Tree Density and Forest Productivity in a Heterogeneous Alpine Environment: Insights from Airborne Laser Scanning and Imaging Spectroscopy

Parviz Fatehi,Mahtab Pir Bavaghar,Michael Schaepman,Mathias Kneubühler,Alexander Damm,Reik Leiterer

doi:10.3390/f8060212

Abstract

We outline an approach combining airborne laser scanning (ALS) and imaging spectroscopy (IS) to quantify and assess patterns of tree density (TD) and forest productivity (FP) in a protected heterogeneous alpine forest in the Swiss National Park (SNP). We use ALS data and a local maxima (LM) approach to predict TD, as well as IS data (Airborne Prism Experiment—APEX) and an empirical model to estimate FP. We investigate the dependency of TD and FP on site related factors, in particular on surface exposition and elevation. Based on reference data (i.e., 1598 trees measured in 35 field plots), we observed an underestimation of ALS-based TD estimates of 40%. Our results suggest a limited sensitivity of the ALS approach to small trees as well as a dependency of TD estimates on canopy heterogeneity, structure, and species composition. We found a weak to moderate relationship between surface elevation and TD (R2 = 0.18–0.69) and a less pronounced trend with FP (R2 = 0.0–0.56), suggesting that both variables depend on gradients of resource availability. Further to the limitations faced in the sensitivity of the applied approaches, we conclude that the combined application of ALS and IS data was convenient for estimating tree density and mapping FP in north-facing forested areas, however, the accuracy was lower in south-facing forested areas covered with multi-stemmed trees.

Highlights

Tree density (TD) and forest productivity (FP) are important structural and functional variables of forest ecosystems
The agreement was assessed by calculating the coefficient of determination (R2 ), the root mean square error (RMSE), and the relative RMSE (RMSE%)
The detection rate was calculated to validate individual tree detection approaches (ITD) and relates the proportion of detected trees to the number of trees measured in the field (Table 2)

Summary

Introduction

Tree density (TD) and forest productivity (FP) are important structural and functional variables of forest ecosystems. TD, defined as the number of trees per unit area [1], along with other structural information (e.g., species composition, canopy closure, tree height, and timber volume), is fundamental for forest management planning [2]. TD provides, indirectly, information on stand basal area, timber volume [3], and aboveground carbon storage [4]. In managed forest ecosystems, TD information allows forest managers to indicate essential treatments such as thinning, or to develop strategies to increase regeneration rates if the number of trees is too small [5]. Gross primary production (GPP), defined as the capacity of a forest to gain carbon through photosynthesis over a given time period, has been widely used to quantify FP [8,9]

Objectives

Methods

Results

Discussion

Conclusion