OpenForest4D - Canopy Height Mapper (NASA GEDI lidar, Sentinel-1 SAR, Sentinel-2 optical imagery)

Canopy Height Mapper (NASA GEDI lidar, Sentinel-1 SAR, Sentinel-2 optical imagery) Beta

Generate a canopy height map for any area of interest. This tool combines NASA Global Ecosystem Dynamics Investigation (GEDI) lidar measurements with Sentinel-2 L2A optical imagery and Sentinel-1 radiometrically terrain-corrected (RTC) SAR data, using machine learning to estimate tree height across a landscape. Optionally include a digital elevation model (Copernicus GLO-30), NLCD land cover, or ESA WorldCover to improve accuracy, and choose between available machine learning models using the settings below.

*This tool is under active development. Results should be validated before use in decision-making

My Canopy Height Map Jobs

1. Select your study area of interest

2. Select Output Year
Choose the year for which you want to generate a canopy height map.

3. Advanced Settings

Advanced settings for data sources used to train and build the canopy height model.

Data Sources & Quality Filters

GEDI lidar measurements date range:

Reference measurements from NASA's GEDI space lidar

Start Date

End Date

Satellite Imagery Date Range:

Sentinel-2 L2A optical imagery & Sentinel-1 SAR data used to map canopy height.

Start Date

End Date

Sentinel-2 Cloud Cover Limit:

Exclude satellite images that are more than X% covered by clouds. Lower = cleaner imagery but fewer available scenes; higher = more data but potentially noisier results.

Include Terrain Elevation

Adding digital elevation model data improves accuracy in high relief or mountainous areas.

Include landcover data (NLCD for US, ESA WorldCover elsewhere)

Helps the model distinguish forests from other vegetation types.

Preview input data layers

Generates intermediate visualizations of the source data (GEDI, Sentinel) used.

4. Prediction Model
Choose the machine learning algorithm used to estimate canopy height.

Random Forest Regression

Histogram Gradient Boosting

Generate model explainability report (shows which data inputs influenced predictions most — useful for validation and publication)

Estimated processing time is dependent on area size.