Robust processing of airborne laser scans to plant area density profiles

Abstract

Abstract. We present a new algorithm for the estimation of plant area density (PAD) profiles and plant area index (PAI) for forested areas based on data from airborne lidar. The new element in the algorithm is to scale and average returned lidar intensities for each lidar pulse, whereas other methods either do not use the intensity information at all, only use average intensity values or do not scale the intensity information, which can cause problems for heterogeneous vegetation. We compare the performance of the new and three previously published algorithms over two contrasting types of forest: a boreal coniferous forest with a relatively open structure and a dense beech forest. For the beech forest site, both summer (full leaf) and winter (bare trees) scans are analyzed, thereby testing the algorithm over a wide spectrum of PAIs. Whereas all tested algorithms give qualitatively similar results, absolute differences are large (up to 400 % for the average PAI at one site). A comparison with ground-based estimates shows that the new algorithm performs well for the tested sites, and further and more importantly – it never produces clearly dubious results. Specific weak points for estimation of PAD from airborne lidar data are addressed; the influence of ground reflections and the effect of small-scale heterogeneity, and we show how the effect of these points is minimized using the new algorithm. We further show that low-resolution gridding of PAD will lead to a negative bias in the resulting estimate according to Jensen’s inequality for concave functions, and that the severity of this bias is method-dependent. As a result, PAI magnitude as well as heterogeneity scales should be carefully considered when setting the resolution for PAD gridding of airborne lidar scans.

Plant phenotyping relevance

航空レーザースキャンから植物面積密度・葉面積指数を推定するアルゴリズムを開発し、既存手法との比較および地上推定値による検証を行っており、植物形態計測手法が研究の中心である。

Code and data availability

The paper's Code availability statement explicitly declares a Python/MATLAB implementation of the ALS-to-PAD/PAI algorithm, freely available on the authors' GitHub repository (ALS2PAD). This is the paper's own computational analysis code for deriving plant area density profiles from airborne laser scans. No public ALS/