The Three-North Shelterbelt Project is the largest ecological engineering initiative in China to date, distinguished by its immense scale, extended construction period, and widespread benefits for the population. The gross ecosystem product (GEP) serves as a crucial indicator for assessing ecological benefits. This study focuses on the Three Northern Protection Forest Project Area, utilizing GEP calculations for the years 2000 to 2020. This study evaluates variations in the production values of different ecosystem services to reflect the ecological conservation benefits of the restoration project. Additionally, it analyzes the spatiotemporal evolution and trends of the GEP calculations, offering data references and decision support for the enduring efficacy of ecological restoration projects. The findings are as follows. (i) Between 2000 and 2020, the GEP of the Three-North region exhibited significant growth with continuous enhancement of various ecosystem service functions; the most substantial rate of change was observed in the water conservation function, followed by carbon sequestration and oxygen release, soil retention, windbreak and sand fixation, flood regulation, and environmental purification functions. (ii) The per-unit area value of different ecosystem types generally increased; the forest ecosystem displayed the largest growth rate at 61.18%, followed by shrubland ecosystems at 49.84%. (iii) The spatial distribution of ecosystem service in the Three-North region displayed a clustering trend alongside notable spatial heterogeneity. High-high clustering zones were identified in areas such as the Tianshan Mountains, Altai Mountains, Qilian Mountains, and Greater and Lesser Khingan Mountains. Conversely, low-low clustering areas were scattered, forming patchy distributions in regions like the Tarim Basin, northern Qinghai-Tibet Plateau, and the Hexi Corridor. This study, by analyzing the gross ecosystem product of the Three-North Shelterbelt Project region, unveils the spatial distribution characteristics, trends, and variations in ecosystem service values over the past two decades. It provides data support and decision guidance for the long-term efficacy of future ecological conservation and restoration projects. This study incorporates the GEP accounting method into the assessment of the effectiveness of major conservation projects. Compared to the traditional methods of effectiveness assessment, this represents a significant exploration and innovation.

Poplar (Poplus spp.) is an important forest species widely distributed in China of great significance in identifying factors that clearly influence its growth rate in order to achieve effective control of poplar growth. In this study, we selected 16 factors, including tree size, competition, climate, location, topography, and soil characteristics, to construct linear regression (LR), multilayer perceptron (MLP), k-nearest neighbor regression (KNN), gradient boosting decision tree (GBDT), extreme gradient boosting (XGB), random forest (RF), and deep neural network (DNN) models based on the poplar growth rate. Using model fusion methods, the fitting accuracy and estimation capability were improved. The relative importance of each variable in estimating the poplar growth rate was analyzed using the permutation importance evaluation. The results showed the following: (1) the model fusion approach significantly improved the estimation accuracy of the poplar growth rate model with an R² of 0.893; (2) the temperature and precipitation exhibited the highest importance in poplar growth; (3) the forest stand density, precipitation, elevation, and temperature had significant variations in growth rates among different-sized poplar trees within different ranges; (4) low-forest stand density, high-precipitation, low-elevation, and high-temperature environments significantly increased the poplar growth rate and had a larger proportion of large-sized individuals with high growth rates. In conclusion, environmental factors significantly influence poplar growth, and corresponding planting and protection measures should be tailored to different growth environments to effectively enhance the growth of poplar plantations.

Climate change may induce regional climate shifts, profoundly affecting plant growth, distribution, and ecosystems. This study collected 37 Sabina chinensis (Sabina chinensis (L.) Ant. cv. Kaizuca) tree cores (74 samples) from a site in the Yangtze River Delta (YRD) coastal region. Utilizing traditional dendrochronological principles and methods, a standardized tree-ring width chronology was developed to detect climate shift points and explore the differences in Sabina chinensis radial growth responses to climate factors, before and after these shifts. The findings are as follows: (1) Between 1967 and 2020, temperature emerged as the main climate factor influencing the radial growth of Sabina chinensis in the study area. (2) There are differences in the correlations between the tree radial growth of Sabina chinensis and climate factors in different months and seasons, before and after climate change. (3) Moving correlation analysis indicated that the relationships between radial growth and precipitation and temperature gradually altered. The study reveals the intricate influencing mechanisms of different climate factors on Sabina chinensis radial growth, before and after climate shifts, offering valuable references for other similar dendrochronological studies.

The traditional volume-derived biomass method is limited because it does not fully consider the carbon sink of young trees, which leads to the underestimation of the carbon sink capacity of a forest ecosystem. Therefore, there is an urgent need to establish an allometric biomass model of young trees to provide a quantitative basis for accurately estimating the carbon storage and carbon sink of young trees. The destructive data that were used in this study included the biomass of the young trees of the two dominant species (Betula pendula subsp. mandshurica (Regel) Ashburner & McAll and Populus × tomentosa Carrière) in China, which was composed of the aboveground biomass (B_a), belowground biomass (B_b), and total biomass (B_t). Univariate and bivariate dimensions were selected and five candidate biomass models were independently tested. Two additive allometric biomass model systems of young trees were established using the proportional function control method and algebraic sum control method, respectively. We found that the logistic function was the most suitable for explaining the allometric growth relationship between the B_a, B_t, and diameter at breast height (D) of young trees; the power function was the most suitable for explaining the allometric growth relationship between the B_b and D of young trees. When compared with the independent fitting model, the two additive allometric biomass model systems provide additive biomass prediction which reflects the conditions in reality. The accuracy of the B_t models and B_a models was higher, while the accuracy of the B_b models was lower. In terms of the two dimensions—univariate and bivariate, we found that the bivariate additive allometric biomass model system was more accurate. In the univariate dimension, the proportional function control method was superior to the algebraic sum control method. In the bivariate dimension, the algebraic sum control method was superior to the proportional function control method. The additive allometric biomass models provide a reliable basis for estimating the biomass of young trees and realizing the additivity of the biomass components, which has broad application prospects, such as the monitoring of carbon stocks and carbon sink evaluation.

Our previous studies discussed the potential of measuring virtual trees using computational virtual measurement (CVM). CVM is a general methodology that employs observational techniques in lieu of mathematical processing. The advantage of CVM lies in its ability to circumvent mathematical assumptions of tree shapes at the algorithmic level. However, due to the current computational limitations of desktop computers, the previously developed CVM application, namely, virtual water displacement (VWD), could only act as a primary theoretical testimonial using an idealized point cloud of a tree. The key problem was that simulating a massive number of virtual water molecules (VMMs) consumed most of the computational resources. As a consequence, an unexpected empirical formula for volume calibration had to be applied to the output measurement results. Aiming to create a more realistic simulation of what occurs when water displacement is used to measure tree volume in the real world, in this study, we developed a new physical scenario for VWMs. This new scenario, namely, a flood area mechanism (FAM), employed footprints of VWMs instead of quantifying VWM counts. Under a FAM, the number of VMMs was reduced to a few from several thousands, making the empirical mathematical process (of the previously developed physical scenario of VWMs) unnecessary. For the same ideal point clouds as those used in our previous studies, the average volume overestimations were found to be 6.29% and 2.26% for three regular objects and two artificial stems, respectively. Consequently, we contend that FAM represents a closer approximation to actual water displacement methods for measuring tree volume in nature. Therefore, we anticipate that the VWD method will eventually utilize the complete tree point cloud with future advancements in computing power. It is necessary to develop methods such as VWD and more CVM applications for future applications starting now.