Methods: This lidar derivative provides information about vegetation height. The 3-foot resolution raster was produced from the 2020 Quality Level 1 classified lidar point cloud, which was provided by Sanborn Map Company, Inc. Tukman Geospatial developed the CHM from the classified point cloud using the following processing steps in LasTools:

Create Tiles (lastile) Height Normalize the Point Cloud (lasheight) Set points classified as buildings and unclassified to 0 height Thin the remaining points, taking the highest point in a 1.5 x 1.5 foot area (lasthin) Convert the thinned point cloud to a DEM (las2dem)

The data was developed based on a horizontal projection/datum of NAD83 (2011). Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data. Uses and Limitations: The CHM provides a raster depiction of the highest vegetation returns for each 3x3 foot raster cell across Santa Clara County. The layer is useful for myriad vegetation and forest-related analysis and is an important input to the automated processes used to develop the Santa Clara fine scale vegetation map. This CHM was derived from the point cloud using only returns classified as vegetation. See the ‘Santa Clara County Canopy Height Model’ for a CHM that also includes points labelled as unclassified. Related Datasets: This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara County

  Dataset


  Description


  Link to Data


  Link to Datasheet




  Canopy Height Model


  Pixel values represent the aboveground height of vegetation and trees.


  https://vegmap.press/clara_chm


  https://vegmap.press/clara_chm_datasheet




  Canopy Height Model – Veg Returns Only


  Same as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)


  https://vegmap.press/clara_chm_veg_returns


  https://vegmap.press/clara_chm_veg_returns_datasheet




  Canopy Cover


  Pixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.


  https://vegmap.press/clara_cover


  https://vegmap.press/clara_cover_datasheet




  Canopy Cover – Veg Returns Only


  Same as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)


  https://vegmap.press/clara_cover_veg_returns


  https://vegmap.press/clara_cover_veg_returns_datasheet





  Hillshade


  This depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. 


  https://vegmap.press/clara_hillshade


  https://vegmap.press/clara_hillshade_datasheet




  Digital Terrain Model


  Pixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).


  https://vegmap.press/clara_dtm


  https://vegmap.press/clara_dtm_datasheet




  Digital Surface Model


  Pixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.


  https://vegmap.press/clara_dsm


  https://vegmap.press/clara_dsm_datasheet

Height of the top of canopy above bare earth (Canopy Height Model (CHM)). Data are mosaicked over the AOP footprint; mosaicked onto a spatially uniform grid at 1 m spatial resolution, and delivered as 1 km by 1 km tiles. Data are provided in GeoTIFF (.tif) format. Associated metadata files include QA reports (.pdf, .md, .html), shapefile boundaries (.shp, .shx, .prj, .dbf), and .kml boundary files; shapefile and kml boundary files may be zipped (.zip).

Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT) mission utilizes a portable, airborne imaging system that aims to simultaneously map the composition, structure, and function of terrestrial ecosystems. G-LiHT primarily focuses on a broad diversity of forest communities and ecoregions in North America, mapping aerial swaths over the Conterminous United States (CONUS), Alaska, Puerto Rico, and Mexico.The purpose of G-LiHT’s Canopy Height Model data product (GLCHMT) is to provide LiDAR-derived maximum canopy height and canopy variability information to aid in the study and analysis of biodiversity and climate change. Scientists at NASA’s Goddard Space Flight Center began collecting data over locally-defined areas in 2011 and that the collection will continue to grow as aerial campaigns are flown and processed.GLCHMT data are processed as a raster data product (GeoTIFF) at a nominal 1 meter spatial resolution over locally-defined areas. A low resolution browse is also provided showing the canopy height with a color map applied in JPEG format.

