f important 
of this work 
ropriate for 
"fish-bone" 
some larger 
| analysis of 
hasis on the 
IM 231/067 
km? and is 
delimited by coordinates 9? 27' S, 61? 08' W; 11? 04' S, 61? 29' 
W; 10? 50' S, 63? 08' W; and 9? 10' S, 62? 47' W. The BR-364 
federal highway (Rondónia's most important road, linking the 
State capital to the Southern part of Brazil) traverses the area in 
the SE-NW direction; most of the developed properties are 
located along the secondary roads that derive from the BR-364 
highway. 
Following FIBGE (1992), the native vegetation in the region 
corresponds to the "Floresta Estacional Semidecidual 
Submontana" class. This tropical seasonal semi-deciduous forest 
covers areas with elevations ranging from 100 m to 600 m, and a 
4 to 6-month dry season. 
The majority of the properties have small areas (around 100 ha); 
some cattle-raising farms have 1,500 ha or more. A number of 
cacao and rubber plantations, funded by subsidies programs in 
the 1980's, have areas between 200 and 500 ha. 
MATERIALS & METHODS 
Satellite Data and Image Processing System 
This study used Landsat Thematic Mapper bands 3, 4 and 5 for 
dates 07/15/94 and 08/05/90 (the area of work was defined 
considering five more images for years 1995, 1992, 1988, 1986 
and 1985, that are being used for further work). TM data 
acquired and processed by INPE was provided in CDs. 
The image processing system SPRING (Cámara et al. 1992) was 
used in this work. This INPE-developed software runs on UNIX 
workstations, and incorporates the functionalities mentioned in 
the following section. 
Image Processing Procedure 
The technique adopted in this work follows the procedure 
described by Alves ef al. (1996) for image co-registration, 
segmentation and classification. 
Six Landsat TM scenes (corresponding to the period 1985-1994) 
were initially read from the CDs. The 1994 image was geo- 
referenced using control points acquired with a Global 
Positioning System device (GPS) during a May 1995 visit to the 
region; the remaining 5 scenes were co-registered to the 1994 
scene and then resampled to 120 m resolution (an August 1995 
scene was later introduced into the data base). 
The co-registered data were processed as described by Alves ef 
al. (1996) to assure that all three bands for each image have the 
same variance and then segmented using the region-growing 
algorithm described by Bins et al. (1996). The result of the 
segmentation algorithm consists of an image of labeled regions 
that is subsequently classified by means of a region-oriented 
unsupervised classifier. 
15 
The segmentation method allows the user to define the minimum 
size of the areas and a minimum distance in digital levels for 
region growing. The un-supervised clustering algorithm classifies 
the regions, merging those that are closer than a specified 
threshold. After clustering, an interactive interpretation 
procedure is performed to assign clusters to one of the following 
classes: 
e forest; 
e use (agriculture and pasture); 
© abandonment; 
e water (rivers, reservoirs and ponds); 
e shadows; 
e undetermined (typically "noises" from different sources 
such as relief or forest texture) 
Segmentation and classification errors are corrected using 
SPRING editing functions. Areas erroneously classified are 
corrected either by masking operations in the raster format or by 
editing polygon attributes after a raster-vector conversion. Areas 
missed by the segmentation procedure are hand-digitized at the 
end of the procedure. 
After processing, the images for years 1990 and 1994 were 
analyzed using SPRING's raster analysis tools and the fractions of 
abandonment for each year and the areas classified as abandoned 
in both images were determined. 
RESULTS AND DISCUSSION 
Segmentation and Classification Results 
As described by Alves ef al. (1996), the segmentation results are 
visually more agreeable than pixel-by-pixel classifications (see 
figure 2). The classified images presented less edge effects and 
unclassified pixels than the pixel approach. As a result, the 
authors' experience indicates that complete classifications 
(including corrections) can be performed in an easier and more 
satisfactory way than pixel-based classifiers. 
The adopted unsupervised classifier presented reasonable results 
for the initial discrimination of forest, use (agriculture and 
pastures) and abandonment. This can be partially explained by 
the relatively high contrast among these classes on TM bands 3, 4 
and 5, that leads to good segmentation and classification results. 
The results, however, did not differentiate abandonment from 
some perennial plantations, like cacao and rubber, occurring in 
the region; it can be noted that the authors are not certain, at the 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996