990 
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
2. MATERIALS AND METHODS 
The materials used in this work were: 
- Landsat images: MSS, TM and ETM+ from February 1973 to 
February 2007, and Aster image (2003); visible, near infrared 
and SWIR (short wave infrared) bands. 
- Topographic maps (2754-19 and 2754-20-I), from the Militar 
Geographic Institute (IGM), 
- Digital Elevation Model (DEM (90m), from NASA-SRTM, 
- Field data, to collect the structure, spatial arrangement and 
plants composition of the forest. 
- Softwares: ERDAS Imagine 9.0, Arcview 9.1, Idrisi, ENVI, 
and statistics tools. 
The work was divided in five steps: 
a. Standarization of satellite data: several factors have 
influence in quantification and qualification of solar radiation 
reflected when different sensors are used along time. The main 
sources of error for identification of physonomic-estructural 
changes in vegetation among dates are: atmospheric conditions, 
errors in image registration, topographic effects, sensor 
variability, the abundance, composition and phenological 
condition of vegetation. This step included: 
a.l. Atmospheric correction: it was evaluated three methods of 
Dark object substraction" (DOSl, DOS2, DOS3 and DOS4) 
1234) (Chavez,1988; Song et al., 2000) and one of Dense 
Dark Vegetation Approach" (DDV) (Kaufman et al.,1988). 
a. 2 Geometric correction (georeferentiation): all the images 
were co-registered from a Landsat TM 224/79 August, 1989 of 
the University of Maryland, (resampling method: cubic 
convolution, RMS less than one pixel, cartographic projection 
system: UTM (WGS84), zone 20 South). 
b. Generation of forest maps: a first categorization of 
vegetation from the field data and satellite images processing 
was carried out. The categories considered and its descriptions 
were: 
- Capuera: cane with small trees (height: 5m). 
- Grassland: grassland (50-75%). 
- Savannah with trees: grassland (50-75%), bare soil (0-20%), 
trees (10-25%, Urunday: Anadenanthera colubrina). 
- Capueron with one trees layer: cane with trees (height 15m), 
dominant species: Helieta apiculata, Trichilla catigua, 
Nectandra spp, Ocotea spp., Lonchocarpus leucantus, L. 
muehlbergianus. 
- Capueron with two trees layer: understorey species, cane, 
first trees layer: 20m, second trees layer: 15m, dominant 
species: Nectandra saligna, Diadenopterix sorbifolia, L. 
muehlbergianus, L. leucanthus, Luhea divaricata, Helieta 
apiculata, Trichilla catigua, Allophylus edulis. 
- Mixed Forest in low land: dominant species: Helieta 
apiculata, Nectadra spp. Patagonula americana, 
Bolfourodendron redelianum, Holocalyx balansae, 
Parapiptadenia rigida, Rupechtria laxiflora. 
- Forest in high land: dominant species: Apuleia leicocarpa, 
Enterolobium contorstiliquum, Tabebuia spp, Cedrela 
tubiflora. 
Grove:dominant species: Helieta apiculata, Nectandra spp, 
Acacia spp., Chorisia speciosa, Melia azedarach 
- Implanted forest: exotic species: Aleuritis fordii, Melia 
azedarach Pinus spp, 
In order to apply the mapping methods to wide scale, the forest 
units of the first categorization were grouped into five classes: - 
- Dense High Forest (MAA) 
- Sparse High Forest (MAD) 
- Low Forest (MB) 
- Capuera 
- Implanted forest 
c. Generation of models of natural and human variables in 
order to interpret the spatial distribution of plant communities 
and predict its location in areas with difficult access. In this 
point we generated two predictive models: one with natural 
variables (height, slope, aspect, NDVI, etc) and other with 
human variables (distance to: rivers, villages, roads, etc.). 
These models were compared with the forest maps, and 
statistical analysis were carried out. 
d. Development of a methodology for multi-temporal 
comparison of forest: the images were grouped by date in 
summer and winter set data. Different classification methods 
were evaluated to separate forest vs other covers as the 
following: 
- MCVT: Variance classification from application of texture 
filters. 
- CnSI: Unsupervised classification (ISODATA). 
- CnSIcp: Unsupervised classification (ISODATA) from PCI 
results. 
After the selection of the best method, we follow with the 
classification of different type of forest (with the same 
categories of the forest maps). The methods evaluated were: 
SAM, ISODATA, Maximum likelihood, PCI, Tasseled Cap, 
NDVI with several combination among the individual results 
of each process. 
e. Multi-temporal comparison of natural and implanted forest: 
this comparison was carried out by Change detection method 
among best classified images (previous point). Also, the NDVI 
differences'were analyzed, and both results were integrated. 
3. RESULTS AND CONCLUSIONS 
We can generate a methodology (a protocol) of standarization 
of data satellite of different dates and sensors. 
The analysis showed that the DOS2 model of atmospheric 
correction (Chavez, 1988; Song et al., 2000) was the more 
adequate for the study area. The details of the geometric 
correction process were as appear in materials and methods. 
It was possible from de satellite and field data analysis to 
elaborate the forest maps to work in two scale of details. 
In the Figure 2 we can see the forest map with nine units of 
vegetation in the area of Cuna Piru Reserve with more details 
than we can observe in the Fig. 3 where are represented only 
four forest classes, while the plantations, the agricultural uses, 
grassland, settlements are grouped as unclassified en the 
Fig.3. 
The methods to generate this last forest map can be used in 
other areas near Cuna Piru Reserve to separate forest vs non