Australia-wide 30 m machine learning-derived canopy height models composites: best pick and median

Brief description

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
Lineage: A total of 26,987 LiDAR and photogrammetry point cloud tiles (1–4 km² each) were obtained from the Elevation and Depth (ELVIS) and Terrestrial Ecosystem Research Network (TERN) open repositories, representing a 5% stratified sample designed to match the distribution of Australia’s 16 vegetation structure classes (Scarth et al., 2019). For each tile, a 0.5 m canopy height model (CHM) was generated using the pit-free algorithm (Khosravipour et al., 2014), and individual tree crowns were delineated with the Dalponte segmentation algorithm (Dalponte & Coomes, 2016) using vegetation-specific optimized parameters (Pucino et al., 2025, under review).

The resulting point-cloud-derived CHMs served as reference data for evaluating the vertical accuracy of four publicly available satellite-based machine-learning or deep learning-derived CHMs: (1) Lang et al. (2023); (2) Liao et al. (2020); (3) Potapov et al. (2021); and (4) Tolan et al. (2024). All datasets were co-registered and resampled to 30 m resolution. Pixel-wise error metrics were computed, and a combined score defined for each vegetation class which publicly available dataset is the most accurate.

Three new continental-scale 30 m CHMs were then produced: (i) a pixel-wise median composite; (ii) a vegetation-class-specific best-pick composite; and (iii) a deep-learning CHM derived from a multi-layer perceptron (MLP - not publicly available).

Note: this document's Start Date and End Date indicate the nominal dates of the datasets we tested, not the publication dates of their associated articles.

References

Dalponte, M., Coomes, D.A., 2016. Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data. Methods Ecol. Evol. 7, 1236–1245. https://doi.org/10.1111/2041-210X.12575

Khosravipour, A., Skidmore, A.K., Isenburg, M., Wang, T., Hussin, Y.A., 2014. Generating Pit-free Canopy Height Models from Airborne Lidar. Photogramm. Eng. Remote Sens.\t 80, 863–872. https://doi.org/10.14358/PERS.80.9.863

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.

Pucino, N., McVicar, T. R., Levick, S.R., van Dijk, A.I.J.M., 2025. Assessing optimization strategies for unsupervised individual tree crown detection and delineation to support continental-scale inventories: role of vegetation type and point cloud data density. Sci. of Remote Sens. (Under Review)

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