Masson Pine Forest Research Articles

Mapping Forest Carbon Stock Distribution in a Subtropical Region with the Integration of Airborne Lidar and Sentinel-2 Data

Forest carbon stock is an important indicator reflecting a forest ecosystem’s structures and functions. Its spatial distribution is valuable for managing natural resources, protecting ecosystems and biodiversity, and further promoting sustainability, but accurately mapping the forest carbon stock distribution in a large area is a challenging task. This study selected Changting County, Fujian Province, as a case study to explore a method to map the forest carbon stock distribution using the integration of airborne Lidar, Sentinel-2, and ancillary data in 2022. The Bayesian hierarchical modeling approach was used to estimate the local forest carbon stock based on airborne Lidar data and field measurements, and then the random forest approach was used to develop a regional forest carbon stock estimation model based on the Sentinel-2 and ancillary data. The results indicated that the Lidar-based carbon stock distribution effectively provided sample plots with good spatial representativeness for modeling regional carbon stock with a coefficient of determination (R2) of 0.7 and root mean square error (RMSE) of 12.94 t/ha. The average carbon stocks were 48.55 t/ha, 55.51 t/ha, and 57.04 t/ha for Masson pine, Chinese fir, and broadleaf forests, respectively. The carbon stock in non-conservation regions was 15.2–16.1 t/ha higher than that in conservation regions. This study provides a promising method through the use of airborne Lidar data as a linkage between sample plots and Sentinel-2 data to map the regional carbon stock distribution in those subtropical regions where serious soil erosion has led to a relatively sparse forest canopy density. The results are valuable for local government to make scientific decisions for promoting ecosystem restoration due to water and soil erosion.

A novel BH3DNet method for identifying pine wilt disease in Masson pine fusing UAS hyperspectral imagery and LiDAR data

Pine Wilt Disease (PWD) is a forest infectious disease that inflicts substantial economic losses to China’s forestry. Its rapid spread and the significant challenges associated with its control make early detection of infected trees crucial for disaster prevention. Unmanned aerial systems (UASs) hyperspectral imaging (HSI) and light detection and ranging (LiDAR) technologies provide high-resolution spectral diagnostic information coupled with intricate three-dimensional structural data, which has potential for fine grained monitoring of PWD. However, how to fuse HSI and LiDAR data to identify the early infected individual trees is still a challenge. This study presents a novel instance segmentation network, BH3DNet, to identify individual trees at different PWD-infected stages by extracting high-level abstract features based on the fusion of drone HSI and LiDAR data. BH3DNet introduces the PointNet++ model as the base network, and incorporates a shared encoder and twin parallel decoders to align semantic category prediction and instance segmentation of individual trees in an end-to-end approach. By applying an enhanced point cloud dataset that fuses drone HSI and LiDAR point cloud data, this model facilitates the identification of PWD infection stages at the individual tree scale. We evaluated the proposed model in a Masson pine forest stand sparsely mixed with broadleaf trees in a variety of infection states ranging from healthy to severely infected by PWD, and compared the performance of the model using the RGB bands, full HSI bands and screened bands as inputs, respectively. BH3DNet achieves an overall accuracy of 89.65 % with a Kappa × 100 of 87.29 for identifying individual trees using screened HSI bands and LiDAR point cloud, significantly outperforming the Mask R-CNN using only HSI data (overall accuracy: 70.81 %, Kappa × 100: 64.16). Moreover, BH3DNet’s accuracy at the early infection stage reaches 83.75 %. It proves that fusing HSI and point cloud data reflects the information of individual trees distribution and infection status, and the BH3DNet is suitable for high-precision monitoring of PWD.

