Using a Multistructural Object-Based LiDAR Approach to Estimate Vascular Plant Richness in Mediterranean Forests With Complex Structure
Author
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Lopatin, Javier
Author
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Galleguillos Torres, Mauricio
Author
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Fassnacht, Fabian E.
Author
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Ceballos, Andrés
Author
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Hernández, Jaime
Admission date
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2015-08-27T18:27:47Z
Available date
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2015-08-27T18:27:47Z
Publication date
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2015
Cita de ítem
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IEEE Geoscience and Remote Sensing Letteres, Vol. 12, No. 5, May 2015
en_US
Identifier
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DOI: 10.1109/LGRS.2014.2372875
Identifier
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https://repositorio.uchile.cl/handle/2250/133235
General note
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Artículo de publicación ISI
en_US
Abstract
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A multistructural object-based LiDAR approach to predict plant richness in complex structure forests is presented. A normalized LiDAR point cloud was split into four height ranges: 1) high canopies (points above 16 m); 2) middle-high canopies (8-16 m); 3) middle-low canopies (2-8 m); and 4) low canopies (0-2 m). A digital canopy model (DCM) was obtained from the full normalized LiDAR point cloud, and four pseudo-DCMs (pDCMs) were obtained from the split point clouds. We applied a multiresolution segmentation algorithm to the DCM and the four pDCMs to obtain crown objects. A partial least squares path model (PLS-PM) algorithm was applied to predict total vascular plant richness using object-based image analysis (OBIA) variables, derived from the delineated crown objects, and topographic variables, derived from a digital terrain model. Results showed that the object-based model was able to predict the total richness with an r(2) of 0.64 and a root-mean-square error of four species. Topographic variables showed to be more important than the OBIA variables to predict richness. Furthermore, high-medium canopies (8-16 m) showed the biggest correlation with the total plant richness within the structural segments of the forest.
en_US
Patrocinador
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CONICYT through Integration of Advanced Human Capital into the Academy Project
791100013