Abstract
Abstract. We present a new algorithm for the estimation of the 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 do not use the intensity information at all, use only average intensity values, or do not scale the intensity information, which can cause problems for heterogeneous vegetation. We compare the performance of the new algorithm to 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-tree) 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. Specific weak points regarding the estimation of the PAD from airborne lidar data are addressed including the influence of ground reflections and the effect of small-scale heterogeneity, and we show how the effect of these points is reduced in the new algorithm, by combining benefits of earlier algorithms. We further show that low-resolution gridding of the PAD will lead to a negative bias in the resulting estimate according to Jensen's inequality for convex functions and that the severity of this bias is method dependent. As a result, the PAI magnitude as well as heterogeneity scales should be carefully considered when setting the resolution for the PAD gridding of airborne lidar scans.
Highlights
Plant area is a key parameter for the quantification of air– vegetation exchange of momentum, latent and sensible heat, and carbon dioxide
Specific weak points regarding the estimation of the plant area density (PAD) from airborne lidar data are addressed including the influence of ground reflections and the effect of small-scale heterogeneity, and we show how the effect of these points is reduced in the new algorithm, by combining benefits of earlier algorithms
There are several other fundamental differences between the ground-based optical methods for determining the plant area index (PAI) and the lidar-based methods: (1) whereas optical methods are typically based on observed radiation of natural light, airborne lidar scans (ALSs) normally use infrared light; (2) the primary information utilized in ALS-based methods is the exact location describing where the radiation is reflected (e.g., Morsdorf et al, 2006; Solberg et al, 2006); and (3) contrary to the homogeneously lit canopies used to determine plant area with ground-based optical methods (Monsi and Saeki, 2005; Bréda, 2003; Yan et al, 2019), ALS data are based on discrete lidar pulses, which have a finite diameter of around 0.1–1 m at the canopy top (e.g., Hopkinson and Chasmer, 2009; Popescu et al, 2011; Almeida et al, 2019)
Summary
Plant area is a key parameter for the quantification of air– vegetation exchange of momentum, latent and sensible heat, and carbon dioxide. In contrast to ground-based optical methods, measurements from ALSs are based on the reflected radiation R Whereas it is clear from Eq (1) that the radiation level above the canopy Q0 is of high importance for the determination of the PAD and PAI, the use of the reflected radiation turns the problem on its head. The high sampling rate of the airborne lidar scanners allows for the detection of multiple reflections from one single emitted pulse, with data sets ranging from one to several returns per pulse, all the way up to the full waveform, depending on the type of scanner Regardless of these fundamental differences, the Beer–Lambert law has been used for the determination of the PAD and PAI with ALS data via the following formulation: 0zR(z)dz hc 0
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