Thinning Effects on Aboveground Biomass Increments in Both the Overstory and Understory of Masson Pine Forests

Thinning Effects on Aboveground Biomass Increments in Both the Overstory and Understory of Masson Pine Forests

Forest age estimation using UAV-LiDAR and Sentinel-2 data with machine learning algorithms- a case study of Masson pine (Pinus massoniana)

ABSTRACT The assessment of ecological functions, such as those of forest structure zoning and carbon sinks, heavily relies on forest age classification. Therefore, monitoring forest age is a crucial element of forest resource surveys. With the increased availability of high-quality open-access satellite data and advancements in Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) technology, remote sensing has emerged as an essential method for acquiring accurate forest age information. In this study, Sentinel-2 remote sensing data, UAV-LiDAR data, and combined Sentinel-2 and LiDAR data are used as data sources. Three machine learning algorithms, Adaptive Boosting (AdaBoost), Random Forest (RF), and Extreme Random Tree (ERT), are used to predict forest age in a Masson pine (Pinus massoniana Lamb.) forest. The optimal model is used to predict the forest age and simulate the spatial age distribution. The machine learning models based on separate Sentinel-2 and LiDAR data accurately predict the age of the Masson pine forest. Nevertheless, the accuracy of the RF model with combined data was higher than that in other cases, with an accuracy R value of 0.81. The model displayed good stability, and the spatial uncertainty of age estimation was low. Compared with the RF model using only Sentinel-2 data (R = 0.43), the RF model with combined LiDAR and Sentinel-2 data achieved the highest accuracy, with R values 88.37% higher. In addition, the forest canopy structure parameters, such as the average height of the forest stand extracted from UAV-LiDAR data, had a significant impact on the estimation of forest age. Thus, when the combined Sentinel-2 and LiDAR data were used to establish these parameters, the highest accuracy in the estimation of Masson pine was obtained. The findings of this study provide new insights for forest age estimation based on multi-source remote sensing data.

Soil PhytOC accumulation of Masson pine forests in Southwest China

Soil PhytOC accumulation of Masson pine forests in Southwest China

Establishment of Pinus massoniana–Lactarius hatsudake Symbiosis

Lactarius hatsudake is a common ectomycorrhizal edible mushroom in Pinus massoniana forests, and has important ecological and potential economic values. However, there are only a few reports on the establishment of Pinus massoniana–Lactarius hatsudake symbiosis. Here, we isolated a new strain of L. Lactarius hatsudake (GX01) from a local masson pine forest and established its ectomycorrhizal symbiosis with the P. massoniana. Potato dextrose agar (PDA) medium was optimal for the growth of L. hatsudake GX01. The saffron-to-brown ectomycorrhiza formed by L. hatsudake GX01 are usually bifurcated or coralloid shape, with a rod and a smooth surface, without emanating hyphae. The characteristic mantle and Hartig net structures of ectomycorrhizae were confirmed by microscope and scanning electron microscope (SEM). L. hatsudake GX01 can significantly promote the formation and development of lateral roots of P. massoniana seedlings during the early interaction. This study thus lays the foundation for subsequent study of the symbiotic molecular mechanism and application of P. massoniana–L. hatsudake symbiosis.

Stratification of vertical canopy structure to improve estimation of forest inventory attributes using airborne LiDAR data in a large subtropical region of China

