Evergreen Coniferous Forest Research Articles

The Impacts of Phenological Stages within the Annual Cycle on Mapping Forest Stock Volume Using Multi-Band Dual-Polarization SAR Images in Boreal Forests

SAR images with two polarizations show strong potential for mapping forest stock volume (FSV) combined with limited samples. However, accurately mapping FSV still presents challenges in selecting the optimal acquisition date to obtain the SAR images during specific phenological stages within the annual forest cycle (growth and dormant stages). To clarify the impacts of phenological stages within the annual cycle on FSV mapping, SAR images with various polarization models and bands (Sentinel-1(S), GaoFen-3(GF-3 (G)) and ALOS-2(A)) were acquired within the growth and dormant stages of an annual cycle in a boreal evergreen coniferous forest (Chinese pine) and a deciduous coniferous forest (Larch). Subsequently, single-band (G, S, and A) and multi-band combined alternative variable sets (A + G, A + S, S + G, and A + S + G) were extracted within the same stage, respectively. Finally, the forward selection approach was utilized in conjunction with four different models (MLR, KNN, RF, and SVR) to obtain the most suitable variable sets and generate FSV mapping. The results demonstrated a strong correlation between the intensity of backscattering coefficients and the phenological stages of the forest. Within the dormant stage, there was a significant decrease in the gaps of backscattering coefficients obtained from the same polarization compared to those within the growth stage. Furthermore, the results also revealed that more signals from inside the canopy could be detected during the dormant stage in both evergreen coniferous forests and deciduous coniferous forests. Subsequently, the accuracy in mapping FSV obtained from single-band SAR images within the dormant stage are slightly higher than that within the growth stage, and the accuracy was still significantly affected by both overestimation and underestimation. Moreover, the combined effects of different bands significantly improve the reliability of mapped FSV. The rRMSE values in four multi-band combinations ranged from 22.37% to 29.40% for Chinese pine forests and from 21.27% to 34.38% for Larch forests, and the optimal result was observed from combinations of A + S + G acquired within the dormant stage. It is confirmed that SAR signal and their sensitivity to FSV depends on the stages of forest annual growth cycle. In comparison to the growth period, dual-polarization SAR data acquired during the dormant stage is more suitable for estimating FSV in boreal forests.

Leaf Functional Traits and Their Influencing Factors in Six Typical Vegetation Communities.

Leaf functional traits (LFTs) have become a popular topic in ecological research in recent years. Here, we measured eight LFTs, namely leaf area (LA), specific leaf area (SLA), leaf thickness (LT), leaf dry matter content (LDMC), leaf carbon content (LCC), leaf nitrogen content (LNC), leaf phosphorus content (LPC), and leaf potassium content (LKC), in six typical vegetation communities (sclerophyllous evergreen broad-leaved forests, temperate evergreen coniferous forests, cold-temperate evergreen coniferous forests, alpine deciduous broad-leaved shrubs, alpine meadows, and alpine scree sparse vegetation) in the Chayu River Basin, southeastern Qinghai-Tibet Plateau. Our aim was to explore their relationships with evolutionary history and environmental factors by combining the RLQ and the fourth-corner method, and the method of testing phylogenetic signal. The results showed that (i) there were significant differences in the eight LFTs among the six vegetation communities; (ii) the K values of the eight LFTs were less than 1; and (iii) except for LCC, all other LFTs were more sensitive to environmental changes. Among these traits, LA was the most affected by the environmental factors, followed by LNC. It showed that the LFTs in the study were minimally influenced by phylogenetic development but significantly by environmental changes. This study further verified the ecological adaptability of plants to changes in environmental factors and provides a scientific basis for predicting the distribution and diffusion direction of plants under global change conditions.

