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Above-ground biomass estimation in closed canopy Neotropical forests using lidar remote sensing: factors affecting the generality of relationships


J.B. Drake, D.B. Warnell School of Forest Resources, University of Georgia, Athens, GA 30602–2152, U.S.A.


Aim Previous studies have developed strong, site-specific relationships between canopy metrics from lidar (light detecting and ranging) remote sensing data and forest structural characteristics such as above-ground biomass (AGBM), but the generality of these relationships is unknown. In this study, we examine the generality of relationships between lidar metrics and forest structural characteristics, including AGBM, from two study areas in Central America with different precipitation patterns.

Location A series of tropical moist forest sites in Panama and a tropical wet forest in Costa Rica.

Methods Canopy metrics (e.g. canopy height) were calculated from airborne lidar data. Basal area, mean stem diameter and AGBM were calculated from measurements taken as a part of ongoing forest dynamics studies in both areas. We examined the generality of relationship between lidar metrics and forest structure, and possible environmental effects (e.g. leaf phenology).

Results We found that lidar metrics were strongly correlated (R2: 0.65–0.92) with mean stem diameter, basal area and AGBM in both regions. We also show that the relationships differed between these regions. Deciduousness of canopy trees in the tropical moist forest area accounted for the differences in predictive equations for stem diameter and basal area. The relationships between lidar metrics and AGBM, however, remained significantly different between the two study areas even after adjusting for leaf drop. We attribute this to significant differences in the underlying allometric relationships between stem diameter and AGBM in tropical wet and moist forests.

Conclusions Important forest structural characteristics can be estimated reliably across a variety of conditions sampled in these closed-canopy tropical forests. Environmental factors such as drought deciduousness have an important influence on these relationships. Future efforts should continue to examine climatic factors that may influence the generality of the relationships between lidar metrics and forest structural characteristics and assess more rigorously the generality of field-derived allometric relationships.