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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
Materials used: 
1) Scenes from WorldView-II sensor obtained in June 10^ 
2010, with off-nadir angle 16° and 11 bits radiometric 
resolution, delivered by DIGITALGLOBE. 
2) Vector files of blocks in the databank of Säo Luis, from the 
city planning agency. 
3) GCPs collected during Field survey in August 2011 with 
TOPCON Hiper L GPS geodetic equipment. 
4) Contour lines in vector format, 1 m equidistance of contours, 
for the Sáo Luis region. 
The following software was used for image processing: ENVI 
4.7 (ITT, 2009), for fusion and preparation of both test sites: 
PCI Geomatics V10.3.1 (PCI Geomatics, 2010) to work with 
the Digital Elevation Model and control points, followed by 
WorldView-2 image orthorectification, InterIMAGE v1.27 
(InterIMAGE 2010) and GeoDMA for the exploratory analysis 
of image attributes and land cover classification. 
Ortho-rectification was performed in order to correct for image 
distortions. In order to accomplish this task, Ground Control 
Points were collected using a DGPS (Differential Global 
Positioning System). The GCPs were collected on the entire 
scene (Figure 5). 
The NDVI was used routinely to calculate the relation NIR - 
RED/NIR + RED for the determination of vegetation covered 
areas according to ROUSE et al (1974). 
In order to evaluate the performance of the additional bands 
from WorldView-II, image classications were made with the 
following procedure: 
Y Considering only the four bands corresponding to 
those found at most high resolution satellites, namely 
blue, green, red and near infrared; 
Y Including all 8 bands of WorldView-II; 
Y Using only bands Red and Near infrared 1 to 
demonstrate the capacity — for vegetation 
discrimination of those bands available traditionally; 
Y  Inserting bands Red edge and Near infrared 2, to 
demonstrate the capacity for target discrimination at 
these new spectral bands; 
Y Testing bands Coastal, Yellow and Red Edge on 
decision rules to improve class separability. 
By visual classification, confusion matrices were tabulated for 
each of the above mentioned classifications, and the respective 
Kappa indices calculated. 
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Figure 5: Distribution of GCPs in the area under study. 
4. RESULTS 
One of the most important results indicates that Red Edge 
(705-745nm) band is sensitive to different spectral behaviour 
of vegetation types, which can be due to its localization on the 
electromagnetic spectrum corresponding to the end of 
absorption of wavelengths red and beginning of infrared by 
vegetation. So it is interesting to calculate the NDVI using this 
band instead of the red one, which is normally used. 
Figure 6 shows the NDVI images from the area under study 
using both Red and Near infrared 1 bands as well as Red Edge 
and Near Infrared 2. Analyzing visually these images, 
enhanced by a color scale where the lowest NDVI values are in 
blue and the highest in red tones, one verifies the capacity of 
the new WorldView-II bands to differentiate vegetation types 
Semi-evergreen Tropical Forest (A) and mangrove (B). For 
further details see SOUZA et al. (2011). 
  
    
Red & Near Inf. 1 Red Edge & Near Inf. 2 
  
  
  
  
  
  
Figure 6: NDVI images from area under study: (A) Semi- 
evergreen Tropical forest, (B) Mangrove. 
An analysis was made to quantify the improvement by the new 
WorldView-II bands mentioned, based on four classifications 
in the area under study and considering the respective 
confusion matrices compared to a visual reference 
classification and the Kappa indices for each classification 
(Figure 7).