[Retrieval of forest topsoil organic matter's spatial pattern based on LiDAR data]

Area of Science:

• Forestry
• Soil Science
• Remote Sensing

Background:

• Forest soils are significant terrestrial carbon pools.
• Soil organic matter (SOM) content is vital for estimating soil carbon storage and evaluating soil carbon sink function.

Purpose of the Study:

• To retrieve the spatial pattern of forest topsoil organic matter in Liangshui National Nature Reserve.
• To identify and analyze variables influencing topsoil organic matter distribution.
• To establish and validate a predictive model for forest soil organic matter content.

Main Methods:

• Utilized LiDAR data and topsoil organic matter measurements from 55 permanent plots.
• Employed partial least squares (PLS) regression to analyze relationships between variables and SOM.
• Extracted and screened relevant variables including LiDAR intensity, counts, elevation, slope, and aspect.

Main Results:

• Topsoil organic matter content showed significant positive correlations with LiDAR intensity (r=0.765), counts (r=0.423), and elevation (r=0.475).
• The predictive model demonstrated high reliability with 83.3% precision and an R-squared value of 0.725.
• Lower SOM content (<100 g x kg⁻¹) was observed at forest edges and in areas with low canopy density.

Conclusions:

• LiDAR-based modeling provides a reliable method for mapping forest topsoil organic matter spatial patterns.
• Understanding the spatial distribution of SOM is critical for effective forest carbon management and conservation strategies.
• The study highlights the importance of factors like LiDAR-derived metrics and topography in influencing soil organic matter dynamics.