All model coefficients are open access.
An R package (RSYC) offers access and visualization tools:
github.com/ptompalski/RSYC

RSYC provides a consistent, scalable way to estimate aboveground biomass across managed and unmanaged forests, supporting carbon accounting and forest monitoring in Canada.

Validation with independent field plots showed that the remote sensing yield curves (RSYC) generally achieved lower bias and RMSE than existing national models, with notable gains for black spruce and jack pine

Using annual Landsat-based estimates of forest age, species, and biomass, we developed over 1,800 yield curves for 27 species and three broader forest-type groups (coniferous, broadleaf, and generic).

Models were fitted at a 150 km tile level, balancing national consistency with local adaptation.

Thanks so much! We had a lot of fun putting this together. Glad you enjoyed the visualization!

It was wonderful to reconnect with old friends and colleagues, and just as exciting to make new ones.

A huge thank you to the organizers — what a well-organized and energizing conference!

#SilviLaser2025 #RemoteSensing #Forestry #LiDAR

@dr-spoon.bsky.social you're on fire!!!

We're also working to keep this information up to date on this website:

📍 Canada's Forest LiDAR Coverage➡️ ptompalski.github.io/CanadaForest...

#ForestScience #LiDAR #SustainableForestry #RemoteSensing #ForestManagement #CanadaForests

📄 White et al. 2025 – Enhanced Forest Inventories in Canada: Implementation, Status, and Research Needs

🔗 Read here: cdnsciencepub.com/doi/10.1139/...

If you'd like to learn more or give it a try, you can find it here: [ptompalski.github.io/lidRmetrics/](ptompalski.github.io/lidRmetrics/ "https://ptompalski.github.io/lidRmetrics/")

#Lidar #Forestry #RemoteSensing #RStats

For convenience, `lidRmetrics` also includes "metric sets"—predefined combinations of multiple metric groups that allow for comprehensive analysis with a single function call.

The package organizes metrics into specific functions:

- `metrics_basic()` – general point cloud statistics
- `metrics_percentiles()` – height percentiles
- `metrics_percabove()` & `metrics_dispersion()` – assess vertical structure
- `metrics_echo()` & `metrics_echo2()` – return type proportions

I was surprised it worked!