Management and Use of LiDAR-Derived Information Elizabeth Cook USDA-NRCS Columbia, MO

Now What?

Study Area

Project Deliverables LAS mass point files, all returns, with and without breaklines, qq quad tiles 1-m ESRI Grid bare earth DEM files, quad and qq quad tiles 1-foot pixel natural color digital orthophotos in geotiff, ecw and sid formats (collected with LiDAR and used for breakline mapping) Independent control points for QA Misc shapefiles – tile indexes, breaklines

Data Management – File Sizes Total for 4-county project area Grid DEMs 31.4 gb LAS 92 gb Imagery gb (one format or version of the data) One 7.5-minute quad Grid DEM – 585 mb LAS 1.8 gb Imagery 4.6 gb

Data Management – Formats, Areas of Coverage, Products Method 1 – deliver “raw” data, such as LAS, ASCII mass point files or ESRI terrains; users clip to areas of interest and process derivatives Method 2 – deliver tiles of derivatives according to anticipated users needs Method 3 – deliver some derivatives, but customize as necessary, and let high-end users process their own

Data Formats – “Raw” LAS – industry standard binary file of x,y,z,i (and more) data not many softwares read (changing fast) may want to generalize, sample per class, etc ASCII x,y,z large and slow ESRI Terrain – multi-resolution database object that “stages” the data for subset, generalization and derivatives requires knowledgeable users lots of time to create

Terrain visualized as a TIN; resolution based on scale of display and pyramid levels of terrain

Derivative Data from LiDAR Digital Elevation Model (bare-earth raster) Digital Surface Model (with canopy) Hillshade TIN – ESRI vs. CAD Contours All are 2-D representations of terrain

Applications to Conservation Planning

LiDAR data can speed design time by eliminating much of the field survey. This dam was designed in 15 minutes without ever going to the field.

Improving Soil Map Unit Boundaries

Standard 10m DEM from hypsography

1m LiDAR

Flooding Frequency Interp. Soils

Using LiDAR First Returns

1.Extract first return LiDAR points 2.Low-pass filter to generalize 3.Polygonize for forest/non-forest layer (red mask)

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