Monitoring post-fire neighborhood competition effects on pine saplings under different environmental conditions by means of UAV multispectral data and structure-from-motion photogrammetry

Abstract

In burned landscapes, the recruitment success of the tree dominant species mainly depends on plant competition mechanisms operating at fine spatial scale, that may hinder resource availability during the former years after the disturbance. Data acquisition at very high spatial resolution from unmanned aerial vehicles (UAV) have promoted new opportunities for understanding context-dependent competition processes in post-fire environments. Here, we explored the potentiality of UAV-borne data for assessing inter-specific competition effects of understory woody vegetation on pine saplings, as well as intra-specific interactions of neighboring saplings, across three burned landscapes located along a climatic/productivity gradient in the Iberian Peninsula. Geographic object-based image analysis (GEOBIA), including multiresolution segmentation and support vector machine (SVM) classification, was used to map pine saplings and understory shrubs at species level. Input data were, on the one hand, multispectral (11.31 cm·pixel^-1) and Structure-from-Motion (SfM) canopy height model (CHM) data fusion, hereafter MS-CHM, and, on the other, RGB (3.29 cm·pixel^-1) and CHM data fusion, hereafter RGB-CHM. A Random Forest (RF) regression algorithm was used to evaluate the effects of neighborhood competition on the relative growth in height of 50 pine saplings randomly sampled across the MS-CHM classified map. Circular plots of 3 m radius were set from the centroid of each target pine sapling to measure percentage cover, mean height of all individuals in the plot and mean height of individuals contacting the target sapling. Competing shrub species were differentiated according to their fire-adaptive traits (i.e. seeders vs resprouters). Object-based image classification applied on MS-CHM yielded higher overall accuracy for the three sites (83.67% ± 3.06%) than RGB-CHM (74.33% ± 3.21%). Intra-specific competitive effects were not detected, whereas increasing cover and height of shrub neighbors had a significant non-linear impact on the growth on pine saplings across the study sites. The strongest competitive effects of seeder shrubs occurred in open areas with low vegetation cover and fuel continuity, following a gap-dependent model. The non-linear relationships evidenced in this study between the structure of neighboring shrubs and the growth of pine seedlings/saplings have profound implications for considering possible competing thresholds in post-fire decision-making processes. These results endorse the use of UAV multispectral and SfM photogrammetry as a valuable post-fire management tool for measuring accurately the effect of competition in heterogeneous burned landscapes.

Keywords: Competition; Fire; Object-based image analysis; Pine; Shrub; Unmanned aerial vehicle.