Description: This High Resolution Landcover product was derived from three dates of aerial imagery, and from elevation information derived from LiDAR elevation data. It has a spatial resolution of one meter, and a class resolution of 15 classes. The target year for the interpretation of the classification is 2009.
The Imagery sources are the 2008 NAIP imagery, the 2009 NAIP imagery, and the Four band Spring imagery collected in 2007, 2009 and 2010. Three dates were used because previous experimentation had shown that using fewer dates lacked sufficient spectral information to produce a reasonably consistent classification at the level required. Because each of these imagery layers were collected at widely different dates, to produce consistent classifications required breaking the state into regions or "tiles" with consistent spectral character. More than 580 such tiles were utilized statewide. Each was independently processed, and separately interpreted and checked. The final products were mosaicked together, then broken out into county files, which is how the files are presented in the NRGIS Library. Although we have done the best job we could to maintain consistency between these many files, it is impossible to maintain absolute consistency in even a small set of files, let alone this large number.
The LiDAR data utilized was created by subtracting the Bare Earth DEM from the First Return DEM, yielding a normalized elevation model, which represents the elevation above ground surface of buildings, trees and other vegetation. This layer made an important contribution to producing an accurate classification of buildings and forests.
A series of post classification steps were performed to correct problems which were consistent enough to be remedied by this kind of solution. These included embedding rasterized roads, recoding certain shadows, and using the LiDAR to refine building footprints, among others. See processing steps for more information.
Copyright Text: R. Peter Kollasch developed this coverage for the Iowa DNR