o expand each 
| larger patch of 
is was done by 
\n unsupervised 
ands yielded a 
litated this task. 
iich noted cover 
ed procedures to 
s but our initial 
. One difficulty 
5, particularly in 
PA. The location 
here is presently 
Sao Paulo so we 
lassification was 
i. Unfortunately, 
vegetation types 
> APA: a “humid 
ind in the upland 
1e highly mesic 
d the more xeric 
eau. 
identify training 
signature of the 
at of mangroves, 
1g the APA into 
ns enabled us to 
arated out based 
ordôes arenosos” 
; near the coast 
f dry upland and 
"his near-shore 
nd was readily 
diversity in the 
n to the Atlantic 
of cover types: 
in. The Atlantic 
Yout the year, in 
n annually. This 
nimized drought 
ation types and 
intainous coastal 
levations ranged 
plex topography, 
ted considerable 
e more effective 
untain shadows 
require a digital elevation model (DEM), but 
appropriate DEM’s were unavailable could not be 
produced within our budget. 
Given these complicating factors, many of 
the original cover types had to be consolidated. 
Some consolidation had been anticipated. For 
example, the four month lag between image date and 
field work meant some crops may have changed 
between the image and field survey dates. We 
combined the various crops into a single cultivated 
class. We also combined the various forest regrowth 
stages together. Lastly, the pasture and bare sand 
categories were combined with the exposed soil 
category, but crops and forest were still difficult to 
separate, as were the mangrove and nearby restinga 
(maritime forest) categories. 
We took a pragmatic approach to our 
classification difficulties which = combined 
unsupervised, supervised, and manual interpretation 
approaches to produce vector coverages of each 
cover type. Unsupervised classification was used to 
identify those land cover categories where it was 
most effective: deep water, shallow or turbid water, 
exposed soil, and beach. Once classified, each of 
these categories was vectorized as a separate 
coverage. The supervised classification image was 
used to manually “photointerpret” the mangrove, 
restinga, planalto, cordóes arenosos, and humid field 
categories, using onscreen digitization of vector 
polygons in Arc/Info. The ratio of bands 3 and 2 
proved particularly useful for this task. 
To provide maximum flexibility in sharing 
data resulting data were made available in Arc/Info's 
transport format for vector data and ERDAS *.IMG 
format for the raster image (land cover only). Each 
of the two source programs plus Idrisi offer 
conversion routines for these formats. 
3.4 Map Production 
While digital coverages were the principal 
product of the project, hardcopy maps were an 
important adjunct product for distribution of study 
results. These were produced using ERDAS. Two 
map products were identified as early products: a 
natural resource characterisation for land managers 
and planners depicting cover types, and an orienting 
map for ecotourists identifying attractions and routes 
of access. 
To produce the map for managers, the 
various vector land cover layers were portrayed as 
patterns and overlaid on a false color image of the 
broader region along with the road network. Names 
of major rivers and communities were added along 
75 
with scale, localization information, and other details 
in a 1:100,000 full color map. 
The map for tourists is still awaiting 
production but is planned to be simpler and cheaper 
to produce, and therefore will be a vector product at 
1:200,000 scale. It will include communities, 
attractions, and roads but not cover types or a 
background image. 
4. RESULTS 
Results of the project include both GIS 
products and the skills acquired in deriving them. 
4.1 Field Work and Georeferencing 
The GPS portion of the project mapped 368 km of 
roads in and near the reserve as well as 13 points of 
special touristic interest. Replicate readings at 
intersections were typically within 10 m of each 
other, although accuracy at intermediate points will 
be less. Flooded fords prevented mapping of the 
distal ends of some roads, and a few road segments 
will need to be digitized from paper maps due to file 
corruption or equipment failure. 
Data on cover types were collected at over 
111 field locations of diverse types although some 
desired types were missed, particularly the humid 
soil (upland wetland), planalto (upland mixed forest), 
and cordóes arenosos (ancient dunes) categories. A 
pleasant surprise was the success of the GPS receiver 
in getting accurate readings even under heavy forest 
canopy. 
Georeferencing adjusted locations on the 
image to within 0.9 pixels (27 m) and within the 
accuracy needs for the project. 
4.2 Image Classification 
Image classification mapped 8 categories of 
land cover: beach, restinga (maritime forest), cordôes 
arenosos, dense forest, upland wetland, upland mixed 
forest, mangrove, and exposed soil. Two categories 
of water were also recognized: deep water and 
shallow or turbid water. These categories met the 
minimum project requirements for use in planning 
and monitoring. 
While SPVS experts on the region were 
consulted on the classification, a quantitative 
assessment of the classification's accuracy is helpful. 
We compared the classification of the image with the 
land cover assignments at the field plots to help 
understand potential misclassifications as well as the 
correspondence between the original categories and