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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Moisture content estimation of forest litter based on remote sensing data.

Xiguang Yang1, Ying Yu2, Haiqing Hu3

  • 1Key Laboratory of Saline-Alkali Vegetation Ecology Restoration (SAVER), Ministry of Education, Alkali Soil Natural Environmental Science Center (ASNESC), Northeast Forestry University, Harbin, China.

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Accurate forest litter moisture data are crucial for fire risk management. A new geometrical-optical model effectively separates background reflectance, improving forest litter moisture estimation from remote sensing images.

Keywords:
Background reflectanceGeometrical-optical modelSpectral analysis

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Area of Science:

  • Forestry
  • Remote Sensing
  • Fire Ecology

Background:

  • Forest litter moisture is vital for fire danger rating systems.
  • Optical remote sensing offers continuous, regional data but struggles to isolate litter moisture information.
  • Pixel reflectance in forests mixes canopy and background signals, obscuring litter moisture.

Purpose of the Study:

  • To develop a method for accurately estimating forest litter moisture content using optical remote sensing.
  • To overcome the challenge of separating background reflectance from canopy reflectance in forest images.

Main Methods:

  • A geometrical-optical model was developed to distinguish background reflectance from total pixel reflectance.
  • A statistical model was employed to estimate forest litter moisture content using the derived background reflectance.

Main Results:

  • The developed model achieved an R-squared value of 0.595.
  • A root mean square error (RMSE) of 0.372 was recorded.
  • The average precision of the forest litter moisture estimation reached 69.654%.

Conclusions:

  • This study presents a novel approach for estimating forest litter moisture using optical remote sensing.
  • The method successfully separates background reflectance, enhancing moisture estimation accuracy.
  • The approach holds potential for large-scale forest litter moisture mapping and improved fire risk management.