Over the last two decades, airborne light detection and ranging (LiDAR) has been developed into an advanced tool for practical forest resource inventory monitoring over large areas. Nonetheless, improving the accuracy of forest inventory attribute estimations remains an ongoing challenge. This paper introduces a novel framework for estimating forest inventory attributes based on the stratification of vertical forest structures (VFS). According to the composition and spatial arrangement of the superior, middle, and inferior strata in the tree layer, the forest stand was classified into six distinct VFS classes. Subsequently, the multiplicative power models were established for the stratification-based estimations of the forest inventory attributes, including stand volume (VOL), basal area (BA), and above-ground biomass (AGB), by using a rule-based exhaustive combination approach, and their performances were comparatively analyzed. The result indicated that: compared to the accuracy (relative root mean squared error, rRMSE) of the species-based estimation, the weighted average rRMSE of stratumbased VOL, BA, and AGB estimations of four forest types (Chinese fir, Masson pine, eucalyptus, and broadleaf forests) decreased by 0.3%–7.3%, +3.6%–9.4%, and 0.7%–8.7%, respectively, and the accuracy was significantly improved after stratification. Even after clustering the VFS into two or three classes using cluster analysis, the accuracy of forest attribute estimations remained superior to that of the species-based estimations. Notably, the coefficients of variation for both forest attributes and LiDAR metrics experienced a substantial decrease, and their statistical relationship considerably strengthened within most strata post-VFS classification, which led to an improvement in the accuracy of the forest attribute estimations. The methodology presented in this paper provides a significant advance in improving the accuracy of forest inventory attributes for large areas using airborne LiDAR data.

PhytOC sequestration characteristics and phytolith carbon sink potential of karst Masson pine forest in southern China

Masson pine forest is an extremely important component of terrestrial ecosystems and plays a significant ecological function in forest carbon sink in China. Phytolith-occluded organic carbon (PhytOC) is the important long-term stable carbon sink mechanism in terrestrial ecosystems. To provide scientific bases for the accurate estimation of phytolith carbon sink and precise management of Masson pine forests, PhytOC sequestration and accumulation of the three typical forest ages of Masson pine in southern China karst area were studied in the present study. Although the sampling pots were still limited, PhytOC sequestration characteristics of the Masson pine forests were successfully clarified. The followed results and conclusions were obtained: 1) The range of PhytOC content, reserves, and sequestration rate of the Masson pine forests was 0.04–0.78 g·kg−1, 0.44–16.93 kg·hm−2, and 0.094–2.557 kg CO2 hm−2·a−1, respectively. 2) Among the three forest ages of Masson pine, the mature forest had the highest accumulation of PhytOC, and the middle-aged forest had the highest sequestration rate of PhytOC. Compared with biomass, vegetation productivity more positively significantly affected the PhytOC sequestration. 3) Among the three organs, leaves had the highest PhytOC content and trunks had the highest PhytOC reserves, which all took significant functions in the sequestration and accumulation of PhytOC. 4) Artificial measures to promote vegetation productivity, such as thinning, artificial tending, and low-efficiency forest transformation, will significantly increase the PhytOC sequestration and accumulation of Masson pine forests.

Ecological stoichiometry of C, N, P and Si of Karst Masson pine forests: Insights for the forest management in southern China

Ecological stoichiometry is an effective method to study the stoichiometric relations and laws of elements in biogeochemical cycle, widely used in studies on nutrient cycles, limiting elements and nutrient utilization efficiency in ecosystems. To explore C, N, P, and Si stoichiometric characteristics and reveal these nutrient cycle processes and mechanisms in the karst Masson pine forests, the typical Masson pine forests of the three different stand ages in southern China were selected as the research objects and the C, N, P, and Si stoichiometric characteristics of soil-plant-litter continuum were studied. The followed results and conclusions were obtained: 1) Content range of TOC (total organic carbon), TN (total N), TP (Total P) and TSi (total Si) of the Masson pine forests was 288.31–334.61, 0.34–6.66, 0.11–1.05, and 0.76–11.4 g·kg−1, respectively. And the ratio range of C:N, C:P, C:Si, N:P, N:Si, and P:Si was 49.95–913.57, 99.98–2872.18, 22.48–429.31, 1.85–6.33, 0.17–6.01, and 0.04–0.91, respectively. 2) The significant differences in C, N, P, and Si stoichiometric characteristics were present between different organs or different forest ages. Leaves had the highest N and P content, while roots were the best enriched organ of Si element. Si content and C:Si were obviously correlated with forest age. 3) Significant N limitation was present in the Masson pine forests. And in the young and middle-aged forests, N limitation was more obvious. 4) The litter nutrients mainly came from branches. And the litter decomposed fast, which played an important role in the nutrient return of barren karst soil. The present results not only revealed the stoichiometric characteristics and cycling processes of C, N, P, and Si elements in the Masson pine forests, but also provided important scientific bases for the artificial management of Masson pine plantations in southern China.

