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

dc.creatorALEJANDRA DEL PILAR OCHOA FRANCO
dc.creatorJOSE RENE VALDEZ LAZALDE
dc.creatorGREGORIO ANGELES PEREZ
dc.creatorHECTOR MANUEL DE LOS SANTOS POSADAS
dc.creatorJOSE LUIS HERNANDEZ STEFANONI
dc.creatorJUAN IGNACIO VALDEZ HERNANDEZ
dc.creatorPAULINO PEREZ RODRIGUEZ
dc.date2019
dc.date.accessioned2023-07-21T19:18:57Z
dc.date.available2023-07-21T19:18:57Z
dc.identifierhttp://cicy.repositorioinstitucional.mx/jspui/handle/1003/1692
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7737253
dc.descriptionTree 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. 
dc.formatapplication/pdf
dc.languageeng
dc.relationinfo:eu-repo/semantics/datasetDOI/10.3390/f10050419
dc.relationcitation:Ochoa-Franco, A. D. 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. (2019). Beta-Diversity Modeling and Mapping with LiDAR and Multispectral Sensors in a Semi-Evergreen Tropical Forest. Forests, 10(5), 419.
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.sourceForests, 10(5), 419, 2019
dc.subjectinfo:eu-repo/classification/Autores/FLORISTIC GRADIENT
dc.subjectinfo:eu-repo/classification/Autores/SPECIES COMPOSITION DISSIMILARITY
dc.subjectinfo:eu-repo/classification/Autores/NMDS
dc.subjectinfo:eu-repo/classification/Autores/RAPIDEYE
dc.subjectinfo:eu-repo/classification/Autores/REMOTE SENSING
dc.subjectinfo:eu-repo/classification/Autores/LIDAR
dc.subjectinfo:eu-repo/classification/Autores/LINEAR MODEL
dc.subjectinfo:eu-repo/classification/Autores/MIXED MODEL
dc.subjectinfo:eu-repo/classification/cti/2
dc.subjectinfo:eu-repo/classification/cti/24
dc.subjectinfo:eu-repo/classification/cti/2417
dc.subjectinfo:eu-repo/classification/cti/241715
dc.subjectinfo:eu-repo/classification/cti/241715
dc.titleBeta-Diversity Modeling and Mapping with LiDAR and Multispectral Sensors in a Semi-Evergreen Tropical Forest
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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