Publications

Schraik, D; Wang, D; Hovi, A; Rautiainen, M (2023). Quantifying stand-level clumping of boreal, hemiboreal and temperate European forest stands using terrestrial laser scanning. AGRICULTURAL AND FOREST METEOROLOGY, 339, 109564.

Abstract
Clumping is critical for quantifying the radiation regime of forest canopies, but challenging to measure. We developed a method to measure clumping index (CI) of forest stands using voxel-based estimates of leaf area density from terrestrial lidar data. Our method uses the principle of the silhouette to total area ratio (STAR), a commonly used shoot clumping correction approach. We adapted the concept to forest stands, and derived that STAR at canopy scale (STARf) is no longer simply a clumping index, but a summary variable for forest structure in general. CI can be calculated from STARf when leaf area index is known.We measured CI and STARf of 38 forest stands in Finland, Estonia, and Czechia to study the natural range of these variables, their relationships to other forest variables, and to Landsat 8 OLI surface reflectance. CI did not include clumping below voxel scale (20 cm), and ranged from 0.6 to 0.9, with the lowest values (i.e., the most clumped canopies) in conifer forests and temperate oak forests, and the highest CI values (i.e., the most random canopies) in boreal broadleaved forests. CI was closely correlated with surface reflectance in conifer forests, which may be explained by contradicting influence of clumping that decreases canopy reflectance, but increases visibility of the forest floor.From the viewpoint of forest reflectance modeling, STAR is a useful variable due to its close relationship with the photon recollision probability, i.e., the probability that a photon will interact with a canopy element after being scattered from another canopy element. The photon recollision probability is used to model the influence of forest structure on reflectance. Our method provides a physically-based means of measuring STARf, and thus the photon recollision probability, hence contributing to the development of new methods for interpreting forest canopy structure from optical remote sensing data.

DOI:
10.1016/j.agrformet.2023.109564

ISSN:
1873-2240