Although the random forest algorithm has been widely applied to remotely sensed data to predict characteristics of forests, such as tree canopy height, the effect of spatial non-stationarity in the modeling process is oftentimes neglected. Previous studies have proposed methods to address the spatial variance at local scales, but few have explored the spatial autocorrelation pattern of residuals in modeling tree canopy height or investigated the relationship between canopy height and model performance. By combining Light Detection and Ranging (LiDAR) and Landsat datasets, we used spatially-weighted geographical random forest (GRF) and traditional random forest (TRF) methods to predict tree canopy height in a mixed dry forest woodland in complex mountainous terrain. Comparisons between TRF and GRF models show that the latter can lower predefined extreme residuals, and thus make the model performance relatively stronger. Moreover, the relationship between model performance and degree of variation of true canopy height can vary considerably within different height quantiles. Both models are likely to present underestimates and overestimates when the corresponding tree canopy heights are high (>95% quantile) and low (<median), respectively. This study provides a critical insight into the relationship between tree canopy height and predictive abilities of random forest models when taking account of spatial non-stationarity. Conclusions indicate that a trade-off approach based on the actual need of project should be taken when selecting an optimal model integrating both local and global effects in modeling attributes such as canopy height from remotely sensed data.

Global and regional Canopy Height Maps (CHM). Created using machine learning models on high-resolution worldwide Maxar satellite imagery.

Method:This lidar derivative provides information about vegetation height. The 3-foot resolution raster was produced from the 2020 Quality Level 1 classified lidar point cloud, which was provided by Sanborn Map Company, Inc. Tukman Geospatial developed the CHM from the classified point cloud using the following processing steps in LasTools:Create Tiles (lastile)Height Normalize the Point Cloud (lasheight)Set points classified as buildings to 0 heightThin the remaining points, taking the highest point in a 1.5 x 1.5 foot area (lasthin)Convert the thinned point cloud to a DEM (las2dem)

The data was developed based on a horizontal projection/datum of NAD83 (2011).

Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area.

An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.Uses and Limitations: The CHM provides a raster depiction of the highest vegetation returns for each 3x3 foot raster cell across Santa Clara County. The layer is useful for myriad vegetation and forest-related analysis and is an important input to the automated processes used to develop the Santa Clara fine scale vegetation map. However, this data product was produced based on a rapid, fully automated point cloud classification and was not manually edited. As such, it includes some ‘false positives’ – pixels with a canopy height in the raster that aren’t vegetation. These false positives include noise from water aboveground non-vegetation returns from bridge decks, powerlines, and edges of buildings.

Related Datasets: This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara CountyDatasetDescriptionLink to DataLink to DatasheetCanopy Height ModelPixel values represent the aboveground height of vegetation and trees.https://vegmap.press/clara_chmhttps://vegmap.press/clara_chm_datasheetCanopy Height Model – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_chm_veg_returnshttps://vegmap.press/clara_chm_veg_returns_datasheetCanopy CoverPixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.https://vegmap.press/clara_coverhttps://vegmap.press/clara_cover_datasheetCanopy Cover – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_cover_veg_returnshttps://vegmap.press/clara_cover_veg_returns_datasheet HillshadeThis depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/clara_hillshadehttps://vegmap.press/clara_hillshade_datasheetDigital Terrain ModelPixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).https://vegmap.press/clara_dtmhttps://vegmap.press/clara_dtm_datasheetDigital Surface ModelPixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.https://vegmap.press/clara_dsmhttps://vegmap.press/clara_dsm_datasheet

Canopy height was calculated by taking the difference between the 2015 LIDAR Digital Surface Model and the 2015 bare earth Digital Elevation Model. These data are available on OpenTopography.org.

DATA OVERVIEW

Mapped attributes are

• above ground biomass, AGB;
• downed wood biomass, i.e., the sum of coarse and fine woody debris, DWB;
• canopy bulk density, CBD;
• canopy height,CH;
• canopy base height, CBH;
• and canopy fuel load, CFL.

