This document discusses the advantages of high-resolution lidar data for quantifying the effects of fine-scale topography on canopy height variation at the landscape scale. It examines how topographical features, canopy gap position, and neighborhood tree density influence canopy height using lidar data from a 230 square km study area in Kyoto, Japan. The analysis found that while these factors drive canopy height variation, they can only describe 40% of the variation, indicating there are other potential drivers like stand age and soil that affect canopy height at the landscape level.

1. Advantage of high resolution lidar data
 Availability of a high‐resolution
topography data (from airborne lidar
scanning ) will able us to quantify the
effect of fine‐scale topographic
heterogeneity on variation of canopy
height at landscape scale.
Ground points- last return
Canopy layer- first return
DSM – Digital surface model
CHM – Canopy height model
DTM – Digital terrain model
 Spatial variation of maximum canopy height are key drivers of many
ecosystem processes.
What creates canopy height variation?
- Topography.

2. 2
Key questions…….
1. Topographical features (elevation,
slope, aspect, topographic convexity,
and topographic wetness index)
We are interested in………
2. Canopy gap position
3. Neighborhood tree density
Influence canopy height.

3. Study area
Kyoto city
Total study area is ~ 230 sq km.

4. Explanatory variables
3-d image
Canopy height
Ground elevation
Slope
Aspect
Topographic
wetness index
Canopy gap
Neighboring tree
density
Topographic
curvature

5. Explanatory variables
3-d image
Canopy height
Ground elevation
Slope
Aspect
Topographic
wetness index
Canopy gap
Neighboring tree
density
Topographic
curvature
High value
low value
Gap detection
Tree top detection
~14 million tree

6. Canopy height and elevation Canopy height and topographic curvature

7. Canopy height and distance
from gap
Canopy height and neighboring
tree density

8. Importance of explanatory variables on canopy height variation

9. Summary
In addition to topographic heterogeneity, canopy gap
position and neighboring tree density drives canopy
height.
However, including all the variables can only
describe 40% of canopy height variation. So there
may be other factors like stand age, soil drive canopy
height variation at landscape level.
This study was conducted in temperate region but
many of the techniques can be used in tropical
regions too.

10. Thank you
Acknowledgement