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chm-3d-visualizer
chm-3d-visualizer PublicA browser-based tool for interactive 3D visualization of canopy height model (CHM) datasets, built entirely in JavaScript. It allows users to load and explore multiple CHM GeoTIFF tiles directly in…
JavaScript 3
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lidar-canopy-metrics-r
lidar-canopy-metrics-r PublicR-based script for processing lidar point cloud data, generating and visualizing Digital Surface Model (DSM) and Digital Terrain Model (DTM), and extracting forest canopy metrics based on the lidR …
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ALS_Notebooks
ALS_Notebooks PublicJupyter notebooks in R and Python that demonstrate end-to-end workflows for generating derived products (e.g., canopy height models and canopy metrics) from Airborne Laser Scanning (ALS) data using…
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gedi-forest-structure-gee-app
gedi-forest-structure-gee-app PublicA Google Earth Engine web application for machine-learning-based canopy height mapping that fuses NASA GEDI lidar data and harmonized Landsat and Sentinel-2 optical imagery to produce continuous fo…
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multi-temporal-lidar-forestry
multi-temporal-lidar-forestry PublicForked from sreejakr/multi-temporal-lidar-forestry
This repository provides an automated workflow for large-scale lidar data processing to map forest structure and change maps. Using lidR, Python libraries, it manages tiling, metric computation, an…
Jupyter Notebook
Repositories
- lidar_basic_concepts_and_exercises Public
Hands-on introductory lab activities for learning lidar and deriving gridded products (DEM, DSM, DTM, and CHM) in R. Designed for undergraduate students with no prior lidar or programming experience, the browser-based lessons guide learners through accessing public lidar data, processing point clouds, and interpreting terrain and canopy datasets.
- lidar_applied_tree_measurements Public
Hands-on CloudCompare activity for measuring tree canopy height and crown width from standard and terrain-normalized lidar point clouds, exploring repeat measurements, averaging, and why height normalization matters.
- ALS_Notebooks Public
Jupyter notebooks in R and Python that demonstrate end-to-end workflows for generating derived products (e.g., canopy height models and canopy metrics) from Airborne Laser Scanning (ALS) data using open-source tools such as lidR. The repository includes reproducible examples for point cloud download, processing, and visualization.
- gedi-forest-structure-gee-app Public
A Google Earth Engine web application for machine-learning-based canopy height mapping that fuses NASA GEDI lidar data and harmonized Landsat and Sentinel-2 optical imagery to produce continuous forest structure maps.
- API_workbench Public
This repository provides practical examples for interacting with the OpenForest4D API, alongside the core code samples underpinning the APIs. Use these resources directly or adapt them to build your own workflows.
- Spaceborne_Notebooks Public
Python and Jupyter notebooks implementing workflows to generate wall-to-wall forest attribute maps (canopy height, AGB, canopy cover) using spaceborne remote sensing data.
- treetop-r Public
R functions for extracting forest information from lidar-derived Canopy Height Models (CHMs). Based on carlos-alberto-silva/weblidar-treetop, the scripts have been refactored into standalone, modular functions better suited for automated or batch processing.
- lidar-canopy-metrics-r Public
R-based script for processing lidar point cloud data, generating and visualizing Digital Surface Model (DSM) and Digital Terrain Model (DTM), and extracting forest canopy metrics based on the lidR package.
- multi-temporal-lidar-forestry Public Forked from sreejakr/multi-temporal-lidar-forestry
This repository provides an automated workflow for large-scale lidar data processing to map forest structure and change maps. Using lidR, Python libraries, it manages tiling, metric computation, and differencing, and is parallelized to handle big data efficiently. The pipeline enables repeatable multi-temporal forest metrics to support conservation
- chm-3d-visualizer Public
A browser-based tool for interactive 3D visualization of canopy height model (CHM) datasets, built entirely in JavaScript. It allows users to load and explore multiple CHM GeoTIFF tiles directly in the browser—no build tools required.
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