Please use this identifier to cite or link to this item: http://cicy.repositorioinstitucional.mx/jspui/handle/1003/1072
Estimating species richness and biomass of tropical dry forests using LIDAR during leaf-on and leaf-off canopy conditions
JOSE LUIS HERNANDEZ STEFANONI
KRISTOFER D. JOHNSON
BRUCE D. COOK
JUAN MANUEL DUPUY RADA
Richard Birdsey
Alicia Peduzzi
FERNANDO JESUS TUN DZUL
Acceso Abierto
Atribución-NoComercial-SinDerivadas
DOI: 10.1111/avsc.12190
LIDAR
ABOVE-GROUND BIOMASS
CANOPY CONDITIONS
FOREST STRUCTURE, SPECIES RICHNESS
TOPOGRAPHY
TROPICAL DRY FOREST
Questions: Is the accuracy of predictions of above-ground biomass (AGB) and plant species richness of tropical dry forests from LIDAR data compromised during leaf-off canopy period, when most of the vegetation is leafless, compared to the leaf-on period? How does topographic position affect prediction accuracy of AGB for leaf-off and leaf-on canopy conditions? Location: Tropical dry forest, Yucatan Peninsula, Mexico. Methods: We evaluated the accuracy of predictions using both leaf-on and leaf-off LIDAR estimates of biomass and species richness, and assessed the adequacy of both LIDAR data sets for characterizing these vegetation attributes in tropical dry forests using multiple regression analysis and ANOVA. The performance of the models was assessed by leave-one-out cross-validation. We also investigated differences in vegetation structure between two topographic conditions using PCA and ANOSIM. Finally, we evaluated the influence of topography on the accuracy of biomass estimates from LIDAR using multiple regression analysis and ANOVA. Results: A higher overall accuracy was obtained with leaf-on vs leaf-off conditions for AGB (root mean square error (RMSE) = 21.6 vs 25.7 ton·ha-1), as well as for species richness (RMSE = 5.5 vs 5.8 species, respectively). However, no significant differences in mean dissimilarities between biomass estimates from LIDAR and in situ biomass estimates comparing the two canopy conditions were found (F1,39 = 0.03, P = 0.87). In addition, no significant differences in dissimilarities of AGB estimation were found between flat and hilly areas (F1,39 = 1.36, P = 0.25). Conclusions: Our results suggest that estimates of species richness and AGB from LIDAR are not significantly influenced by canopy conditions or slope, indicating that both leaf-on and leaf-off models are appropriate for these variables regardless of topographic position in these tropical dry forests. We evaluated the accuracy of predictions using both leaf-on and leaf-off LiDAR estimates of biomass and species richness in tropical dry forest. Estimations of biomass and species richness from LiDAR data were not influenced by canopy conditions, indicating that LiDAR estimates of these variables can be obtained during the dry season. Moreover, biomassestimates were unaffected by topography.
2015
Artículo
Applied vegetation science, 18(4), 724-732, 2015
Inglés
Hernández‐Stefanoni, J. L., Johnson, K. D., Cook, B. D., Dupuy, J. M., Birdsey, R., Peduzzi, A., & Tun‐Dzul, F. (2015). Estimating species richness and biomass of tropical dry forests using LIDAR during leaf‐on and leaf‐off canopy conditions. Applied vegetation science, 18(4), 724-732.
BIOLOGÍA Y QUÍMICA
Versión publicada
publishedVersion - Versión publicada
Appears in Collections:Artículos de Investigación Arbitrados

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