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| Figure 1 - Forest Analysis Part 1 |
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| Figure 2- Forest Analysis Part 2 |
This week’s
lab represented a decent challenge for me! Technology was not cooperating, but
overall, with proper troubleshooting, I was able to overcome and complete the
assignment.
We
conducted a LiDAR‑based forest analysis in Virginia to better understand canopy
structure, terrain, and vegetation patterns relevant to forest management. We
began by downloading and converting the Virginia LiDAR tile into an
uncompressed LAS dataset, then explored the point cloud in a 3D local scene to
observe landscape form, topography, and vegetation distribution. Using ground
and non‑ground returns, we generated a DEM and DSM and subtracted them to
estimate tree height across the study area. We evaluated height accuracy,
identified outliers, and interpreted negative values in relation to roads and
clearings. To assess biomass‑related canopy density, we converted LiDAR classes
to multipoint features, rasterized them, and calculated vegetation to total
return ratios to produce a canopy density surface. We then visualized height
distribution with a histogram and created a series of maps to illustrate forest
structure, highlight man‑made features, and support forestry applications such
as biomass estimation, forest health assessment, and terrain‑based planning.
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