Responses of Plant Species Diversity and Biomass to Forest Management Practices after Pine Wilt Disease

Pine wilt disease (PWD), which is caused by the Bursaphelenchus xylophilus, is one of the most serious biological invasions in East Asia. Removal of infected pine trees is generally used to prevent the spread of PWD. However, how this strategy affects plant species diversity and ecosystem functions needs to be clarified. We compared alpha diversity, species composition, and biomass for all woody plant species, adults, saplings, and seedlings in infected Masson pine forests where removal of infected trees occurred (CTD) and where infected trees were retained (UTD), uninfected Masson pine forests (PMF), and evergreen broad-leaved forests (EBF). UTD had the highest alpha diversity of all species and saplings, and UTD and PMF had the lowest alpha diversity of seedlings. CTD and EBF had a similar composition of seedlings, and CTD and UTD had similar species composition of all plant species and saplings. UTD had the lowest biomass for all species and adults but had the highest saplings biomass. Soil properties were significantly related to plant biomass. The retention of infected trees likely maintained soil fertility which increased species alpha diversity and the biomass of saplings, and unchanged species composition compared to the removal of infected trees, indicating that the retention strategy could help to maintain ecosystem functions.

Linkages between Plant Community Composition and Soil Microbial Diversity in Masson Pine Forests.

Plant species identity influences soil microbial communities directly by host specificity and root exudates, and indirectly by changing soil properties. As a native pioneer species common in early successional communities, Masson pine (Pinus massoniana) forests are widely distributed in subtropical China, and play a key role in improving ecosystem productivity. However, how pine forest composition, especially the dominance of plant functional groups, affects soil microbial diversity remains unclear. Here, we investigated linkages among woody plant composition, soil physicochemical properties, and microbial diversity in forests along a dominance gradient of Masson pine. Soil bacterial and fungal communities were mainly explained by woody plant community composition rather than by woody species alpha diversity, with the dominance of tree (without including shrub) species and ectomycorrhizal woody plant species accounting for more of the variation among microbial communities than pine dominance alone. Structural equation modeling revealed that bacterial diversity was associated with woody plant compositional variation via altered soil physicochemical properties, whereas fungal diversity was directly driven by woody plant composition. Bacterial functional groups involved in carbohydrate and amino acid metabolism were negatively correlated with the availability of soil nitrogen and phosphorus, whereas saprotrophic and pathogenic fungal groups showed negative correlations with the dominance of tree species. These findings indicate strong linkages between woody plant composition than soil microbial diversity; meanwhile, the high proportion of unexplained variability indicates great necessity of further definitive demonstration for better understanding of forest-microbe interactions and associated ecosystem processes.

Effects of Simulated Acid Rain and Nitrogen Deposition on Soil Bacterial Community Structure and Diversity in the Masson Pine Forest

