Crown width(CW)is one of the most important tree metrics,but obtaining CW data is laborious and timeconsuming,particularly in natural forests.The Deep Learning(DL)algorithm has been proposed as an alternative to tradi...Crown width(CW)is one of the most important tree metrics,but obtaining CW data is laborious and timeconsuming,particularly in natural forests.The Deep Learning(DL)algorithm has been proposed as an alternative to traditional regression,but its performance in predicting CW in natural mixed forests is unclear.The aims of this study were to develop DL models for predicting tree CW of natural spruce-fir-broadleaf mixed forests in northeastern China,to analyse the contribution of tree size,tree species,site quality,stand structure,and competition to tree CW prediction,and to compare DL models with nonlinear mixed effects(NLME)models for their reliability.An amount of total 10,086 individual trees in 192 subplots were employed in this study.The results indicated that all deep neural network(DNN)models were free of overfitting and statistically stable within 10-fold cross-validation,and the best DNN model could explain 69%of the CW variation with no significant heteroskedasticity.In addition to diameter at breast height,stand structure,tree species,and competition showed significant effects on CW.The NLME model(R^(2)=0.63)outperformed the DNN model(R^(2)=0.54)in predicting CW when the six input variables were consistent,but the results were the opposite when the DNN model(R^(2)=0.69)included all 22 input variables.These results demonstrated the great potential of DL in tree CW prediction.展开更多
Assessing the changes in forest carbon stocks over time is critical for monitoring carbon dynamics,estimating the balance between carbon uptake and release from forests,and providing key insights into climate change m...Assessing the changes in forest carbon stocks over time is critical for monitoring carbon dynamics,estimating the balance between carbon uptake and release from forests,and providing key insights into climate change mitigation.In this study,we quantitatively characterized spatiotemporal variations in aboveground carbon density(ACD)in boreal natural forests in the Greater Khingan Mountains(GKM)region using bi-temporal discrete aerial laser scanning(ALS)data acquired in 2012 and 2016.Moreover,we evaluated the transferability of the proposed design model using forest field plot data and produced a wall-to-wall map of ACD changes for the entire study area from 2012 to 2016 at a grid size of 30 m.In addition,we investigated the relationships between carbon dynamics and the dominant tree species,age groups,and topography of undisturbed forested areas to better understand ACD variations by employing heterogeneous forest canopy structural characteristics.The results showed that the performance of the temporally transferable model(R^(2)=0.87,rRMSE=18.25%),which included stable variables,was statistically equivalent to that obtained from the model fitted directly by the 2016 field plots(R^(2)=0.87,rRMSE=17.47%).The average rate of change in carbon sequestration across the entire study region was 1.35 Mg⋅ha^(-1)⋅year^(-1) based on the changes in ALS-based ACD values over the course of four years.The relative change rates of ACD decreased as the elevation increased,with the highest and lowest ACD growth rates occurring in the middle-aged and mature forest stands,respectively.The Gini coefficient,which represents forest canopy surface structure heterogeneity,is sensitive to carbon dynamics and is a reliable predictor of the relative change rate of ACD.This study demonstrated the applicability of bi-temporal ALS for predicting forest carbon dynamics and fine-scale spatial change patterns.Our research contributed to a better understanding of the in-fluence of remote sensing-derived environmental variables on forest carbon dynamic patterns and the development of context-specific management approaches to increase forest carbon stocks.展开更多
Soil organic carbon(SOC) is an effective indicator of soil fertility and productivity, and it varies spatially and temporally in relation to other soil properties. Spatial variability of SOC in the forestlands of nort...Soil organic carbon(SOC) is an effective indicator of soil fertility and productivity, and it varies spatially and temporally in relation to other soil properties. Spatial variability of SOC in the forestlands of northeast China was characterized using geostatistics. Soil samples at the depths of 0?20 cm, 20?40 cm and 40?60 cm were collected from sixty-three temporary plots to evaluate SOC concentration and density(SOCD) and other soil properties. We analyzed correlations between SOC and soil properties. Soil organic carbon concentrations were high. The total amount of C stored in soil(0?60 cm) was 16.23 kg·m-2 with the highest SOCD of 7.98 kg?m-2 in topsoil. Soil properties in most cases differed by horizon, suggesting different processes and effects in each horizon. Soil organic carbon had positive relationships with total N, P and K as well as readily available K, but did not show a significant positive correlation with available P. Spatial factors including elevation, slope and aspect affected SOC distribution. Soil organic carbon at 0?60 cm had strong spatial autocorrelation with nugget/sill ratio of 5.7%, and moderate structured dependence was found at 0?20 cm, which indicated the existence of a highly developed spatial structure. Spatial distributionsof SOC concentration and SOCD were estimated using regression-kriging, with higher prediction accuracy than ordinary kriging. The fractal dimension of SOC indicated the preferential pattern of SOC distribution, with the greatest spatial heterogeneity and strongest spatial dependence in the northeast-southwest direction.展开更多
Background: In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future pr...Background: In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future predictions of these responses, the current forest biomass carbon storage(FCS) should first be clarified as much as possible,especially at national scales. However, few studies have introduced how to verify an FCS estimate by delimiting the reasonable ranges. This paper addresses an estimation of national FCS and its verification using two-step process to narrow the uncertainty. Our study focuses on a methodology for reducing the uncertainty resulted by converting from growing stock volume to above-and below-ground biomass(AB biomass), so as to eliminate the significant bias in national scale estimations.Methods: We recommend splitting the estimation into two parts, one part for stem and the other part for AB biomass to preclude possible significant bias. Our method estimates the stem biomass from volume and wood density(WD), and converts the AB biomass from stem biomass by using allometric relationships.Results: Based on the presented two-step process, the estimation of China’s FCS is performed as an example to explicate how to infer the ranges of national FCS. The experimental results demonstrate a national FCS estimation within the reasonable ranges(relative errors: + 4.46% and-4.44%), e.g., 5.6–6.1 PgC for China’s forest ecosystem at the beginning of the 2010 s. These ranges are less than 0.52 PgC for confirming each FCS estimate of different periods during the last 40 years. In addition, our results suggest the upper-limits by specifying a highly impractical value of WD(0.7 t·m-3) on the national scale. As a control reference, this value decides what estimate is impossible to achieve for the FCS estimates.Conclusions: Presented methodological analysis highlights the possibility to determine a range that the true value could be located in. The two-step process will help to verify national FCS and also to reduce uncertainty in related studies. While the true value of national FCS is immeasurable, our work should motivate future studies that explore new estimations to approach the true value by narrowing the uncertainty in FCS estimations on national and global scales.展开更多
Environmental heterogeneity is a constant presence in the natural world that significantly affects plant behavior at a variety of levels of complexity. In order to estimate the spatial pattern of fine root biomass in ...Environmental heterogeneity is a constant presence in the natural world that significantly affects plant behavior at a variety of levels of complexity. In order to estimate the spatial pattern of fine root biomass in the Three Gorges Reservoir Area, the spatial heterogeneity of fine root biomass in the upper layer of soils (0-10 cm) in three Masson pine (Pinus massoniana) stands in the Three Gorges Reservoir Area, China, was studied in 30 m × 30 m plots with geostatistical analysis. The results indicate that 1) both the live and dead fine root biomass of stand 2 were less than those of other stands, 2) the spatial variation of fine roots in the three stands was caused together by structural and random factors with moderate spatial dependence and 3) the magnitude of spatial heterogeneity of live fine roots ranked as: stand 3 > stand 1 > stand 2, while that of dead fine roots was similar in the three stands. These findings suggested that the range of spatial autocorrelation for fine root biomass varied considerably in the Three Gorges Reservoir Area, while soil properties, such as soil bulk density, organic matter and total nitrogen, may exhibit great effect on the spatial distribution of fine roots. Finally, we express our hope to be able to carry out further research on the quantitative relationship between the spatial heterogeneous patterns of plant and soil properties.展开更多
