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Please use this identifier to cite or link to this item: http://dspace.utalca.cl/handle/1950/8934

Title: Discrimination of araucaria forests (Araucaria araucana) in the Conguillio National Park, Southern-Center Chile, using Landsat TM data
Other Titles: Discriminación de bosques de Araucaria araucana en el Parque Nacional Conguillío, centro-sur de Chile, mediante datos Landsat TM
Authors: Ojeda, N.
Sandoval, V.
Soto, H.
Casanova, J.L.
Herrera, M.A.
Morales, L.
Espinosa, A.
Martin, J.S.
Keywords: supervised classification
vegetation index
digital elevation model
araucaria forest
remote sensing
Issue Date: 2011
Publisher: UNIV AUSTRAL CHILE, FAC CIENCIAS FORESTALES
Citation: Bosque vol.:32 iss:2 pág.:113 -125
Abstract: 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 Conguillio 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.
Description: Martin, JS (San Martin, Jose). Univ Talca, Inst Biol Vegetal & Biotecnol, Talca, Chile
URI: http://dspace.utalca.cl/handle/1950/8934
ISSN: 0304-8799
Appears in Collections:Artículos en publicaciones ISI - Universidad de Talca

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