Vegetation Structure Maps for the Upper East River Domain Derived from 2015 and 2019 LiDAR Data

Description

<p>This is a map of various vegetation canopy structure metrics derived from high-density airborne LiDAR scans collected in August - September 2015 and 2019. The different raster bands represent statistical summaries of the normalized LiDAR point cloud. In the normalized point cloud, ground elevations have been subtracted, leaving the Z coordinate to represent heights above the ground surface. T</p><p>he 14 raster bands represent: </p><p>1. "maxht", the maximum height above ground (in m) of points in a raster cell. 2. "meanht",the mean height above ground (in m) of points in a raster cell. 3. "ht99", 99th-percentile height above ground (in m) of points in a raster cell. 4. "ht80", 80th-percentile height above ground (in m) of points in a raster cell. 5. "ht20", 20th-percentile height above ground (in m) of points in a raster cell. 6. "htsd", standard deviation of point heights. 7. "intmean", mean of return intensities. 8. "intmax", maximum of return intensities. 9. "intsd", standard deviation of return intensities. 10. "intcan_ratio", ratio of summed return intensities from the canopy (&gt; 2m height) to total summed intensities. 11. "intund_ratio", ratio of summed return intensities from the understory (0.25 - 2 m height) to total summed intensities below 2m height. 12. "prop_can", ratio of return numbers from the canopy (&gt; 2m height) to total number of returns. 13. "prop_und", ratio of return numbers from the understory (0.25 - 2 m height) to the total number of returns below 2m height. 14. "p_crown", ratio of the number of returns inside vegetation crowns to the total number of returns. </p><p>Processing summary: Classified LiDAR point clouds from both datasets were re-projected to a common coordinate system (EPSG:32613, WGS84 UTM Zone 13N). A 0.33m canopy height model was constructed for each dataset by triangulating ground returns and subtracting the elevation of the ground surface. Statistical summaries described above were then calculated from the normalized point cloud. All point cloud processing was done using the R package lidR version 2.1. The two summary datasets from the 2015 and 2019 LiDAR datasets were mosaiced to produce a continous dataset, with the 2019 data taking priority where there was coverage (approximately 80% of the domain). Note that this causes visible inconsistencies in return intensity data between the two domains because of the different characteristics of the LiDAR instruments used.</p>

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