By analyzing the effects of acid rain and nitrogen deposition on the structure and diversity of soil bacterial communities, the response mechanism of Masson pine forests to environmental stress was investigated, providing a theoretical reference basis for resource management and conservation in Tianmu Mountain National Nature Reserve. Four treatments of the simulated acid rain and nitrogen deposition were set up in 2017 to 2021 in Tianmu Mountain National Nature Reserve (pH value of 5.5 and 0 kg·(hm2·a)-1, CK; pH value of 4.5 and 30 kg·(hm2·a)-1, T1; pH value of 3.5 and 60 kg·(hm2·a)-1, T2; pH value of 2.5 and 120 kg·(hm2·a)-1, T3). The differences in soil bacterial community composition and structure among treatments and their influencing factors were analyzed by collecting soils from four treatments, using the Illumina MiSeq PE300 second-generation high-throughput sequencing platform. The results showed that acid rain and nitrogen deposition significantly reduced soil bacterial α-diversity (P<0.05) in a Masson pine forest. The Masson pine forest soils consisted of 36 phylum groups of mycota, with Acidobacteria, Proteobacteria, Actinobacteria, and Chloroflexi as the main bacterial phyla (relative abundance>1%) in the Masson pine forest soils. Flavobacterium, Nitrospira, Haliangium, Candidatus_Koribacter, Bryobacter, Occallatibacter, Acidipla, Singulisphaera, Pajaroellobacter, and Acidothermus, which showed significant changes in relative abundance under the four treatments, could be used as indicator species for changes in soil bacterial communities under acid rain and nitrogen deposition stress. Soil pH and total nitrogen were influential factors in the diversity of soil bacterial communities. As a result, acid rain and nitrogen deposition increased the potential ecological risk, and the loss of microbial diversity will change the ecosystem function as well as reduce the stability of the ecosystem.

Plant Community Degradation Inquiry and Ecological Restoration Design in South Lake Scenic Area of China

The most visited provincially administered park in Hanzhong City, the South Lake Scenic Area, is degrading the Masson Pine forest communities. Determining and repairing the landscape degradation and impacts on recreational value due to the degraded community structure is essential for restoring the environment of the southern Qinling Mountains. By evaluating the degree, trend, and pattern (DTP) of impacts, we identified the degradation status of the plant community in the South Lake Scenic Area in the past 20 years. We show that the scenic area has experienced an increase in the degradation of vegetation cover in the last 20 years. The area of degraded vegetation cover is significantly larger than the area of improvement, and the overall area is changing, with fewer stable areas. The area of reduced forest cover in the South Lake Scenic Area from 2000–2010 and 2010–2020 has been expanding, and the area of forest land transferred to nonforest land from 2010–2020 has been accelerating compared with 2000–2010; the landscape pattern index has decreased year over year, fragmentation has become serious, landscape connectivity is declining, woodland patches are subject to human disturbance, and patch shapes are simplifying. Based on theories of natural succession and moderate disturbance, the Miyawaki method and interlogging are used to promote plant community renewal and biodiversity restoration. This is intended to shorten the natural succession process in the scenic area and to rapidly restore the ecological foundation of the scenic area. Recovery will meet the aesthetic and ecological values of the South Lake Scenic Area.

Allometry, Biomass and Productivity in Pinus massoniana Forests of China: An Updated Review of Published Data

Field measurements of forest biomass are labor-consuming and usually destructive, but such direct datasets are the foundation for further developing indirect estimation such as biomass equation and biomass expansion factors that were used to scale biomass estimation or carbon storage from site or local surveys up to regional or national level. Pinus massoniana is widely distributed throughout 17 provinces of China and has a high economic value. This study is an updated review of the allometric equations, biomass allocation data and productivity estimations in Pinus massoniana forests of China published in articles during the period from 1982 to 2019. Different allometric equations of Pinus massoniana showed site-specific dependences. The aboveground and total tree biomass were expressed as power equations of mean age for Pinus massoniana forests. The mean value of aboveground tree biomass, total tree biomass and ecosystem biomass across all ages for Masson pine forests was 109.81, 127.65 and 138.76 Mg/ha, respectively. The mean proportion of stem, branch and foliage to aboveground tree biomass for Pinus massoniana was 77.1%, 15.9% and 7%, respectively. The tree root biomass continuously increased with the aboveground tree biomass and a power relationship was found between the tree root biomass and aboveground tree biomass for Pinus massoniana forests. The tree net primary productivity increased with aboveground tree biomass for Pinus massoniana forests, and the relationship was expressed as a linear equation. The mean tree, litter and ecosystem net primary productivity was 7.04, 4.88 and 8.30 Mg/ha/year, respectively. These findings provide key parameters for the biomass estimation and carbon accounting studies of widely planted Pinus massoniana forests in China.

