Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest

Abstract

The so-called clumping factor (Ω) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Ω is essential to convert effective Plant or Leaf Area Index into actual LAI or PAI, which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural configurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15° was the best performing clumping method, matching the reference values to within 0.05Ω on average near zenith. Clumping error increased linearly with zenith angle to >0.3Ω (equivalent to a 30% PAI error) at 75° for all structural configurations. At larger zenith angles, PAI errors were found to be around 25–30% on average when derived from the 55–60° zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1m of unrepresentative large tree stems, so these situations should be avoided in practice if possible.

Plant phenotyping relevance

仮想森林と全天空写真を用いて、林冠クラ ​​ンピング係数およびLAI/PAI推定手法を比較・検証しており、植物構造形質の取得精度評価が研究の中心である。

Code and data availability

The supplied blocks describe simulated hemispherical photography scenes and clumping retrieval analyses, but contain no data or code availability statement, no public repository, and no author-provided URL for the virtual scenes, tree models, HP images, or analysis scripts. All URLs in the text are reference DOIs or a

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