Models were fit using auxiliary information that included lidar data from 20 acquisitions in Oregon and climate data. Measurements in plots of the Forest Inventory and Analysis program (FIA) were used to obtain plot-level ground observations for predictive modeling. Tree and transect measurements in FIA plots were respectively used to obtain plot-level values of AGB and DWB. To obtain plot-level values of CBD, CH, CBH and CFL, tree measurements in FIA plots were processed with FuelCalc. Plot level auxiliary variables were obtained intersecting the axiliary information layers with the FIA plots. Predictive models were random forest models in which a parametric component was added to model the error variance. The error variance was modeled as a power function of the predictive value and was used to produce uncertainty maps. A different model was fit for each variable and the resulting models were used to obtain maps of synthetic predictions for all areas covered by the 20 lidar acquisitions. The modeled error variance was used to generate uncertainty maps for the predictions of each response variable. Model accuracy was assessed globally (for the entire dataset) and separately for each one of the 20 lidar acquisitions included in the dataset.

Results from the accuracy assessment can be found in Appendix A and Appendix B of Mauro et al. (2021).

Each variable has two associated maps. These maps are named using the following convention where VARIABLE is the acronym for each variable (AGB, DWB, CBD, CH, CBH or CFL):

Predictions of forest attributes:

VARIABLE.tif

Standard deviation of modeled errors:

SD_VARIABLE.tif

### There are two additional rasters. The first one, year.tif is necessary to obtain the reference year for each lidar acquisition. The second one, forest_mask.tif provides a forest vs non-forest mask. Forested areas are coded as 1s and non-forested areas with no-datas. This mask is a resampled subset of the PALSAR JAXA 2014 ‘New global 25m-resolution PALSAR mosaic and forest/non-forest map (2007-2010) - version 1’ from the Japan Aerospace Exploration Agency Earth Observation Research Center (www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm). Its reference year is 2009. Models to predict forest attributes were created using ground observations in forested areas. For many applications it is advisable to use the provided mask to excluded non-forested areas from analyses. This can be done, for example, multiplying the desired raster by the forest mask. Exceptions to this may occur in relatively open forested lands where the mask eliminates areas that actually sustain forest. In those areas, the use of an add-hoc forest mask might be more appropriate. ### Reference year: year.tif ### Forest mask: forest_mask.tif ###

UNITS:

For a given variable, both predictions and standard deviation of model errors have the same units. These units are:

• Variable (Abreviation): Units

• Above ground biomass (AGB): Mg/ha

• Downed wood biomass (DWB):Mg/ha

• Canopy bulk density (CBD): Kg/m3 (Kilogram per cubic meter)

• Canopy height (CH): m

• Canopy base height (CBH): m

  Canopy fuel load (CFL):Mg/ha

COORDINATE REFERENCE SYSTEM:

The reference system for all maps is EPSG 5070

USAGE

These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights.

Please include the following citation in any publication that uses these data:

Mauro, F., Hudak, A.T., Fekety, P.A., Frank, B., Temesgen, H., Bell, D.M., Gregory, M.J., McCarley, T.R., 2021. Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon. Remote Sensing 13. https://doi.org/10.3390/rs13020261

Monitoring tree canopy height is crucial for assessing forest health, biodiversity, and carbon sequestration potential, as it provides insights into forest structure and ecosystem dynamics. Lidar data, which is preferable for this use, isn't always available and other measurement methods can be labor-intensive and time-consuming, often limited to small areas. This model can be used to estimate tree canopy height given high-resolution satellite imagery where Lidar data isn't available.This Deep Learning Package (DLPK) contains Meta's High-Resolution Canopy Height model. The model employs a vision transformer backbone pretrained using self-supervised learning on millions of high-resolution satellite images from around the globe. It then uses a convolutional decoder trained on a LiDAR-derived canopy height dataset to generate canopy height estimates, expressed in meters above ground. Use this model to automate the workflow for estimating tree canopy height from high-resolution satellite imagery over large areas.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.Input8-bit, 3-band high resolution (0.6 - 1 meter) satellite imagery.OutputClassified raster with each pixel value representing the height of tree canopy in meters.Applicable geographiesThis model is expected to work well globally.Model architectureThis model packages Meta's High Resolution Canopy Height model (Tolan et al., 2023).Accuracy metricsThe model produces an average Mean Absolute Error (MAE) of 2.8 m and Mean Error (ME) of 0.6 m on NEON dataset.LimitationsLidar data can provide more accurate measurements where it is available and this model should only be used where such data isn't available. Prediction on regions with tree shadows, terrains with slope might have inconsistent results.Predicted canopy height values vary drastically with cell size. The recommended cell size should be used for inference.Sample resultsHere are a few results from the model.See this web scene for examples of 3D Trees derived from this model.

