Mixed Broadleaf Research Articles

Assessing the carbon stock in the Alps: Considerations on three different approaches

Alpine protected areas provide a wide range of ecosystem services, with climate regulation being one of the most significant. In line with the European Biodiversity Strategy for 2030, which emphasizes the conservation and enhancement of ecosystem services, there is an urgent need to correctly manage these areas in order to maximize biodiversity conservation and the supply of ecosystem services. To achieve efficient management and decision-making processes, it is crucial to first assess the current supply of ecosystem services and to have a basic reference for monitoring activities. Various approaches can be used to evaluate the carbon storage, a widely used indicator of the climate regulation service. In this study three approaches were compared: fieldwork data collection, the Italian National Inventory and the TESSA toolkit. Discrepancies in the results emerged, in the Aosta Valley, TESSA reported 423 Gg for OC stock in mixed broadleaves, compared to 263 Gg from field data and 210 Gg from the National Inventory. Fieldwork data collection, while the most accurate, was the most time and resource intensive. The national inventory yielded values similar to fieldwork data; for example, in the Adamello spruce forest, the National Inventory reported 1838 Gg, while field data measured 1964 Gg. However, TESSA depicted qualitatively the same organic carbon stock distribution across the habitats compared to the other approaches. Based on the results, we propose different applications for these approaches, considering the advantages and disadvantages of each. Specifically, we suggest using the TESSA toolkit for preliminary and a qualitative screening of a study area to identify potential areas of interest for the carbon stock, while more precise but demanding approaches should be employed for local studies.

Disaggregation of canopy photosynthesis among tree species in a mixed broadleaf forest.

Carbon dioxide sequestration from the atmosphere is commonly assessed using the eddy covariance method. Its net flux signal can be decomposed into gross primary production and ecosystem respiration components, but these have seldom been tested against independent methods. In addition, eddy covariance lacks the ability to partition carbon sequestration among individual trees or species within mixed forests. Therefore, we compared gross primary production from eddy covariance versus an independent method based on sap flow and water-use efficiency, as measured by the tissue heat balance method and δ13C of phloem contents, respectively. The latter measurements were conducted on individual trees throughout a growing season in a mixed broadleaf forest dominated by three tree species, namely English oak, narrow-leaved ash and common hornbeam (Quercus robur L., Fraxinus angustifolia Vahl, and Carpinus betulus L., respectively). In this context, we applied an alternative ecophysiological method aimed at verifying the accuracy of a state-of-the-art eddy covariance system while also offering a solution to the partitioning problem. We observed strong agreement in the ecosystem gross primary production estimates (R2=0.56; P<0.0001), with correlation being especially high and nearly on the 1:1 line in the period before the end of July (R2=0.85; P<0.0001). After this period, the estimates of gross primary production began to diverge. Possible reasons for the divergence are discussed, focusing especially on phenology and the limitation of the isotopic data. English oak showed the highest per-tree daily photosynthetic rates among tree species, but the smaller, more abundant common hornbeam contributed most to the stand-level summation, especially early in the spring. These findings provide a rigorous test of the methods and the species-level photosynthesis offers avenues for enhancing forest management aimed at carbon sequestration.

Inclusion of bedrock vadose zone in dynamic global vegetation models is key for simulating vegetation structure and function