Soil Nitrogen Transformation Process Influenced by Litterfall Manipulation in Two Subtropical Forest Types.

Nitrogen (N) is often recognized as the primary limiting nutrient element for the growth and production of forests worldwide. Litterfall represents a significant pathway for returning nutrients from aboveground parts of trees to the soils and plays an essential role in N availability in different forest ecosystems. This study explores the N transformation processes under litterfall manipulation treatments in a Masson pine pure forest (MPPF), and Masson pine and Camphor tree mixed forest (MCMF) stands in subtropical southern China. The litterfall manipulation included litterfall addition (LA), litterfall removal (LR), and litterfall control (LC) treatments. The project aimed to examine how litterfall inputs affect the soil N process in different forest types in the study region. Results showed that soil ammonium N (NH4+-N) and nitrate N (NO3−-N) content increased under LA treatment and decreased under LR treatment compared to LC treatment. LA treatment significantly increased soil total inorganic N (TIN) content by 41.86 and 22.19% in MPPF and MCMF, respectively. In contrast, LR treatment decreased the TIN content by 10 and 24% in MPPF and MCMF compared to LC treatment. Overall, the soil net ammonification, nitrification, and N mineralization rates were higher in MCMF than in MPPF; however, values in both forests were not significantly different. LA treatment significantly increased annual net ammonification, nitrification, and mineralization in both forest types (p < 0.05) compared to LC treatment. LR treatment significantly decreased the values (p < 0.05), except for ammonification, where LR treatment did not differ substantially compared to LC treatment. Our results suggested that changes in litterfall inputs would significantly alter soil N dynamics in studied forests of sub-tropical region. Moreover, mixed forest stands have higher nutrient returns due to mixed litter and higher decomposition compared to pure forest stands.

Variations in Litterfall Dynamics, C:N:P Stoichiometry and Associated Nutrient Return in Pure and Mixed Stands of Camphor Tree and Masson Pine Forests

Litterfall, directly and indirectly, affects the soil physicochemical properties, microbial activity, and diversity of soil fauna and flora by adding organic matter and nutrients to the soil. This study explores litterfall dynamics such as litterfall production, litter decomposition rate, and associated nutrient return in three forest types, that is, camphor tree forest (CTF), Masson pine forest (MPF), and camphor tree and Masson pine mixed forest (CMF), in subtropical China. Results showed that CMF had the highest mean annual litterfall production (4.30 ± 0.22 t ha−1), which was significantly higher than that of MPF (3.41 ± 0.25 t ha−1) and CTF (3.26 ± 0.17 t ha−1). Leaf represented the major fraction of litterfall, constituting over 71% of the total litterfall mass in the three forest types. The contribution of branch litter was 16.3, 8.9, and 16.9%, and miscellaneous litter was 12.6, 18.9, and 11.1% in CTF, MPF, and CMF, respectively. The concentration of macronutrients ranked as N &amp;gt; Ca &amp;gt; K &amp;gt; Mg &amp;gt; P in all litter fractions. The total annual macronutrient return to the soil from the litterfall was in order as CTF (74.2 kg ha−1‧yr−1) &amp;gt; CMF (70.7 kg ha−1‧yr−1) &amp;gt; MPF (33.6 kg ha−1‧yr−1). The decomposition rate was higher in leaf litter than in branch litter throughout the three forests. Among the forest types, the leaf and branch decomposition rates were in a pattern: CTF &amp;gt; CMF &amp;gt; MPF. The ratio of C/N in both leaf and branch litters was significantly higher in MPF than in CTF and CMF, while no significant differences in N/P ratio were found in these litters among the three forests. The high N:P ratios in leaf litter (23/30) and the branch (24/32) litter indicated the high N returning and low nutrient returning to the soil. Our results suggested that the broadleaved forests have faster litter decomposition and higher macronutrient returns than conifer forests. Moreover, the litter decomposition rate was mainly associated with litterfall quality and chemical composition. The introduction of broadleaved trees into monoculture coniferous stands could increase litter production nutrients return, and thus, it had advantages in soil nutrients restoration and sustainable forest management.

