Height of the top of canopy above bare earth (Canopy Height Model (CHM)); data are mosaicked over AOP footprint; mosaicked onto a spatially uniform grid at 1 m spatial resolution in 1 km by 1 km tiles. Data are provided in geotiff format.

Height of the top of canopy above bare earth (Canopy Height Model; CHM). The CHM is derived from theNEON 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 classes …

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

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

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

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

LANFIRE’s (LF) 2016 Remap (Remap) Forest Canopy Height (CH) describes the average height of the top of the canopy for a stand. In disturbed locations CH is calculated from linear regression equations derived from Forest Vegetation Simulator (FVS) plot data output, but at non-disturbed locations it is assigned the midpoint of Fuel Vegetation Height (FVH) forested classes. 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. LF Remap Annual Disturbance products are incorporated into CH 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 reporting of pre-disturbance scenarios helps to calculate CH. Vegetation adjustments are then modeled in disturbance areas based on disturbance type and severity. CH is then used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). CH supplies information for fire behavior models, such as FARSITE (Finney 1998), that can determine; the starting point for embers in the spotting model, wind reductions, and the volume of crown fuels. CH 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.

Goddard’s LiDAR, Hyperspectral, and Thermal Imager (G-LiHT(https://gliht.gsfc.nasa.gov/)) 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.

This dataset represents global tree heights based on a fusion of spaceborne-lidar data (2005) from the Geoscience Laser Altimeter System (GLAS) and ancillary geospatial data. See Simard et al. (2011) for details.

This datasheet describes a set of 5 lidar derived, 5-meter resolution rasters that cover the entire extents of Santa Cruz and Santa Clara Counties. The rasters are slope (Degrees), aspect, elevation, canopy height, and canopy cover. These rasters were derived from the early-2020 Quality Level 1 (QL1) points clouds for Santa Cruz and Santa Clara County. As such, these rasters represent the state of the landscape in 2020 before the CZU and SCU complex fires. The horizontal coordinate system of these rasters is UTM zone 10 NAD 83.
Higher resolution, single-county versions of each of these rasters exist and are available on https://pacificvegmap.org. These 5-meter versions were produced for the entire 2 county area and are used – along with the 5-meter Scott and Burgan fuel model – as landscape (.LCP) file rasters to accompany the Santa Cruz / Santa Clara 5-meter fuel model.
Table 1 provides links to download these lidar derived rasters.
Table 1. lidar derivatives for Santa Clara County

  Dataset


  Description


  Link to Data


  Link to Datasheet




  Slope (Degrees)


  Aspect (or ‘slope direction’) shows the downslope direction of the maximum rate of change in elevation value from each 5m x 5m cell to its neighbors. 


  https://vegmap.press/scc_scz_5_meter_slope_degrees


  https://vegmap.press/scc_scz_5_meter_datasheet




  Aspect


  Aspect (or ‘slope direction’) shows the downslope direction of the maximum rate of change in elevation value from each 5m x 5m cell to its neighbors. 


  https://vegmap.press/scc_scz_5_meter_aspect


  https://vegmap.press/scc_scz_5_meter_datasheet




  Elevation


  Elevation above sea level (in feet) for each 5m x 5m cell. 


  https://vegmap.press/scc_scz_5m_elevation


  https://vegmap.press/scc_scz_5_meter_datasheet




  Canopy Height


  Pixel values represent the aboveground height of vegetation and trees.


  https://vegmap.press/scc_scz_5_meter_can_height


  https://vegmap.press/scc_scz_5_meter_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/scc_scz_5_meter_can_cov


  https://vegmap.press/scc_scz_5_meter_datasheet

In this study, the accuracy of forest canopy height estimation was assessed using Synthetic Aperture Radar (SAR), including backscatter and Polarimetric SAR (PolSAR), as well as optical indices derived from optical imagery, and Random Forest (RF) and Support Vector Machine (SVM) models were applied by using canopy heights derived from Light Detection and Ranging (LiDAR) as a reference for validation. Accurate measurement of canopy height is critical for effective forest management, biodiversity conservation, and climate change analysis, so this study attempted to address the challenges posed by traditional measurement methods, which are time-consuming and limited in scope. SAR with its all-weather, day and night imaging capability, has the distinct advantage of being able to continuously monitor forest canopy dynamics over a wide area, thus overcoming the spatial time and cost constraints of ground-based observations. Approaches in this study involved pre-processing of SAR and LiDAR data to reduce inherent data inaccuracies, as well as calculating optical indices to facilitate indirect estimation of canopy height. This study provided a comparative assessment of the performance of RF and SVM models using various data integrations, highlighted the higher accuracy was achieved through the synergistic combination of PolSAR and optical indices. The results showed that the data-integrated approach improved the accuracy of canopy height estimation, with the RF model performing slightly better than the SVM model in terms of prediction under the optimal data configurations of the two models in this study. These findings support the advanced application of incorporating remote sensing techniques, validated against LiDAR benchmarks, as a viable strategy for refining forest canopy height estimation, thereby providing insights for forest management and ecological modelling programs.

