Estimating species richness and biomass of tropical dry forests using LIDAR during leaf‐on and leaf‐off canopy conditions. Issue 4 (10th August 2015) - Hernández‐Stefanoni, Jose Luis; Johnson, Kristofer D.; Cook, Bruce D.; Dupuy, Juan Manuel; Birdsey, Richard; Peduzzi, Alicia; Tun‐Dzul, Fernando; Goslee, Sarah
Abstract 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.