Background:Process-based models are widely used to simulate forest productivity,but complex parameterization and calibration challenge the application and development of these models.Sensitivity analysis of numerous p...Background:Process-based models are widely used to simulate forest productivity,but complex parameterization and calibration challenge the application and development of these models.Sensitivity analysis of numerous parameters is an essential step in model calibration and carbon flux simulation.However,parameters are not dependent on each other,and the results of sensitivity analysis usually vary due to different forest types and regions.Hence,global and representative sensitivity analysis would provide reliable information for simple calibration.Methods:To determine the contributions of input parameters to gross primary productivity(GPP)and net primary productivity(NPP),regression analysis and extended Fourier amplitude sensitivity testing(EFAST)were conducted for Biome-BGCMuSo to calculate the sensitivity index of the parameters at four observation sites under climate gradient from ChinaFLUX.Results:Generally,GPP and NPP were highly sensitive to C:Nleaf(C:N of leaves),Wint(canopy water interception coefficient),k(canopy light extinction coefficient),FLNR(fraction of leaf N in Rubisco),MRpern(coefficient of linear relationship between tissue N and maintenance respiration),VPDf(vapor pressure deficit complete conductance reduction),and SLA1(canopy average specific leaf area in phenological phase 1)at all observation sites.Various sensitive parameters occurred at four observation sites within different climate zones.GPP and NPP were particularly sensitive to FLNR,SLA1 and Wint,and C:Nleaf in temperate,alpine and subtropical zones,respectively.Conclusions:The results indicated that sensitivity parameters of China's forest ecosystems change with climate gradient.We found that parameter calibration should be performed according to plant functional type(PFT),and more attention needs to be paid to the differences in climate and environment.These findings contribute to determining the target parameters in field experiments and model calibration.展开更多
Remote sensing provides key inputs to a wide range of models and methods developed for quantifying forest carbon.In particular,carbon inventory methods recommended by IPCC require biomass data and a suite of forest di...Remote sensing provides key inputs to a wide range of models and methods developed for quantifying forest carbon.In particular,carbon inventory methods recommended by IPCC require biomass data and a suite of forest disturbance products.Significant progress has been made in deriving these products by leveraging publicly available remote sensing assets,including observations acquired by the legendary Landsat mission and new systems launched within the past decade,including Sentinel-2,Sentinel-1,GEDI,and ICESAT-2.With the L-band NISAR and P-band BIOMASS missions to be launched in 2023,the Earth’s land surfaces will be imaged by optical and multi-band(including C-,L-,and P-bands)radar systems that can provide global,sub-weekly observations at sub-hectare spatial resolutions for public use.Fine scale products derived from these observations will be crucial for developing monitoring,reporting,and verification(MRV)capabilities needed to support carbon trade,REDD+,and other market-driven tools aimed at achieving climate mitigation goals through forest management at all levels.Following a brief discussion of the roles of forests in the global carbon cycle and the wide range of models and methods available for evaluating forest carbon dynamics,this paper provides an overview of recent progress and forthcoming opportunities in using remote sensing to map forest structure and biomass,detect forest disturbances,determine disturbance attribution,quantify disturbance intensity,and estimate harvested timber volume.Advances in these research areas require large quantities of well—distributed reference data to calibrate remote sensing algorithms and to validate the derived products.In addition,two of the forest carbon pools-dead organic matter and soil carbon—are difficult to monitor using modern remote sensing capabilities.Carefully designed inventory programs are needed to collect the required reference data as well as the data needed to estimate dead organic matter and soil carbon.展开更多
Background:With the loss of species worldwide due to anthropogenic factors,especially in forested ecosystems,it has become more urgent than ever to understand the biodiversity-ecosystem functioning relationship(BEFR)....Background:With the loss of species worldwide due to anthropogenic factors,especially in forested ecosystems,it has become more urgent than ever to understand the biodiversity-ecosystem functioning relationship(BEFR).BEFR research in forested ecosystems is very limited and thus studies that incorporate greater geographic coverage and structural complexity are needed.Methods:We compiled ground-measured data from approx.one half mil ion forest inventory sample plots across the contiguous United States,Alaska,and northeastern China to map tree species richness,forest stocking,and productivity at a continental scale.Based on these data,we investigated the relationship between forest productivity and tree species diversity,using a multiple regression analysis and a non-parametric approach to account for spatial autocorrelation.Results:In general,forests in the eastern United States consisted of more tree species than any other regions in the country.The highest forest stocking values over the entire study area were concentrated in the western United States and Central Appalachia.Overall,96.4 % of sample plots(477,281)showed a significant positive effect of species richness on site productivity,and only 3.6 %(17,349)had an insignificant or negative effect.Conclusions:The large number of ground-measured plots,as well as the magnitude of geographic scale,rendered overwhelming evidence in support of a positive BEFR.This empirical evidence provides insights to forest management and biological conservation across different types of forested ecosystems.Forest timber productivity may be impaired by the loss of species in forests,and biological conservation,due to its potential benefits on maintaining species richness and productivity,can have profound impacts on the functioning and services of forested ecosystems.展开更多
Climate warming is expected to influence forest growth,composition and distribution.However,accurately estimating and predicting forest biomass,potential productivity or forest growth is still a challenge for forest m...Climate warming is expected to influence forest growth,composition and distribution.However,accurately estimating and predicting forest biomass,potential productivity or forest growth is still a challenge for forest managers dealing with land-use at the stand to regional levels.In the present study,we predicted the potential productivity(PP)of forest under current and future climate scenarios(RCP2.6,RCP4.5,RCP6.0 and RCP8.5)in Jilin province,northeastern China by using Paterson’s Climate Vegetation and Productivity(CVP)index model.The PP was validated by comparing it with the mean and maximum net primary production calculated from light energy utilization(GLM_PEM).Our results indicated that using the CVP index model is partially valid for predicting the potential forest productivity in northeastern China.PP exhibited obvious spatial heterogeneity varying from 4.6 to 8.9 m3 ha-1 year-1 with an increasing tendency from northwest to southeast driven by the precipitation across the region.The number of vegetation-active months,precipitation and insolation coefficient were identified as the primary factors affecting PP,but no significant relationship was found for warmest temperature or temperature fluctuation.Under future climate scenarios,PP across the Jilin Province is expected to increase from 1.38%(RCP2.6 in 2050)to 15.30%(RCP8.5 in 2070),especially in the eastern Songnen Plain(SE)for the RCP8.5 scenarios.展开更多