High-precision estimation of plant alpha diversity in different ecosystems based on Sentinel-2 data

At present, the accuracy of remote sensing estimation models of plant alpha diversity is generally low, and high-precision estimation models in deciduous broadleaved forest (DBF), deciduous coniferous forest (DCF) and evergreen coniferous forest (ECF) are still lacking. The main purpose of this study is to construct high-precision remote sensing models for plant alpha diversity in multiple ecosystems at global scale. Normalized Difference Vegetation Index (NDVI) were derived from Sentinel-2 data. NDVI and NDVI based spectral diversity/heterogeneity indices were selected as predictive variables, and alpha diversity indices were selected as response variables. Simple linear regression (SLR), partial linear regression (PLSR), and random forest (RF) models were used to evaluate the predictive ability of the predictive variables against the response variables under six ecosystems (evergreen broadleaved forest (EBF), DBF, ECF, DCF, shrub, and grassland), and to compare the estimated robustness of various spectral diversity indices. In terms of prediction accuracy, the SLR models were the worst, and the PLSR model were average. RF performed best, outperforming most current models. Especially in DBF, ECF, shrub and grassland, the determination coefficient R2 of RF models can be as high as 0.9. In terms of the prediction of α-diversity, the prediction effect of species richness was better than that of Shannon index, Simpson index and Pielou index. The higher the vegetation complexity, the more accurate the assessment of vegetation α-diversity tends to be, especially in DBF, shrub and grassland. According to the importance of predictive variables and model stability evaluation results, NDVI, standard deviation of NDVI (SD), and NDVI derived Shannon’s diversity index (Sha), Cumulative Residual Entropy (CRE), Pielou’s evenness index (Pie), Hill’s numbers (Hill), Berger-Parker’s diversity index (Ber), Parametric Rao’s index of quadratic entropy(paRao) are all powerful indicators for predicting plant alpha diversity. Among them, the prediction performance of NDVI and SD is better. This study is not only an exploration of the practicability of R package “rasterdiv”, but also an attempt to construct high-precision remote sensing estimation models of plant alpha diversity at global scale.

Trends in the Altitudinal Gradient Evolution of Vegetation Ecological Functions in Mountainous Areas

Natural vegetation protects, maintains, and improves the environment through its ecological functions and is, thus, an important component of Earth’s ecosystems. The distribution of natural vegetation and its corresponding ecological roles vary with the topographic gradient. Understanding this role is essential for effective ecosystem management and conservation efforts. This study analyzes vegetation composition across altitude gradients and the spatiotemporal evolution of water conservation, soil conservation, and carbon storage in the southern hill and mountain belt of China. We then explored the drivers of the ecological functions of vegetation at different altitude gradients. The results showed that water conservation increased by 108.56%, soil conservation increased by 97.04%, and carbon storage increased only slightly. The ecological functions of vegetation varied across altitude gradients, with the 500–800 m gradient exhibiting markedly higher ecological functions than the other gradients. The effect of precipitation on soil conservation increases with altitude. In addition, at higher altitudes, evergreen coniferous forests had a greater effect on carbon storage. Based on the results, we propose vegetation management measures for different altitudes. This study provides a reference for decision-makers to develop and adjust ecological restoration programs in mountainous areas for the improvement of the local ecological environment.

Measuring the cooling effects of green cover on urban heat island effects using Landsat satellite imagery

ABSTRACT Rapid urbanization has resulted in urban heat island (UHI) effects becoming a global environmental issue. Urban green space (UGS) can effectively alleviate UHI effects; however, how their spatial arrangements and the cooling radiation region effects on UGS are relatively poorly understood. To advance our understanding, we analyzed Landsat imagery from 2010 to 2021 for the world’s first fine classification product of surface cover to quantitatively analyze the cooling radiation range of UGS. We used buffer analysis to measure the impact of the spatial distribution of UGS on UHI effects from normalized difference vegetation index (NDVI) and variance mean ratio (VMR). UGS has its greatest cooling effect in summer, among which evergreen coniferous forests contribute the most to cooling and grasslands contribute the least. The cooling radiation range of UGS on UHI effects is 300 m, and the cooling effect is most effective from 0 to 200 m. The more the UGS is dispersed, the more significant the cooling effect of UGS. Our study demonstrates that the cooling mechanism of UGS on UHI includes radiation range, types and spatial distribution of UGS, and the approach provides a basis for evaluating UHI effects for urban planning.

