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Estimating species richness and biomass of tropical dry forests using LIDAR during leaf‐on and leaf‐off canopy conditions

Hernández‐Stefanoni, Jose Luis ; Johnson, Kristofer D ; Cook, Bruce D ; Dupuy, Juan Manuel ; Birdsey, Richard ; Peduzzi, Alicia ; Tun‐Dzul, Fernando ; Goslee, Sarah Goslee, Sarah ; Goslee, Sarah

Applied vegetation science, 2015-10, Vol.18 (4), p.724-732 [Periódico revisado por pares]

Malden: Opulus Press

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  • Título:
    Estimating species richness and biomass of tropical dry forests using LIDAR during leaf‐on and leaf‐off canopy conditions
  • Autor: Hernández‐Stefanoni, Jose Luis ; Johnson, Kristofer D ; Cook, Bruce D ; Dupuy, Juan Manuel ; Birdsey, Richard ; Peduzzi, Alicia ; Tun‐Dzul, Fernando ; Goslee, Sarah
  • Goslee, Sarah ; Goslee, Sarah
  • Assuntos: Above-ground biomass ; aboveground biomass ; analysis of variance ; canopy ; Canopy conditions ; data collection ; dry forests ; Forest structure ; Forest structure, Species richness ; LIDAR ; prediction ; regression analysis ; species diversity ; Species richness ; Topography ; Tropical dry forest
  • É parte de: Applied vegetation science, 2015-10, Vol.18 (4), p.724-732
  • Notas: http://dx.doi.org/10.1111/avsc.12190
    United States Forest Service (USFS) and USAID
    Appendix S1. Summary statistics of multiple linear regression of above-ground biomass (AGB) and species richness from LIDAR metrics for leaf-on and leaf-off canopy conditions.Appendix S2. PCA ordination diagram of vegetation structure (abundance, number of species, basal area, plant cover, average height and canopy height) comparing flat areas and hills.
    ark:/67375/WNG-175ZB2DV-Q
    Reinforcing REDD+ and South-South Cooperation Project
    Mexican National Forest Commission (CONAFOR)
    ArticleID:AVSC12190
    istex:375A12584CDA58F546C4AC4C5C41340D50EAEC43
    ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: 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.
  • Editor: Malden: Opulus Press
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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