林分的三维结构复杂性取决于单株树冠内外结构属性的大小

IF 5.6 1区农林科学 Q1 AGRONOMY

Agricultural and Forest Meteorology Pub Date : 2025-01-31 DOI:10.1016/j.agrformet.2025.110424

Alexandra Koller , Matthias Kunz , Maria D. Perles-Garcia , Goddert von Oheimb

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引用次数: 0

摘要

林分结构的复杂性影响着森林生态系统的各种功能，如碳储量或生产力。然而，定义和测量林分结构复杂性并非易事，因为不同的结构属性可以用来描述林分结构。研究了基于地面激光扫描的林分结构复杂性指数（SSCI）及其组成、平均分形维数（MeanFrac）和有效层数（ENL）。这些指数现在被广泛使用，但仍然缺乏对它们的确切构成的理解。在这项研究中，我们分析了在哪个空间尺度上，单个树木的哪些结构属性决定了这些指数。为了进行分析，我们使用了高分辨率地面激光扫描（TLS）数据集，该数据集包含来自中国亚热带30个研究样地的1300多棵树的11个结构属性。我们的结果表明，单个树的结构属性的平均值优于描述变化的值。因此，我们认为SSCI、MeanFrac和ENL取决于林分中树木结构属性的大小而不是变化。我们还发现SSCI主要由内部（即分支强度和分支数）和外部冠结构（即树冠密实度）决定。MeanFrac最好用分支强度来描述。因此，更高的冠层空间填充，即更高的密度，导致更高的林分结构复杂性。树高和胸径是ENL的主要决定因素。对于我们选择的空间尺度，只有MeanFrac被证明是敏感的。通径分析表明，不同结构属性（分枝强度与树高）对林分结构复杂性的影响不同（水平与垂直），为林分结构复杂性的提高提供了多种管理选择。研究结果表明，为了更好地理解林分结构复杂性，在林分结构评价中纳入林冠结构属性至关重要。

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3D structural complexity of forest stands is determined by the magnitude of inner and outer crown structural attributes of individual trees

Stand structural complexity influences various forest ecosystem functions, such as carbon storage or productivity. However, defining and measuring stand structural complexity is not trivial, as different structural attributes can be used to describe stand structure. We focus on a terrestrial laser scan-based stand structural complexity index (SSCI) and its components, mean fractal dimension (MeanFrac) and effective number of layers (ENL). These indices are now widely used, but there is still a lack of understanding of what exactly constitutes them. In this study, we analysed which structural attributes of individual trees determine these indices at which spatial scale. For our analysis, we used a high-resolution terrestrial laser scanning (TLS) dataset consisting of 11 structural attributes of over 1300 individual trees from 30 study plots of a young tree experiment in subtropical China. Our results show that averaged values of structural attributes of individual trees outperform values describing variation. Therefore, we suggest that SSCI, MeanFrac, and ENL depend on the magnitude rather than the variation of structural attributes among trees in a stand. We also found that SSCI is mainly determined by inner, i.e. branching intensity and branch number, and outer crown structure, i.e. crown compactness. MeanFrac is best described by branching intensity. Thus, a higher canopy space filling, i.e. higher density, leads to a higher stand structural complexity. Tree height and diameter at breast height are the main determinants of ENL. For the spatial scales we selected, only MeanFrac proved to be sensitive. The path analysis showed that different structural attributes (branching intensity vs. tree height) promote different aspects of stand structural complexity (horizontal vs. vertical), providing a variety of management options to increase stand structural complexity. Our results show that to better understand stand structural complexity, it is essential to include crown structural attributes in the assessment of stand structure.

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来源期刊

Agricultural and Forest Meteorology 农林科学-林学

CiteScore

10.30

自引率

9.70%

发文量

415

审稿时长

69 days

期刊介绍： Agricultural and Forest Meteorology is an international journal for the publication of original articles and reviews on the inter-relationship between meteorology, agriculture, forestry, and natural ecosystems. Emphasis is on basic and applied scientific research relevant to practical problems in the field of plant and soil sciences, ecology and biogeochemistry as affected by weather as well as climate variability and change. Theoretical models should be tested against experimental data. Articles must appeal to an international audience. Special issues devoted to single topics are also published. Typical topics include canopy micrometeorology (e.g. canopy radiation transfer, turbulence near the ground, evapotranspiration, energy balance, fluxes of trace gases), micrometeorological instrumentation (e.g., sensors for trace gases, flux measurement instruments, radiation measurement techniques), aerobiology (e.g. the dispersion of pollen, spores, insects and pesticides), biometeorology (e.g. the effect of weather and climate on plant distribution, crop yield, water-use efficiency, and plant phenology), forest-fire/weather interactions, and feedbacks from vegetation to weather and the climate system.