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Litter decomposition is a vital link between material circulation and energy flow in forest ecosystems and is intensely affected by global change factors, such as increased nitrogen (N) deposition and altered precipitation regimes. As essential nutrients, calcium (Ca), magnesium (Mg), and manganese (Mn) play crucial roles in plant energy metabolism, photosynthesis, and membrane transport of plants, and the major source of these nutrients is litter decomposition. However, the dynamics of Ca, Mg, and Mn during decomposition have been largely ignored. Thus, to better understand Ca, Mg, and Mn dynamics during leaf litter decomposition in the scenario of increasing N deposition and decreasing precipitation, we carried out a two-year field litterbag experiment in a natural evergreen broad-leaved forest in the central area of the rainy area of Western China. Two levels of N deposition (ambient N deposition and 150 kg·N·ha−1·y−1) and precipitation reduction (no throughfall reduction and 10% throughfall reduction) were set, i.e., control (Ctr: without nitrogen deposition or throughfall reduction), N deposition (N, 150 kg·N·ha−1·y−1), throughfall reduction (T, 10% throughfall reduction), and N deposition and throughfall reduction (NT, 150 kg·N·ha−1·y−1 and 10% throughfall reduction). We found that leaf litter Ca concentration increased in the early decomposition stage and then decreased, while Mg and Mn concentrations generally decreased during the whole period of decomposition. The amount of Ca showed an accumulation pattern, while Mg and Mn generally showed a release pattern. N deposition and throughfall reduction affected the Ca, Mg, and Mn dynamics, varying with different decomposition stages; i.e., N deposition significantly affected the concentration and amount of Ca, regardless of the decomposition stages, while throughfall reduction significantly affected the Ca concentration in the whole and early decomposition stages. N deposition significantly affected the concentration and amount of Mg in the whole and early decomposition stages, while throughfall reduction had no significant effects. Throughfall reduction significantly affected the concentration and amount of Mn in the whole and late decomposition stages, while N deposition had no significant effects. Ca concentration generally showed a significant positive linear relationship with mass loss in the early decomposition stage; Mg concentration showed a significant positive linear relationship with mass loss in the Ctr and N treatments in the early and late decomposition stages; Mn generally showed a significant negative linear relationship with mass loss, regardless of the decomposition stage. Overall, the results suggest that Ca accumulation is more likely affected by N deposition, while Mg and Mn releases are more likely affected by N deposition combined with throughfall reduction, particularly in the early decomposition stage. [ABSTRACT FROM AUTHOR]

Tropical forest degradation is a major contributor to greenhouse gas emissions. Tree height can be used as an important predictor of forest growth, and yield models can provide basic data for forest degradation assessments. As an important parameter of unmanned aerial vehicle-light detection and ranging (UAV-LiDAR), it is not clear how the point cloud density affects the extraction accuracy of tree height in degraded tropical rain forests. To solve this problem, we collected UAV-LiDAR data at a flight altitude of 150 m, and then resampled the UAV-LiDAR data obtained according to the point cloud density percentage resampling method and obtained UAV-LiDAR data for five different point cloud densities, namely, 12, 17, 28, 64, and 108 points/m2. On the basis of the resampled LiDAR data, we generated a canopy height model (CHM) to extract the height of Dacrydium pierrei (D. pierrei). The results show that (1) With the increase in the point cloud density, the accuracy of tree height extraction gradually increased, with a maximum accuracy at 108 points/m2 (root mean squared error (RMSE)% = 22.78%, bias% = 14.86%). The accuracy (RMSE%) increased by 6.92% as the point cloud density increased from 12 points/m2 to 17 points/m2, but only increased by 0.99% as the point cloud density increased from 17 points/m2 to 108 points/m2, indicating that 17 points/m2 is a critical point for tree height extraction of D. pierrei. (2) Compared with the results from broad-leaved forests, the accuracy of D. pierrei height extraction from coniferous forest was higher. With the increase in point cloud density, the difference in the accuracy of D. pierrei height between two stands gradually increased. When the point cloud density was 108 points/m2, the differences in RMSE% and bas% were 3.55% and 6.22%, respectively. When the point cloud density was 12 points/m2, the differences in RMSE% and bias% were 2.71% and 4.69%, respectively. Our research identified the lowest LiDAR data point cloud density required to ensure a certain accuracy in tree height extraction, which will help scholars formulate UAV-LiDAR forest resource survey plans. [ABSTRACT FROM AUTHOR]

Knowledge of forest structure is vital for sustainable forest management decisions. Terrestrial laser scanning cannot describe the canopy trees in a large area, and it is unclear whether unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have the ability to capture the forest canopy structural parameters in tropical forests. In this study, we estimated five forest canopy structures (stand density (N), basic area (G), above-ground biomass (AGB), Lorey's mean height (HL), and under-crown height (hT)) with four modeling algorithms (linear regression (LR), bagged tree (BT), support vector regression (SVR), and random forest (RF)) based on UAV-LiDAR data and 60 sample plot data from tropical forests in Hainan and determined the optimal algorithms for the five canopy structures by comparing the performance of the four algorithms. First, we defined the canopy tree as a tree with a height ≥70% HL. Then, UAV-LiDAR metrics were calculated, and the LiDAR metrics were screened by recursive feature elimination (RFE). Finally, a prediction model of the five forest canopy structural parameters was established by the four algorithms, and the results were compared. The metrics' screening results show that the most important LiDAR indexes for estimating HL, AGB, and hT are the leaf area index and some height metrics, while the most important indexes for estimating N and G are the kurtosis of heights and the coefficient of variation of height. The relative root mean squared error (rRMSE) of five structure parameters showed the following: when modeling HL, the rRMSEs (10.60%–12.05%) obtained by the four algorithms showed little difference; when N was modeled, BT, RF, and SVR had lower rRMSEs (26.76%–27.44%); when G was modeled, the rRMSEs of RF and SVR (15.37%–15.87%) were lower; when hT was modeled, BT, RF, and SVR had lower rRMSEs (10.24%–11.07%); when AGB was modeled, RF had the lowest rRMSE (26.75%). Our results will help facilitate choosing LiDAR indexes and modeling algorithms for tropical forest resource inventories. [ABSTRACT FROM AUTHOR]

Forest landscape change is affected by a complex mix of multiple interacting factors, including the biophysical environment, socioeconomic activities, cultural contexts, and forest management. Here, we investigated the temporal and spatial changes in forested land in Sichuan, China, using forest resource inventory data from 1980 to 2015. The factors that drove forest landscape conversion included environmental and socioeconomic characteristics, and forest operations. We also used spatial techniques to allow for neighborhood effects from forest land use activities in neighboring areas. We found that forest landscapes were very dynamic, with high change and high turnover in forest type and cover, but with an overall net gain. Spatial regression models showed strong neighborhood effects. Forest operations such as afforestation and protected areas had positive effects on forest gain. Meanwhile, forest land use changes resulting from forest programs (in Sichuan, mainly the Grain to Green Program and Natural Forest Conservation Program) were the major driving factors for increasing forest areas and improving forest conditions, tempered by local conditions of topography, climate, demography, and economy. The effective implementation of sustainable forest management strategy and policy can increase forest quality and quantity and maintain ecological function.