Background: The accurate estimation of soil nutrient content is particularly important in view of its impact on plant growth and forest regeneration. In order to investigate soil nutrient content and quality for the n...Background: The accurate estimation of soil nutrient content is particularly important in view of its impact on plant growth and forest regeneration. In order to investigate soil nutrient content and quality for the natural regeneration of Dacrydium pectinatum communities in China, designing advanced and accurate estimation methods is necessary.Methods: This study uses machine learning techniques created a series of comprehensive and novel models from which to evaluate soil nutrient content. Soil nutrient evaluation methods were built by using six support vector machines and four artificial neural networks.Results: The generalized regression neural network model was the best artificial neural network evaluation model with the smallest root mean square error(5.1), mean error(-0.85), and mean square prediction error(29). The accuracy rate of the combined k-nearest neighbors(k-NN) local support vector machines model(i.e. k-nearest neighbors-support vector machine(KNNSVM)) for soil nutrient evaluation was high, comparing to the other five partial support vector machines models investigated. The area under curve value of generalized regression neural network(0.6572) was the highest, and the cross-validation result showed that the generalized regression neural network reached 92.5%.Conclusions: Both the KNNSVM and generalized regression neural network models can be effectively used to evaluate soil nutrient content and quality grades in conjunction with appropriate model variables. Developing a new feasible evaluation method to assess soil nutrient quality for Dacrydium pectinatum, results from this study can be used as a reference for the adaptive management of rare and endangered tree species. This study, however, found some uncertainties in data acquisition and model simulations, which will be investigated in upcoming studies.展开更多
Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,f...Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,from Landsat-8(L8)and Sentinel-2(S2),have been proven useful in mapping general forest types,yet we do not know quantitatively how their spectral features(e.g.,red-edge)and temporal frequency of data acquisitions(e.g.,16-day vs.5-day)contribute to plantation forest mapping to the species level.Moreover,it is unclear to what extent the fusion of L8 and S2 will result in improvements in tree species mapping of northern plantation forests in China.Methods:We designed three sets of classification experiments(i.e.,single-date,multi-date,and spectral-temporal)to evaluate the performances of L8 and S2 data for mapping keystone timber tree species in northern China.We first used seven pairs of L8 and S2 images to evaluate the performances of L8 and S2 key spectral features for separating these tree species across key growing stages.Then we extracted the spectral-temporal features from all available images of different temporal frequency of data acquisition(i.e.,L8 time series,S2 time series,and fusion of L8 and S2)to assess the contribution of image temporal frequency on the accuracy of tree species mapping in the study area.Results:1)S2 outperformed L8 images in all classification experiments,with or without the red edge bands(0.4%–3.4%and 0.2%–4.4%higher for overall accuracy and macro-F1,respectively);2)NDTI(the ratio of SWIR1 minus SWIR2 to SWIR1 plus SWIR2)and Tasseled Cap coefficients were most important features in all the classifications,and for time-series experiments,the spectral-temporal features of red band-related vegetation indices were most useful;3)increasing the temporal frequency of data acquisition can improve overall accuracy of tree species mapping for up to 3.2%(from 90.1%using single-date imagery to 93.3%using S2 time-series),yet similar overall accuracies were achieved using S2 time-series(93.3%)and the fusion of S2 and L8(93.2%).Conclusions:This study quantifies the contributions of L8 and S2 spectral and temporal features in mapping keystone tree species of northern plantation forests in China and suggests that for mapping tree species in China's northern plantation forests,the effects of increasing the temporal frequency of data acquisition could saturate quickly after using only two images from key phenological stages.展开更多
Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals.However,the key environmental factors affecting volume growth diff...Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals.However,the key environmental factors affecting volume growth differ across various scales and plant functional types.This study was,therefore,conducted to estimate the volume growth of Larix and Quercus forests based on national-scale forestry inventory data in China and its influencing factors using random forest algorithms.The results showed that the model performances of volume growth in natural forests(R^(2)=0.65 for Larix and 0.66 for Quercus,respectively)were better than those in planted forests(R^(2)=0.44 for Larix and 0.40 for Quercus,respectively).In both natural and planted forests,the stand age showed a strong relative importance for volume growth(8.6%–66.2%),while the edaphic and climatic variables had a limited relative importance(<6.0%).The relationship between stand age and volume growth was unimodal in natural forests and linear increase in planted Quercus forests.And the specific locations(i.e.,altitude and aspect)of sampling plots exhibited high relative importance for volume growth in planted forests(4.1%–18.2%).Altitude positively affected volume growth in planted Larix forests but controlled volume growth negatively in planted Quercus forests.Similarly,the effects of other environmental factors on volume growth also differed in both stand origins(planted versus natural)and plant functional types(Larix versus Quercus).These results highlighted that the stand age was the most important predictor for volume growth and there were diverse effects of environmental factors on volume growth among stand origins and plant functional types.Our findings will provide a good framework for site-specific recommendations regarding the management practices necessary to maintain the volume growth in China's forest ecosystems.展开更多
Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification,it has not been widely used over large areas.A comprehensive p...Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification,it has not been widely used over large areas.A comprehensive process based on multi-flightline airborne hyperspectral data is lacking over large,forested areas influenced by both the effects of bidirectional reflectance distribution function(BRDF)and cloud shadow contamination.In this study,hyperspectral data were collected over the Mengjiagang Forest Farm in Northeast China in the summer of 2017 using the Chinese Academy of Forestry's LiDAR,CCD,and hyperspectral systems(CAF-LiCHy).After BRDF correction and cloud shadow detection processing,a tree species classification workflow was developed for sunlit and cloud-shaded forest areas with input features of minimum noise fraction reduced bands,spectral vegetation indices,and texture information.Results indicate that BRDF-corrected sunlit hyperspectral data can provide a stable and high classification accuracy based on representative training data.Cloud-shaded pixels also have good spectral separability for species classification.The red-edge spectral information and ratio-based spectral indices with high importance scores are recommended as input features for species classification under varying light conditions.According to the classification accuracies through field survey data at multiple spatial scales,it was found that species classification within an extensive forest area using airborne hyperspectral data under various illuminations can be successfully carried out using the effective radiometric consistency process and feature selection strategy.展开更多
Forests,trees,and agroforestry(FTA)are ecosystem hotspots.They exemplify the contributions of biodiversity to sustainable and resilient landscapes,green circular economy and to sustainable agriculture and food systems...Forests,trees,and agroforestry(FTA)are ecosystem hotspots.They exemplify the contributions of biodiversity to sustainable and resilient landscapes,green circular economy and to sustainable agriculture and food systems for healthy diets.However,most research on these topics have been performed separately and lack comparison.The International FTA-Kunming Conference'Forests,trees and agroforestry for diverse sustainable landscapes'22nd–24th June 2021,focused on these contributions,brought together scientists NGOs,and policy makers to further the understanding of tree diversity;provided a communication platform for scientists to share their research results;evaluated the role of tree diversity in agroecology and circular agriculture;assessed benefits of landscape restoration;and explored applied research in mountain ecosystems and food security.The goals were to gather evidence that ground the design of solutions that can contribute to the implementation of the post 2020 Global Biodiversity Framework and towards the UN Food Systems Summit,and the overall implementation of the SDGs.This paper summarizes the outcomes of the international FTA Conference in Kunming 2021 and points out the highlights of research involved in six major themes.展开更多
