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Research article - Peer-reviewed, 2019

Single-image photogrammetry for deriving tree architectural traits in mature forest stands: a comparison with terrestrial laser scanning

Kedra, Kamil; Barbeito, Ignacio; Dassot, Mathieu; Vallet, Patrick; Gazda, Anna


Key messageWe compared two methods for detailed individual tree measurements: single image photogrammetry (SIP), a simplified, low-cost method, and the state-of-the-art terrestrial laser scanning (TLS). Our results provide evidence that SIP can be successfully applied to obtain accurate tree architectural traits in mature forests.ContextTree crown variables are necessary in forest modelling; however, they are time consuming to measure directly, and they are measured in many different ways. We compare two methods to obtain crown variables: laser-based and image-based. TLS is an advanced technology for three-dimensional data acquisition; SIP is a simplified, low-cost method.AimsTo elucidate differences between the methods, and validate SIP accuracy and usefulness for forest research, we investigated if (1) SIP and TLS measurements are in agreement in terms of the most widely used tree characteristics; (2) differences between the SIP traits and their TLS counterparts are constant throughout tree density and species composition; (3) tree architectural traits obtained with SIP explain differences in laser-based crown projection area (CPA), under different forest densities and stand compositions; and (4) CPA modelled with SIP variables is more accurate than CPA obtained with stem diameter-based allometric models. We also examined the correspondence between local tree densities extracted from images and from field measurements.MethodsWe compared TLS and SIP in a temperate pure sessile oak and mixed with Scots pine stands, in the Orleans Forest, France. Standard major axis regression was used to establish relations between laser-based and image-based tree height and diameter at breast height. Four SIP-derived traits were compared between the levels of stand density and species composition with a t test, in terms of deviations and biases to their TLS counterparts. We created a set of linear and linear mixed models (LMMs) of CPA(TLS), with SIP variables. Both laser-based and image-based stem diameters were used to estimate CPA with the published allometric equations; the results were then compared with the best predictive LMM, in terms of similarity with CPA(TLS) measurement. Local tree density extracted from images was compared with field measurements in terms of basic statistics and correlation.ResultsTree height and diameter at breast height were reliably represented by SIP (Pearson correlation coefficients r=0.92 and 0.97, respectively). SIP measurements were affected by the stand composition factor; tree height attained higher mean absolute deviation (1.09m) in mixed stands, compared to TLS, than in pure stands (0.66m); crown width was more negatively biased in mixed stands (-0.79m), than in pure stands (-0.05m); and diameter at breast height and crown asymmetry were found unaffected. Crown width and mean branch angle were key SIP explanatory variables to predict CPA(TLS). The model was approximately 2-fold more accurate than the CPA allometric estimations with both laser-based and image-based stem diameters. SIP-derived local tree density was similar to the field-measured density in terms of mean and standard deviation (9.6 (3.5) and 9.4 (3.6) trees per plot, respectively); the correlation between both density measures was significantly positive (r=0.76).ConclusionSIP-derived variables, such as crown width, mean branch angle, branch thickness, and crown asymmetry, were useful to explain tree architectural differences under different densities and stand compositions and may be implemented in many forest research applications. SIP may also provide a coarse measure of local competition, in terms of number of neighbouring trees. Our study provides the first test in mature forest stands, for SIP compared with TLS.


Tree architecture; Branching system; Variable selection; Temperate pure and mixed forests; Remote sensing; Allometry

Published in

Annals of Forest Science
2019, Volume: 76, number: 1, article number: 5

    UKÄ Subject classification

    Forest Science
    Remote Sensing

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