LANFIRE’s (LF) Remap Forest Canopy Height (CH) describes the average height of the top of the canopy for a stand. In disturbed locations CH is calculated from linear regression equations derived from Forest Vegetation Simulator (FVS) plot data output, but at non-disturbed locations it is assigned the midpoint of Fuel Vegetation Height (FVH) forested classes. 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. LF Remap Annual Disturbance products are incorporated into CH 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 reporting of pre-disturbance scenarios helps to calculate CH. Vegetation adjustments are then modeled in disturbance areas based on disturbance type and severity. CH is then used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). CH supplies information for fire behavior models, such as FARSITE (Finney 1998), that can determine; the starting point for embers in the spotting model, wind reductions, and the volume of crown fuels. CH 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.

LANDFIRE's (LF) 2022 Forest Canopy Height (CH) describes the average height of the top of the canopy for a stand. CH is used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). CH supplies information for fire behavior models, such as FARSITE (Finney 1998), that can determine the starting point of embers in the spotting model, wind reductions, and the volume of crown fuels. To create this product, plot level CH values are calculated using the canopy fuel estimation software, Forest Vegetation Simulator (FVS). Pre-disturbance Canopy Cover and CH are used as predictors of disturbed CH using a linear regression equation per Fuel Vegetation Type (FVT), disturbance type/severity, and time since disturbance. CH is used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). In LF 2022, fuel products are created with LF 2016 Remap vegetation in areas that were un-disturbed in the last ten years. To designate disturbed areas where CH is modified, the aggregated Annual Disturbance products from 2013 to 2022 in the Fuel Disturbance (FDist) product are used. All existing disturbances between 2013-2022 are represented in the LF 2022 update, and the products are intended to be used in 2023 (the year of release). The "capable" year terminology used in LF 2020 and LF 2016 Remap is no longer specified, due to reduction in latency from when a disturbance occurs to the release date of fuel products accounting for that disturbance. However, users should still consider adjusting fuel layers for disturbances that occurred after the end of the 2022 fiscal year (after October 1st, 2022) when using the LF 2022 fuel products. Because those changes would not be accounted for. Learn more about LF 2022 at https://landfire.gov/lf_230.php

This dataset provides information on tree canopy cover percentage and mean canopy height, both at a 10m resolution, for the North Pennines & Dales in northern England. The data was derived from LiDAR analysis, which was used to create a vegetation height model for the region. From this model, tree crowns were identified and subsequently processed into two raster datasets: one representing the percentage of tree canopy cover and the other depicting mean canopy height, both specific to the North Pennines & Dales landscape. While significant efforts were made to exclude non-vegetative structures, some non-vegetative objects may still be present in the dataset. Full details about this dataset can be found at https://doi.org/10.5285/9e3055a3-a56b-4210-9628-4acd096ed9b7

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. 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.

LANDFIRE's 2023 Update (LF 2023) Forest Canopy Height (CH) describes the average height of the top of the canopy for a stand. CH is used in the calculation of Forest Canopy Bulk Density (CBD) and Base Height (CBH). CH supplies information for fire behavior models, such as FARSITE (Finney 1998), that can determine the starting point of embers in the spotting model, wind reductions, and the volume of crown fuels. To create CH, LANDFIRE's Existing Vegetation Height (EVH) product must be produced first. EVH is a continuous scaled product which assigns height to all life forms in the LF data, this product is created using an image-based process (within the Conterminous United States (CONUS)) to assess canopy structure for areas disturbed in the past twenty years. CH is then derived from EVH by assigning bins of 13 height classes for fuel production and use in fire behavior software. In LF 2023, fuel products are created with LF 2016 Remap vegetation in areas that were un-disturbed in the last twenty years. To designate disturbed areas where CH is modified, the aggregated Annual Disturbance products from 2014 to 2023 in the LF Fuel Disturbance (FDist) product are used. All existing disturbances between 2014-2023 are represented in LF 2023, and the products are intended to be used in 2024 (the year of release). When using any product from the LF 2023 fuel product suite, users should consider adjusting fuel layers for disturbances that occurred after the end of the 2023 fiscal year (after October 1st, 2023). Disturbances that occurred after the end of the 2023 fiscal year are not accounted for within LF 2023 fuel products. Learn more about LF 2023 at https://www.landfire.gov/data/lf2023.

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 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.

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 folder contains models created from lidar to investigate the impact of Green Infrastructure on vegetation growth. The lidar data used is stored within this folder as well as all the models and shapefiles developed during this investigation. This project focused on two washes, Bronx and High School, located in Tucson, Arizona. DEMs and DTMs were created from the lidar in order to develop canopy height models. The CHMs were then used to calculate differentials over time spans to measure how tree heights changed. Boundaries were identified to compare tree growth/loss for regions with and without Green Infrastructure.