Version 1
: Received: 25 October 2018 / Approved: 26 October 2018 / Online: 26 October 2018 (05:32:52 CEST)
How to cite:
Silva, E.D.O.; Candido Xavier, A.; Nogueira de Souza Tedesco, A.; Azevedo Barreto Neto, A.; de Lima, L.E.M.; Pezzopane, J.E.M.; Tognella, M.M.P. Estimates of the Leaf Area Index Using Unmanned Aerial Vehicle Images of an Urban Mangrove in the Vitória Bay, Brazil. Preprints2018, 2018100617. https://doi.org/10.20944/preprints201810.0617.v1
Silva, E.D.O.; Candido Xavier, A.; Nogueira de Souza Tedesco, A.; Azevedo Barreto Neto, A.; de Lima, L.E.M.; Pezzopane, J.E.M.; Tognella, M.M.P. Estimates of the Leaf Area Index Using Unmanned Aerial Vehicle Images of an Urban Mangrove in the Vitória Bay, Brazil. Preprints 2018, 2018100617. https://doi.org/10.20944/preprints201810.0617.v1
Silva, E.D.O.; Candido Xavier, A.; Nogueira de Souza Tedesco, A.; Azevedo Barreto Neto, A.; de Lima, L.E.M.; Pezzopane, J.E.M.; Tognella, M.M.P. Estimates of the Leaf Area Index Using Unmanned Aerial Vehicle Images of an Urban Mangrove in the Vitória Bay, Brazil. Preprints2018, 2018100617. https://doi.org/10.20944/preprints201810.0617.v1
APA Style
Silva, E.D.O., Candido Xavier, A., Nogueira de Souza Tedesco, A., Azevedo Barreto Neto, A., de Lima, L.E.M., Pezzopane, J.E.M., & Tognella, M.M.P. (2018). Estimates of the Leaf Area Index Using Unmanned Aerial Vehicle Images of an Urban Mangrove in the Vitória Bay, Brazil. Preprints. https://doi.org/10.20944/preprints201810.0617.v1
Chicago/Turabian Style
Silva, E.D.O., José Eduardo Macedo Pezzopane and Mônica Maria Pereira Tognella. 2018 "Estimates of the Leaf Area Index Using Unmanned Aerial Vehicle Images of an Urban Mangrove in the Vitória Bay, Brazil" Preprints. https://doi.org/10.20944/preprints201810.0617.v1
Abstract
The urban mangrove of the Vitória Bay, Espírito Santo, Southern Brazil suffers from anthropogenic impacts, which interfere in the foliar spectral response of its species. Identifying the spectral behavior of these species and creating regression models to indirectly obtain structure data like the Leaf Area Index (LAI) are powerful environmental monitoring tools. In this study, LAI was obtained in 32 plots distributed in four stations. In situ LAI regression analysis with the SAVI resulted in significant positive relationships (r2 = 0.58). Forest variability regarding the degree of maturity and structural heterogeneity and LAI influenced the adjustment of vegetation indices (VIs). The highest regression values were obtained for the homogeneous field data, represented by R. mangle plots, which also had higher LAI values. The same field data were correlated with SAVI of a RapidEye image for comparison purposes. The results showed that, images obtained by a UAV have higher spatial resolution than the Rapideye image, and therefore had a greater influence of the background. Another point is that the statistical analysis of the field data with the IVs obtained from the RapidEye image did not present high regression coefficient (r2 = 0.7), suggesting that the use of VIs applied to the study of urban mangroves needs to be better evaluated, observing the factors that influence the leaf spectral response.
Keywords
UAV images; mangrove; vegetation indices; Leaf Area Index (LAI)
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
