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Open AccessArticle

Beta-Diversity Modeling and Mapping with LiDAR and Multispectral Sensors in a Semi-Evergreen Tropical Forest

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Postgrado en Ciencias Forestales, Colegio de Postgraduados, Texcoco, Edo. de México 56230, Mexico
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Unidad de Recursos Naturales, Centro de Investigación Científica de Yucatán A.C, Mérida, Yucatán 97205, Mexico
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Postgrado en Estadística, Colegio de Postgraduados, Texcoco, Edo. de México 56230, Mexico
*
Author to whom correspondence should be addressed.
Forests 2019, 10(5), 419; https://doi.org/10.3390/f10050419
Received: 28 February 2019 / Revised: 12 April 2019 / Accepted: 23 April 2019 / Published: 15 May 2019
Tree beta-diversity denotes the variation in species composition at stand level, it is a key indicator of forest degradation, and is conjointly required with alpha-diversity for management decision making but has seldom been considered. Our aim was to map it in a continuous way with remote sensing technologies over a tropical landscape with different disturbance histories. We extracted a floristic gradient of dissimilarity through a non-metric multidimensional scaling ordination based on the ecological importance value of each species, which showed sensitivity to different land use history through significant differences in the gradient scores between the disturbances. After finding strong correlations between the floristic gradient and the rapidEye multispectral textures and LiDAR-derived variables, it was linearly regressed against them; variable selection was performed by fitting mixed-effect models. The redEdge band mean, the Canopy Height Model, and the infrared band variance explained 68% of its spatial variability, each coefficient with a relative importance of 49%, 32.5%, and 18.5% respectively. Our results confirmed the synergic use of LiDAR and multispectral sensors to map tree beta-diversity at stand level. This approach can be used, combined with ground data, to detect effects (either negative or positive) of management practices or natural disturbances on tree species composition. View Full-Text
Keywords: floristic gradient; species composition dissimilarity; nMDS; RapidEye; remote sensing; LiDAR; linear model; mixed model floristic gradient; species composition dissimilarity; nMDS; RapidEye; remote sensing; LiDAR; linear model; mixed model
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Ochoa-Franco, A.P.; Valdez-Lazalde, J.R.; Ángeles-Pérez, G.; de los Santos-Posadas, H.M.; Hernández-Stefanoni, J.L.; Valdez-Hernández, J.I.; Pérez-Rodríguez, P. Beta-Diversity Modeling and Mapping with LiDAR and Multispectral Sensors in a Semi-Evergreen Tropical Forest. Forests 2019, 10, 419.

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