Fossil flying squirrels (Petauristinae, Sciuridae, Rodentia) from the Yumidong Cave in Wushan County, Chongqing, China

Flying squirrels are important forest environment indicators. There have been many reports on them from fossil localities of the Late Cenozoic in southwest China, but relatively few detailed studies have been carried out on them. Numerous flying squirrel fossils of the Mid-Late Pleistocene were unearthed from the Yumidong Cave in Wushan County, Chongqing Municipality, China, providing excellent materials for morphological comparison and further research on this group. Four species have been recognised from this locality, including Pteromys volans, Trogopterus xanthipes, Belomys pearsonii and Aeretes melanopterus. P. volans and A. melanopterus are Palearctic species, which adapted to the cold environment and had been completely extinct in the study area since the Holocene Megathermal period. Based on the analyses of paleozoogeography and paleoecology of these four species, it could be concluded that the Yumidong Cave area was dominated by subalpine evergreen coniferous forest or coniferous and broad-leaved mixed forest during MIS 2 and MIS 4 periods, which were colder and had more coniferous forest than now, while the vegetation landscape of MIS 3 and MIS 5 periods were similar to that of nowadays.

The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China’s evergreen coniferous forests

ABSTRACT Accurate assessments of forest biomass carbon are invaluable for managing forest resources, evaluating effects on ecological protection, and achieving goals related to climate change and sustainable development. Currently, the integration of optical and synthetic aperture radar (SAR) data has been extensively utilized in estimating forest aboveground biomass carbon (AGC), while it is limited by using single-phase remote sensing images. Time-series data, which capture the interannual dynamic growth and seasonal variations of photosynthetic phenology in forests, can sufficiently describe forest growth characteristics. However, there remains a gap in research focusing on utilizing satellite-based time-series data for AGC estimation, especially for SAR sensors. This study investigated the potential of satellite-based optical and SAR time-series data for estimating AGC. Here, we undertook nine quantitative experiments of AGC estimation from Landsat 8 and Sentinel-1 and tested several regression algorithms (including multiple linear regression (MLR), random forests (RF), artificial neural network (ANN), and extreme gradient boosting (XGBoost)) to explore the contributions of spatiotemporal features to AGC estimation. The results suggested that the XGBoost algorithm was suitable for AGC estimation with explanatory solid power and stable performance. The temporal features representing forest growth trends and periodic change characteristics (such as coefficients of continuous wavelet transform) were more valuable for AGC estimation than spatial features for both sensor types, accounting for around 40% ~50% of the variance compared to 17% ~25%. The combination of optical and SAR time-series data produced the best performance (R2 = 0.814, RMSE = 18.789 Mg C/ha, rRMSE = 26.235%), compared with when utilizing optical or SAR time-series data alone (optical: R2 of 0.657 and rRMSE of 35.317%; SAR: R2 of 0.672 and rRMSE of 34.701%). Feature importance analysis also verified that temporal features of optical vegetation indices, SWIR 1/2 bands, and SAR backscatter from VV polarization were the most critical variables for AGC estimation. Furthermore, incorporating temporal features into the modeling is illustrated to be effective in reducing saturation effects within high-biomass forests. This study demonstrated the superiority of time-series data for forest carbon estimation. While the applicability of this methodology has only been investigated in evergreen coniferous forests, it may provide a viable approach needed to make full use of increasingly better and free satellite time-series data to estimate forest AGC with high accuracy, supporting policy making of forest management and sustainable development.

Spatiotemporal variations and driving forces analysis of ecosystem water conservation in coastal areas of China

China's coastal zones, characterized by their ecological vulnerability and significant economic growth, demand effective water conservation management. Such management is pivotal not only for safeguarding water resources but also for ensuring ecological stability and promoting sustainable socio-economic development. In this study, based on the water balance model, the spatiotemporal dynamics of water conservation in the ecosystems of China's coastal areas from 2001 to 2021 were characterized by three dimensions: overall, zonal, and raster. The results show that: ① the distribution pattern of ecosystem water conservation in China's coastal areas from 2001 to 2021 is “high in the south and low in the north”, with a total amount of 117.726 billion m3/a, with the Pearl River Delta and Southwest Coastal Areas as the main high-value areas, and the overall water conservation and its capacity show a “ decreasing-rising-decreasing” pattern. The overall water conservation and its capacity show a fluctuating trend of “falling-rising-falling”. Additionally, water conservation capacity varied significantly among different vegetation types and across marine-terrestrial gradients, with the highest capacity recorded in evergreen coniferous forests and areas situated 140 km from the coastline. ②From 2001 to 2021, the water conservation capacity of China's coastal ecosystems exhibited significant temporal patterns and spatial differentiation along the marine-terrestrial gradient. The research suggests that these patterns of change are intricately linked with the stages of regional development strategies and ecological construction within these coastal zones. ③In China's coastal areas, a mere 11.63 % of the rasters showed significant changes in water conservation, dominated by highly significant and significant increases, mainly in the Southeast Coastal Area, the Pearl River Delta and Southwest Coastal Areas, and the Bohai Bay Coastal Area. This included areas such as the Yangtze River Delta silty coastal plain area, the Bohai Bay silty coastal plain area, the sandy silt coastal hilly terrace area of western Guangdong and Guinan, and other China coastal protection forest system construction projects. ④ Water conservation function within China's coastal ecosystems demonstrates a profound interconnection with both natural and socio-economic determinants. Natural elements such as elevation, slope, and precipitation positively correlate with water conservation metrics. Conversely, evapotranspiration and temperature are inversely related to conservation outcomes. In terms of social factors, water conservation has a significant negative correlation with GDP density and population density, with the highest negative correlation with the GDP2 density.

