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

Abstract

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 (R 2 = 0.18-0.69) and a less pronounced trend with FP (R 2 = 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.

Comparison of Machine Learning Methods for Mapping the Stand Characteristics of Temperate Forests Using Multi-Spectral Sentinel-2 Data

“…Valbuena et al [76] quoted from several researchers that lower R 2 value is not always an indicator for lower accuracy of predictions. Fatehi et al [77] also experienced very low R 2 value especially for stem density estimation using digital terrain model of 1-m grid (airborne laser scanning) and multi-spectral image of 30-m resolution (imaging spectroscopy) to predict tree density and forest productivity in a heterogeneous Alpine landscape. The authors came to conclusion that low R 2 was due to the presence of small and diverse tree species and mentioned stem density was dependent on the mixture of different species, structures, and non-homogenous canopy.…”

Feature selection and modeling forest tree canopies using supervised and unsupervised neural network self-organizing maps (case study: District 2, Kacha, Rasht, Iran)

“…Forest statistics presents forest information on the quantitative and qualitative conditions of forest structural features, which underpin forest planning and policy (Fatehi et al, 2017). Traditional canopy estimation methods involve eld surveys and measurements, which can be labor-intensive and time-consuming (Jing et al, 2023).…”