IRSS at UBC
@irssubc.bsky.social
The Integrated Remote Sensing Studio at the Faculty of Forestry
of UBC led by Prof Nicholas Coops.
of UBC led by Prof Nicholas Coops.
✔️Comprehensive review of PlanetScope data for forest monitoring
✔️Assessment of spatiotemporal characteristics of PlanetScope datasets
✔️Evaluation of methods for preprocessing and analyzing PlanetScope datasets
✔️Suggestions of future research directions and uses
✔️Assessment of spatiotemporal characteristics of PlanetScope datasets
✔️Evaluation of methods for preprocessing and analyzing PlanetScope datasets
✔️Suggestions of future research directions and uses
October 21, 2025 at 11:12 PM
✔️Comprehensive review of PlanetScope data for forest monitoring
✔️Assessment of spatiotemporal characteristics of PlanetScope datasets
✔️Evaluation of methods for preprocessing and analyzing PlanetScope datasets
✔️Suggestions of future research directions and uses
✔️Assessment of spatiotemporal characteristics of PlanetScope datasets
✔️Evaluation of methods for preprocessing and analyzing PlanetScope datasets
✔️Suggestions of future research directions and uses
✔️Framework for automated generation of point-cloud segmentation & species labeling
✔️Novel dual-stream deep learning model for predicting species from point-clouds
✔️Evaluation of point-based deep learning layers for species prediction
✔️Inclusion of point-cloud metrics improved accuracy by ∼ 11 %
✔️Novel dual-stream deep learning model for predicting species from point-clouds
✔️Evaluation of point-based deep learning layers for species prediction
✔️Inclusion of point-cloud metrics improved accuracy by ∼ 11 %
October 20, 2025 at 4:12 PM
✔️Framework for automated generation of point-cloud segmentation & species labeling
✔️Novel dual-stream deep learning model for predicting species from point-clouds
✔️Evaluation of point-based deep learning layers for species prediction
✔️Inclusion of point-cloud metrics improved accuracy by ∼ 11 %
✔️Novel dual-stream deep learning model for predicting species from point-clouds
✔️Evaluation of point-based deep learning layers for species prediction
✔️Inclusion of point-cloud metrics improved accuracy by ∼ 11 %
📖 Published in Canadian Journal of Forest Research (@canjforestres.bsky.social)
Funded by #Silva21, #NSERC
Funded by #Silva21, #NSERC
September 8, 2025 at 7:05 PM
📖 Published in Canadian Journal of Forest Research (@canjforestres.bsky.social)
Funded by #Silva21, #NSERC
Funded by #Silva21, #NSERC
1️⃣FAM forests adapt to #ClimateChange, but limited #SeedSupply, infrastructure & policy frameworks hinder large-scale implementation
2️⃣Tools like genomic selection, seed databases offer opportunities for future FAM
3️⃣Trust, collaboration & #DiverseKnowledgeSystems are essential for policy & practice
2️⃣Tools like genomic selection, seed databases offer opportunities for future FAM
3️⃣Trust, collaboration & #DiverseKnowledgeSystems are essential for policy & practice
September 8, 2025 at 7:05 PM
1️⃣FAM forests adapt to #ClimateChange, but limited #SeedSupply, infrastructure & policy frameworks hinder large-scale implementation
2️⃣Tools like genomic selection, seed databases offer opportunities for future FAM
3️⃣Trust, collaboration & #DiverseKnowledgeSystems are essential for policy & practice
2️⃣Tools like genomic selection, seed databases offer opportunities for future FAM
3️⃣Trust, collaboration & #DiverseKnowledgeSystems are essential for policy & practice
✨Research Highlights:
The approach keeps existing stand boundaries without land cover change and updates regenerating areas.
Stands grew larger and more complex over time, approaching pre-disturbance values.
The algorithm is transparent and efficient, ideal for forest change monitoring.
#IRSSlab
The approach keeps existing stand boundaries without land cover change and updates regenerating areas.
Stands grew larger and more complex over time, approaching pre-disturbance values.
The algorithm is transparent and efficient, ideal for forest change monitoring.
#IRSSlab
September 3, 2025 at 11:28 PM
✨Research Highlights:
The approach keeps existing stand boundaries without land cover change and updates regenerating areas.
Stands grew larger and more complex over time, approaching pre-disturbance values.
The algorithm is transparent and efficient, ideal for forest change monitoring.
#IRSSlab
The approach keeps existing stand boundaries without land cover change and updates regenerating areas.
Stands grew larger and more complex over time, approaching pre-disturbance values.
The algorithm is transparent and efficient, ideal for forest change monitoring.
#IRSSlab
We’re so glad you made the trip to join us!
August 27, 2025 at 7:33 PM
We’re so glad you made the trip to join us!