Libro de Resúmenes / Book of Abstracts (Español/English)

Resumenes 122
azedarach) in El Palmar National Park Using ETM Landsat
images
El Palmar National Park (EPNP) is a 8400 Ha relict of temperate
savannas of Butia yatay palms with serious conservation threats due to
exotic woody expansion, mainly produced by chinaberry (Melia azedarach).
In this presentation we assess the ability of several supervised machine
learning approaches to detect the stage of invasion from Landsat ETM
spectral data.
Data were compiled from non-differencial GPS located vegetation
samples, which were classified as not-invaded, incipient and established,
according to the presence and relative age of Melia trees. Spectral data for
these locations were extracted from two Landsat ETM radiometric and
geometrically calibrated images, corresponding to winter 2000 and summer
2001. A 3x3 pixel window was used to reduce the effect of positional errors.
Five different learning approaches were used: Naive Bayes, k-nearest
neighbors (k-nn), C4.5 decision trees, Jrip rules, and a backpropagation
thrilayered perceptron neural net. Performance was evaluated using ROC
curves, confusion matrices, total positive rate and total false positive rate.
The following variables resulted selected as indicators of invasion
stage: summer-winter differences in the near infrared spectral reflectance,
NDVI, SAVI and NDWI; summer reflectance in the blue region, and winter
reflectance in the middle infrared. Not-invaded and established stages could
be identified by all methods with greater than 70% confidence. The incipient
stage instead, which is the most critical in terms of park management
decisions, was detected with acceptable performance only by 1-nn and
neural nets with very long training periods. The use of k-nn as classification
algorithm in satellite image processing looks promising because it is both
easy to implement for semestral monitoring, and to interpret conceptually
by park managers.