Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines. The provided land cover maps follow the high carbon stock approach (HCSA) stratifying vegetation based on the estimated carbon density (aboveground biomass). A deep convolutional neural network was trained to estimate canopy top height from Sentinel-2 optical satellite images using reference data derived from GEDI lidar waveforms. Carbon density and high carbon stock classes were derived from these dense canopy height maps using calibration data from an airborne lidar campaign in Sabah, Borneo. The resulting maps have a ground sampling distance (GSD) of 10 m and are based on images between 1st of September 2020 and 1st of March 2021. The style files (color_style_HCS.qml, color_style_canopy_top_height.qml) contain the color coding and can be loaded for visualization (e.g. in QGIS). The indicative HCS maps contain 9 land cover categories noted as "Label: name [colorcode]": 0: Open land (OL) [#440154] 1: Scrub (S) [#404387] 2: Young regenerating forest (YRF) [#29788e] 3: Low density forest (LDF) [#22a884] 4: Medium density forest (MDF) [#7ad251] 5: High density forest (HDF) [#fde725] 10: Oil palm [#fcffa4] 11: Coconut [#a4feff] 50: Urban [#fa0000] 255: No data Citation: Use of these data require citation of this dataset and the original research articles. These citations are as follows: Lang, N., Schindler, K., & Wegner, J. D. (2021). High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR. arXiv preprint arXiv:2107.07431. Rodríguez, A. C., D'Aronco, S., Schindler, K., & Wegner, J. D. (2021). Mapping oil palm density at country scale: An active learning approach. Remote Sensing of Environment, 261, 112479. Lang, N., Rodríguez, A. C., Schindler, K., & Wegner, J. D. (2021). Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.5012448

The project received funding from Barry Callebaut Sourcing AG, as part of a Research Project Agreement.