Abstract. Across many upland environments, soils are thin and plant roots extend into fractured and weathered bedrock where moisture and nutrients can be obtained. Root water extraction from unsaturated weathered bedrock is widespread and, in many environments, can explain gradients in vegetation community composition, transpiration, and plant sensitivity to climate. Despite increasing recognition of its importance, the “rock moisture” reservoir is rarely incorporated into vegetation and Earth system models. Here, we address this weakness in a widely used dynamic global vegetation model (DGVM; LPJ-GUESS). First, we use a water flux-tracking deficit approach to more accurately parameterize plant-accessible water storage capacity across the contiguous United States, which critically includes the water in bedrock below depths typically prescribed by soil databases. Secondly, we exploit field-based knowledge of contrasting plant-available water storage capacity in weathered bedrock across two bedrock types in the Northern California Coast Ranges as a detailed case study. For the case study in Northern California, climate and soil water storage capacity are similar at the two study areas, but the site with thick weathered bedrock and ample rock moisture supports a temperate mixed broadleaf–needleleaf evergreen forest, whereas the site with thin weathered bedrock and limited rock moisture supports an oak savanna. The distinct biomes, seasonality and magnitude of transpiration and primary productivity, and baseflow magnitudes only emerge from the DGVM when a new and simple subsurface storage structure and hydrology scheme is parameterized with storage capacities extending beyond the soil into the bedrock. Across the contiguous United States, the updated hydrology and subsurface storage improve annual evapotranspiration estimates as compared to satellite-derived products, particularly in seasonally dry regions. Specifically, the updated hydrology and subsurface storage allow for enhanced evapotranspiration through the dry season that better matches actual evapotranspiration patterns. While we made changes to both the subsurface water storage capacity and the hydrology, the most important impacts on model performance derive from changes to the subsurface water storage capacity. Our findings highlight the importance of rock moisture in explaining and predicting vegetation structure and function, particularly in seasonally dry climates. These findings motivate efforts to better incorporate the rock moisture reservoir into vegetation, climate, and landscape evolution models.

Maize Cultivation Three Hundred Years Ago Triggered Severe Rocky Desertification in Southwest China

AbstractUnderstanding the forest evolution is vital to answering the reforestation potential in karst areas. Here, we present the first‐ever pollen record in karst depression sediment, combined with comprehensive dating methods (137Cs, 210Pbex, and 14C) and historical documents, to reveal plant change history in southwest Guangxi, a severe rocky‐desertification region. We inferred three stages of “virgin forest–deforestation–sparse tree planting” over the past three centuries. Before the 1780s, the barren mountains used to be a lush mixed broadleaf forest probably. However, maize cultivation, along with explosive population growth and migration, accelerated mountain reclamation and deforestation, leading to severe rocky desertification since the 1780s, featured by the co‐occurrence of Zea pollen appearance and Dicranopteris spore surge from 0.92% to 12.18%. Since the 1930s, sparse tree planting began, as Cupressaceae/Taxodiaceae pollen abruptly increased by 32%. Our study is significant in understanding the rocky desertification causes and guiding ecological restoration in the region.

The Impact of Canopy on Nutrient Fluxes through Rainfall Partitioning in a Mixed Broadleaf and Coniferous Forest

Rainfall constitutes the primary input in the nutrient flux within forest ecosystems. The forest canopy modulates this flux by partitioning rainfall and selectively absorbing or adding nutrients. In mixed forests, variation in tree species composition regulates rainwater chemical composition, potentially leading to spatial heterogeneity in nutrient distribution and influencing nutrient cycling processes. This study examined the partitioning of rainfall into throughfall and stemflow, as well as their associated nutrient concentrations and fluxes, in a mixed broadleaf and coniferous forest on Changbai Mountain in Northeast China. We observed a rising trend in nutrient contents from rainfall to throughfall and then stemflow. The nutrient contents of stemflow varied largely with tree species due to the differences in canopy structure and bark morphological characteristics. The nutrient input contributed by throughfall and stemflow was 92.30 kg ha−1 during the observation period, and most elements underwent passive leaching through washout except for F− and Na+. We note that the nutrient fluxes in stemflow differed among tree species, with Pinus koraiensis (PK) delivering more acid group anions and Quercus mongolica (QM) providing more cations. Our research provides new insights into nutrient cycling within mixed forest canopies, sparking a transformative advancement in forest management and protection strategies through hydrochemistry-driven solutions.

Empirical Study on the Impact of Different Types of Forest Environments in Wuyishan National Park on Public Physiological and Psychological Health