The role of tropical forests in the global carbon budget remains controversial,as carbon emissions from deforestation are highly uncertain.This high uncertainty arises from the use of either fixed forest carbon stock ...The role of tropical forests in the global carbon budget remains controversial,as carbon emissions from deforestation are highly uncertain.This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation.New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar.We found that lost forests are special cases,and their spatial structures can be directly measured by combining archived data acquired before and after deforestation by space missions principally aimed at measuring topography.Thus,using biomass mapping,we obtained new estimates of carbon loss from deforestation ahead of forthcoming space missions.Here,using a high-resolution map of forest loss and the synergy of radar and lidar to estimate the aboveground biomass density of forests,we found that deforestation in the 2000s in Latin America,one of the severely deforested regions,mainly occurred in forests with a significantly lower carbon stock density than typical mature forests.展开更多
Estimating individual tree biomass is critical to forest carbon accounting and ecosystem service modeling.In this study,we developed one-(tree diameter only) and two-variable(tree diameter and height) biomass equation...Estimating individual tree biomass is critical to forest carbon accounting and ecosystem service modeling.In this study,we developed one-(tree diameter only) and two-variable(tree diameter and height) biomass equations,biomass conversion factor(BCF) models,and an integrated simultaneous equation system(ISES) to estimate the aboveground biomass for five conifer species in China,i.e.,Cunninghamia lanceolata(Lamb.) Hook.,Pinus massoniana Lamb.,P.yunnanensis Faranch,P.tabulaeformis Carr.and P.elliottii Engelm.,based on the field measurement data of aboveground biomass and stem volumes from 1055 destructive sample trees across the country.We found that all three methods,including the one-and two-variable equations,could adequately estimate aboveground biomass with a mean prediction error less than 5%,except for Pinus yunnanensis which yielded an error of about 6%.The BCF method was slightly poorer than the biomass equation and the ISES methods.The average coefficients of determination(R^2) were 0.944,0.938 and 0.943 and the mean prediction errors were 4.26,4.49 and 4.29% for the biomass equation method,the BCF method and the ISES method,respectively.The ISES method was the best approach for estimating aboveground biomass,which not only had high accuracy but also could estimate stocking volumes simultaneously that was compatible with aboveground biomass.In addition,we found that it is possible to develop a species-invariant one-variable allometric model for estimating aboveground biomass of all the five coniferous species.The model had an exponent parameter of 7/3 and the intercept parameter a_0 could be estimated indirectly from stem basic density(a_0= 0.294 q).展开更多
Calorific value of plants is an important parameter for evaluating and indexing material cycles and energy conversion in forest ecosystems. Based on mensuration data of 150 sample sets, we analyzed the calorific value...Calorific value of plants is an important parameter for evaluating and indexing material cycles and energy conversion in forest ecosystems. Based on mensuration data of 150 sample sets, we analyzed the calorific value(CV) and ash content(AC) of different parts of Masson pine(Pinus massoniana) trees in southern China using hypothesis testing and regression analysis. CV and AC of different tree parts were almost significantly different(P<0.05). In descending order, ash-free calorific value(AFCV) ranked as foliage > branch > stem bark > root > stem wood, and AC ranked as foliage > stem bark > root > branch > stem wood. CV and AC of stem wood from the top, middle and lower sections of trees differed significantly. CV increased from the top to the lower sections of the trunk while AC decreased. Mean gross calorific value(GCV) and AFCV of aboveground parts were significantly higher than those of belowground parts(roots). The mean GCV, AFCV and AC of a whole tree of Masson pine were 21.54 kJ/g, 21.74 kJ/g and 0.90%, respectively. CV and AC of different tree parts were, to some extent, correlated with tree diameter, height and origin.展开更多
Background: Soil temperature and moisture are sensitive indicators in soil organic matter decomposition because they control global carbon and water cycles and their potential feedback to climatic variations. Although...Background: Soil temperature and moisture are sensitive indicators in soil organic matter decomposition because they control global carbon and water cycles and their potential feedback to climatic variations. Although the Biome-Biogeochemical Cycles (Biome-BGC) model is broadly applied in simulating forest carbon and water fluxes, its single-layer soil module cannot represent vertical variations in soil moisture. This study introduces the Biome-BGC MuSo model, which is composed of a multi-layer soil module and new modules pertaining to phenology and management for simulations of carbon and water fluxes. Although this model considers soil processes among active layers, estimates of soil-related variables might be biased, leading to inaccurate estimates of carbon and water fluxes. Methods: To improve the estimations of soil-related processes in Biome-BGC MuSo, this study assimilates ground-measured multi-layer daily soil temperature and moisture at the Changbai Mountains forest flux site by using the Ensemble Kalman Filter algorithm. The modeled estimates of water and carbon fluxes were evaluated with measurements using determination coefficient (R2) and root mean square error (RMSE). The differences in the RMSEs from Biome-BGC MuSo and the assimilated Biome-BGC MuSo were calculated (ΔRMSE), and the relationships between ΔRMSE and the climatic and biophysical factors were analyzed. Results: Compared with the original Biome-BGC model, Biome-BGC MuSo improved the simulations of ecosystem respiration (ER), net ecosystem exchange (NEE) and evapotranspiration (ET). Data assimilation of the soil-related variables into Biome-BGC MuSo in real time improved the accuracies of the simulated carbon and water fluxes (ET: R^2=0.81, RMSE=0.70 mm·d^-1;ER: R^2=0.85, RMSE=1.97 gC·m^-2·d^-1;NEE: R^2=0.70, RMSE=1.16 gC·m^-2·d^-1). Conclusions: This study proved that seasonal simulation of carbon and water fluxes are more accurate when using Biome-BGC MuSo with a multi-layer soil module than using Biome-BGC with a single-layer soil module. Moreover, assimilating the observed soil temperature and moisture data into Biome-BGC MuSo improved the modeled estimates of water and carbon fluxes via calibrated soil-related simulations. The assimilation strategy is applicable to various climatic and biophysical conditions, particularly densely forested areas, and for local or regional simulation.展开更多
Knowledge on the potential suitability of tree species to the site is very important for forest management planning.Natural forest distribution provides a good reference for afforestation and forest restoration.In thi...Knowledge on the potential suitability of tree species to the site is very important for forest management planning.Natural forest distribution provides a good reference for afforestation and forest restoration.In this study,we developed species distribution model(SDM)for 16 major tree species with 2,825 permanent sample plots with natural origin from Chinese National Forest Inventory data collected in Jilin Province using the Maxent model.Three types of environmental factors including bioclimate,soil and topography with a total of 33 variables were tested as the input.The values of area under the curve(AUC,one of the receiver operating characteristics of the Maxent model)in the training and test datasets were between 0.784 and 0.968,indicating that the prediction results were quite reliable.The environmental factors affecting the distribution of species were ranked in terms of their importance to the species distribution.Generally,the climatic factors had the greatest contribution,which included mean diurnal range,annual mean temperature,temperature annual range,and iosthermality.But the main environmental factors varied with tree species.Distribution suitability maps under current(1950-2000)and future climate scenarios(CCSM4-RCP 2.6 and RCP 6.0 during 2050)were produced for 16 major tree species in Jilin Province using the model developed.The predicted current and future ranges of habitat suitability of the 16 tree species are likely to be positively and negatively affected by future climate.Seven tree species were found to benefit from future climate including B etula costata,Fraxinus mandshurica,Juglans mandshurica,Phellodendron amurense,Populus ussuriensis,Quercus mongolica and Ulmus pumila;five tree species will experience decline in their suitable habitat including B.platyphylla,Tilia mongolica,Picea asperata,Pinus sylvestris,Pinus koraiensis;and four(Salix koreensis,Abies fabri,Pinus densiflora and Larix olgensis)showed the inconsistency under RCP 2.6 and RCP 6.0 scenarios.The maps of the habitat suitability can be used as a basis for afforestation and forest restoration in northeastern China.The SDMs could be a potential tool for forest management planning.展开更多
基金funded by National Natural Science Foundation of China(Grant No.31870623)National Key R&D Program of China(Grant No.2022YFD2200501).
