Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 3; p. 261
• Main Authors: Domingo, Darío, Alonso, Rafael, Lamelas, María Teresa, Montealegre, Antonio Luis, Rodríguez, Francisco, de la Riva, Juan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2019
• Subjects: Airborne lasers; ALS; Artificial intelligence; backdating; Biomass; Carbon; Coniferous forests; Data acquisition; Datasets; Density; Ecosystems; Estimates; forest inventory; Forest management; Forestry; Forests; Lasers; Machine learning; Mediterranean forest; model temporal transferability; Plantations; Regression analysis; Regression models; Scanning; Support vector machines; Timber inventory; Trees; Portugal; Spain
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs11030261

This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones.