## LiDAR and Hyperspectral Data Product Fusion

Last modified: Jun 23, 2016 Time: 10:00Introduction to data fusion

Source:
National Ecological Observatory Network (NEON)

Data tutorials that use the `R`

`sp`

package.

Introduction to data fusion

Bring LiDAR-derived raster data (DSM and DTM) into R to create a final canopy height model representing the actual vegetation height with the influence of elevation removed. Then compare lidar derived height (CHM) to field measured tree height to estimate uncertainty in lidar estimates.

This two-day workshop, taught at the USGS National Training Center in Denver, CO on 12-13 April 2016, will cover working with spatio-temporal data in R.

This post explains the fundamental principles, functions and metadata that you need to work with raster data, in image format in R. Topics include raster stacks, raster bricks plotting RGB images and exporting an RGB image to a Geotiff.

This workshop will present how to work with Lidar Data derived rasters in R. Learn how to import rasters into R. Learn associated key metadata attributed needed to work with raster formats. Analyzing the data performing basic raster math to create a canopy height model. Export raster results as a (spatially located) geotiff.

This activity walks you through creating square polygons from a plot centroid (x,y format) in R.

Bring LiDAR-derived raster data (DSM and DTM) into R to create a final canopy height model representing the actual vegetation height with the influence of elevation removed. Then compare lidar derived height (CHM) to field measured tree height to estimate uncertainty in lidar estimates.