Tropical tree size-frequency distributions from airborne lidar

Area of Science:

• Forestry
• Remote Sensing
• Ecology

Background:

• Tropical rainforests are complex ecosystems where tree size and density are influenced by numerous unpredictable factors.
• Accurate forest inventory data is crucial for understanding these dynamics but is often difficult to obtain comprehensively.

Purpose of the Study:

• To quantify tree size-frequency distributions in an old-growth tropical rainforest using airborne lidar.
• To assess the accuracy of an individual tree crown (ITC) algorithm for estimating tree parameters and biomass.

Main Methods:

• Airborne lidar data was processed using an individual tree crown (ITC) algorithm at La Selva Biological Station, Costa Rica.
• The ITC algorithm derived tree height, crown area, and predicted tree diameter and aboveground biomass using field allometry.
• Results were validated against field observations for tree number density, basal area, and biomass.

Main Results:

• The ITC method demonstrated strong agreement with field observations for tree number density (97.4%) and predicted tree diameter and height distributions.
• Basal area estimates derived from lidar were unbiased (0.8% plot-level) with low uncertainty (6%).
• Biomass estimates showed no significant bias at plot (-5.2%) and tree-height-class (2.1%) levels.

Conclusions:

• The individual tree crown (ITC) algorithm applied to lidar data is a powerful tool for tropical forest inventory and biomass estimation.
• This approach overcomes the calibration limitations of lidar area-based methods, enabling tree- to landscape-level analysis.
• The study provides a scalable and accurate method for assessing tropical forest structure and carbon stocks.