Predicting and calibrating height to crown base: a case for Dahurian larch (Larix gmelinii Rupr.) in Northeastern China

Abstract

The applicability of height to crown base (HCB) appears to be particularly broad since the important impact on explaining tree growth dynamics. Given the high cost of measuring HCB, accurate prediction with a few trees is popular in forestry practice. In this study, a fixed-effects model (FEM), a mixed-effects model (MEM), and quantile regressions (QR) were adopted to predict HCB using data from natural secondary forests of Dahurian larch (Larix gmelinii Rupr.) in Northeastern China. Corresponding calibration techniques were applied to trees with different sample designs (random selection, the thickest tree selection, the intermediate tree selection, and the thinnest tree selection) and sample sizes (1-10 trees per plot). The results showed that all models achieved accurate HCB predictions. The QR calibration technique of 3-quantiles achieved simple and accurate consequences compared to 5-quantiles and 9-quantiles. MEM displayed the most robust and superior statistics. The selection of the two thickest trees was recommended for the MEM. For FEM and QR, the sample size should be exceeded by 5. Generally, a combination of MEM and sampling two thickest trees per plot to predict HCB is recommended as a win-win solution that neither sacrifices model prediction accuracy nor minimizes the required measurement cost.

Assessment of Potential Prediction and Calibration Methods of Crown Width for Dahurian Larch (Larix gmelinii Rupr.) in Northeastern China

“…Furthermore, as the sample size increased, the differences in predictive accuracy among the models gradually decreased. The observed variation trend in predictive accuracy for these three models in this study aligns with the findings of Özçelik et al [30] and Wang et al [31], who also compared GMC, QRC, and MIXED models for predicted tree height and height to crown base, respectively. Additionally, several studies comparing QRC and MIXED have reported similar results [41,68].…”

“…The choice of MIXED as the final model is not only because the validation statistics consistently outperformed QRC and GMC, but also because MIXED demonstrates extremely high robustness. Even when the four sampling designs caused significant variations in CW prediction accuracy for QRC and GMC (Figure 7), MIXED was able to maintain a high level of CW prediction accuracy [30,31].…”

“…Furthermore, calibration techniques applying GM and quantile regression (QR) have become prevalent in recent years to improve model prediction accuracy [30,31]. GM using the ordinary least squares method (OLS) [26] to estimate the model parameters is widely employed in modeling, and therefore research on calibrating GM has a generalizable value.…”

“…QR is a flexible statistical method that offers several advantages, including estimates of the conditional quantile of the dependent variable distribution, not requiring further assumptions about the distribution of the error term, stronger robustness and insensitivity to outliers, and the fitted model not needing to satisfy equal variances [34,35]. Consequently, QR has been widely applied in forestry studies in recent years [30,31,[36][37][38][39], and the method of combining multiple QR curves for calibration has shown promising results [30,31,38,39].…”

Tree Crown Affects Biomass Allocation and Its Response to Site Conditions and the Density of Platycladus orientalis Linnaeus Plantation

“…The correlation between crown and tree growth was affected by density. Lower density is associated with greater light, water, and soil nutrient availability, which can enhance the tree photosynthetic and respiratory capacity, resulting in an increased positive correlation between CW, CL, and FT and tree growth indicators [98][99][100]. At higher density, the vertical direction of crown growth was more strongly correlated with tree growth, suggesting that taller crowns are needed to maximize light interception capacity [99,101,102].…”