文摘Crown width(CW)is one of the most important tree metrics,but obtaining CW data is laborious and timeconsuming,particularly in natural forests.The Deep Learning(DL)algorithm has been proposed as an alternative to traditional regression,but its performance in predicting CW in natural mixed forests is unclear.The aims of this study were to develop DL models for predicting tree CW of natural spruce-fir-broadleaf mixed forests in northeastern China,to analyse the contribution of tree size,tree species,site quality,stand structure,and competition to tree CW prediction,and to compare DL models with nonlinear mixed effects(NLME)models for their reliability.An amount of total 10,086 individual trees in 192 subplots were employed in this study.The results indicated that all deep neural network(DNN)models were free of overfitting and statistically stable within 10-fold cross-validation,and the best DNN model could explain 69%of the CW variation with no significant heteroskedasticity.In addition to diameter at breast height,stand structure,tree species,and competition showed significant effects on CW.The NLME model(R^(2)=0.63)outperformed the DNN model(R^(2)=0.54)in predicting CW when the six input variables were consistent,but the results were the opposite when the DNN model(R^(2)=0.69)included all 22 input variables.These results demonstrated the great potential of DL in tree CW prediction.
基金We acknowledge grants from the National Key R&D Program of China(Project Number:2020YFE0200800)National Science and Technology Major Project of China's High Resolution Earth Observation System(Project Number:21-Y20B01-9001-19/22-1).
文摘Assessing the changes in forest carbon stocks over time is critical for monitoring carbon dynamics,estimating the balance between carbon uptake and release from forests,and providing key insights into climate change mitigation.In this study,we quantitatively characterized spatiotemporal variations in aboveground carbon density(ACD)in boreal natural forests in the Greater Khingan Mountains(GKM)region using bi-temporal discrete aerial laser scanning(ALS)data acquired in 2012 and 2016.Moreover,we evaluated the transferability of the proposed design model using forest field plot data and produced a wall-to-wall map of ACD changes for the entire study area from 2012 to 2016 at a grid size of 30 m.In addition,we investigated the relationships between carbon dynamics and the dominant tree species,age groups,and topography of undisturbed forested areas to better understand ACD variations by employing heterogeneous forest canopy structural characteristics.The results showed that the performance of the temporally transferable model(R^(2)=0.87,rRMSE=18.25%),which included stable variables,was statistically equivalent to that obtained from the model fitted directly by the 2016 field plots(R^(2)=0.87,rRMSE=17.47%).The average rate of change in carbon sequestration across the entire study region was 1.35 Mg⋅ha^(-1)⋅year^(-1) based on the changes in ALS-based ACD values over the course of four years.The relative change rates of ACD decreased as the elevation increased,with the highest and lowest ACD growth rates occurring in the middle-aged and mature forest stands,respectively.The Gini coefficient,which represents forest canopy surface structure heterogeneity,is sensitive to carbon dynamics and is a reliable predictor of the relative change rate of ACD.This study demonstrated the applicability of bi-temporal ALS for predicting forest carbon dynamics and fine-scale spatial change patterns.Our research contributed to a better understanding of the in-fluence of remote sensing-derived environmental variables on forest carbon dynamic patterns and the development of context-specific management approaches to increase forest carbon stocks.
基金supported by Natural ScienceFoundation of China(No.31270697)the Fundamental Research Fundsfor the Central Universities(TD2011-2)+1 种基金State Forestry Administrative public service sector project"Key management techniques for the health of typical forest types in China"(20100400201)National‘973’project"Soil carbon stock and its temporal and spatial distribution pattern in natural forests"(2011CB403201)
文摘Soil organic carbon(SOC) is an effective indicator of soil fertility and productivity, and it varies spatially and temporally in relation to other soil properties. Spatial variability of SOC in the forestlands of northeast China was characterized using geostatistics. Soil samples at the depths of 0?20 cm, 20?40 cm and 40?60 cm were collected from sixty-three temporary plots to evaluate SOC concentration and density(SOCD) and other soil properties. We analyzed correlations between SOC and soil properties. Soil organic carbon concentrations were high. The total amount of C stored in soil(0?60 cm) was 16.23 kg·m-2 with the highest SOCD of 7.98 kg?m-2 in topsoil. Soil properties in most cases differed by horizon, suggesting different processes and effects in each horizon. Soil organic carbon had positive relationships with total N, P and K as well as readily available K, but did not show a significant positive correlation with available P. Spatial factors including elevation, slope and aspect affected SOC distribution. Soil organic carbon at 0?60 cm had strong spatial autocorrelation with nugget/sill ratio of 5.7%, and moderate structured dependence was found at 0?20 cm, which indicated the existence of a highly developed spatial structure. Spatial distributionsof SOC concentration and SOCD were estimated using regression-kriging, with higher prediction accuracy than ordinary kriging. The fractal dimension of SOC indicated the preferential pattern of SOC distribution, with the greatest spatial heterogeneity and strongest spatial dependence in the northeast-southwest direction.
基金supported by the National Key Research and Development Program of China(Grant Nos:2017YFA0604401,2016YFC0501101)the Open Fund of State Key Laboratory of Remote Sensing Science(No.OFSLRSS201704)+1 种基金the Meteorology Scientific Research Fund in the Public Welfare of China(No.GYHY201506010)partly supported by the National Basic Research Program in China(No.2013CB956602)
文摘Background: In recent decades the future of global forests has been a matter of increasing concern, particularly in relation to the threat of forest ecosystem responses under potential climate change. To the future predictions of these responses, the current forest biomass carbon storage(FCS) should first be clarified as much as possible,especially at national scales. However, few studies have introduced how to verify an FCS estimate by delimiting the reasonable ranges. This paper addresses an estimation of national FCS and its verification using two-step process to narrow the uncertainty. Our study focuses on a methodology for reducing the uncertainty resulted by converting from growing stock volume to above-and below-ground biomass(AB biomass), so as to eliminate the significant bias in national scale estimations.Methods: We recommend splitting the estimation into two parts, one part for stem and the other part for AB biomass to preclude possible significant bias. Our method estimates the stem biomass from volume and wood density(WD), and converts the AB biomass from stem biomass by using allometric relationships.Results: Based on the presented two-step process, the estimation of China’s FCS is performed as an example to explicate how to infer the ranges of national FCS. The experimental results demonstrate a national FCS estimation within the reasonable ranges(relative errors: + 4.46% and-4.44%), e.g., 5.6–6.1 PgC for China’s forest ecosystem at the beginning of the 2010 s. These ranges are less than 0.52 PgC for confirming each FCS estimate of different periods during the last 40 years. In addition, our results suggest the upper-limits by specifying a highly impractical value of WD(0.7 t·m-3) on the national scale. As a control reference, this value decides what estimate is impossible to achieve for the FCS estimates.Conclusions: Presented methodological analysis highlights the possibility to determine a range that the true value could be located in. The two-step process will help to verify national FCS and also to reduce uncertainty in related studies. While the true value of national FCS is immeasurable, our work should motivate future studies that explore new estimations to approach the true value by narrowing the uncertainty in FCS estimations on national and global scales.
基金supported by the Special Fund of National Forestry Public Welfare of the State Forestry Administration (No.201104008)a Special Fund of the Research Institute of Forest Ecology, Environment and Protection of the Chinese Academy of Forestry, China (No. CAFRIFEEP201006)
文摘Environmental heterogeneity is a constant presence in the natural world that significantly affects plant behavior at a variety of levels of complexity. In order to estimate the spatial pattern of fine root biomass in the Three Gorges Reservoir Area, the spatial heterogeneity of fine root biomass in the upper layer of soils (0-10 cm) in three Masson pine (Pinus massoniana) stands in the Three Gorges Reservoir Area, China, was studied in 30 m × 30 m plots with geostatistical analysis. The results indicate that 1) both the live and dead fine root biomass of stand 2 were less than those of other stands, 2) the spatial variation of fine roots in the three stands was caused together by structural and random factors with moderate spatial dependence and 3) the magnitude of spatial heterogeneity of live fine roots ranked as: stand 3 > stand 1 > stand 2, while that of dead fine roots was similar in the three stands. These findings suggested that the range of spatial autocorrelation for fine root biomass varied considerably in the Three Gorges Reservoir Area, while soil properties, such as soil bulk density, organic matter and total nitrogen, may exhibit great effect on the spatial distribution of fine roots. Finally, we express our hope to be able to carry out further research on the quantitative relationship between the spatial heterogeneous patterns of plant and soil properties.