A 2m by 2m canopy height model (CHM) grid developed from the 2011 aerial LiDAR survey of Christmas Island. As with the 2011 DEM, the CHM was provided to Geoscience Australia in 1km by 1km ESRI grid tiles, which were then joined together using ESRI ArcMap. Each grid cell (2m x 2m) contains the maximum vegetation height in metres. Canopy height was generated by subtracting the ground height from the first laser return classified as vegetation. As a guide, the data is vertically accurate to 15cm and horizontally accurate to 30cm. For a detailed description of the survey accuracy see the AAM Survey Report. The CHM grid file was provided in GDA94 MGA zone 48 and has been left in this projection. The CHM data can be used to find the average vegetation canopy height for defined areas. LiDAR vegetation heights, along with vegetation density values have been used in other organisations to create vegetation maps, estimate carbon content, characterise species habitats and assist in decision making. Disclaimer

Rasterized canopy top height models (CTHM) at 10m ground sampling distance (GSD) derived from airborne LIDAR.

The CTHMs were created to be comparable to GEDI canopy top heights (within 25m footprints) using two sources:

1) NASA's LVIS airborne LIDAR campaigns (here we rasterized the RH98).
2) High-resolution canopy height models derived from small-footprint airborne laser scanning campaigns in Europe (max pooled with a circular 25m footprint corresponding to the GEDI footprint).

The original LVIS LIDAR data is available here: https://lvis.gsfc.nasa.gov

Links to the original ALS data are available here: https://publications.jrc.ec.europa.eu/repository/bitstream/JRC126223/jrc126223_jrc126223_lidaropensourcedata.pdf

Code to create GEDI-like canopy top heights from high-resolution ALS data is available here: https://github.com/langnico/global-canopy-height-model

More information is available in the Lang et al. (2022). Please cite our paper if you use these derived data in your own work.

Reference:

Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 1-12, https://doi.org/10.1038/s41559-023-02206-6

This dataset provides 30 m gridded estimates of aboveground biomass density (AGBD), forest canopy height, and tree canopy coverage for the New England Region of the U.S., including the state of Maine, Vermont, New Hampshire, Massachusetts, Connecticut, and Rhode Island, for the nominal year 2015. It is based on inputs from 1 m resolution Leaf-off LiDAR data collected from 2010 through 2015, high-resolution leaf-on agricultural imagery, and FIA plot-level measurements. Canopy height and tree cover were derived directly from LiDAR data while AGBD was estimated by statistical models that link remote sensing data and FIA plots at the pixel level. Error in AGBD was calculated at the 90% confidence interval. This approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA's spaceborne LiDAR missions.

This data set provides a regression-based canopy height model of the contiguous United States (CONUS) using data from ATLAS/ICESat-2 L3A Land and Vegetation Height (ATL08), as well as data from Landsat, LANDFIRE, and NASADEM.

This is a full 1km by 1km tile of NEON AOP lidar data from the Smithsonian Environmental Research Center (SERC). This dataset contains a canopy height model at 1m spatial resolution. All NEON AOP missions are flown at or near peak 'greenness'. This dataset is uploaded here for teaching purposes, in particular with the tutorials found on https://www.neonscience.org/resources/data-tutorials. This particular tile makes for a good teaching example because it contains a wide variety of recognizable land cover types.Citation:National Ecological Observatory Network. 2020. Data Product DP3.30015.001, Ecosystem structure. Provisional data downloaded from http://data.neonscience.org on December 15, 2020. Battelle, Boulder, CO, USA NEON. 2020.