Amidst the challenges of global environmental change and urbanization, the salutary effects of natural environments on public health are increasingly being recognized. This study investigates the specific effects of varied forest environments in China’s Wuyishan National Park on physiological and psychological health. Eight distinct forest environments were carefully selected, and a repeated-measures ANOVA approach was used to evaluate 41 participants over three days. Physiological assessments included Heart Rate Variability, Skin Conductance Level, and surface Electromyography, complemented by psychological evaluations using the Profile of Mood States. The key findings include the following: (1) Notable variations in physiological indicators were observed among different forest types. In valley tea gardens and broadleaf forest streamside, significant changes in heart rate indicators highlighted the influence of these settings on autonomic nervous activities. Skin Conductance Level and surface Electromyography also indicated varying emotional arousal and pleasure across the forests. The mixed broadleaf and coniferous forest valley, along with the rock-bedded streamscape, elicited emotions of low arousal but high pleasure, inducing feelings of calmness and pleasure. The valley’s tea gardens were associated with low arousal and pleasure, suggesting tranquility without positive emotional induction, while the broadleaf ridge forest induced high arousal and pleasure, reflecting an exciting and joyful environment. (2) The study found that different forest environments had a notable impact on participants’ mood states, indicating reductions in tension, anger, fatigue, and depression, along with an increase in vigor levels. In summary, forest environments offer unique psychological and physiological health benefits compared to urban settings. These findings underscore the importance of integrating forest environments into urban development and public health frameworks, and the need to further explore their impact on the health of diverse populations.

Improved Tree Segmentation Algorithm Based on Backpack-LiDAR Point Cloud

For extracting tree structural data from LiDAR point clouds, individual tree segmentation is of great significance. Most individual tree segmentation algorithms miss segmentation and misrecognition, requiring manual post-processing. This study utilized a hierarchical approach known as segmentation based on hierarchical strategy (SHS) to improve individual tree segmentation. The tree point cloud was divided into the trunk layer and the canopy layer to carry out trunk detection and canopy segmentation, respectively. The effectiveness of SHS was evaluated on three mixed broadleaf forest plots. The segmentation efficacy of SHS was evaluated on three mixed broadleaf forest plots and compared with the point cloud segmentation algorithm (PCS) and the comparative shortest-path algorithm (CSP). In the three plots, SHS correctly identified all the trunk portion, had a recall (r) of 1, 0.98, and 1, a precision (p) of 1, and an overall segmentation rate (F) of 1, 0.99, and 1. CSP and PCS are less accurate than SHS. In terms of overall plots, SHS had 10%–15% higher F-scores than PCS and CSP. SHS extracted crown diameters with R2s of 0.91, 0.93, and 0.89 and RMSEs of 0.24 m, 0.23 m, and 0.30 m, outperforming CSP and PCS. Afterwards, we evaluate the three algorithms’ findings, examine the SHS algorithm’s parameters and constraints, and discuss the future directions of this research. This work offers an enhanced SHS that improves upon earlier research, addressing missed segmentation and misrecognition issues. It improves segmentation accuracy, individual tree segmentation, and provides both theoretical and data support for the LiDAR application in forest detection.

Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors, site quality, and aridity index

The diameter distribution function (DDF) is a crucial tool for accurately predicting stand carbon storage (CS). The current key issue, however, is how to construct a high-precision DDF based on stand factors, site quality, and aridity index to predict stand CS in multi-species mixed forests with complex structures. This study used data from 70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest, Hebei Province, China, to construct the DDF based on maximum likelihood estimation and finite mixture model (FMM). Ordinary least squares (OLS), linear seemingly unrelated regression (LSUR), and back propagation neural network (BPNN) were used to investigate the influences of stand factors, site quality, and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests. The results showed that FMM accurately described the stand-level diameter distribution of the mixed P. davidiana and B. platyphylla forests; whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution. The combined variable of quadratic mean diameter (Dq), stand basal area (BA), and site quality improved the accuracy of the shape parameter models of FMM; the combined variable of Dq, BA, and De Martonne aridity index improved the accuracy of the scale parameter models. Compared to OLS and LSUR, the BPNN had higher accuracy in the re-parameterization process of FMM. OLS, LSUR, and BPNN overestimated the CS of P. davidiana but underestimated the CS of B. platyphylla in the large diameter classes (DBH ≥18 ​cm). BPNN accurately estimated stand- and species-level CS, but it was more suitable for estimating stand-level CS compared to species-level CS, thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.

