Tropical tree size–frequency distributions from airborne lidar. Issue 7 (6th July 2020)

• Record Type: Journal Article
• Authors: Ferraz, António; Saatchi, Sassan S.; Longo, Marcos; Clark, David B.
• Is Part Of: Ecological applications. Volume 30:Issue 7(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-06
• Subjects: airborne lidar -- crown area -- individual tree crown -- plot‐level aboveground biomass -- size‐classes‐level basal area -- tree diameter -- tree height -- tree size frequency; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1002/eap.2154
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract: In tropical rainforests, tree size and number density are influenced by disturbance history, soil, topography, climate, and biological factors that are difficult to predict without detailed and widespread forest inventory data. Here, we quantify tree size–frequency distributions over an old‐growth wet tropical forest at the La Selva Biological Station in Costa Rica by using an individual tree crown (ITC) algorithm on airborne lidar measurements. The ITC provided tree height, crown area, the number of trees >10 m height and, predicted tree diameter, and aboveground biomass from field allometry. The number density showed strong agreement with field observations at the plot‐ (97.4%; 3% bias) and tree‐height‐classes level (97.4%; 3% bias). The lidar trees size spectra of tree diameter and height closely follow the distributions measured on the ground but showed less agreement with crown area observations. The model to convert lidar‐derived tree height and crown area to tree diameter produced unbiased (0.8%) estimates of plot‐level basal area and with low uncertainty (6%). Predictions on basal area for tree height classes were also unbiased (1.3%) but with larger uncertainties (22%). The biomass estimates had no significant bias at the plot‐ and tree‐height‐classes level (−5.2% and 2.1%). Our ITC method provides a powerful tool for tree‐ to landscape‐level tropical forest inventory and biomass estimation by overcoming the limitations of lidar area‐based approaches that require local calibration using a large number of inventory plots.