Association between Soil Physicochemical Properties and Bacterial Community Structure in Diverse Forest Ecosystems.

Although the importance of the soil bacterial community for ecosystem functions has long been recognized, there is still a limited understanding of the associations between its community composition, structure, co-occurrence patterns, and soil physicochemical properties. The objectives of the present study were to explore the association between soil physicochemical properties and the composition, diversity, co-occurrence network topological features, and assembly mechanisms of the soil bacterial community. Four typical forest types from Liziping Nature Reserve, representing evergreen coniferous forest, deciduous coniferous forest, mixed conifer-broadleaf forest, and its secondary forest, were selected for this study. The soil bacterial community was analyzed using Illumina MiSeq sequencing of 16S rRNA genes. Nonmetric multidimensional scaling was used to illustrate the clustering of different samples based on Bray-Curtis distances. The associations between soil physicochemical properties and bacterial community structure were analyzed using the Mantel test. The interactions among bacterial taxa were visualized with a co-occurrence network, and the community assembly processes were quantified using the Beta Nearest Taxon Index (Beta-NTI). The dominant bacterial phyla across all forest soils were Proteobacteria (45.17%), Acidobacteria (21.73%), Actinobacteria (8.75%), and Chloroflexi (5.06%). Chao1 estimator of richness, observed ASVs, faith-phylogenetic diversity (faith-PD) index, and community composition were distinguishing features of the examined four forest types. The first two principal components of redundancy analysis explained 41.33% of the variation in the soil bacterial community, with total soil organic carbon, soil moisture, pH, total nitrogen, carbon/nitrogen (C/N), carbon/phosphorous (C/P), and nitrogen/phosphorous (N/P) being the main soil physicochemical properties shaping soil bacterial communities. The co-occurrence network structure in the mixed forest was more complex compared to that in pure forests. The Beta-NTI indicated that the bacterial community assembly of the four examined forest types was collaboratively influenced by deterministic and stochastic ecological processes.

Restoring Subtropical Forests: Alleviating P Limitation and Introducing C Limitation Using Evergreen Broad-Leaved Tree Species

Determining which species to utilize for the artificial restoration of subtropical secondary forests has become a focal point in forestry and ecology. To compare the effects of the subtropical secondary forest artificial restoration model on soil microbial nutrient acquisition and limitation, we examined secondary forests (CKs), evergreen coniferous forests (GCPs), evergreen coniferous mixed broad-leaved forests (GCBMs), evergreen mixed broad-leaved forests (GBMs), and natural deciduous broad-leaved mixed forests (DBMs) as research subjects. Among them, GCPs, GCBMs, and GBMs were dominated by the species of the early, middle, and climax stages of subtropical forest succession, respectively. The activities and stoichiometry of β-1,4-glucosidase (BG), β-1,4-N-acetylglucosaminidase (NAG), leucine aminopeptidase (LAP), and acid phosphatase (ACP) in the topsoil were analyzed. The results showed that the forest type significantly affects the activities of BG and LAP rather than NAG or ACP. The BG activity in DBMs was the lowest, while the LAP activity in CKs was significantly higher than that in plantations. Furthermore, the nutrient limitation of microbes was quantitatively analyzed by using the vector analysis of enzyme stoichiometry. The soil microbes in the study area were co-limited by C and P, and the nutrient limitation was in the order of C > P > N. Among the forests, the enzyme stoichiometric ratios in GCPs and DBMs were closest to 1:1:1. From CKs to GBMs, the microbial C limitation was increased, while the P limitation was decreased. The C limitation in DBMs was slightly lower than that in CKs. Overall, the P limitation in evergreen plantations was less than that in CKs. The soil bulk density, C/P, and N/P significantly influenced enzyme activities and stoichiometry. These results suggest that the artificial restoration of subtropical forests using evergreen species alleviated P limitation, while using deciduous broad-leaved species offered potential for alleviating microbial C limitation. Compared with evergreen broad-leaved species, employing pioneer and mid-successional or deciduous broad-leaved species can better achieve balanced microbial nutrient requirements.