Variation and drivers of soil fungal and functional groups among different forest types in warm temperate secondary forests

Soil fungal communities play a crucial role in maintaining and regulating ecosystem functions. The soil fungal structure of different plant communities in forests has been widely studied. However, the drivers of changes in soil fungal community dynamics in warm temperate secondary forests and the pathways influencing them remain to be explored. The Illumina high-throughput sequencing and FUNGuild platform were used to characterize the soil fungal diversity and community composition of six typical plant communities (JM, Juglans mandshurica; PD, Populus davidiana; QM, Quercus mongolica; MB, mixed broadleaf forest; MC, mixed conifer forest; PT, Pinus tabuliformis) in warm temperate secondary forests. The drivers of structural changes in dominant fungal taxa and functional groups were also explored. The pathways through which forest type, soil properties, altitude, climate, and plant diversity affected fungal community structure were further clarified. The results suggested that Ascomycota, Basidiomycota, and Zygomycota dominated the soil fungal communities in warm temperate secondary forests. Except for symbiotic fungi, there was no significant difference in soil fungal α-diversity among forest types. However, there were significant differences in total fungi and functional group community composition. The drivers of diversity and community composition of different soil fungal taxa in warm temperate secondary forests differed. In addition, the partial least squares path model indicated that the composition of soil fungal community in warm temperate secondary forests was directly influenced by forest type and was less dependent on soil properties. Collectively, our study highlights the importance of forest type, soil properties, and other factors (climate, altitude, plant diversity) in driving changes in soil fungal community structure.

Impact of nitrogen addition on single and mixed tree leaf litter decomposition depends on N forms in subtropical China

Increased nitrogen (N) inputs via atmospheric deposition are believed to influence leaf litter decomposition rate in forests. However, whether inorganic and organic N deposition produces a consistent influence on leaf litter decomposition is uncertain in forests, especially leaf litter mixtures. Using a fertilization experiment, we investigated the impact of inorganic (NH4Cl) and organic (urea and glycine) N on decomposition of single leaf litter (Liquidambar formosana, Pinus elliottii, Pinus massoniana, and Schima superba) and mixed broadleaf and coniferous (equal mass ratio) leaf litter using the litterbag method in a subtropical forest in southern China. Both inorganic and organic N addition increased mineral soil β-1,4-glucosidase, cellobiohydrolase, β-1,4-N-acetylglucosaminidase, L-leucine aminopeptidase, and acid phosphatase enzyme activities. During 780-day incubation, single litter decomposition was accelerated by NH4Cl addition but was not affected by either urea or glycine addition. Irrespective of added N forms, N addition shifted litter mixing effects on decomposition from synergistic effects to either additive or antagonistic effects. Accordingly, N addition generally decelerated litter mixture decomposition rates. Compared to coniferous litter, N addition-induced changes in litter mixing effects were often lower for broadleaf litter within the litter mixtures. These findings suggest that the impact of N enrichment on litter decomposition depends upon litter types and N forms, and highlight that N forms should be incorporated into assessing the consequence of atmospheric N deposition on the biogeochemical cycles in subtropical forests.

Climatically induced anomalies in tree-ring structure of Abies pindrow (Royal ex D. Don) and Taxus baccata (L.) growing in Hindu-Kush mountainous region of Pakistan

Dendroclimatic potential from Abies pindrow Royal ex D. Don. (fir) and Taxus baccata L. (yew) was investigated by developing four different types of tree-ring width chronologies (by ARSTAN program) in a mixed broadleaf forest of Kalam, KP, Pakistan. Firstly, measured tree-ring width series were cross-dated and identified the best and least (cross-matched with master plot) samples with the most applied Skeleton Plot Model (SPM) method alongside checking the quality through statistical program (COFECHA). Tree-ring attributes (age, growth rate and earlywood and latewood) were also measured with maximum age (677 years) was observed in fir plant and maximum growth rate (6.096 mm year−1) in yew plant with clear demarcations of earlywood and latewood formation. Yew trees were observed to be more sensitive species towards climatic variations (expressed population signal = 0.945, mean sensitivity = 0.321 and signal to noise ratio = 3.214) while fir trees were comparatively less affected by climatic alterations of the study site (expressed population signal = 0.954, mean sensitivity = 0.314 and signal to noise ratio = 2.453). Correlation between tree-ring series of T. baccata with the master series was found to be more significant as compared to A. pindrow after developing series plots of both the species by using package dplR in R. Potential ecological and anatomical explanations for these results were also discussed. Sensitivity of samples showed good climatic signals which are valuable for dendroclimatic growth response investigations leading to reconstruction of past climate as well as possible future climate predictions.

