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Vegetation height estimation using ubiquitous foot-based wearable platform.

Sofeem Nasim1, Mourad Oussalah2, Bjorn Klöve3

  • 1Centre of Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland.

Environmental Monitoring and Assessment
|November 21, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a low-cost wearable platform using pressure sensors for vegetation height estimation. The method proves effective, especially in forestry regions, offering an affordable alternative for ecological monitoring.

Keywords:
Machine learningUbiquitous sensor platformVegetation height

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Wearable Technology

Background:

  • Vegetation height is crucial for environmental applications like biodiversity assessment and disaster management.
  • Traditional methods (in situ, LiDAR) are costly and labor-intensive.
  • Wearable technology presents a cost-effective alternative, particularly for rural and developing areas.

Purpose of the Study:

  • To develop and implement a locally designed, ubiquitous wearable platform for vegetation height estimation.
  • To create and test a regression model utilizing pressure sensor data for vegetation height prediction.
  • To provide an affordable solution for ecological monitoring and environmental planning.

Main Methods:

  • A locally designed data acquisition ubiquitous wearable platform was developed.
  • A regression model was trained using attributes from a pressure sensor.
  • The method was tested in the Oulu region, focusing on forestry areas.

Main Results:

  • Linear regression model achieved r² = 0.81 and RMSE = 16.73 cm.
  • Multi-regression model achieved r² = 0.82 and RMSE = 15.73 cm.
  • The approach demonstrated high effectiveness in forestry regions.

Conclusions:

  • The developed wearable platform offers a promising, cost-effective alternative for vegetation height estimation.
  • This method is suitable for environments lacking traditional remote sensing infrastructure or budget.
  • Facilitates ecological monitoring and environmental sustainability planning by reducing costs.