Olive trees, which are planted widely in China, are economically significant. Timely and accurate acquisition of olive tree crown information is vital in monitoring olive tree growth and accurately predicting its fruit yield. The advent of unmanned aerial vehicles (UAVs) and deep learning (DL) provides an opportunity for rapid monitoring parameters of the olive tree crown. In this study, we propose a method of automatically extracting olive crown information (crown number and area of olive tree), combining visible-light images captured by consumer UAV and a new deep learning model, U2-Net, with a deeply nested structure. Firstly, a data set of an olive tree crown (OTC) images was constructed, which was further processed by the ESRGAN model to enhance the image resolution and was augmented (geometric transformation and spectral transformation) to enlarge the data set to increase the generalization ability of the model. Secondly, four typical subareas (A–D) in the study area were selected to evaluate the performance of the U2-Net model in olive crown extraction in different scenarios, and the U2-Net model was compared with three current mainstream deep learning models (i.e., HRNet, U-Net, and DeepLabv3+) in remote sensing image segmentation effect. The results showed that the U2-Net model achieved high accuracy in the extraction of tree crown numbers in the four subareas with a mean of intersection over union (IoU), overall accuracy (OA), and F1-Score of 92.27%, 95.19%, and 95.95%, respectively. Compared with the other three models, the IoU, OA, and F1-Score of the U2-Net model increased by 14.03–23.97 percentage points, 7.57–12.85 percentage points, and 8.15–14.78 percentage points, respectively. In addition, the U2-Net model had a high consistency between the predicted and measured area of the olive crown, and compared with the other three deep learning models, it had a lower error rate with a root mean squared error (RMSE) of 4.78, magnitude of relative error (MRE) of 14.27%, and a coefficient of determination (R2) higher than 0.93 in all four subareas, suggesting that the U2-Net model extracted the best crown profile integrity and was most consistent with the actual situation. This study indicates that the method combining UVA RGB images with the U2-Net model can provide a highly accurate and robust extraction result for olive tree crowns and is helpful in the dynamic monitoring and management of orchard trees.

/pdf/extraction-of-olive-crown-based-on-uav-visible-images-and-2p3f7r8z.pdf

Extraction of Olive Crown Based on UAV Visible Images and the U2-Net Deep Learning Model

A climate-sensitive mixed-effects tree recruitment model for oaks (Quercus spp.) in Hunan Province, south-central China

The growth and production potential of a tree depends on its crown dimensions as these are closely related to a tree’s photosynthetic capacity. However, tree crowns have been studied less compared to their main stems because of their lower market value and because the measurement of crown dimensions, such as crown volume or surface area, is difficult. Frequently, an individual tree’s live crown ratio (LCR) is predicted by linear or nonlinear models that are a function of easy-to-measure dendrometric variables using ordinary least-squares techniques. Using the long-term data from established genetic and spacing trials, we developed and evaluated the predictive performance of three nonlinear models and introduced a new generalized linear model for predicting LCR. The nonlinear models were fit using exponential, Weibull, and Richards functions. The generalized linear model was based on beta regression. This resulted in a slightly smaller error than the other models in predicting the LCR of loblolly pine trees used in this study. Crown ratio is measured in percentage unit and should be modeled using generalized linear models that assume a beta distribution for error terms.

Live Crown Ratio Models for Loblolly Pine (Pinus taeda) with Beta Regression

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.

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

Does climate play a more important role than competition in modeling height to crown base of Larix principis-rupprechtii in northern China?

Crown ratio (CR) and height to crown base (HCB) are important crown characteristics influencing the behavior of forest canopy fires. However, the labor-intensive and costly measurement of CR and HCB have hindered their wide application to forest fire management. Here, we use 301 sample trees collected in 11 provinces in China to produce predictive models of CR and HCB for Masson pine forests (Pinus massoniana Lamb.), which are vulnerable to forest canopy fires. We first identified the best basic model that used only diameter at breast height (DBH) and height (H) as independent variables to predict CR and HCB, respectively, from 11 of the most used potential candidate models. Second, we introduced other covariates into the best basic model of CR and HCB and developed the final CR and HCB predictive models after evaluating the model performance of different combinations of covariates. The results showed that the Richards form of the candidate models performed best in predicting CR and HCB. The final CR model included DBH, H, DBH0.5 and height-to-diameter ratio (HDR), while the final HCB model was the best basic model (i.e., it did not contain any other covariates). We hope that our CR and HCB predictive models contribute to the forest crown fire management of Masson pine forests.

https://www.mdpi.com/1999-4907/11/11/1216/pdf

Development of Crown Ratio and Height to Crown Base Models for Masson Pine in Southern China

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Development of Crown Ratio and Height to Crown Base Models for Masson Pine in Southern China