Improved Object-Based Mapping of Aboveground Biomass Using Geographic Stratification with GEDI Data and Multi-Sensor Imagery

Aboveground biomass (AGB) mapping using spaceborne LiDAR data and multi-sensor images is essential for efficient carbon monitoring and climate change mitigation actions in heterogeneous forests. The optimal predictors of remote sensing-based AGB vary greatly with geographic stratification, such as topography and forest type, while the way in which geographic stratification influences the contributions of predictor variables in object-based AGB mapping is insufficiently studied. To address the improvement of mapping forest AGB by geographic stratification in heterogeneous forests, satellite multisensory data from global ecosystem dynamics investigation (GEDI) and series of advanced land observing satellite (ALOS) and Sentinel were integrated. Multi-sensor predictors for the AGB modeling of different types of forests were selected using a correlation analysis of variables calculated from topographically stratified objects. Random forests models were built with GEDI-based AGB and geographically stratified predictors to acquire wall-to-wall biomass values. It was illustrated that the mapped biomass had a similar distribution and was approximate to the sampled forest AGB. Through an accuracy comparison using independent validation samples, it was determined that the geographic stratification approach improved the accuracy by 34.79% compared to the unstratified process. Stratification of forest type further increased the mapped AGB accuracy compared to that of topography. Topographical stratification greatly influenced the predictors’ contributions to AGB mapping in mixed broadleaf–conifer and broad-leaved forests, but only slightly impacted coniferous forests. Optical variables were predominant for deciduous forests, while for evergreen forests, SAR indices outweighed the other predictors. As a pioneering estimation of forest AGB with geographic stratification using satellite multisensory data, this study offers optimal predictors and an advanced method for obtaining carbon maps in heterogeneous regional landscapes.

Asynchronous destruction of marsh and forest in Neolithic age: An example from Luotuodun site, Lower Yangtze

The natural marshland and forest landscapes in the Lower Yangtze region have undergone a long history of human-induced destruction; however, little is known about the beginning and process of this destruction. In this study, we investigate the anthropogenic impact on the marsh and forest using cores collected from the vicinity of the Neolithic Luotuodun site, employing palaeoenvironmental and palaeovegetation methods. Our results indicate that the marsh was disturbed by the Neolithic community at 7500 cal yr BP and was completely destroyed at 6500 cal yr BP. Deforestation began at 6500 cal yr BP, and the original mixed broadleaf evergreen and deciduous forest was completely cleared at 4800 cal yr BP. Our findings demonstrate that the Neolithic community in the Luotuodun site prioritized the transformation of marsh in low-lying areas before deforestation. Given that most recent research has focused on pollen-based forest dynamics to study terrestrial landscape changes, the emergence and evolution of anthropogenic landscapes may be greatly underestimated.

The medium‐term effects of forest operations on a mixed broadleaf forest: Changes in soil properties and loss of nutrients

AbstractThis study took place in a mixed deciduous forest located in Northern Iran to investigate the effects of two types of silvicultural methods, specifically the single‐selection method and the group‐selection method. The study also included the observation of skid trails in terms of runoff, sediment yield, and the loss of NO3‐N and PO4‐P. The process of recovery was studied by the assessment of these characteristics after the completion of forest operations for a period of 3 years. Although there is a considerable amount of literature on the topic of soil erosion caused by forest harvesting, there is little information regarding the medium‐term effects of different sizes of gaps in the forest canopy. The attempt to gather data was done by designating hillslope plots on the soil directly affected by machinery passage on skid trails, as well as on the felling gaps in correlation with the two types of silvicultural methods. Plots were also marked on an unharvested area as a control. The results confirmed the suitability of retention forestry within the framework of sustainable forest management. A majority of the data gathered showed no difference between the single selection and the control area. What is more, the values obtained from the felling gaps correlated to the group selection resulted to be the same as the control values 2 years after harvesting. However, the disturbance caused by machinery passage on skid trails showed a significantly higher level of impact, and the recovery time needed was at least 3 years.

