Assessing and correcting topographic effects on forest canopy height retrieval using airborne LiDAR data

Area of Science:

• Forestry
• Remote Sensing
• Geospatial Analysis

Background:

• Topography introduces significant errors in forest canopy height retrieval using airborne Light Detection and Ranging (LiDAR) data.
• These errors can affect the accurate extraction of individual tree locations and crown dimensions.

Purpose of the Study:

• To develop and validate a method for correcting topographic effects on forest canopy height measurements derived from LiDAR data.
• To improve the accuracy of individual tree crown segmentation and height estimation in varied terrain.

Main Methods:

• Individual tree crown segmentation was performed on digital orthophoto maps (DOMs).
• Individual tree point clouds were extracted based on segmented crown boundaries.
• A precise digital elevation model (DEM) was derived from classified point clouds.
• A height-weighted correction method was applied to mitigate topographic influences.

Main Results:

• Terrain significantly impacts individual tree canopy height, causing elevated downslope and depressed upslope sides of tree trunks.
• A strong correlation was found between slope gradient and the proportion of LiDAR returns with height differences.
• The proposed method demonstrated effectiveness in correcting for topographic distortions in LiDAR-derived canopy heights.

Conclusions:

• The developed method effectively corrects for topographic distortions in airborne LiDAR data for forest canopy height assessment.
• Accurate DEM generation and individual tree crown segmentation are crucial for reliable forest structure analysis.
• This approach enhances the utility of LiDAR data in complex terrain for ecological and forestry applications.