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Remote sensing

Researchers Develop Advanced Remote Sensing Method for Urban Vegetation Monitoring

Strategic Insights

— 2 min read

A new study by Aalto University investigators has aimed to overcome the shortcomings of conventional remote sensing approaches by integrating terrestrial laser scanning (TLS) with UAV-based photogrammetry for effective vegetation monitoring using change detection methods. The research findings demonstrate the method's capability to identify growth in urban vegetation up to 2.8 m, with accuracy evaluations indicating a 95% confidence interval corresponding to a difference of approximately 4 cm for both TLS and UAV photogrammetric datasets.

Key Takeaways:

  • The study highlights the importance of monitoring urban green areas for environmental quality and inhabitant well-being, with remote sensing methods being essential for this purpose.
  • The research used a novel approach by integrating terrestrial laser scanning (TLS) with UAV-based photogrammetry to overcome traditional remote sensing limitations, such as cloud coverage, illumination issues, and sensor-specific issues.
  • The study focused on detecting changes in the Malminkartano area of Helsinki during the leaf-off and leaf-on periods of 2022, using 2D point cloud data and the Multiscale Model-to-Model Cloud Comparison algorithm.
  • The findings demonstrate the method's capability to identify growth in urban vegetation up to 2.8 m, with accuracy evaluations indicating a 95% confidence interval corresponding to a difference of approximately 4 cm for both TLS and UAV photogrammetric datasets.
  • The research concluded that addressing processing-related uncertainties, including point density, alignment, vertical accuracy, and scale variation, is essential for reliable estimation of tree attributes.
  • The study highlights the potential of the integrated TLS and UAV photogrammetry method for change detection analysis, which can be applied in various urban planning and environmental monitoring applications.
  • Osama Bin Shafaat, Heikki Kauhanen, Arttu Julin, and Matti T. Vaaja contributed to the research, which was funded by the European Regional Development Fund.

Statistics:

  • 2.8 m: the highest growth in urban vegetation identified by the method
  • 4 cm: the difference in accuracy between TLS and UAV photogrammetric datasets
  • 95%: the confidence interval for accuracy evaluations
  • 2022: the year in which the Malminkartano area of Helsinki was studied
  • Malminkartano area, Helsinki: the location where the study was conducted
  • Leaf-off and leaf-on periods: the timeframes of the study

Sources:

  • 3D Change Detection of Urban Vegetation Using Integrated TLS and UAV Photogrammetry Point Clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18(): 24976-24989. (IEEE)
  • NewsRx. Aalto University Researchers Highlight Recent Research in Remote Sensing (3D Change Detection of Urban Vegetation Using Integrated TLS and UAV Photogrammetry Point Clouds). Journal of Engineering. October 20, 2025; p 75. (NewsRx LLC)

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