733a2ecb-56f1-4dd0-a9fb-89e5570d2f2b urn:uuid eng UTF-8 nonGeographicDataset nonGeographicDataset 2025-11-19T09:28:25 creation ISO 19115 2016 Australia-Wide 30 m Machine Learning-Derived Canopy Height Models Composites: Best Pick and Median 733a2ecb-56f1-4dd0-a9fb-89e5570d2f2b This dataset is part of the OzTreeMap project and provides two new 30 m spatial resolution canopy height products for continental Australia: the best-pick canopy height model (pick-CHM) and the median canopy height model (med-CHM). Both products represent estimates of vegetation canopy height across Australia and were developed to improve the accuracy and consistency of existing large-scale canopy height models, which were generated by researchers to represent canopy heights from variable time periods ranging from 2007 until 2020. The best-pick and median products are composites and derive from an extensive validation of the 4 original CHMs. Each product is provided as a single-band GeoTIFF raster in the Australian Albers (EPSG:3577) coordinate reference system, with 30 m spatial resolution and float32 data type. These datasets support applications in forest structure monitoring, carbon accounting, and ecosystem assessment across Australia. {"type": "Polygon", "coordinates": [[[112.92, -43.74], [153.64, -43.74], [153.64, -9.03], [112.92, -9.03], [112.92, -43.74]]]} 112.92 153.64 -43.74 -9.03 CHM 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. documentDigital Method documentation HTML Khosravipour, A., Skidmore, A.K., Isenburg, M., Wang, T., Hussin, Y.A., 2014. Generating Pit-free Canopy Height Models from Airborne Lidar. Photogrammetric Engineering & Remote Sensing 80, 863–872. documentDigital Method documentation HTML 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. documentDigital Method documentation HTML 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. documentDigital Method documentation HTML 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. documentDigital Method documentation HTML 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 Sensing 11, 147 documentDigital Method documentation HTML 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. documentDigital Method documentation HTML Canopy Height Model Composite - Best Pick Data documentDigital HTML 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. documentDigital Method documentation HTML Khosravipour, A., Skidmore, A.K., Isenburg, M., Wang, T., Hussin, Y.A., 2014. Generating Pit-free Canopy Height Models from Airborne Lidar. Photogrammetric Engineering & Remote Sensing 80, 863–872. documentDigital Method documentation HTML 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. documentDigital Method documentation HTML 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. documentDigital Method documentation HTML 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. documentDigital Method documentation HTML 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 Sensing 11, 147 documentDigital Method documentation HTML 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. documentDigital Method documentation HTML Canopy Height Model Composite - Median Data documentDigital HTML 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. documentDigital Method documentation HTML Khosravipour, A., Skidmore, A.K., Isenburg, M., Wang, T., Hussin, Y.A., 2014. Generating Pit-free Canopy Height Models from Airborne Lidar. Photogrammetric Engineering & Remote Sensing 80, 863–872. documentDigital Method documentation HTML 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. documentDigital Method documentation HTML 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. documentDigital Method documentation HTML 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. documentDigital Method documentation HTML 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 Sensing 11, 147 documentDigital Method documentation HTML 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. documentDigital Method documentation HTML ro-crate-metadata.json documentDigital HTML OzTreeMap - best pick and median documentDigital PNG Unnamed resource documentDigital PNG