LANDFIRE’s (LF) 2016 Remap (Remap) Canopy Base Height (CBH) supplies information used in fire behavior models to determine the critical point at which a surface fire will transition to a crown fire. CBH data are continuous from 0 to 9.9 meters (to the nearest 0.1 meter) and describe the lowest point in a stand where there is enough available fuel (0.25 in dia.) to propagate fire vertically through the canopy. Critical CBH is defined as the lowest point at which the canopy bulk density is .012 kg m-3. The CBH mapping process starts by deriving field referenced estimates of canopy characteristics through LF Reference Database (LFRDB) plot analysis. Utilizing LFRDB plots, a training data set to model CBH was created. Field referenced CBH values are calculated for each plot using the canopy fuel estimation software in Forest Vegetation Simulator (FVS). Statistical analysis of plot variables indicates that LF Remap Existing Vegetation Type (EVT) and Existing Vegetation Height (EVH) demonstrate some influence on CBH, with Existing Vegetation Cover (EVC) affecting CBH values within certain EVTs. To model the relationsIAp LF Remap Canopy Cover (CC) and Canopy Height (CH) are used as predictors of CBH; developing a linear regression equation per EVT and disturbance type/severity. These CBH regression models account for roughly two-thirds of EVT assignments. To account for missing EVTs, regression equations from similar EVTs are utilized as surrogates ensuring consistent values throughout. In some, instances LF Remap assumes the potential burnable biomass in the tree canopy has been accounted for in the surface fuel model. For example, young or short conifer stands where the trees are represented by a shrub type fuel model will not have canopy characteristics. For tree stands dominated by broadleaf species that are fire resistant (e.g. Populus spp.) are coded with a CBH of 10 meters. the artificial increase in CBH is done to prevent false simulation of crown fires that rarely occur in these areas. All non-forest values, including herbaceous/shrub systems and non-burnable types (urban, barren, snow and ice, and agriculture) are coded as 0. However, certain types of agriculture and urban vegetation that are burnable are assigned a constant value by LF Remap Total Fuel Change Toolbar (LFTFC) rulesets based on region and vegetation type. LF Remap Annual Disturbance products are incorporated into CBH to provide informed changes by disturbance type, severity, and time since disturbance (TSD). Annual Disturbance products provide a pre-disturbance scenario represented by LF Remap existing vegetation products. The combination of pre-disturbance and non-disturbance vegetation are used to calculate CBH by vegetation type, cover, and height acted on by a disturbance event. Vegetation adjustments are modeled in disturbed areas based on disturbance type and severity using FVS derived linear equations. With the use of Annual Disturbance products, CBH also has capable fuels functionality. Capable fuels calculate TSD assignments for disturbed areas using an "effective year." For example, year 2020 fuels may be calculated for the year 2020. the new process considers all the existing disturbances included in LF Remap and adjusts the TSD for these to the effective year (2020 in the example), making the products "2020 capable fuels." More information about capable fuels can be found at https://www.landfire.gov/lf_remap.php.

This folder contains canopy height models of Bronx wash and High School wash created from liar data collected in 2008

A tree crowns layer was derived from 2018 NAIP and 2019 LiDAR, and then each tree crown polygon was populated with the 95th percentile nDSM (height above ground) values from LiDAR collected in 2014 and in 2019. Object-based image analysis techniques (OBIA) were employed to extract potential tree crowns including the area of the crown and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2000 and all observable errors were corrected.

The dataset contains a Canopy Height Model (CHM) of a 1m x 1m resolution (pixel size) for the entire Denmark. One of the derivatives of the CHMs is a model excluding village, urban, and summerhouse areas.

This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency.

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)

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