Table1.DOCX

<p>Mensuration of tree growth habits is of considerable importance for understanding forest ecosystem processes and forest biophysical responses to climate changes. However, the complexity of tree crown morphology that is typically formed after many years of growth tends to render it a non-trivial task, even for the state-of-the-art 3D forest mapping technology—light detection and ranging (LiDAR). Fortunately, botanists have deduced the large structural diversity of tree forms into only a limited number of tree architecture models, which can present a-priori knowledge about tree structure, growth, and other attributes for different species. This study attempted to recruit Hallé architecture models (HAMs) into LiDAR mapping to investigate tree growth habits in structure. First, following the HAM-characterized tree structure organization rules, we run the kernel procedure of tree species classification based on the LiDAR-collected point clouds using a support vector machine classifier in the leave-one-out-for-cross-validation mode. Then, the HAM corresponding to each of the classified tree species was identified based on expert knowledge, assisted by the comparison of the LiDAR-derived feature parameters. Next, the tree growth habits in structure for each of the tree species were derived from the determined HAM. In the case of four tree species growing in the boreal environment, the tests indicated that the classification accuracy reached 85.0%, and their growth habits could be derived by qualitative and quantitative means. Overall, the strategy of recruiting conventional HAMs into LiDAR mapping for investigating tree growth habits in structure was validated, thereby paving a new way for efficiently reflecting tree growth habits and projecting forest structure dynamics.</p

Season-dependence of remote sensing indicators of tree species diversity

<div><p>During recent years, many studies have been undertaken to investigate how spectral characteristics of forests can provide information on spatial patterns of tree species diversity (TSD). Important advances have been made, and significant relationships between TSD and remotely sensed indicators of net primary productivity and environmental heterogeneity have been reported. However, the season-dependence of these relationships has not yet been fully investigated, and the influence of phenology remains poorly understood. In this study, we aim to assess how the relationships between remote sensing indicators and TSD depend on the season of the year. TSD measures, including species richness, Shannon’s diversity and Simpson’s diversity, were determined for 82 field plots in the Afromontane cloud forests of Taita Hills, Kenya. A time series of 15 Landsat images were used to calculate a set of spectral and heterogeneity metrics. The relationship between remote-sensing metrics and TSD measures was analysed by simple and multivariate regression analysis. We conclude that the relationships between remote-sensing metrics and TSD are season-dependent. Hence, it is demonstrated the date of image acquisition is an important aspect to be considered in biodiversity studies. Given that the dependence of the relationships is closely linked to climate seasonality defining vegetation phenology, the relationships may also vary according to geographical conditions.</p></div

Sensitivity of tree height (<i>H</i>) and tree-level above-ground biomass (<i>AGB</i>) predictions to changes in the environmental stress parameter (<i>E</i>).

<p><b>(a)</b> Tree <i>H</i> predictions by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e029" target="_blank">Eq 16</a>, including (white colour) those obtained by the value of <i>E</i> = 0.7 extracted from Chave et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.ref024" target="_blank">24</a>] (<b>B0</b>; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.g003" target="_blank">Fig 3A</a>) and the calibrated baseline value (<b>B0*</b>; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.t005" target="_blank">Table 5</a>), and compared with those measured in the field (black colour). <b>(b)</b> Pair-wise differences of those <i>H</i> predictions against measured values. <b>(c)</b> Tree-level <i>AGB</i> predictions, also including <b>B0</b> and <b>B0*</b> (white colour), and compared with the <i>AGB</i> predictions obtained directly from field measurements, applying the corresponding <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e025" target="_blank">Eq 13</a> (black colour). <b>(d)</b> Pair-wise differences of those <i>AGB</i> predictions and those obtained directly from field measurements. Outliers have been omitted from (c) and (d) to improve the clarity of figures.</p

Comparison of above-ground biomass (<i>AGB</i>) estimates including summary of plot-level , pair-wise differences among methods (<i>diff</i>; row minus column), and significances yielded by paired Wilcoxon signed rank tests (H₀: <i>diff</i> = 0).

<p>Levels of significance: ^NS, not significant; ^•, <0.1; *, <0.05;***, < .001. SD: standard deviation. IQR: inter-quartile range. <i>AGB</i>: above-ground biomass; <i>D</i>: tree diameter; <i>H</i>: tree height; <i>sp</i>: species; <i>j</i>: plot; <i>k</i>: cluster; <i>E</i>: environmental stress. <b>B0</b>: <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e026" target="_blank">Eq 14</a>; <b>B1</b>: Eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e025" target="_blank">13</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e016" target="_blank">7</a>; <b>B2</b>: Eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e025" target="_blank">13</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e020" target="_blank">9</a>; <b>B3</b>: Eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e025" target="_blank">13</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e022" target="_blank">11</a>; <b>B4</b> Eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e025" target="_blank">13</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e024" target="_blank">12</a>; <b>B0*</b>: Eqs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e026" target="_blank">14</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0158198#pone.0158198.e029" target="_blank">16</a>.</p

Baseline determination (<i>E</i> ‘calibration’) (M0*).

<p><i>σ</i>: standard deviations. SE: standard error. RMSE: root mean square error. Level of significance: ***, <0.001.</p

The frequencies of the twenty most common tree species ordered by the proportion of the total basal area.

<p>Frequencies of stems (proportions of total density) for those same species are also shown.</p

Plot of residuals versus predictions for fixed-effects <i>H</i>-<i>D</i> model (M1).

<p>Species which were found to significantly include additional effects are highlighted.</p

Parameter estimates for the models M1-M4.

<p><i>σ</i>: standard deviations. SE: standard errors. RMSE: root mean square error. <i>D</i>: tree diameter; <i>j</i>: plot; <i>k</i>: cluster. Levels of significance: **, <0.01; ***, <0.001.</p

Likelihood ratio tests comparing nested models.

<p>-LogLik.:—log of likelihood; <i>D</i>: tree diameter. Levels of significance: **, <0.01; ***, <0.001.</p

Descriptive statistics for the sample plots with trees (n = 152).

<p>Descriptive statistics for the sample plots with trees (n = 152).</p