基金This study was funded by the National Natural Science Foundation of China(grant number 41871279 and 41901364).
文摘Background:Process-based models are widely used to simulate forest productivity,but complex parameterization and calibration challenge the application and development of these models.Sensitivity analysis of numerous parameters is an essential step in model calibration and carbon flux simulation.However,parameters are not dependent on each other,and the results of sensitivity analysis usually vary due to different forest types and regions.Hence,global and representative sensitivity analysis would provide reliable information for simple calibration.Methods:To determine the contributions of input parameters to gross primary productivity(GPP)and net primary productivity(NPP),regression analysis and extended Fourier amplitude sensitivity testing(EFAST)were conducted for Biome-BGCMuSo to calculate the sensitivity index of the parameters at four observation sites under climate gradient from ChinaFLUX.Results:Generally,GPP and NPP were highly sensitive to C:Nleaf(C:N of leaves),Wint(canopy water interception coefficient),k(canopy light extinction coefficient),FLNR(fraction of leaf N in Rubisco),MRpern(coefficient of linear relationship between tissue N and maintenance respiration),VPDf(vapor pressure deficit complete conductance reduction),and SLA1(canopy average specific leaf area in phenological phase 1)at all observation sites.Various sensitive parameters occurred at four observation sites within different climate zones.GPP and NPP were particularly sensitive to FLNR,SLA1 and Wint,and C:Nleaf in temperate,alpine and subtropical zones,respectively.Conclusions:The results indicated that sensitivity parameters of China's forest ecosystems change with climate gradient.We found that parameter calibration should be performed according to plant functional type(PFT),and more attention needs to be paid to the differences in climate and environment.These findings contribute to determining the target parameters in field experiments and model calibration.
基金funded by NASA’s Carbon Cycle Science and Land Cover and Land Use Change Programs,the Laboratory of Environmental Model and Data Optima(EMDO),and PIESAT-Australiasupport was provided by the Department of Geographical Sciences of the University of Maryland and the Central PublicInterest Scientific Institution Basic Research Fund(CAFYBB2018GB01)。
文摘Remote sensing provides key inputs to a wide range of models and methods developed for quantifying forest carbon.In particular,carbon inventory methods recommended by IPCC require biomass data and a suite of forest disturbance products.Significant progress has been made in deriving these products by leveraging publicly available remote sensing assets,including observations acquired by the legendary Landsat mission and new systems launched within the past decade,including Sentinel-2,Sentinel-1,GEDI,and ICESAT-2.With the L-band NISAR and P-band BIOMASS missions to be launched in 2023,the Earth’s land surfaces will be imaged by optical and multi-band(including C-,L-,and P-bands)radar systems that can provide global,sub-weekly observations at sub-hectare spatial resolutions for public use.Fine scale products derived from these observations will be crucial for developing monitoring,reporting,and verification(MRV)capabilities needed to support carbon trade,REDD+,and other market-driven tools aimed at achieving climate mitigation goals through forest management at all levels.Following a brief discussion of the roles of forests in the global carbon cycle and the wide range of models and methods available for evaluating forest carbon dynamics,this paper provides an overview of recent progress and forthcoming opportunities in using remote sensing to map forest structure and biomass,detect forest disturbances,determine disturbance attribution,quantify disturbance intensity,and estimate harvested timber volume.Advances in these research areas require large quantities of well—distributed reference data to calibrate remote sensing algorithms and to validate the derived products.In addition,two of the forest carbon pools-dead organic matter and soil carbon—are difficult to monitor using modern remote sensing capabilities.Carefully designed inventory programs are needed to collect the required reference data as well as the data needed to estimate dead organic matter and soil carbon.
基金supported in parts by the United States Department of Agriculture Mc Intire-Stennis Act Fund WVA00104the Division of Forestry and Natural Resources,West Virginia University
文摘Background:With the loss of species worldwide due to anthropogenic factors,especially in forested ecosystems,it has become more urgent than ever to understand the biodiversity-ecosystem functioning relationship(BEFR).BEFR research in forested ecosystems is very limited and thus studies that incorporate greater geographic coverage and structural complexity are needed.Methods:We compiled ground-measured data from approx.one half mil ion forest inventory sample plots across the contiguous United States,Alaska,and northeastern China to map tree species richness,forest stocking,and productivity at a continental scale.Based on these data,we investigated the relationship between forest productivity and tree species diversity,using a multiple regression analysis and a non-parametric approach to account for spatial autocorrelation.Results:In general,forests in the eastern United States consisted of more tree species than any other regions in the country.The highest forest stocking values over the entire study area were concentrated in the western United States and Central Appalachia.Overall,96.4 % of sample plots(477,281)showed a significant positive effect of species richness on site productivity,and only 3.6 %(17,349)had an insignificant or negative effect.Conclusions:The large number of ground-measured plots,as well as the magnitude of geographic scale,rendered overwhelming evidence in support of a positive BEFR.This empirical evidence provides insights to forest management and biological conservation across different types of forested ecosystems.Forest timber productivity may be impaired by the loss of species in forests,and biological conservation,due to its potential benefits on maintaining species richness and productivity,can have profound impacts on the functioning and services of forested ecosystems.
文摘Climate warming is expected to influence forest growth,composition and distribution.However,accurately estimating and predicting forest biomass,potential productivity or forest growth is still a challenge for forest managers dealing with land-use at the stand to regional levels.In the present study,we predicted the potential productivity(PP)of forest under current and future climate scenarios(RCP2.6,RCP4.5,RCP6.0 and RCP8.5)in Jilin province,northeastern China by using Paterson’s Climate Vegetation and Productivity(CVP)index model.The PP was validated by comparing it with the mean and maximum net primary production calculated from light energy utilization(GLM_PEM).Our results indicated that using the CVP index model is partially valid for predicting the potential forest productivity in northeastern China.PP exhibited obvious spatial heterogeneity varying from 4.6 to 8.9 m3 ha-1 year-1 with an increasing tendency from northwest to southeast driven by the precipitation across the region.The number of vegetation-active months,precipitation and insolation coefficient were identified as the primary factors affecting PP,but no significant relationship was found for warmest temperature or temperature fluctuation.Under future climate scenarios,PP across the Jilin Province is expected to increase from 1.38%(RCP2.6 in 2050)to 15.30%(RCP8.5 in 2070),especially in the eastern Songnen Plain(SE)for the RCP8.5 scenarios.
