This assignment was fun in that it compared to the work I do at my internship related to QA/QC of county LiDAR imagery. In this case, we were looking at the use of LiDAR classification for forest details such as land elevation, distribution of tree height, and density of the canopy. We created a digital elevation model (DEM), digital surface model (DSM), used LAS Dataset to Raster to create:
At this point, we have reused many of the same tools (the Plus, Minus, and Con tools for example) multiple times, and this has been helpful in gaining a deeper understanding of what is taking place in the raster images when they are being used in this type of analysis. I recognized quickly the process of creating a DEM and the problems that may occur with mixing pixels and confused flow, which are things we look for at my internship. Elevation changes that are inconsistent are reviewed, along with other notable characteristics such as an unexplained, sudden change in flow magnitude or direction, plus more obvious issues like locating and assessing tiles with missing pixels.
Something less familiar to me is the process of calculating the forest height distribution and canopy density, and I am grateful to be gaining this knowledge - it may be very useful in the future for other professional and personal projects. Knowing this type of analysis can help in making choices related to maintaining the health of forests being monitored, conserved and/or used for logging or other commercial purposes, which covers a multitude of scenarios over a broad number of disciplines.
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