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OpenForest4D @openforest4d.org · 24/02/2026

Identifying #forest canopy gaps is important for understanding forest development & biological diversity. New R notebook outlines a workflow to detect canopy gaps from Canopy Height Models derived from ALS #lidar data. Available at: github.com/OpenForest4D... #RemoteSensing #Forestry #GoogleColab

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OpenForest4D @openforest4d.org · 17/02/2026

How can lidar track a forest's recovery after a fire? 🌲🔥 In this study of the #Kaibab National Forest, researchers used multi-temporal #lidar data to map forest structure and canopy changes over time. Explore the interactive map here: of4d.sdsc.edu/usecases/kai... #RemoteSensing #Wildfire

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OpenForest4D @openforest4d.org · 10/12/2025

Join us at our #AGU25 poster to discover how we harnesses airborne & satellite #lidar data using advanced workflows to uncover powerful insights on forest structure, biomass, and how landscapes change over time. agu.confex.com/agu/agu25/me... #OpenScience #WildfireRecovery #EcosystemChange #RStats

Monitoring forest structure change using multi-temporal ALS workflows in OpenForest4D

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OpenForest4D @openforest4d.org · 09/12/2025

Stop by our #AGU25 poster-showcasing our #GoogleEarthEngine app that fuses NASA ICESat‑2 & GEDI #lidar with Sentinel‑1 SAR & Landsat‑8 using machine learning to map 30 m canopy height & biomass, achieving ~6 m RMSE in a hurricane‑impacted Florida case study. agu.confex.com/agu/agu25/me... #ICESat2

OpenForest4D-GEE Mapper - Google Earth Engine web app for forest structure mapping. Thur 18th Dec 2025 2:15pm. Hall EFG

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OpenForest4D @openforest4d.org · 01/12/2025

Generate #forestry products from #lidar topography data with lidR! Reproducible R notebook guides users through #pointcloud filtering, raster generation & forest metrics extraction to produce key outputs like canopy height models. Explore notebook: github.com/OpenForest4D... #googlecolab #geospatial

+ representing tree tops on smoothed CHM

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OpenForest4D @openforest4d.org · 20/11/2025

Multi-temporal #lidar reveals how trees grow - but interpreting change isn’t simple, given differences in point density & season. An #NYC case study (2014 vs 2017) reveals impact of data resolution and timing. #Forestry #RemoteSensing #Geospatial #PointClouds openforest4d.org/density-and-...

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OpenForest4D @openforest4d.org · 05/11/2025

We created an #OpenSource automated workflow for multi-temporal #lidar processing to map forest change at scale. Generate canopy height, cover & change products critical for monitoring #wildfire impacts and ecosystem dynamics. Code and example from #Kaibab forest #AZ at github.com/OpenForest4D...

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OpenForest4D @openforest4d.org · 30/10/2025

Discover how #lidar reveals the size & location of individual Ponderosa Pines at Sunset Crater, AZ! Using advanced algorithms in lidR, we map tree height and crowns in stunning detail, informing forest ecology and management. openforest4d.org/tree-identif... #Forestry #RemoteSensing #RStats

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OpenForest4D @openforest4d.org · 27/10/2025

Ever wondered what #lidar point clouds reveal about forests? OpenForest4D’s new guide breaks down how elevation, return number, intensity, and classification describe forest structure. openforest4d.org/lidar-point-... #pointcloud #laserscanning #forestry #geospatial

A lidar point cloud along central California’s Big Sur coast displaying elevation, classification, return number, and intensity. The airborne lidar point cloud was collected in 2022 has a point density of 85 points/m2.

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