This dataset is part of the OzTreeMap project and provides two new 30 m spatial resolution canopy height products for continental Australia: (1) the best-pick canopy height model (pick-CHM); and (2) the median canopy height model (med-CHM). Both products were generated and validated as part of the study titled “Accuracy of Machine Learning-Derived Canopy Height Models at Continental Scale.”
The pick-CHM is a composite model in which each 30 m pixel adopts the most accurate canopy height value among four publicly available machine learning-derived CHMs—Tolan et al. (2024), Lang et al. (2023), Potapov et al. (2021), and Liao et al. (2020)—based on the vegetation class (Scarth et al., 2019) that the pixel represents and our vegetation-specific accuracy assessment (see lineage). The med-CHM represents a pixel-wise median composite of the same four CHMs and achieved the highest overall accuracy when validated against 22,967 km² of reference airborne point cloud data across 16 Australian vegetation classes.
Both datasets are provided as single-band GeoTIFF rasters in EPSG:3577 (Australian Albers) coordinate reference system, with 30 m spatial resolution and float32 data type. These CHMs offer improved accuracy and spatial consistency compared to the individual global products supporting continental-scale applications in forest structure monitoring, carbon accounting, and ecosystem assessment.
References
Lang, N., Jetz, W., Schindler, K., Wegner, J.D., 2023. A high-resolution canopy height model of the Earth. Nat Ecol Evol 7, 1778–1789. https://doi.org/10.1038/s41559-023-02206-6
Liao, Z., van Dijk, A.I.J.M., He, B., Larraondo, P.R., Scarth, P.F., 2020. Woody vegetation cover, height and biomass at 25 m resolution across Australia derived from multiple site, airborne and satellite observations. Int. J. Appl. Earth Obs. Geoinf. 93, 102209. https://doi.org/10.1016/j.jag.2020.102209
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M.C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C.E., Armston, J., Dubayah, R., Blair, J.B., Hofton, M., 2021. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165. https://doi.org/10.1016/j.rse.2020.112165
Scarth, P., Armston, J., Lucas, R., Bunting, P., 2019. A structural classification of Australian vegetation using ICESat/GLAS, ALOS PALSAR, and Landsat sensor data. Remote Sens. 11, 147. https://doi.org/10.3390/rs11020147
Tolan, J., Yang, H.-I., Nosarzewski, B., Couairon, G., Vo, H.V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J., Moutakanni, T., Bojanowski, P., Johns, T., White, B., Tiecke, T., Couprie, C., 2024. Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar. Remote Sens. Environ. 300, 113888. https://doi.org/10.1016/j.rse.2023.113888
