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Bosque (Valdivia)

versión On-line ISSN 0717-9200


OJEDA, Nelson et al. Discrimination of araucaria forests (Araucaria araucana) in the Conguillio National Park, Southern-Center Chile, using Landsat TM data. Bosque (Valdivia) [online]. 2011, vol.32, n.2, pp.113-125. ISSN 0717-9200.

Araucaria forests (Araucaria araucana) have a tremendous ecological relevance; however, the information concerning their spatial distribution is still insufficient. They have only been classified according to small management scales, using satellite photos and images processed through conventional methods. The present study had as its objective to discriminate and characterize types of A. araucana forests in the Conguillío National Park, located in the Southern-Center Chile, through data derived from the Landsat-5 TM satellite and geographic information systems. The normalized difference vegetation index (NDVI) was satisfactorily related with variables corresponding to crown coverage and the diameter at breast height; thus, these index values were incorporated to the classification process. Using the digital elevation model and the NDVI, the effect provoked by the shadow was minimized. Seven types of forests, between dense and semi-dense-open, were discriminated in accordance with the accompanying species. The global reliability of the classification was 83.8 %. The greatest reliability for the producer was for the medium crown density forest of A. araucana - N. dombeyi (B1) (87.5 %); and for the consumer, for the high crown density forests of A. araucana - N. dombeyi (B1) and also for those of medium density (B2) (93 %). It is concluded that incorporating NDVI values and data derived from the digital elevation model to the satellite classification process, it is possible to discriminate araucaria forests with satisfactory reliability in areas of rough relief, which is very useful information for the management of these forestry ecosystems.

Palabras clave : supervised classification; vegetation index; digital elevation model; araucaria forest; remote sensing.

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