These files are rasters of tree canopy heights derived from 23 sets of aerial lidar collected during 2014-2018 in Texas. Canopy heights are expressed in meters. These data were used to model golden-cheeked warbler habitat.

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

This map provides access to vector data layers that incorporate Landsat reflectance data and LiDAR elevation data within the state of New Hampshire. These data were developed by NH GRANIT using data collected between 2011 and 2018. The polygon boundaries represent "image objects" that were derived using eCognition image segmentation software, grouping image pixels into contiguous segments based on spectral reflectance and LiDAR. The attributes for each polygon were then calculated from the LiDAR crown height layer. Attributes include maximum, minimum, and mean elevation above the ground and standard deviation within each polygon. Elevation is in meters. The boundary of each layer is determined by the extent of a LiDAR data collection; Landsat reflectance data were clipped to the extent of the LiDAR. The dates of data collection are listed in the metadata for each data layer.These data were developed by NH GRANIT using funding from the New Hampshire Space Grant Consortium.

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).

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 (

Data Use
License: CC-BY-NC-SA 4.0
Recommended Citation: Wang, H., Seaborn, T., & Wang, Z. (2021). Data from: Modeling tree canopy height using machine learning over mixed vegetation landscapes [Data set]. University of Idaho. https://doi.org/10.7923/VJ7D-KS92

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.

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

This data set contains land-use, canopy height, and aboveground carbon estimates derived from LiDAR data collected at La Selva Biological Station in Costa Rica in March 1998 and March 2005. The data are provided as GeoTIFFs (*.tif) of 100-m (1-ha) resolution. A look-up table is provided that relates modeled changes in height to changes in stand characteristics (including age and carbon content). The data were used to test the accuracy and scale-dependency of high-resolution predictions of vegetation dynamics and carbon flux by the Ecosystem Demography (ED). The ED model is an individual-based terrestrial ecosystem model that predicts both ecosystem structure and corresponding ecosystem fluxes from climate, soil, and land-use inputs.

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

Many maps provide details of land classification types such as grassland, scrub, or tree cover. However, the total amount of biomass in these areas can vary greatly. Better mapping of the canopy height is important for understanding potential biodiversity, ecosystem function, or loss of carbon biomass due to deforestation, development, or fires. Scientists at ETH Zurich created a method to estimate canopy height using the best available LiDAR from space and airborne sensors, including the Global Ecosystem Dynamics Investigation (GEDI) on board the International Space Station. These LiDAR observations were used as ground truth data to train a deep convolutional neural network to regress canopy height from Sentinel-​2 images at a 10-m spatial resolution. Units are in meters. The accuracy of the vegetation height is ± 5 m.This map was financed in a partnership with Barry Callebaut AG, the world's largest chocolate maker, to better allow companies to track deforestation in their supply chains. The intent is objective, highly automated tool to guide sustainable agribusiness. More about this project may be read at ETH Zurich website here.

This dataset provides 30-meter gridded estimates of aboveground biomass (AGB), forest canopy height, and canopy coverage for Maryland, Pennsylvania, and Delaware in 2011. Leaf-off LiDAR data were combined with high-resolution leaf-on agricultural imagery in a model-based stratification that was used to select 848 sampling sites for AGB estimation. Field-based estimates were then related to LiDAR height and volume metrics through random forest regression models across three physiographic regions. Spatial errors were estimated at the pixel level using standard prediction intervals to assess the accuracy of the modeling approach. Estimates of biomass were further validated against the permanent network of FIA plots and compared with existing coarse resolution national biomass maps.

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.