Spatial-temporal heterogeneity of spring phenology in boreal forests as estimated by satellite solar-induced chlorophyll fluorescence and vegetation index

Changes in spring phenology in boreal forests around the subarctic region have a profound impact on the global carbon and water cycles. To date, photosynthesis and greenness dynamics in evergreen coniferous forests have been found to be decoupled on a large scale, but whether the spatial heterogeneity of seasonal dynamics in boreal forests is coupled in various datasets remains unclear. Therefore, based on the solar-induced chlorophyll fluorescence (SIF) retrieved by satellite and the modelled SIF (GOME-2 SIF and GOSIF) during 2007–2018, we calculated the start of the growing season (SOS) of boreal forests (45–70°N) and compared the results with 2-band Enhanced Vegetation Index (EVI2) phenological products (MCD12Q2 C6) and flux data (Gross primary productivity, GPP) phenology. The spatial-temporal variations in the SOS were analyzed by linear regression and piecewise linear regression. The results exhibited that GOME-2 SIF showed an SOS of 11 days earlier than GOSIF, 8 days earlier than EVI2 in North America, and 5 days earlier than GOSIF, 3 days earlier than EVI2 in Eurasia. However, all relationships between SOSs of the three remote sensing datasets were significant in North America and Eurasia, and the significance was higher in Eurasia. With increasing latitude and longitude, almost all SOSs estimated from the three datasets significantly increased. Also, both in North America and Eurasia, the SOSs exhibited a trend of being advanced to being delayed from the western to the eastern parts of the continents over 2007–2018. This indicated the spatial consistency of photosynthesis and greenness dynamics in the spring phenology of boreal forests, especially in Eurasia. In this study, the validity of the spatial-temporal variation in spring phenology of boreal forests based on GOME-2 SIF retrieved from satellites and MCD12Q2 C6 was confirmed, and further exploration is needed to determine their potential advantages.

Contrasting impact of extreme soil and atmospheric dryness on the functioning of trees and forests

Recent studies indicate an increase in the frequency of extreme compound dryness days (days with both extreme soil AND air dryness) across central Europe in the future, with little information on their impact on the functioning of trees and forests. This study aims to quantify and assess the impact of extreme soil dryness, extreme air dryness, and extreme compound dryness on the functioning of trees and forests. For this, >15 years of ecosystem-level (carbon dioxide and water vapor fluxes) and 6–10 years of tree-level measurements (transpiration and growth) each from a montane mixed deciduous forest (CH-Lae) and a subalpine evergreen coniferous forest (CH-Dav) in Switzerland, is used. The results showed extreme air dryness limitation on CO2 fluxes and extreme soil dryness limitations on water vapor fluxes. Additionally, CH-Dav was mainly affected by extreme air dryness whereas CH-Lae was affected by both extreme soil dryness and extreme air dryness. The impact of extreme compound dryness on net CO2 uptake (about 75 % decrease) was more due to higher increased ecosystem respiration (40 % and 70 % increase at CH-Dav and CH-Lae, respectively) than decreased gross primary productivity (10 % and 40 % decrease at CH-Dav and CH-Lae, respectively). A significant negative impact on evapotranspiration and transpiration was only observed at CH-Lae during extreme soil and compound dryness (about 25 % decrease). Furthermore, with some differences, the tree-level impact on tree water deficit, transpiration, and growth were consistent with the ecosystem-level impact on carbon uptake and evapotranspiration. Finally, the impact of extreme dryness showed no significant relationship with tree allometry (diameter and height) but across different tree species. The projected future is likely to expose these forest areas to more extreme and frequent dryness conditions, thus compromising the functioning of trees and forests, thereby calling for management interventions to increase the adaptive capacity and resistance of these forests.