Changes in Carbon Storage of Masson Pine Forests along a Latitudinal Gradient with Different Stand Structures in Southern China

The spatial patterns of carbon pool in landscape vary greatly with variation of forest structures and climate conditions. In this field study, the carbon storage in overstory, understory, litter layer of plants and soil carbon in forests was investigated in 26-28 year-old Masson pine (Pinus massoniana) pure and mixed forests along a latitudinal gradient (20–30 °N) crossing Hunan and Guangxi provinces of China, during the period of May 2015–August 2017. We found that the total carbon storage in Masson pine forests ranged 88.92–149.41 Mg/ha, of which 54.03% occurred in aboveground compound and 45.97% occurred belowground. The overall total carbon storage distribution was 34.62–68.72 Mg/ha, 3.82–10.19 Mg/ha, 2.37–3.96 Mg/ha, and 6.06–12.08 Mg/ha in stems, branches, leaves, and roots, respectively. The carbon storage in the overstory of forest stands and forest soils significantly decreased with increasing latitude, while the carbon storage in the understory and litter of the forest stands significantly increased as the latitude increased. The overall carbon storage significantly decreases by 8.26 Mg/ha in Masson pine forests as the latitude increased by one degree. The carbon storage increased by 8.43% in Masson pine mixed forests compared to the pure forest stands after the transformation from the pure forest stands into the mixed forest stands ∼ 15 years later. The results of changes in carbon storage in the studied forests with the latitudinal gradient could be attributed to variations in hydrothermal conditions with changing latitudes. The forest structure with different tree species composition was another important factor regulating carbon storage in forest ecosystems at the same latitude. The results provided a scientific basis for better understanding of latitudinal variation and spatial distribution of carbon storage in Masson pine forest stands with optimal forest stand structures in subtropical region of China.

Comparison of Variable Selection Methods among Dominant Tree Species in Different Regions on Forest Stock Volume Estimation

The forest stock volume (FSV) is one of the crucial indicators to reflect the quality of forest resources. Variable selection methods are usually used for FSV estimated models. However, few studies have explored which variable selection methods can make the selected data set have better explanatory and robustness for the same dominant tree species in different regions after the feature variables were filtered by the feature selection methods. In this study, we chose six dominant tree species from Lin’an District, Anji County, and a part of Longquan City. The tree species include broad-leaved, coniferous, Masson pine, Chinese fir, coniferous and broad-leaved mixed forest, and all tree species which include the above five groups of tree species. The last two tree species were represented by mixed and all, respectively. Then, the satellite images, terrain factors, and forest inventory data were selected by six variable selection methods (least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), stepwise regression (Step-Reg), permutation importance (PI), mean decrease impurity (MDI), and SelectFromModel based on LightGBM (SFM)), according to different dominant tree types in different regions. The selected variables were formed into a new dataset divided by different dominant trees. Besides, extreme gradient boosting (XGBoost) was used, combined with variable selection methods to estimate the FSV. The performed results are as follows: In the feature selection of coniferous, RFE performed better both in the average and in the separate regions. In the feature selection of Chinese fir and all, PI performed better both in the average and in the separate regions. In the feature selection of Masson pine, MDI performed better both in the average and in the separate regions. In the feature selection of mixed, MDI performed better in the average while RFE performed better in the separate regions comprehensively. The results showed that not only in separate regions, but the average result two factors, RFE, MDI, and PI all performed well to select variables to estimate the FSV. Furthermore, we selected the top five high feature-importance factors of different tree types, and the results showed that tree age and canopy density were both of great importance to the estimation of FSV. Besides, in the exhibited results of feature selection methods, compared with no variable selection, the research also found that variable selection can improve the performance of the model. Additionally, from the results of different tree types in different regions, we also found that small-scale and diversity of dominant tree types may lead to the instability and unreliability of experimental results. The study provides some insight into the application the optimal variable selection methods of the same dominant tree type in different regions. This study will help the development of variable selection methods to estimate FSV.

