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Terrain mobility estimation using TWI and airborne gamma-ray data.

U Mattila1, T Tokola1

  • 1University of Eastern Finland, School of Forest Sciences, Yliopistokatu 7, FIN-80100 Joensuu, Finland.

Journal of Environmental Management
|December 4, 2018
PubMed
Summary
This summary is machine-generated.

Predicting forest terrain mobility using open data sources in Finland is crucial for minimizing logging damage. Gamma-ray data and topographic wetness index effectively explained variations in rut depth, aiding sustainable forestry practices.

Keywords:
Can bus dataForest trafficabilityGamma-ray radiationRut depthTopographic wetness index

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Area of Science:

  • Forestry science
  • Environmental science
  • Geospatial analysis

Background:

  • Forestry operations require site trafficability data for economic and ecological reasons.
  • Minimizing logging damage necessitates understanding terrain variations.
  • Open data sources offer potential for predicting site trafficability.

Purpose of the Study:

  • To evaluate the prediction of terrain mobility using open data sources in Finland.
  • To assess the utility of Lidar-based terrain features and gamma-ray soil wetness data.
  • To correlate predicted mobility with measured rut depths and forwarder operational data.

Main Methods:

  • Utilized Lidar-derived terrain features and gamma-ray radiation data for soil wetness.
  • Collected forwarder can bus data (net power) and measured rut depths.
  • Integrated digital elevation models (DEM), soil data, and topographic wetness index (TWI) for analysis.

Main Results:

  • Rut depth was best explained by TWI from a 2m grid DEM or a combination of 16m grid TWI, thorium gamma radiation, and engine net power.
  • Gamma-ray data potentially offered more precise soil type and wetness detection than traditional soil maps.
  • The model achieved an R-squared value of 0.20-0.33 in explaining rut depth variations.

Conclusions:

  • Open data sources, particularly gamma-ray and TWI, can effectively predict terrain mobility in forest logging areas.
  • Gamma-ray data shows promise in identifying specific soil types and localized wetness, improving rut depth prediction.
  • This approach supports informed decision-making for sustainable forest management and reduced operational impact.