Geographical distribution and potential distribution prediction of thirteen species of Citrus L. in China.

Using DIVA-GIS software to study the spatial accumulation of Citrus species, an important economic crop in China. Draw the distribution maps of Citrus to concerning altitude and vegetation, and use DIVA-GIS' Bioclim ecological model and maximum information entropy model Maxent software to estimate the potential distribution areas of various Citrus species. The results show that the Citrus genus is located in the south of Qinling Mountains, mainly in the southwest of China and the coast of southeastern China. Sichuan and Chongqing are the most densely distributed regions of Citrus. The distribution of Citrus is closely related to the vegetation type and altitude. The vegetation types in the distribution area is evergreen broad-leaved forest, evergreen shrub, deciduous broad-leaved forest, mixed-leaf forest coverage area, deciduous coniferous forest, farmland, trees, other vegetation coverage, and evergreen coniferous forest. The current potential distribution area of Citrus is mainly in Guizhou, Hubei, Hunan, Guangdong, Guangxi, Yunnan,Sichuan, Chongqing, and other provinces and municipalities and their borders, while the potential distribution area in the future moves northward and enter Henan and southern Gansu. At the same time, climate warming changes the distribution of suitable areas of Citrus, which makes the suitable areas of C. sinensis Osbeck, C. reticulata Blanco, and other crops greatly increased. Planning the planting area will effectively improve the yield and quality in the future. Planning presents new challenges.

Effects of urban green spaces landscape pattern on carbon sink among urban ecological function areas at the appropriate scale: A case study in Xi’an

At appropriate scales, optimizing landscape pattern in urban green spaces is shown to augment carbon sink. However, existing research primarily concentrates on regional or city-wide areas, often overlooking the nuanced effects of landscape pattern on carbon sink within specialized urban ecological function areas. Focusing on the city of Xi'an and emphasizing the forest within green spaces as the principal subject, this study investigated the spatial interplay between forest landscape pattern and carbon sink across the Ecological Control Area, the Urban Ecological Construction Buffer Area, and the Tsinling Mountains Ecological Protection Area. Grounded in field surveys, this study employed GF1-WFV remote sensing imagery to ascertain the 2021 land-use data in Xi'an and further classified forests into five distinct types: deciduous broad-leaved forest, evergreen coniferous forest, evergreen broad-leaved forest, coniferous and broad-leaved mixed forest, and evergreen-deciduous broad-leaved mixed forest. Employing the Carnegie-Ames-Stanford Approach model and landscape metrics methods, the study evaluated the carbon sink and landscape pattern at multi-scale. Through semi-variance analyses, we discerned the appropriate grid scales. Finally, multiple linear stepwise regression model elucidated the significant landscape metrics, while geographically weighted regression model were employed to scrutinize spatial relationships. Our findings suggested that grid scales of 5 km for the Ecological Control Area, 3 km for the Urban Ecological Construction Buffer Area, and 6 km for the Tsinling Mountains Ecological Protection Area are most appropriate for effective management and planning. Metrics related to area, edge, and fragmentation were pivotal in enhancing carbon sink, and their influence was spatially variable. This study furnished an in-depth analysis of how landscape pattern and carbon sink interact spatially within various urban ecological function areas at appropriate grid scales, thereby providing a scientific framework for the sustainable planning and management of green spaces.

Integrating leaf functional traits improves modelled estimates of carbon and water fluxes at a subtropical evergreen conifer forest

