Abstract
The Leaf Area Index (LAI) is a key parameter that helps to understand the connection between canopy structure and ecosystem functions. In this study, the main aims were to examine the impact of forest management on canopy structure using LiDAR data to characterize the canopy vertical profile, as well as to develop LAI models and an LAI mapping tool for sweet chestnut (Castanea Sativa Mill.) coppice. Twenty-one circular plots (r = 10 m) were established, each of which was submitted to one of the following forest management treatments: Control, with no intervention (3300–3700 stems ha−1); Treatment 1, one thinning to leave a living stock density of 900–600 stems ha−1; or Treatment 2, a more intensive thinning, leaving 400 stems ha−1. A LAI field measurement was made in all plots and the study area was recorded by LiDAR. With the LiDAR, two types of metrics were calculated: standard elevation metrics and canopy metrics. The results showed the different canopy layers of the study area, highlighting how the resprout layer influences the canopy structure of sweet chestnut coppice. By combining the LiDAR data and the LAI field estimates, various linear and nonlinear models were developed and tested, the linear model being found to have the best performance (R2 = 0.79) for the study area. With the selected linear model and other LiDAR data of interest such as the 95th percentile, an automatic mapping tool was designed. This tool allows spatially information to be generated that can be used to implement management strategies.
Highlights
Forests are highly complex systems influenced by numerous external and internal factors, making the development of sustainable forest management strategies, though crucial, somewhat challenging (Ceccherini et al, 2020).Forest management operations impact on the complete forest ecosystem
The mapping tool developed in this paper allows spatial information to be generated that is of value in implementing management strategies that improve productivity, ecosystem functions, forest management planning and fuel management. While this tool could be further improved with new models and the inclusion of new variables based on LiDAR metrics, the results provided here indicate that the statistics derived from the LiDAR data, and the models developed based on them, are suitable for calculating Canopy Vertical Profile (CVP) and great potential for its analysis (Lovell et al, 2003)
The methodology used and developed is of great interest because the characterization of the canopy vertical profile is of enormous benefit in terms of improving forest management planning, ecosystem functioning, structural biodiversity and ecosystem services
Summary
Forests are highly complex systems influenced by numerous external and internal factors, making the development of sustainable forest management strategies, though crucial, somewhat challenging (Ceccherini et al, 2020).Forest management operations impact on the complete forest ecosystem. In a forest, the laser pulses from the LiDAR are reflected back from the canopy, and from the ground and other vegetation elements in those places where the laser pulse travels through the gaps that exists in the canopy This ability of LiDAR allows information to be retrieved about the underlying terrain, enabling better characterization of the different canopy layers (Nelson et al, 1988; Lesfky et al, 2002; Mkaouar et al, 2018). For these reasons, LiDAR technology has been investigated in depth in terms of its value in the acquisition of data related to forest biomass, wood volume, stem density, canopy height, canopy cover and forest inventory parameters and structural characteristics, among other applications LiDAR technology has been investigated in depth in terms of its value in the acquisition of data related to forest biomass, wood volume, stem density, canopy height, canopy cover and forest inventory parameters and structural characteristics, among other applications (e.g. Popescu et al, 2002; Coops et al, 2007; Bergen et al, 2009; Leeuwen and Nieuwenhuis, 2010; Magnussen et al, 2018)
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