Comparing forest structural attributes derived from UAV-based point clouds with conventional forest inventories in the dry chaco

Gobbi, Beatriz [UCL] Van Rompaey, A. Loto, D. Gasparri, I. Vanacker, Veerle [UCL]

Anthropogenic activity leading to forest structural and functional changes needs specific ecological indicators and monitoring techniques. Since decades, forest structure, composition, biomass, and functioning have been studied with ground-based forest inventories. Nowadays, satellites survey the earth, producing imagery at different spatial and temporal resolutions. However, measuring the ecological state of large extensions of forest is still challenging. To reconstruct the three-dimensional forest structure, the structure from motion (SfM) algorithm was applied to imagery taken by an unmanned aerial vehicle (UAV). Structural indicators from UAV-SfM products are then compared to forest inventory indicators of 64 circular plots of 1000 m2 in a subtropical dry forest. Our data indicate that the UAV-SfM indicators provide a valuable alternative for ground-based forest inventory’ indicators of the upper canopy structure. Based on the correlation between ground-based measures and UAV-SfM derived indicators, we can state that the UAV-SfM technique provides reliable estimates of the mean and maximum height of the upper canopy. The performance of UAV-SfM techniques to characterize the undergrowth forest structure is low, as UAV-SfM indicators derived from the point cloud in the lower forest strata are not suited to provide correct estimates of the vegetation density in the lower strata. Besides structural information, UAV-SfM derived indicators, such as canopy cover, can provide relevant ecological information as the indicators are related to structural, functional, and/or compositional aspects, such as biomass or compositional dominance. Although UAV-SfM techniques cannot replace the wealth of data collected during ground-based forest inventories, its strength lies in the three-dimensional (3D) monitoring of the tree canopy at cm-scale resolution, and the versatility of the technique to provide multi-temporal datasets of the horizontal and vertical forest structure. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.