Prediction of Competition Indices in a Norway Spruce and Silver Fir-Dominated Forest Using Lidar Data

Versace Versace,Garfì Garfì,Dalponte Dalponte,Tognetti Tognetti,Gianelle Gianelle,Frizzera Frizzera

doi:10.3390/rs11232734

Versace Versace, Garfì Garfì + Show 4 more

Open Access

https://doi.org/10.3390/rs11232734

Copy DOI

Journal: Remote sensing	Publication Date: Nov 21, 2019
Citations: 17	License type: CC BY 4.0

Affiliation: Fondazione Edmund Mach, University of Molise

Abstract

Competitive interactions are important predictors of tree growth. Spatial and temporal changes in resource availability, and variation in species and spatial patterning of trees alter competitive interactions, thus affecting tree growth and, hence, biomass. Competition indices are used to quantify the level of competition among trees. As these indices are normally computed only over small areas, where field measurements are done, it would be useful to have a tool to predict them over large areas. On this regard, remote sensing, and in particular light detection and ranging (lidar) data, could be the perfect tool. The objective of this study was to use lidar metrics to predict competition (on the basis of distance-dependent competition indices) of individual trees and to relate them with tree aboveground biomass (AGB). The selected study area was a mountain forest area located in the Italian Alps. The analyses focused on the two dominant species of the area: Silver fir (Abies alba Mill.) and Norway spruce (Picea abies (L.) H. Karst). The results showed that lidar metrics could be used to predict competition indices of individual trees (R2 above 0.66). Moreover, AGB decreased as competition increased, suggesting that variations in the availability of resources in the soil, and the ability of plants to withstand competition for light may influence the partitioning of biomass.

Highlights

Tree growth is influenced by several factors, including climate patterns, site conditions, and competition processes [1,2,3,4] Among them, tree competition measures are the main predictors of individual trees’ growth [5]
Our results showed that lidar metrics have a good capacity to predict competition indices
We developed a system that, after detecting individual tree crowns (ITCs) in the forest, on the basis of lidar metrics extracted in the neighborhood of the detected ITC, predicts two competition indices related to height and diameter at breast height (DBH)

Summary

Introduction

Tree growth is influenced by several factors, including climate patterns, site conditions, and competition processes [1,2,3,4] Among them, tree competition measures are the main predictors of individual trees’ growth [5]. Competition among trees is defined as the negative effects that neighboring trees have on a subject tree These negative effects depend on the interactions between trees in acquiring limited resources, such as light, water, and nutrients [6,7]. Distance-independent models use only non-spatial competition indices. These indices are based on the size distribution of competitor trees within a given area, without considering their spatial distribution. Distance-dependent models are based on spatial competition indices that incorporate both the size and the spatial distribution of competitors [17]. In forests with a spatially inhomogeneous distribution of trees, and in particular in unmanaged mountainous areas, there is usually stronger growth competition between neighboring trees and biomass growth can be more influenced by the available light intensity and site quality

Objectives

Methods

Findings

Conclusion