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 is a 20ft resolution canopy height layer for the eastern 59 counties of North Carolina. This data was derived from the 2014 and 2015 QL2 LiDAR datasets collected by USGS and the North Carolina Department of Public Safety. This data was processed using Laszip software to compress the LAS data to LASZIP format, GDAL gdalbuildvirt was used to create county mosaics of 5ft resolution. The counties are: Alamance, Beaufort, Bertie, Bladen, Brunswick, Camden, Carteret, Caswell, Chatham, Chowan, Columbus, Craven, Cumberland, Currituck, Dare, Duplin, Durham, Edgecombe, Franklin, Gates, Granville, Greene, Guilford, Halifax, Harnett, Hertford, Hoke, Hyde, Johnston, Jones, Lee, Lenoir, Martin, Montgomery, Moore, Nash, New Hanover, Northampton, Onslow, Orange, Pamlico, Pasquotank, Pender, Perquimans, Person, Pitt, Randolph, Richmond, Robeson, Rockingham, Sampson, Scotland, Tyrrell, Vance, Wake, Warren, Washington, Wayne, and Wilson. LINK TO THE DATA SET: https://www.sciencebase.gov/catalog/item/5a591b25e4b00b291cd6a949

Canopy top height (RH98) is estimated from GEDI L1B waveforms globally between 51.6° N & S. The map is based on the first four months of L1B Version 1 data (April-July 2019). The sparse footprint level predictions are averaged at 0.5 degree resolution (approx. 55 km raster cells at the equator) to obtain a dense map. We refer to the original research article below for further information, especially on how the predictions were filtered before the aggregation.

The footprint level RH98 predictions are stored in hdf5 files corresponding to the orbit files of the GEDI L1B Version 1 data. The file load_pred_RH98_files.py contains more information on how to parse and load the prediction orbit files.

GEDI mission website: https://gedi.umd.edu/.

Citation: Use of these data require citation of this dataset and the original research article. These citations are as follows:

Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022). Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sensing of Environment, 268, 112760.

Lang, Nico, Kalischek, Nikolai, Armston, John, Schindler, Konrad, Dubayah, Ralph, & Wegner, Jan Dirk. (2021). Global canopy top height estimates from GEDI LIDAR waveforms for 2019 (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5704852

The global forest canopy height map with a resolution of 30 m for 2020 (GlobeFCH_2020_30m_v1) was generated by integrating the new-generation space-borne LiDAR (Global Ecosystem Dynamics Investigation, GEDI; Ice, Cloud, and Land Elevation Satellite-2, ICESat-2), Sentinel-1 SAR images, Sentinel-2 optical images and other ancillary data based on Google Earth Engine (GEE) platform. The coordinate system of the GlobeFCH_2020_30m_v1 is World Geodetic System 1984 (WGS 84) and the unit of the forest canopy height value is centimeter. The GlobeFCH_2020_30m_v1 was divided into 305 files, and the range of each file is 10°×10°.

Methods: This lidar derivative provides information about tree (and tall shrub) cover. 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 canopy cover raster 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) Assign all pixels with values >= 15 feet to 1 (tree canopy), and all others to 0 (no tree canopy)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 canopy cover raster provides a raster of tree and shrub canopy greater than or equal to 15 feet in height. All pixels with any vegetation exceeding this height threshold have a pixel value of 1; all others have a 0. 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 may include 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

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.

This data set provides estimates of forest canopy height derived from the Geoscience Laser Altimeter System (GLAS) LiDAR instrument that was aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite. A global GLAS waveform data set (n=12,336,553) from collection periods between October 2004 and March 2008 was processed to obtain canopy height estimates.Estimates of GLAS maximum canopy height and crown-area-weighted Lorey's height are provided for 18,578 statistically-selected globally distributed forested sites in a point shapefile. Country is included as a site attribute.Also provided is the average canopy height for the forested area of each country, plus the number of GLAS data footprints (shots), number of selected sample sites, and estimates of the variance for each country.

Height of the top of canopy above bare earth (Canopy Height Model; CHM). The CHM is derived from the NEON LiDAR point cloud and is generated by creating a continuous surface of canopy height estimates across the entire spatial domain of the LiDAR survey. The point cloud is separated into …