performed with assistance of the Technological 
Foundation of Acre (FUNTAC) and University of Acre 
(UFAC). During the field campaign, several land use 
classes were observed, and several vegetation profiles 
(arboreal and bush individuals along sections of 60 m x 2 
m size) were made at natural vegetation regrowth 
including: the description and collection of flora 
composition, DBH above 3 cm, total height, percentage 
of crown cover. In this study, the general allometric 
equation for secondary forest, 
In Y 7 -2.17 * 1.02 In (DBH) + 0.39 x In Height 
according to Uhl et al. (1988), was used to estimate the 
biomass values, mainly of regrowth areas. An overview 
of the entire area, as well as 35 mm photos were obtained 
during an overflight. 
The TM images were analyzed considering the following 
steps: atmospheric and geometric corrections, image 
segmentation based on the algorithm for the growth of 
regions (similarity threshold = 6; area threshold = 10); 
labeling of segment samples of thematic classes: Forest 
(F) Initial (IR) and Intermediate (AR) Regrowth, 
Overgrown Pasture (OP), Fresh Pasture (FP), Pasture 
with Bare Soil (PS); application of the ISOSEG 
Classifier; and generation of a thematic map. The SIR-C 
data were analyzed according to the following procedure: 
speckle reduction filtering (MAP filter) and merging of 
SAR data at different polarizations (HH, VV, HV, LL 
and Total Power) with TM images, application of a new 
version of the EBIS texture classifier (Evidence Based 
Interpretation of Satellite Images, developed at DLR) and 
also plotting of the mean backscatter values as a function 
of wavelength and polarization for each land cover 
identified, and generation of a thematic map. EBIS is a 
algorithm used for classifying textures, based on co- 
occurrence feature vectors, that are modeled as 
multinomial density functions (Lohmann, 1991). 
Additionally to the classes mentioned above, for the 
SIR-C data, the floodplain forest (V) was included. 
4. RESULTS 
Figures 1 and 2 are histograms showing the behavior of 
land use classes at both SIR-C/L-band and TM-Landsat 
data. In the signature plots of these figures, the original 
data have been used in association with the variance 
values of the training areas. The combination of bands 
TM 3 and 5 present the best performance, as compared to 
the other bands for the general discrimination of the set 
of thematic classes, i.e. discrimination of forest, regrowth 
and pasture areas. During the thematic classification, we 
could observe that TM images alone did not allow the 
discrimination of “Terra Firme” (Uplands) from 
“Varzea” (Floodplain) Forests. In contrast to that, L-band 
images, due to the several polarizations available and 
textural characteristics, allowed a very good 
210 
discrimination among these two important forest types. It 
is known that it is possible to separate different forest 
types as well as logged forest using texture information, 
and in this case, the different moisture and relief 
conditions of these two environments, -are the main 
reasons for its’ discrimination. 
Regrowth areas are best discriminated with TM band 4 
data, in the succession “Initial” and “Intermediate”, while 
SIR-C/L-band data does not allow this discrimination. 
The same finding applies for the discrimination among 
"regrowth" and "overgrown pasture" classes. For the 
identification “initial” regrowth and overgrown pasture, 
TM band 3 was a better indication. The misclassification 
in SIR-C data (L band) is mainly due to larger amounts 
of shrubs and palms, like Maximiliana maripa and 
Orbignia martiniana, which abound in these former 
pastures and lead to higher backscatter values (sometimes 
as corner reflectors) as well as to texture variances. 
For the discrimination among "pasture with bare soil" 
and “fresh pasture” classes, the L-band image presented a 
similar performance as TM-Landsat data. The soil type 
and the physical-structural conditions are also an 
important factor for this discrimination analysis. 
Generally, optical sensors are most sensitive to plant 
structure at micrometer scales, whereas radar interacts 
mainly at centimeter scales. Of special interest to radar 
are the vertical stems of plants and the trunks of trees, 
because wave propagation and backscattering through 
these media are polarization-dependent (NASA, 1989). 
Being so, the different polarizations provide different 
views of the canopy’s structure. From another overview, 
Figure 1, shows that the HV polarization of L-band is 
more sensitive to the vegetation growth and it is 
appropriated for land occupation/management studies. 
As far as backscattering from vegetated surface is 
concerned, three scattering components must be 
considered: vegetation, soil and also the interaction 
component of the L-band data studied here. Leaves and 
stalks of vegetation are relatively transparent to radiation 
and so the significance of the soil and the interaction 
component predominate. The variability found in the 
sample areas in each of the thematic classes of L-band 
data, shows a certain sensitivity of this sensor to the 
horizontal and vertical polarizations, but a lower 
sensitivity to Total Power (TP) polarization. 
Taking into account the actual interest of the scientific 
community on specific studies of regrowth areas, it is fo 
say that at these areas, the contribution of the horizontal 
polarization is moderately higher than the vertical one. 
The first one is of interest to define parameters such as 
the spectral and textural amplitude of the physiognomic 
structural variations of secondary succession areas 
(Figure 3). Considering the vegetation data inventoried 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996