Geoscience Reference
In-Depth Information
Table 6.2
Number of Pixels Sampled for Classifier Training and Map
Validation for the 1999 Image
No. of Pixels/Polygons
Mean
Land-Cover
Class
Total No.
of Polygons
Total No.
of Pixels
Variance
Forest
189
16,755
89
5,349
Secondary forest
108
3,060
28
401
Transition
43
10,054
33
917
Crops
306
2,693
63
1,358
Pasture
261
4,496
17
120
Bare soil
17
140
8
18
Water
106
1,705
16
244
Total
1,030
38,903
38
2,089
types (i.e.,
1% land area). To address this issue, we randomly sampled individual pixels within
these polygons of the rare cover types and equally partitioned the pixels into the two groups used
for classifier training and map validation.
<
6.2.4
Image Classification
Spectral signature files were generated to be used in supervised classification using a maximum
likelihood algorithm. The spectral signatures included both image and tasseled-cap bands created
for each image of each analysis year. LC maps were produced for each of the 3 years containing
all seven LC types in each of the resulting maps. Postclassification 3
3 pixel majority convolution
filter was applied to all three LC maps to eliminate some of the speckled pattern (noise) of individual
pixels. The result of this filter was to eliminate pixels that differed in LC type from their neighbors,
which tended thereby to eliminate both rare cover types and those that exist in small patches on
the landscape (such as crops). However, we concluded that the filtering process introduced an
unreasonable amount of homogeneity onto the landscape and obscured valuable information
relevant to the spatial pattern of important cover types within our unit of analysis, which was the
land parcel. All subsequent analyses were performed on the unfiltered LC maps for all three dates
of imagery.
¥
6.2.5
Accuracy Assessment
We assessed the accuracy of the three LC maps at the pixel level using a proportional sampling
scheme based on the distribution of validation sample points (pixels) for each of the cover types
in the study. This methodology was efficiently applied in this study because the distribution of our
field-collected validation sample points was representative of the distribution in area of each cover
type in the study area (Table 6.2).
The proportional sample of pixels used for the accuracy assessment for each year was selected
by first taking into account the cover type having the smallest area based on the number of validation
pixels we had for that cover type. Once the number of pixels in the validation data set was determined
for the cover type occupying the smallest area, the total number of validation pixels to be used for
each analysis year was calculated by the general formula:
S
=
N
/
P
(6.1)
t
s
s
where
S
= the total number of validation pixels to be sampled for use in accuracy assessment,
N
t
s
= the number of pixels in the land cover type with the smallest number of validation pixels, and
P
= the proportion of the classified map predicted to be the cover type with the smallest amount
of validation pixels.
s
