Comparison of IKONOS Derived Vegetation Index and LiDAR Derived Canopy Height Model for Grassland Management. Page: 29
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assess how well values matched the field data. To improve these initial correlations, SPSS 17
was used to calculate 10 residuals that represented outliers within the dataset; the outliers were
then removed. After initial analysis, if either the NDVI or CHM did not show high enough
correlations they would have been eliminated from further analysis. Insufficient correlations at
this stage would have suggested that those particular data sets were not suited to this particular
study.
In line with previous studies, a polynomial 2nd order regression was used in Excel to
relate field values with calculated values. The resulting regression equation was then used in
conjunction with the image classification. NDVI values were calculated for the pixels that were
classified as juniper; for the NDVI analysis, this involves multiple pixels per tree, as discussed
previously. Once NDVI values were assigned to individual pixels, the total NDVI per
management unit was calculated. Following that, the total NDVI score was converted to biomass
using ArcGIS. Similarly, the CHM used the juniper objects from the classification and then zonal
statistics in ArcGIS were used to find the maximum CHM value within each juniper object. The
maximum height value was then used in the regression equation to calculate a biomass value for
each tree in a management unit, and then totaled for the unit. Estimated biomass values for
juniper and Fringe juniper were normalized by area of each management unit. The resulting
normalized biomass per area (BPA) calculation provided forest service managers with
information concerning relative concentrations of juniper, allowing them to assess different
techniques for management purposes.29
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Parker, Gary. Comparison of IKONOS Derived Vegetation Index and LiDAR Derived Canopy Height Model for Grassland Management., thesis, December 2009; Denton, Texas. (https://digital.library.unt.edu/ark:/67531/metadc12179/m1/36/: accessed April 19, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; .