Comparing the Use of Indigenous Knowledge with Classification and Ordination Techniques for Assessing the Species Composition and Structure of Vegetation in a Tropical Forest |
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Authors: | J Luis Hernandez-Stefanoni Javier Bello Pineda Gabriela Valdes-Valadez |
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Institution: | (1) Watershed Ecosystems Graduate Program, Trent University, Peterborough, Ontario, Canada;(2) Facultad de Ciencias Politicas y Sociales, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico;(3) Present address: Servicio de Información Estadística Agroalimentaria y Pesquera, Av. Benjamín Franklin 146, Col. Escandon, C.P., 11800, D.F., México |
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Abstract: | dentification of groups that are similar in their floristic composition and structure (habitat types) is essential for conservation
and forest managers to allocate high priority areas and to designate areas for reserves, refuges, and other protected areas.
In this study, the use of indigenous knowledge for the identification of habitat types in the field was compared against an
ecological characterization of habitat types, including their species composition obtained by using classification and ordination
techniques for a tropical landscape mosaic in a rural Mayan area of Quintana Roo, Mexico. Plant diversity data calculated
from 141 sampled sites chosen randomly on a vegetation class’s thematic map obtained by multispectral satellite image classification
were used for this propose. Results indicated high similarity in the categorization of vegetation types between the Mayan
classification and those obtained by cluster and detrended correspondence analysis. This suggests that indigenous knowledge
has a practical use and can be comparable to that obtained by using science-based methods. Finally, identification and mapping
of vegetation classes (habitat types) using satellite image classification allowed us to discriminate significantly different
species compositions, in such a way that they can provide a useful mechanism for interpolating diversity values over the entire
landscape. |
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Keywords: | Indigenous knowledge Plant diversity Remote sensing Satellite image classification Tropical forest |
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