Creation of a 3D Urban GIS Database: Data Fusion Approach Technical Session on "Photogrammetry and 3D Visualization"


Los Lunas, NM Data Acquisition and Processing
The Los Lunas, NM test area included the simultaneous acquisition of high-resolution LIDAR data using Spectrum's D3 LIDAR system, which provided 1.0-meter spacing data, the acquisition of 0.5-foot pixel Digital Color IR imagery and the collection of hyperspectral imagery using the SPECTIR hyperspectral system. All data was preprocessed to real world coordinates using the on board airborne GPS and IMU data (New Mexico State Plane coordinates NAD83, feet Central Zone). All LIDAR data was preprocessed into a LAS file format, with the Multispectral Digital Color IR camera imagery being delivered as orthorectifed TIFF file formatted files. The hyperspectral imagery was captured as a binary file that was later converted to a georeferenced ERDAS imagine file that contained 63 bands of image data.

The Los Lunas study area is characterized as a rural-urban zone that is dominated by agricultural fields, urban-residential subdivisions, the Rio Grande Valley and associated riverine morphology and riparian forest vegetation, figure 1.


Figure 1. Los Lunas New Mexico Color IR Digital Camera imagery.

Because of the nature of the vegetation in this project area Spectrum chose to fly Color IR digital camera imagery in order to best distinguish vegetation features from the background soil and urban features. The imagery was collected a pixel resolution of 0.5-ft, and provided and excellent cartographic base from which to tie in both the LIDAR and hyperspectral imagery. The imagery was orthophoto processed using the LIDAR data as the DEM base and laid out in a project-tiling scheme and with the resultant 8 orthophotos being mosaiced to form the final project ortho base seen in figure 1.

Once the ortho base was generated feature extraction was initiated using the LIDAR data. This included the extraction of the following earth surface features:
  • Bare Earth surface generation
  • Building footprint generation
  • Vegetation canopy generation
The LIDAR data resolution was 1.0-meter point spacing. Features were extracted using Spectrum's LID-MAS software using a TIN and Fast Fourier Filter (FFT) in combination. The resultant data included extracted bare-earth surface, buildings and vegetation (trees) in a LAS elevation point cloud format, figures 2.

The LAS extracted elevation point cloud features were then converted to their appropriate feature formats in which they would be delivered. The bare earth surface elevation points were first converted to an ESRI GRID format using an IDW interpolation algorithm (figure 3), and then exported into the ARA deliverable format, which is a 32-bit GeoTIFF file. Buildings were converted from the LAS point cloud format into an ArcView Shapefile as a polygon footprint. Each building polygon in this dataset was assigned the following attributes that included: Area, Perimeter, max building height and min building height.


Figure 2. 3D Perspective of extracted bare-earth surface, buildings and tree point clouds


Figure 3. (Left) Bare Earth Surface-ESRI GRID, (Right) Bare Earth Surface-32bit GeoTIFF, with both images being colored by increasing elevation values.

LIDAR did an excellent job in defining and capturing tree canopies. Tree Canopy point clouds were converted to ArcView Shape files in three distinct formats:
  • Individual Tree Points
  • Individual Trees as polygons
  • Tree Clusters (Riparian Forest) as Polygons
The hyperspectral imagery was used to define and classify the following classes of features, figure 4:
  • Water bodies (river, irrigation ditches, ponds)
  • Non-tree vegetation (Agriculture and native vegetation)
  • Soils: Sandy Loam, Sandy soils, Wet-Clay Soils
  • Road Pavement (Asphalt)

Figure 4. Final Los Lunas Area extracted Land Cover Features. This includes LIDAR extracted buildings, and tree cover; and Hyperspectral extracted, non-tree vegetation cover, agriculture, Soils, road-asphalt and water classes.

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