Distribution characteristics of radionuclides (137Cs, 239+240Pu, 237Np, and 241Am) in vertical vegetation zone in Changbai Mountain, China

Changbai Mountain has vegetation distribution characteristics, which is an important location in northeast China to study the variation characteristics of radionuclides with vegetation distribution. Soils from three vegetation zones of mixed broadleaf and coniferous forest (MBCF), coniferous forest (CF), and alpine birch forest (ABF) in Changbai Mountain were collected to measure the artificial radionuclides of 239+240Pu, 237Np, and 241Am by SF-ICP-MS, as well as 137Cs by anti-Compton HPGe. The mean values of 240Pu/239Pu and 237Np/239Pu atomic ratios, 241Am/239+240Pu and 137Cs/239+240Pu activity ratios were 0.19 ± 0.02, 0.37 ± 0.23, 0.56 ± 0.31, 22 ± 17, respectively, indicating that the global fallout was the main source of 239+240Pu, 237Np, 137Cs and 241Am in these regions. 137Cs, 239+240Pu, 237Np, and 241Am activities in the samples are 1.1–218, 0.065–9.1, (0.018–2.9) × 10-3, and 0.039–2.0 Bq/kg, respectively. The activities of 137Cs, 239+240Pu, and 237Np in the surface soil have a similar distribution characteristic, i.e. CF > MBCF > ABF, and 241Am is CF > ABF > MBCF. The simulation results of the CDE model suggest that the migration of these radionuclides is dominant in Changbai Mountain forest soil. The inventories of 137Cs, 239+240Pu, 237Np, and 241Am in the soil cores are 1263–20843, 110–1425, 0.017–0.38, and 91–493 Bq/m2, respectively. The inventories distribution of these nuclides in the vegetation zone were as follows: 137Cs and 239+240Pu had similar distribution characteristics as ABF > CF > MBCF; 237Np as CF > MBCF > ABF; 241Am as ABF > MBCF > CF. These results showed that the content, accumulated inventory, and vertical distribution characteristics of these radionuclides were influenced by the vegetation zone distribution in the Changbai Mountain.

Forest management schemes based on carbon sequestration models

This research requires the carbon sequestration model to determine the capacity of the forest and its products to sequester carbon and then to choose the most effective forest management plan in terms of sequestering CO2.we need to create a carbon sequestration model to assess how much trees and their products can absorb carbon dioxide. We compared how effective each forest management plan is at sequestering CO2 after developing a dynamic model for accounting for physical quantities of carbon sequestered and oxygen released by forests, analyzing forest resource flows and stocks and determining the willingness to pay for carbon sequestration and oxygen release by forests using a benefit transfer approach.we selected Chinese forestry as a representative to give the results of a study on selective logging to promote carbon sequestration in temperate coniferous and mixed broadleaf forests in China at reasonable time intervals.

Individual tree detection from unmanned aerial vehicle (UAV) derived point cloud data in a mixed broadleaf forest using hierarchical graph approach

ABSTRACT Studying individual trees is a common way that scientists employ to study forests and estimate forest parameters. In this study, a graph-based approach was developed for detecting individual trees in a broadleaf, complex forest region based on UAV-derived point cloud data. Horizontal cross-sections at different heights were applied to the Canopy Height Model (CHM) to extract initial candidates for graph nodes. The graph was processed in multiple steps, and individual treetop locations were detected based on graph nodes’ properties. The impact of various parameters, such as minimum area of connected components and minimum tree heights, on the performance of the developed method was investigated. The evaluation step demonstrated the potential of the proposed graph-based method for individual tree detection in a complex forest region in Mazandaran, Iran. In particular, the graph-based method obtained Precision, Recall, and F1-score values of 0.64, 0.73, and 0.68, respectively. Furthermore, the intercomparison with the well-known and most used Local Maximum (LM) suggested the applicability of the proposed method. After point cloud generation, the proposed method was implemented entirely in Python using open-source packages, which increases its applicability for other scholars and managers. The source code of the proposed algorithm can be found at https://github.com/Seyed-Ali-Ahmadi/Graph-based_ITCD.

