A collection of functions to browse lidar data collections, query collections for coverage for specific locations, and retrieve data covering locations.
There are substantial distortions associated with the web mercator projection for CONUS. This means that samples defined to be a specific size in the web mercator projection will be a very different size in a projection that has less distortion of distances and areas. The example builds PDAL pipelines to clip 1000m square areas. In reality, the actual sample areas will be much smaller. A simple work-around is to call prepareTargetData with data in a projection with less distortion to do the buffering of point locations. However, selection of a suitable projection to use for CONUS is problematic. UTM is an option but this will involve multiple zones for some/most states. Albers equal area conic is another option but this introduces rotation in the sample areas that may necessitate a larger sample size to ensure coverage for areas around a plot locations. Changing to a 1400m square would ensure coverage 1000m around a plot but this doubles the sample area and will result in longer retrieval times and larger point and derived output files.
There are substantial distortions associated with the web mercator projection for CONUS. This means that samples defined to be a specific size in the web mercator projection will be a very different size in a projection that has less distortion of distances and areas. The example builds PDAL pipelines to clip 1000m square areas. In reality, the actual sample areas will be much smaller. A simple work-around is to call prepareTargetData with data in a projection with less distortion to do the buffering of point locations. However, selection of a suitable projection to use for CONUS is problematic. UTM is an option but this will involve multiple zones for some/most states. Albers equal area conic is another option but this introduces rotation in the sample areas that may necessitate a larger sample size to ensure coverage for areas around a plot locations. Changing to a 1400m square would ensure coverage 1000m around a plot but this doubles the sample area and will result in longer retrieval times and larger point and derived output files.