基金financially supported by the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (CAFBB2017ZB004)。
文摘Background: The accurate estimation of soil nutrient content is particularly important in view of its impact on plant growth and forest regeneration. In order to investigate soil nutrient content and quality for the natural regeneration of Dacrydium pectinatum communities in China, designing advanced and accurate estimation methods is necessary.Methods: This study uses machine learning techniques created a series of comprehensive and novel models from which to evaluate soil nutrient content. Soil nutrient evaluation methods were built by using six support vector machines and four artificial neural networks.Results: The generalized regression neural network model was the best artificial neural network evaluation model with the smallest root mean square error(5.1), mean error(-0.85), and mean square prediction error(29). The accuracy rate of the combined k-nearest neighbors(k-NN) local support vector machines model(i.e. k-nearest neighbors-support vector machine(KNNSVM)) for soil nutrient evaluation was high, comparing to the other five partial support vector machines models investigated. The area under curve value of generalized regression neural network(0.6572) was the highest, and the cross-validation result showed that the generalized regression neural network reached 92.5%.Conclusions: Both the KNNSVM and generalized regression neural network models can be effectively used to evaluate soil nutrient content and quality grades in conjunction with appropriate model variables. Developing a new feasible evaluation method to assess soil nutrient quality for Dacrydium pectinatum, results from this study can be used as a reference for the adaptive management of rare and endangered tree species. This study, however, found some uncertainties in data acquisition and model simulations, which will be investigated in upcoming studies.
基金supported by National Natural Science Foundation of China(Grant No.41901382)Open Fund of State Key Laboratory of Remote Sensing Science(Grant No.OFSLRSS201917)the HZAU research startup fund(No.11041810340,No.11041810341).
文摘Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,from Landsat-8(L8)and Sentinel-2(S2),have been proven useful in mapping general forest types,yet we do not know quantitatively how their spectral features(e.g.,red-edge)and temporal frequency of data acquisitions(e.g.,16-day vs.5-day)contribute to plantation forest mapping to the species level.Moreover,it is unclear to what extent the fusion of L8 and S2 will result in improvements in tree species mapping of northern plantation forests in China.Methods:We designed three sets of classification experiments(i.e.,single-date,multi-date,and spectral-temporal)to evaluate the performances of L8 and S2 data for mapping keystone timber tree species in northern China.We first used seven pairs of L8 and S2 images to evaluate the performances of L8 and S2 key spectral features for separating these tree species across key growing stages.Then we extracted the spectral-temporal features from all available images of different temporal frequency of data acquisition(i.e.,L8 time series,S2 time series,and fusion of L8 and S2)to assess the contribution of image temporal frequency on the accuracy of tree species mapping in the study area.Results:1)S2 outperformed L8 images in all classification experiments,with or without the red edge bands(0.4%–3.4%and 0.2%–4.4%higher for overall accuracy and macro-F1,respectively);2)NDTI(the ratio of SWIR1 minus SWIR2 to SWIR1 plus SWIR2)and Tasseled Cap coefficients were most important features in all the classifications,and for time-series experiments,the spectral-temporal features of red band-related vegetation indices were most useful;3)increasing the temporal frequency of data acquisition can improve overall accuracy of tree species mapping for up to 3.2%(from 90.1%using single-date imagery to 93.3%using S2 time-series),yet similar overall accuracies were achieved using S2 time-series(93.3%)and the fusion of S2 and L8(93.2%).Conclusions:This study quantifies the contributions of L8 and S2 spectral and temporal features in mapping keystone tree species of northern plantation forests in China and suggests that for mapping tree species in China's northern plantation forests,the effects of increasing the temporal frequency of data acquisition could saturate quickly after using only two images from key phenological stages.
基金supported by the Major Program of the National Natural Science Foundation of China(No.32192434)the Fundamental Research Funds of Chinese Academy of Forestry(No.CAFYBB2019ZD001)the National Key Research and Development Program of China(2016YFD060020602).
文摘Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals.However,the key environmental factors affecting volume growth differ across various scales and plant functional types.This study was,therefore,conducted to estimate the volume growth of Larix and Quercus forests based on national-scale forestry inventory data in China and its influencing factors using random forest algorithms.The results showed that the model performances of volume growth in natural forests(R^(2)=0.65 for Larix and 0.66 for Quercus,respectively)were better than those in planted forests(R^(2)=0.44 for Larix and 0.40 for Quercus,respectively).In both natural and planted forests,the stand age showed a strong relative importance for volume growth(8.6%–66.2%),while the edaphic and climatic variables had a limited relative importance(<6.0%).The relationship between stand age and volume growth was unimodal in natural forests and linear increase in planted Quercus forests.And the specific locations(i.e.,altitude and aspect)of sampling plots exhibited high relative importance for volume growth in planted forests(4.1%–18.2%).Altitude positively affected volume growth in planted Larix forests but controlled volume growth negatively in planted Quercus forests.Similarly,the effects of other environmental factors on volume growth also differed in both stand origins(planted versus natural)and plant functional types(Larix versus Quercus).These results highlighted that the stand age was the most important predictor for volume growth and there were diverse effects of environmental factors on volume growth among stand origins and plant functional types.Our findings will provide a good framework for site-specific recommendations regarding the management practices necessary to maintain the volume growth in China's forest ecosystems.
基金supported by the National Natural Science Foundation of China (Grant No.42101403)the National Key Researchand Development Program of China (Grant No.2017YFD0600404)。
文摘Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification,it has not been widely used over large areas.A comprehensive process based on multi-flightline airborne hyperspectral data is lacking over large,forested areas influenced by both the effects of bidirectional reflectance distribution function(BRDF)and cloud shadow contamination.In this study,hyperspectral data were collected over the Mengjiagang Forest Farm in Northeast China in the summer of 2017 using the Chinese Academy of Forestry's LiDAR,CCD,and hyperspectral systems(CAF-LiCHy).After BRDF correction and cloud shadow detection processing,a tree species classification workflow was developed for sunlit and cloud-shaded forest areas with input features of minimum noise fraction reduced bands,spectral vegetation indices,and texture information.Results indicate that BRDF-corrected sunlit hyperspectral data can provide a stable and high classification accuracy based on representative training data.Cloud-shaded pixels also have good spectral separability for species classification.The red-edge spectral information and ratio-based spectral indices with high importance scores are recommended as input features for species classification under varying light conditions.According to the classification accuracies through field survey data at multiple spatial scales,it was found that species classification within an extensive forest area using airborne hyperspectral data under various illuminations can be successfully carried out using the effective radiometric consistency process and feature selection strategy.
文摘Forests,trees,and agroforestry(FTA)are ecosystem hotspots.They exemplify the contributions of biodiversity to sustainable and resilient landscapes,green circular economy and to sustainable agriculture and food systems for healthy diets.However,most research on these topics have been performed separately and lack comparison.The International FTA-Kunming Conference'Forests,trees and agroforestry for diverse sustainable landscapes'22nd–24th June 2021,focused on these contributions,brought together scientists NGOs,and policy makers to further the understanding of tree diversity;provided a communication platform for scientists to share their research results;evaluated the role of tree diversity in agroecology and circular agriculture;assessed benefits of landscape restoration;and explored applied research in mountain ecosystems and food security.The goals were to gather evidence that ground the design of solutions that can contribute to the implementation of the post 2020 Global Biodiversity Framework and towards the UN Food Systems Summit,and the overall implementation of the SDGs.This paper summarizes the outcomes of the international FTA Conference in Kunming 2021 and points out the highlights of research involved in six major themes.
基金National Natural Science Foundation of China(42022009)National Key Research and Development Program of China(2017YFA0603002)+2 种基金National Natural Science Foundation of China(41471311)as well as by partial support from the National Key Research and Development Program of China(2020YFE0200800)National Natural Science Foundation of China(42090013).
文摘The role of tropical forests in the global carbon budget remains controversial,as carbon emissions from deforestation are highly uncertain.This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation.New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar.We found that lost forests are special cases,and their spatial structures can be directly measured by combining archived data acquired before and after deforestation by space missions principally aimed at measuring topography.Thus,using biomass mapping,we obtained new estimates of carbon loss from deforestation ahead of forthcoming space missions.Here,using a high-resolution map of forest loss and the synergy of radar and lidar to estimate the aboveground biomass density of forests,we found that deforestation in the 2000s in Latin America,one of the severely deforested regions,mainly occurred in forests with a significantly lower carbon stock density than typical mature forests.
