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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Expanding forest research with terrestrial LiDAR technology.

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  • 1Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland. eduardo.maeda@helsinki.fi.

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Summary

Terrestrial laser scanning (TLS) provides detailed 3D forest structure data. Advancements in TLS and AI are improving our understanding of forest ecosystems and their responses to environmental change.

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Area of Science:

  • Forestry science
  • Ecology
  • Geospatial technology

Background:

  • Forest three-dimensional structure is crucial for ecosystem function and environmental response.
  • Terrestrial laser scanning (TLS) offers detailed insights into forest architecture.
  • TLS aids in ecological process studies, disturbance analysis, and forest inventories.

Purpose of the Study:

  • To review recent advancements in TLS technology for forest science.
  • To explore the integration of increasing computational power and artificial intelligence (AI) with TLS data.
  • To highlight the potential for breakthroughs in understanding dynamic forest ecosystems.

Main Methods:

  • Review of recent literature on terrestrial laser scanning (TLS) applications in forest science.
  • Analysis of emerging trends in computational power and artificial intelligence (AI).
  • Examination of how these advancements address complex ecological questions.

Main Results:

  • TLS has significantly enhanced the characterization of forest 3D structure.
  • AI and increased computing power enable more sophisticated analysis of TLS data.
  • These tools facilitate deeper insights into forest productivity, light regimes, and physiological processes.

Conclusions:

  • TLS is a pivotal technology for modern forest science.
  • The synergy of TLS, AI, and computational power is transforming our ability to study forest ecosystems.
  • This integration is essential for understanding forest dynamics in a changing global environment.