Several finer categories of forest types were 
dropped during the initial field testing because key 
tree species were too difficult to determine quickly in 
the field. Some less common categories were added 
later based on descriptions on the field forms. 
The field crew was instructed to collect as 
many diverse examples of each cover type as 
possible in their two month field season. Stratified- 
random plot selection methods proved too difficult to 
implement in the field, although geographic 
dispersion and patch size constraints were 
emphasized. We were pleased to find that the GPS 
unit was able to get useable location readings even 
under heavy forest canopies, but this typically 
required elevating the GPS antenna on a 10 m 
telescoping pole. 
While scheduling field work during summer 
vacation made hiring students easier, rainfall is 
heaviest during these months. Such rains prevented 
location readings and crossing of river fords common 
on smaller roads, limiting efficiency of field plot 
establishment. 
3.2 Preprocessing and Georeferencing 
We used ERDAS Imagine 8.1 software to 
process the 1993 Landsat TM image, including scene 
stitching, georeferencing, and finally classification. 
While most of the subject area fell within a single 
satellite path and scene, a small area at the east end 
of the APA had to be stitched to the main scene 
before subsequent steps could be performed. The 
added area had similar radiometric characteristics to 
the main scene but the stitched seam remained 
faintly visible in subsequent images. No other 
preprocessing steps were performed however, such 
as contrast enhancements or destriping. 
We  georeferenced the images before 
classification using maximum likelihood techniques. 
Georeferencing the images before classification 
simplified use of GPS locations for the training sites. 
An alternative approach would have required 
classification first followed by  georeferencing 
through convolution. Although this alternative 
promises greater cartographic accuracy, our 
comparisons found little difference between the two 
approaches, and using the maximum likelihood 
method avoided creation of mixed pixels in the 
procedure. 
Based on preliminary analyses and the 
literature, we confined our analyses to bands 2, 3, 4 
and 5 although in a few cases radiometric 
interference was too severe to use band 2. 
Before conducting the actual supervised 
74 
classification it was necessary to expand each 
training site’s point location into a larger patch of 
similar pixels surrounding it. This was done by 
digitizing polygons on screen. An unsupervised 
classification of the original bands yielded a 
provisional image that greatly facilitated this task. 
Also helpful were field records, which noted cover 
type changes near the plot location. 
3.3 Image Classification 
We tried using parallel piped procedures to 
distiguish the different cover types but our initial 
efforts revealed several difficulties. One difficulty 
was unanticipated vegetation types, particularly in 
the plateau at the north end of the APA. The location 
of the State and APA boundaries here is presently 
disputed by the states of Paraná and Sáo Paulo so we 
did not target field work there, but classification was 
nonetheless attempted for the area. Unfortunately, 
cover there is dominated by two vegetation types 
which do not occur elsewhere in the APA: a “humid 
soil” marshland type in the valleys, and in the upland 
“planalto” a transition between the highly mesic 
dense forest of the coastal region and the more xeric 
Araucaria forests of the interior plateau. 
Expert judgement helped to identify training 
pixels for these northern types. The signature of the 
planalto type was confused with that of mangroves, 
located along the bay, but stratifying the APA into 
northern (plateau) and southern regions enabled us to 
distinguish the two types. 
Another new type was separated out based 
on examination of the image: the “cordôes arenosos” 
type, a complex of ancient dunes near the coast 
characterized by alternating strips of dry upland and 
seasonally flooded wetlands. This  near-shore 
complex is visually distinctive and was readily 
identifiable on the image. 
In addition to the extreme diversity in the 
region, two edaphic factors common to the Atlantic 
Forest complicated discrimination of cover types: 
mesic conditions and complex terrain. The Atlantic 
Forest enjoys plentiful rain throughout the year, in 
this region totalling about 1500 mm annually. This 
well-watered condition probably minimized drought 
stress among the different vegetation types and 
resulting signature differences. 
The Atlantic Forest is a mountainous coastal 
system. In the APA Guaraquegaba, elevations ranged 
from sea level to 1580 m. This complex topography, 
coupled with a low solar angle, created considerable 
mountain shadow in the image. The more effective 
approaches for dealing with mountain shadows 
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