Simulations of gross primary productivity (GPP) and evapotranspiration (ET) by terrestrial biosphere models (TBMs) are subject to significant uncertainty, in part due to the spatiotemporal variability in leaf photosynthetic capacity, which is not well represented in models. Recent studies have shown the potential for using leaf chlorophyll content (Chlleaf) to constrain GPP and ET modeling in deciduous vegetation with a strong seasonal phenology. However, little is known about how integrating physiological trait information affects modelled GPP and ET in evergreen plants. In this study, we investigated the feasibility of incorporating Chlleaf and leaf age into a TBM, as a proxy for leaf maximum carboxylation rate at 25 °C (Vcmax25) for improving GPP and ET simulations. Measurements of Chlleaf and Vcmax25 from different leaf age classes (current-year and 1-year-old) for Masson pine and Slash pine species, and leaf area index (LAI) were made in a subtropical Evergreen Needleleaf Forest (ENF) eddy covariance flux tower site. The parameterization of Vcmax25 using combined information on Chlleaf and leaf age considerably reduced the biases in simulated GPP and ET, relative to the cases of i) constant Vcmax25 and ii) Chlleaf based Vcmax25. The largest improvements in GPP and ET simulations were found in growing season (May to August), when monthly absolute errors (AEs) of modeled GPP were ∼40 % reduced, from 120.5 to 71.2 g C m−2 mon−1, with a 25 % decrease of monthly AEs of modeled ET from 52.3 to 39.1 mm mon−1. Chlleaf plays a different role in modelled photosynthesis and transpiration between sunlit and shaded leaves. The modeled water use efficiency (WUE) and light use efficiency (LUE) of the shaded leaves were both higher than those of sunlit leaves. This study presents the newly use of Chlleaf and leaf age as a proxy for improving Vcmax25 modeling at an ENF stand, which highlights the importance of using plant physiological traits and leaf age for improving ecosystem carbon-water coupling simulations.

Temporal and spatial dynamics in emission of water-soluble ions in fine particulate matter during forest fires in Southwest China

AimsThe aim of this study was to analyze changes in emission of water-soluble ions in fine particulate matter over time and in different southwest forest areas in China based on China’s Forestry Statistical Yearbook and MODIS satellite fire point data.MethodsWe took 6 dominant tree species samples in the southwestern forest region of China and simulated combustion using controllable biomass combustion devices. Based on the spatial analysis method of ArcGIS, combining satellite fire point data and official statistical yearbooks, we analyzed the spatial and temporal dynamics of emissions of water-soluble ions in PM2.5 released by forest fires in southwestern forest areas from 2004 to 2021.ResultsThe total amount of forest biomass combusted in southwest forest areas was 64.43 kt. Among the different forest types, the proportion of burnt subtropical evergreen broad-leaved forest was the largest (60.49%) followed by subtropical mixed coniferous and broad-leaved forest (22.78%) and subtropical evergreen coniferous forest (16.72%). During the study period, 61.19 t of water-soluble ions were released in PM2.5 from forest fires, and the emissions of Li+, Na+, NH4+, K+, Mg2+, Ca2+, F−, Cl−, Br−, NO3−, PO43− and SO42− were 0.48 t, 11.54 t, 2.51 t, 19.44 t, 2.12 t, 2.92 t, 1.94 t, 12.70 t, 1.12 t, 1.18 t, 1.17 t and 4.07 t, respectively. Yunnan was the province with the highest emissions of water-soluble ions in PM2.5 in the southwest forest areas, and the concentration K+ was the highest. Emission of water-soluble ions in Yunnan and Sichuan all showed a significant downward trend, while the overall decrease in Tibet, Chongqing and Guizhou was not significant. The peak emission of water-soluble ions in PM2.5 during forest fires appeared in spring and winter, which accounted for 87.66% of the total emission.DiscussionThis study reveals the spatiotemporal changes in water-soluble ion emissions from forest fires, by studying the spatiotemporal dynamics of water-soluble ions in PM2.5, we can better understand the sources, distribution, and change patterns of these ions, as well as their impact on the atmospheric environment, ecosystems, and climate change. This information is crucial for predicting and managing air pollution, as well as developing effective forest management and environmental protection policies to respond to fires; and hence concerted fire prevention efforts should be made in each province, taking into account the season with higher probability of fire occurrence to reduce the potential impact of fire-related pollutions.

Carbon budget of different forests in China estimated by an individual-based model and remote sensing.