Contribution of Litter Layer to Greenhouse Gas Fluxes between Atmosphere and Soil Varies with Forest Succession

Surface litter layer strongly influences CO2, N2O, and CH4 fluxes (FCO2, FN2O, and FCH4) between the atmosphere and forest floor through litter decomposition (litter-internal, fL-L) or interactions between litter and mineral soil (litter-induced, fL-S). However, the relative contribution of fL-L or fL-S to these greenhouse gas (GHG) fluxes in forests at different succession stages remain unclear. We conducted a field experiment where surface litter was either removed (LR), left intact (CT), doubled (LD), or exchanged (LE) in a Masson pine forest (PF, early stage of succession) and an evergreen broadleaved forest (BF, climax of succession) at the Dinghushan Nature Reserve in southern China, and studied the responses of FCO2, FN2O, and FCH4 from August 2012 to July 2013. The results showed that both FCO2 and FN2O were increased by LD treatment with a greater increase in BF (41% for FCO2 and 30% for FN2O) and decreased by LR treatment with the greater decrease in PF (−61% for FCO2 and −58% for FN2O). LD treatment decreased FCH4 by 14% in PF and 6% in BF, and LR treatment increased FCH4 by 5% in PF and 18% in BF. fL-S contributed more to FCO2 (36%) and FN2O (45%) than fL-L in PF, whereas contributions of fL-L to FCO2 (41%) and FN2O (30%) were much bigger than fL-S in BF. The greater FCH4 in PF and BF resulted from the contributions of fL-L (−14%) and fL-S (−12%), respectively. Our results indicated that fL-L is the major source of GHG fluxes in BF, whereas fL-S dominates GHG fluxes in PF. The results provide a scientific reference for quantifying the contributions of fL-L and fL-S to GHG fluxes during the subtropical forest succession and should be considered in ecosystem models to predict global warming in the future.

Phosphorus is the key soil indicator controlling productivity in planted Masson pine forests across subtropical China

Soil physiochemical properties are critical to understanding forest productivity and carbon (C) finance schemes in terrestrial ecosystems. However, few studies have focused on the effects of the soil physiochemical properties on the productivity in planted forests. This study was therefore conducted at 113 sampling plots located in planted Masson pine forests across subtropical China to test what and how the aboveground net primary productivity (ANPP) would be explained by the soil physiochemical properties, stand attributes, and functional traits using regression analysis and structural equation modelling (SEM). Across subtropical China, the ANPP ranged from 1.79 to 14.04 Mg ha−1 year−1 among the plots, with an average value of 6.05 Mg ha−1 year−1. The variations in ANPP were positively related to the stand density, root phosphorus (P) content and soil total P content but were negatively related to the stand age, root C:P and N:P ratios. Among these factors, the combined effects of stand density, stand age and soil total P content explained 35% of the ANPP variations. The SEM results showed the indirect effect of the soil total P content via the root P content and C:P ratio on the ANPP and indirect effects of other soil properties (e.g., pH, clay, and bulk density) via the soil total P content and root functional traits (e.g., root P, C:P, and N:P) on the ANPP. By considering all possible variables and paths, the best-fitting SEM explained only 11–13% of the ANPP variations, which suggested that other factors may be more important in determining the productivity in planted forests. Overall, this study highlights that soil total P content should be used as a key soil indicator for determining the ANPP in planted Masson pine forests across subtropical China, and suggests that the root functional traits mediate the effects of soil properties on the ANPP.