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2. Geographic and geometric compatibilization
This compatibilization was done through the digital registration procedure, which in the first phase was applied only for
the SPOT image. In this phase, topographic maps were used to extract the geographical coordinates, also recognizable
in the digital image. These coordinates were used as referential points for geometric corrections. The registered SPOT
image was used in the digital aerial photo registration through the *image to image" register mode. Submitted to this
procedure, the digital aerial photo resultant had the same spatial resolution of the SPOT image, which reduced the
details of the original aerial photo. These procedures made both remote sensing data compatible because they had the
same size and the same georeference system. It is important to note that after this compatibilization, both remote
sensing data were sampled in order to distinguish different land cover classes. These classes were preconceived from a
classification system with four land cover classes: “tropical rainforest (Ca)”, “degraded tropical rainforest (Cb)",
“herbaceous (Ps)", and “grass (Pa)” (ANDERSON, 1979). The first and second classes are two types of forest. The
third and the fourth are two types of grass. In this way, it was possible to establish a pre-classification of the Ibaté-
Mirim watershed land cover map based on those samples, which is shown in Table 1.
Table 1: Gray level intervals found in samples of both remote sensing source data.
Aerial photograph SPOT image
Classes | Min. Max. Mean S.D. CV | Min. Max. Mean SD. CV.
Ca 29 67 43,26 7,64 17,66 15 65 35,32 7,97 22,56
Cb 61 121 83,37 15,92 18,65 37 77 61,80 4,59 7,43
Ps 73 150 102,09 16,58 16,24 65 90 74,37 5,08 6,83
Pa 148 211 176,97. 15,66 . 885 88 140 2108,68 11,96 11,00
Mean - - - 13,87. 15,33 - - ^ 7,4 11,95
S.D. = standard deviation, C.V. = coefficient of variation.
Note through the dispersion measures, standard deviation and variation coefficient, that the variability of the gray level
values of the aerial photo are higher than those of the SPOT image. This was clearly noticed in all the land cover classes
with the exception of the “Ca” class. This variability is related to the mapping effort or with the class limits delineation
when producing the land cover map from a digital image.
23 The “true” land cover map
The same vertical aerial photographs were used to produce the true land cover map of the same watershed. For that, the
Kartoflex-Zeiss stereoscopic device was employed in order to rectify the pair of stereo photos. The land cover map
obtained by this process was used to analyze the accuracy of both pre-classification data described above. It is important
to notice that the map scale adopted for the “true” land map cover was the 1:25000.
3 THE ACCURACY ANALYSIS
Both remote sensing data sources pre-classifications were compared with the true land cover map through the
misclassification matrix method (GOODCHILD & KEMP, 1991; JENSEN, 1986). Using this method it was possible to
evaluate the accuracy level of each remote sensing source through the omission and comission error indexes. The
accuracy coefficient and the kappa index were calculated from the misclassification matrix too. It is important to
consider that each land cover class described previously was individually compared with the true land cover map. This
accuracy analysis was facilitated by the Envi software.
4 RESULTS
The results are presented in the Table 2. These results are the arithmetic mean calculated from each land cover class
misclassification matrix. In this way, these results summarize four matrixes for each land cover class from each remote
sensing data source. Observing the results, it is possible to notice that the digital aerial photograph presents a higher
variability of the gray level values in comparison with the SPOT image. This consideration is based on the accuracy
coefficient: 91.11 % for the SPOT image and 88.62 % for the aerial photograph, and on the kappa index: 0.62 for the
SPOT image and 0.57 for the aerial photo.
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 99