysis of image feature properties. Tests on the given data set show that all methods but the optical flow estimation have great potential. The mask comparison method captured plant growth and translocation most satisfactory. Individual plant detection and statistical analysis further helped to evaluate changes in biodiversity. When combined, the proposed methods give an immediate overview about relevant changes in the multi-temporal transects, which has not been done before for close-distance images of alpine plants.

Original languageEnglish
Publication statusPublished - Sept 2022
EventInternational Workshop on Simulation for Energy, Sustainable Development & Environment 2022 - Rom, Italy
Duration: 19 Sept 202221 Sept 2022


ConferenceInternational Workshop on Simulation for Energy, Sustainable Development & Environment 2022
Abbreviated titleSESDE
Internet address


  • Alpine Plants
  • Change Localization
  • Optical Flow
  • Structural Plant Analysis
  • Visual Change Monitoring
  • Visualization


Dive into the research topics of 'Visual Change Detection in Multi-Temporal Vegetation Transects of Alpine Plants'. Together they form a unique fingerprint.

Cite this