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 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)

• 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.

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 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)

• 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.