Arboreal forests growth carbon sink and assessment of carbon storage in China’s Henan Province

Henan province is located in central China and the assessment of its arboreal forests’ carbon sink and carbon storage is important to the sustainable use of forest resources. This study used 2018 forest inventory data to estimate forest biomass and carbon storage. The results showed that: (1) By origin, the carbon storage of natural forest is lower than that of artificial forest. (2) By type, Populus L. accounted for the highest proportion, and together with the carbon storage of Mixed broadleaf and Quercus spp. accounted for 80%. The carbon density of Populus L. is the highest, and that of Cupressus funebris is the lowest. (3) By region, the carbon storage of arboreal forest in Western and southern Henan is the highest, and that in Southeast is the lowest. (4) The carbon storage of the arboreal forest has increased to 8.891 million tons which is equivalent to 32.60 million tons of carbon dioxide equivalent. This paper’s evaluation of carbon storage is a valuable reference to forest ecosystems in mitigating global climate change.

Classification of forest types in Cuigang Forest Farm based on time series data of Landsat 8

To explore the practical role of enhanced vegetation index (EVI) time series data in improving the accuracy of forest type recognition could promote the deep application of optical remote sensing data in forest resources investigation and monitoring. With Cuigang Forest Farm of Xinlin Forestry Bureau in Daxing'anling as the object, we constructed six classification schemes, using random forest algorithm with spectral feature, texture feature and EVI time series feature. The data sources were 20-view Landsat 8 OLI time series data from 2014 to 2018, 56 fixed plots data from 2017-2019, and the 2016 Class II survey data. Our aims were to realize the classification of forest types in Cuigang Forest Farm and to evaluate the accuracy of different classification schemes. The results showed the EVI values of Larix gmelinii forest, Betula platyphylla forest, coniferous-broadleaved mixed forest, coniferous mixed forest and broadleaved mixed forest were significantly different in non-growing seasons (36-111 days and 287-367 days), with the EVI value of mixed conifer forest being significantly higher, and that of mixed broadleaf forest being always lower than the other four forest types. In the early growing season (111-143 days), the EVI value of B. platyphylla forest were higher than L. gmelinii forest, which could effectively distinguish the two forests. Among the six classification schemes, spectral feature, texture feature, and EVI time series feature had the highest classification accuracy, with a Kappa of 0.82 and a classification accuracy of 86.1%. The comparison results showed that the overall accuracy of adding vegetation index time series feature was improved by 14.3% compared with that of spectral feature. The random forest algorithm with combined spectral, texture and EVI time series features could effectively classify forest stand types in Cuigang Forest Farm, with good recognition accuracy and confidence.

Direct and Indirect Effects of Long-Term Field Warming Methods on the Physical Environment and Biological Responses in a Subtropical Forest

Tree growth may be affected by rising temperature. We conducted two long-term, independent warming experiments in a subtropical forest; one experiment used translocation warming and one experiment used infra-red (IR) warming. Both warming techniques are designed to increase air and soil temperatures (Tair and Tsoil), but may also differentially affect other environmental variables, including soil volumetric water content (SVWC), air relative humidity (RH) and vapor pressure deficit (VPD). Hence, tree response ascribed to Tair and Tsoil may be dependent on the indirect effects of the warming techniques. We experimentally tested these ideas on three native tree species (Machilus breviflora, Syzygium rehderianum, and Schima superba), which occurred at all experimental sites, in subtropical China. We translocated trees from higher elevation sites to lower elevation sites in the coniferous and broadleaf mixed forest (Tair was 0.68 ± 0.05°C higher; 8 years) and mountain evergreen broadleaf forest (Tair was 0.95 ± 0.06°C and 1.63 ± 0.08°C higher; 8 years). IR warming was imposed at an experimental site in a monsoon evergreen broadleaf forest (Tair was 1.82 ± 0.03°C higher; 5 years). We found that both methods directly increased Tair and Tsoil (although to varying degrees), while translocation warming indirectly dried the soil (lower SVWC) and IR warming indirectly dried the air (lower RH and higher VPD). Machilus breviflora exposed to translocation warming exhibited lower photosynthesis due to higher Tsoil and lower SVWC, leading to declining growth. Higher Tair and Tsoil due to translocation warming increased photosynthesis and growth for S. superba. Trees exposed to IR warming exhibited reduced photosynthesis due to lower RH (M. breviflora) and to lower stomatal conductance (gs) as a function of higher Tair (S. rehderianum and S. superba). This study highlights the potential direct and indirect effects of different warming techniques on the physical environment of forest ecosystems, and subsequently their impacts on biological traits of trees. Hence, different warming techniques may provide different outcomes when assessing the impact of warming on trees in future climates.