Forests play a key role in the regional or global carbon cycle. Determining the forest carbon budget is of great significance for estimating regional carbon budgets and formulating forest management policies to cope with climate change. However, the carbon budget of Chinese different forests and their relative contributions are not completely clear so far. We evaluated the carbon budget of different forests from 1981 to 2020 in China through combining model with remote sensing observation. In addition, we also determined the relative contribution of carbon budget of each forest type to all forests in China. Eight forest types were studied: evergreen coniferous forest (ECF), deciduous coniferous forest (DCF), coniferous and broad-leaved mixed forest (CBF), deciduous broad-leaved forest (DBF), evergreen broad-leaved forest (EBF), evergreen deciduous broad-leaved mixed forest (EDBF), seasonal rain forest (SRF), and rain forest (RF). The results indicated that the Chinese forests were mainly carbon sink from 1981 to 2020, particularly the annual average carbon budget of forest from 2011 to 2020 was 0.191 PgC·a-1. Spatially, the forests' carbon budget demonstrated obvious regional differences, gradually decreasing from Southeast China to Northwest China. The relative contributions of carbon budget in different forests to all forests in China were different. During 2011-2020, the ECF forests contributed the most carbon budget (34.45%), followed by DBF forests (25.89%), EBF forests (24.82%), EDBF forests (13.10%), RF forests (2.23%), SRF forests (3.14%) and CBF forests (1.14%). However, the DCF forests were found mainly as carbon source. These results contribute to our understanding of regional carbon budget of forests.

A watershed-scale evapotranspiration model considering forest type, stand parameters, and climate factors

We developed a model to estimate evapotranspiration (ET) for temperate coniferous and broadleaf forests that considers stand structure (density and height) and meteorological data (precipitation, air temperature, solar radiation, and vapor pressure deficit) as input. We used globally available data from 65 publications, as well as 37 datasets from sources such as FLUXNET, that were collected mainly from temperate climates. The wet canopy evaporation (Ewet) submodel consisted of a nonlinear function to determine the ratio of interception loss to precipitation from stand density for coniferous forests or stand height for broadleaf forests. The parameters of the Ewet submodel were determined separately for rainfall and snowfall. The dry canopy evaporation (Edry) submodel consisted of a simplified Penman–Monteith equation, including a nonlinear function to determine the reference surface conductance from stand height. We tested the prediction ability of the model using data from three locations covered by evergreen coniferous (ET, 13–83 mm/month) and deciduous coniferous forests (24–104 mm/month) in central Japan, and a deciduous broadleaf forest (1–59 mm/month) in northern Wisconsin, USA, for a mixture of rain and snow. Our model reproduced seasonal ET patterns, with mean absolute errors of 10 ± 5, 12 ± 7, and 6 ± 9 mm/month for the evergreen coniferous, deciduous coniferous, and deciduous broadleaf forests, respectively. The model is applicable to ET estimation over watershed-scale areas with heterogeneous biotic (forest condition) and abiotic (meteorological) factors in landscapes with complex topography.

Synchronous Changes of GPP and Solar-Induced Chlorophyll Fluorescence in a Subtropical Evergreen Coniferous Forest.

Using in situ near-surface observations of solar-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) of a subtropical evergreen coniferous forest in southern China, this study analyzed the dynamics of SIF, GPP and their environmental responses, and explored the potential of SIF in characterizing the variation of GPP. The results showed that SIF and GPP have similar diurnal and seasonal variation and both reach the highest value in summer, indicating that the SIF can be applied to indicate the seasonal variation of GPP for the subtropical evergreen co-niferous. With the increase in temporal scale, the correlation between SIF and GPP becomes more linear. The diurnal variations of both SIF and GPP were characterized by photosynthetically active radiation (PAR), the seasonal variations of SIF and GPP were influenced by air temperature (Ta) and PAR. Probably due to the absent of drought stress during the study period, no significant correlation was detected between soil water content (SWC) and either SIF or GPP. With the in-crease in Ta, PAR or SWC, the linear correlation between the SIF and GPP gradually decreased, and when Ta or PAR was relatively higher, the correlation between SIF and GPP become weakly. Further research is still needed to illustrate the relationship between SIF and GPP under drought condition which occurred frequently in this region based on longer observation.

Dataset for "Incorporating leaf chlorophyll content and leaf age into a two-leaf terrestrial biosphere model for estimating carbon and water fluxes at a subtropical evergreen conifer forest"

Dataset for "Incorporating leaf chlorophyll content and leaf age into a two-leaf terrestrial biosphere model for estimating carbon and water fluxes at a subtropical evergreen conifer forest"