基金funded by National Natural Science Foundation of China(Grant Nos.31270697,31370634,31570628)supported by State Forestry Administration of China(Grant No.2030208)
文摘Estimating individual tree biomass is critical to forest carbon accounting and ecosystem service modeling.In this study,we developed one-(tree diameter only) and two-variable(tree diameter and height) biomass equations,biomass conversion factor(BCF) models,and an integrated simultaneous equation system(ISES) to estimate the aboveground biomass for five conifer species in China,i.e.,Cunninghamia lanceolata(Lamb.) Hook.,Pinus massoniana Lamb.,P.yunnanensis Faranch,P.tabulaeformis Carr.and P.elliottii Engelm.,based on the field measurement data of aboveground biomass and stem volumes from 1055 destructive sample trees across the country.We found that all three methods,including the one-and two-variable equations,could adequately estimate aboveground biomass with a mean prediction error less than 5%,except for Pinus yunnanensis which yielded an error of about 6%.The BCF method was slightly poorer than the biomass equation and the ISES methods.The average coefficients of determination(R^2) were 0.944,0.938 and 0.943 and the mean prediction errors were 4.26,4.49 and 4.29% for the biomass equation method,the BCF method and the ISES method,respectively.The ISES method was the best approach for estimating aboveground biomass,which not only had high accuracy but also could estimate stocking volumes simultaneously that was compatible with aboveground biomass.In addition,we found that it is possible to develop a species-invariant one-variable allometric model for estimating aboveground biomass of all the five coniferous species.The model had an exponent parameter of 7/3 and the intercept parameter a_0 could be estimated indirectly from stem basic density(a_0= 0.294 q).
基金initiated as part of the National Biomass Modeling Program in Continuous Forest Inventory(NBMP-CFI)funded by the State Forestry Administration of China
文摘Calorific value of plants is an important parameter for evaluating and indexing material cycles and energy conversion in forest ecosystems. Based on mensuration data of 150 sample sets, we analyzed the calorific value(CV) and ash content(AC) of different parts of Masson pine(Pinus massoniana) trees in southern China using hypothesis testing and regression analysis. CV and AC of different tree parts were almost significantly different(P<0.05). In descending order, ash-free calorific value(AFCV) ranked as foliage > branch > stem bark > root > stem wood, and AC ranked as foliage > stem bark > root > branch > stem wood. CV and AC of stem wood from the top, middle and lower sections of trees differed significantly. CV increased from the top to the lower sections of the trunk while AC decreased. Mean gross calorific value(GCV) and AFCV of aboveground parts were significantly higher than those of belowground parts(roots). The mean GCV, AFCV and AC of a whole tree of Masson pine were 21.54 kJ/g, 21.74 kJ/g and 0.90%, respectively. CV and AC of different tree parts were, to some extent, correlated with tree diameter, height and origin.
基金supported by the Fundamental Research Funds for the Central Non-profit Research Institution of CAF under grant CAFYBB2017QC005General Financial Grant from the China Postdoctoral Science Foundation(2017M611036)+1 种基金National Natural Science Foundation of China(Grant No.41771392)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19030302)
文摘Background: Soil temperature and moisture are sensitive indicators in soil organic matter decomposition because they control global carbon and water cycles and their potential feedback to climatic variations. Although the Biome-Biogeochemical Cycles (Biome-BGC) model is broadly applied in simulating forest carbon and water fluxes, its single-layer soil module cannot represent vertical variations in soil moisture. This study introduces the Biome-BGC MuSo model, which is composed of a multi-layer soil module and new modules pertaining to phenology and management for simulations of carbon and water fluxes. Although this model considers soil processes among active layers, estimates of soil-related variables might be biased, leading to inaccurate estimates of carbon and water fluxes. Methods: To improve the estimations of soil-related processes in Biome-BGC MuSo, this study assimilates ground-measured multi-layer daily soil temperature and moisture at the Changbai Mountains forest flux site by using the Ensemble Kalman Filter algorithm. The modeled estimates of water and carbon fluxes were evaluated with measurements using determination coefficient (R2) and root mean square error (RMSE). The differences in the RMSEs from Biome-BGC MuSo and the assimilated Biome-BGC MuSo were calculated (ΔRMSE), and the relationships between ΔRMSE and the climatic and biophysical factors were analyzed. Results: Compared with the original Biome-BGC model, Biome-BGC MuSo improved the simulations of ecosystem respiration (ER), net ecosystem exchange (NEE) and evapotranspiration (ET). Data assimilation of the soil-related variables into Biome-BGC MuSo in real time improved the accuracies of the simulated carbon and water fluxes (ET: R^2=0.81, RMSE=0.70 mm·d^-1;ER: R^2=0.85, RMSE=1.97 gC·m^-2·d^-1;NEE: R^2=0.70, RMSE=1.16 gC·m^-2·d^-1). Conclusions: This study proved that seasonal simulation of carbon and water fluxes are more accurate when using Biome-BGC MuSo with a multi-layer soil module than using Biome-BGC with a single-layer soil module. Moreover, assimilating the observed soil temperature and moisture data into Biome-BGC MuSo improved the modeled estimates of water and carbon fluxes via calibrated soil-related simulations. The assimilation strategy is applicable to various climatic and biophysical conditions, particularly densely forested areas, and for local or regional simulation.
基金supported by the forestry public welfare scientific research project(Grant No.201504303)。
文摘Knowledge on the potential suitability of tree species to the site is very important for forest management planning.Natural forest distribution provides a good reference for afforestation and forest restoration.In this study,we developed species distribution model(SDM)for 16 major tree species with 2,825 permanent sample plots with natural origin from Chinese National Forest Inventory data collected in Jilin Province using the Maxent model.Three types of environmental factors including bioclimate,soil and topography with a total of 33 variables were tested as the input.The values of area under the curve(AUC,one of the receiver operating characteristics of the Maxent model)in the training and test datasets were between 0.784 and 0.968,indicating that the prediction results were quite reliable.The environmental factors affecting the distribution of species were ranked in terms of their importance to the species distribution.Generally,the climatic factors had the greatest contribution,which included mean diurnal range,annual mean temperature,temperature annual range,and iosthermality.But the main environmental factors varied with tree species.Distribution suitability maps under current(1950-2000)and future climate scenarios(CCSM4-RCP 2.6 and RCP 6.0 during 2050)were produced for 16 major tree species in Jilin Province using the model developed.The predicted current and future ranges of habitat suitability of the 16 tree species are likely to be positively and negatively affected by future climate.Seven tree species were found to benefit from future climate including B etula costata,Fraxinus mandshurica,Juglans mandshurica,Phellodendron amurense,Populus ussuriensis,Quercus mongolica and Ulmus pumila;five tree species will experience decline in their suitable habitat including B.platyphylla,Tilia mongolica,Picea asperata,Pinus sylvestris,Pinus koraiensis;and four(Salix koreensis,Abies fabri,Pinus densiflora and Larix olgensis)showed the inconsistency under RCP 2.6 and RCP 6.0 scenarios.The maps of the habitat suitability can be used as a basis for afforestation and forest restoration in northeastern China.The SDMs could be a potential tool for forest management planning.
基金This study was supported by the Fundamental Research Funds for the Central Non-profit Research Institution of Chinese Academy of Forestry （CAFYBB2017QC005） and the National Natural Science Foundation of China （21-Y20A06-9001-17/18）.
