Purdue GDSLab (https://gdsl.org) and the collaborators collected, processed, and published high-quality, comprehensive UAS (Uncrewed Aerial System) data for the Amazon rainforest. This Git Hub repository provides an exemplary Python code to generate a CHM (Canopy Height Model) from the published lidar data (point cloud and DTM; Digital Terrain Model) based on a user-defined percentile value and spatial resolution.
(1) Preparation: Please download the point cloud from the TBS STAC repository. We recommend checking d2spy if you are not interested in the entire TBS region. You can download the data only for your ROI (region of interest). Please check the guide at https://py.d2s.org.
(2) Copy (or download) the gen_chm.py to your workplace.
(3) Set the parameters in the code as you wish.
*Laspy, Numpy, GDAL, rasterio are required.
We provide high-quality UAS data over 700-ha of Tiputini Biodiversity Station (TBS, https://www.tiputini.com) in the Ecuadorian Amazon. The dataset consists of:
- Multispectral orthomosaics (three RGB and four multispectral bands)
- Multispectral-derived DSMs (Digital Surface Model)
- LiDAR point clouds
- LiDAR-derived DEMs (Digital Elevation Model; i.e., DTM, DSM, and CHMs)
Data Repository: You can download the data from a Data-to-Science (D2S) central STAC repository: TBS STAC Catalog
Please cite the information following when you use the data.
Jung, M., Chang, A., Jung, J., Cannon, C., Rivas-Torres, G. (2025). Comprehensive high-quality UAS data for Amazon rainforest: Tiputini Biodiversity Station. Purdue Unversity Research Repository. https://doi.org/10.4231/FV2H-VR18