Judging and predicting tree suitability is of great significance in the cultivation and management of forests. Background and Objectives: Due to the diversity of tree species for afforestation in China and the lack of experts or the limitations of expert knowledge, the site rules of tree species in some regions are lacking or incomplete, so that a small number of tree suitability empirical site rules are difficult to adapt to the afforestation expert system’s diverse needs. Research Highlights: This paper explores an intelligent method to automatically extract rules for selecting favorable site conditions (tree suitability site rules) from a large amount of data to solve the problem of knowledge acquisition, updating and maintenance of suitable forest site rules in the expert system. Materials and Methods: Based on the method of site quality evaluation and the theory of the decision tree in knowledge discovery and machine learning, the dominant species of Chinese fir and Masson pine in the forest resources subcompartment data (FRSD) of Jinping County, Guizhou Province were taken as examples to select the important site factors affecting the forest quality and based on the site quality of potential productivity. Assessment methodology was proposed to determine the afforestation of a stand site by nonlinear quantile regression, the decision tree was constructed from the ID3, C5.0 and CART algorithms. Results: Finally, the best-performing CART algorithm was selected to construct the model, and the extractor of the afforestation rules was constructed. After validating the rules for selecting favorable site conditions of Chinese fir and Masson pine, the production representation method was used to construct the relationship model of the knowledge base. Conclusions: Intelligent extraction of suitable tree rules for afforestation design in an expert system was realized, which provided the theoretical basis and technical support for afforestation land planning and design.

As a key functional trait, leaf photosynthetic pigment content (LPPC) plays an important role in the health status monitoring and yield estimation of apples. Hyperspectral features including vegetation indices (VIs) and derivatives are widely used in retrieving vegetation biophysical parameters. The fractional derivative spectral method shows great potential in retrieving LPPC. However, the performance of fractional derivatives and machine learning (ML) for retrieving apple LPPC still needs to be explored. The objective of this study is to test the capacity of using fractional derivative and ML methods to retrieve apple LPPC. Here, the hyperspectral data in the 400–2500 nm domains was used to calculate the fractional derivative order of 0.2–2, and then the sensitive bands were screened through feature dimensionality reduction to train ML to build the LPPC estimation model. Additionally, VIs-based ML methods and empirical regression models were developed to compare with the fractional derivative methods. The results showed that fractional derivative-driven ML methods have higher accuracy than the ML methods driven by the original spectra or vegetation index. The results also showed that the ML methods perform better than empirical regression models. Specifically, the best estimates of chlorophyll content and carotenoid content were achieved using support vector regression (SVR) at the derivative order of 0.2 (R² = 0.78) and 0.4 (R² = 0.75), respectively. The fractional derivative maintained a good universality in retrieving the LPPC of multiple phenological periods. Therefore, this study highlights that the fractional derivative and ML improved the estimation of apple LPPC.

Crown profile models were developed for Chinese fir (Cunninghamia lanceolata) in Shunchang County, Fujian Province. We used data from 360 trees located in 65 pure, even-aged, and unthinned temporary plots. The data were divided into three groups according to site index. Nine models, including models for upper crown, lower crown, and entire crown, were fit; the optimal ones in each group were validated and chosen to estimate crown shape. The optimal models explained at least 70% variability in crown radius (CR). In addition, models for crown width, height above ground to crown base, and height above ground to largest CR were also developed to facilitate the convenient simulation of crown profile models. These three models explained 85.4%, 85.1%, and 86.9% variability. All models also passed the F-test and residual test. The 3D images of a single tree and stands were presented by OpenGL technology on visual c++ platform based on the proposed models. Tree growth was compared and analyzed using crown profile curves under constrained conditions. The analysis results accorded with plant growth.

Research Highlights: Improving the prediction accuracy represents a popular forest simulation modeling issue, and exploring the optimal maximum entropy (MaxEnt) distribution is a new effective method for improving the diameter distribution model simulation precision to overcome the disadvantages of Weibull. Background and Objectives: The MaxEnt distribution is the closest to the actual distribution under the constraints, which are the main probability density distributions. However, relatively few studies have addressed the optimization of stand diameter distribution based on MaxEnt distribution. The objective of this study was to introduce application of the MaxEnt distribution on modeling and prediction of stand diameter distribution. Materials and Methods: The long-term repeated measurement data sets consisted of 260 diameter frequency distributions from China fir (Cunninghamia lanceolate (Lamb.) Hook) plantations in the southern China Guizhou. The Weibull distribution and the MaxEnt distribution were applied to the fitting of stand diameter distribution, and the modeling and prediction characteristics of Weibull distribution and MaxEnt distribution to stand diameter distribution were compared. Results: Three main conclusions were obtained: (1) MaxEnt distribution presented a more accurate simulation than three-parametric Weibull function; (2) the Chi-square test showed diameter distributions of unknown stands can be well estimated by applying MaxEnt distribution based on the plot similarity index method (PSIM) and Weibull distribution based on the parameter prediction method (PPM); (3) the MaxEnt model can deal with the complex nonlinear relationship and show strong prediction ability when predicting the stand distribution structure. Conclusions: With the increase of sample size, the PSIM has great application prospects in the dynamic prediction system of stand diameter distribution.

Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long-term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time-varying parameter vector autoregressive model (TVP-VAR) was used to analyze the temporal and spatial differences of the time-lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time-lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP-VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP-VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP-VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time-lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management.

Improving prediction accuracy is a prominent modeling issue in relation to forest simulations, and ensemble learning is a new effective method for improving the precision of crown profile model simulations in order to overcome the disadvantages of statistical modeling. Background: Ensemble learning (a machine learning paradigm in which multiple learners are trained to achieve better performance) has strong nonlinear problem learning ability and flexibility in terms of analyzing longitudinal data, and it remains rarely explored so far in the field of crown profile modeling forest science. In this study, we explored the application of ensemble learning to the modeling and prediction of crown profiles. Methods: We evaluated the performance of ensemble learning procedures and marginal model in modeling crown profile using the crown profile database from China fir plantations in Fujian, in southern China. Results: The ensemble learning approach for the crown profile model appeared to have better performance and higher efficiency (R² > 0.9). The crown equation model 18 showed an intermediate performance in its estimation, whereas GBDT (MAE = 0.3250, MSE = 0.2450) appeared to have the best performance and higher efficiency. Conclusions: The ensemble learning method can combine the advantages of multiple learners and has higher model accuracy, robustness and overall induction ability, and is thus an effective technique for crown profile modeling and prediction.