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Divergent response of vegetation phenology to extreme temperatures and precipitation of different intensities on the Tibetan Plateau

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Science China Earth Sciences 2023年第10期66卷 2200-2210页

作者： Mai SUN peng LI Peixin REN Jiayi TANG Cicheng ZHANG Xiaolu ZHOU changhui peng School of Geographic Sciences Hunan Normal UniversityChangsha410081China Department of Biology Sciences Institute of Environment SciencesUniversity of Quebec at MontrealMontrealH3C 3P8Canada

Quantifying how climate factors affect vegetation phenology is crucial for understanding climate-vegetation interactions and carbon and water cycles under a changing ***,the effects of different intensities of extreme climatic events on vegetation phenology remain poorly *** a long-term solar-induced chlorophyll fluorescence dataset,we investigated the response of vegetation phenology to extreme temperatures and precipitation events of different intensities across the Tibetan Plateau(TP)from 2000 to *** found that the effect of maximum temperature exposure days(TxED)and minimum temperature exposure days(TnED)on the start of the growing season(SOS)was initially delayed and shifted to advance along the increasing temperature ***,the response of the end of the growing season(EOS)to TxED and TnED shifted from an advance to a delay with increasing temperature gradients until the temperature thresholds were reached,above which thresholds produced an unfavorable response to vegetation growth and brought the EOS to an early *** corresponding maximum and minimum temperature thresholds were 10.12 and 2.54℃,*** contrast,cumulative precipitation(CP)was more likely to advance SOS and delay EOS as the precipitation gradient increased,but the advance of SOS is gradually *** vegetation types(i.e.,forest,shrubland,meadow,and steppe)showed similar trends in response to different climates,but the optimal climatic conditions varied between the vegetation ***,meadow and steppe had lower optimal temperatures and precipitation than forest and *** findings revealed the divergent responses of vegetation phenology to extreme climate events of different intensities,implying that the SOS will continue to advance with warming,whereas the EOS may undergo a partial transformation from delayed areas to advanced areas with continued warming.

关键词： Vegetation phenology Extreme climates Optimal climate Solar-induced chlorophyll fluorescence Tibetan Plateau

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Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques

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Forest Ecosystems 2020年第3期7卷 378-391页

作者： Chunyan Wu Yongfu Chen Xiaojiang Hong Zelin Liu changhui peng State Key Laboratory of Tree Genetics and Breeding Key Laboratory of Tree Breeding and Cultivation of the State Forestry AdministrationResearch Institute of ForestryChinese Academy of ForestryBeijing100091China Research Institute of Forest Resource Information Techniques Chinese Academy of ForestryBeijing100091China Hainan Bawangling National Natural Reserve Changjiang572722HainanChina Department of Biological Science Institute of Environment SciencesUniversity of Quebec at MontrealMontrealQCCanada

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 ***: 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 ***: 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.

关键词： Support vector machine KNNSVM Generalized regression neural network Nutrient grade Rare and endangered tree species

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Divergent contributions of spring and autumn photosynthetic phenology to seasonal carbon uptake of subtropical vegetation in China

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Journal of Geographical Sciences 2024年第7期34卷 1280-1296页

作者： peng Ying LI peng ZHOU Xiaolu LUO Yunpeng ZHANG Cicheng WANG Longjun LI Tong peng changhui School of Geographic Sciences Hunan Normal UniversityChangsha410081China Department for Biogeochemical Integration Max-Planck-Institute for BiogeochemistryJena07745Germany Department of Biology Sciences Institute of Environment SciencesUniversity of Quebec at MontrealC.P.8888Succ.Centre-VilleMontrealH3C 3P8Canada

Phenological changes play a central role in regulating seasonal variation in the ecological processes,exerting significant impacts on hydrologic and nutrient cycles,and ultimately influencing ecosystem functioning such as carbon ***,the potential impact mechanisms of phenological events on seasonal carbon dynamics in subtropical regions are *** knowledge gaps hinder from accurately linking photosynthetic phenology and carbon sequestration *** chlorophyll fluorescence remote sensing and productivity data from 2000 to 2019,we found that an advancement in spring phenology increased spring gross(GPP)and net primary productivity(NPP)in subtropical vegetation of China by 2.1 gC m^(-2)yr^(-1)and 1.4 gC m^(-2)yr^(-1),respectively.A delay in autumn phenology increased the autumnal GPP and NPP by 0.4 gC m^(-2)yr^(-1)and 0.2 gC m^(-2)yr^(-1),***,the contribution of the spring phenology to spring carbon uptake increased significantly during the study period,while this positive contribution showed a nonsignificant trend in *** comparison,the later autumn phenology could significantly contribute to the increase in autumnal carbon uptake;however,this contributing effect was *** analysis indicated that these phenomena have been caused by the increased leaf area and enhanced photosynthesis due to earlier spring and later autumn phenology,*** results demonstrate the diverse impacts of vegetation phenology on the seasonal carbon sequestration ability and it is imperative to consider such asymmetric effects when modeling ecosystem processes parameterized under future climate change.

关键词： subtropics vegetation phenology remote sensing carbon cycle climate change

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Meta-analysis and its application in global change research

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Chinese Science Bulletin 2007年第3期52卷 289-299,M0300-M0302页

作者： LEI XiangDong peng changhui TIAN DaLun SUN JianFeng Institute of Forest Resource Information Techniques Chinese Academy of Forestry Beijing 100091 China ECO-MCS Lab Institute of Environment Sciences University of Quebec at Montreal (UQAM) Montreal H3C 3P8 Canada Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China College of Life Science and Technology Central-South University of Forestry and Technology Changsha 410004 China

Meta-analysis is a quantitative synthetic research method that statistically integrates results from in- dividual studies to find common trends and differences. With increasing concern over global change, meta-analysis has been rapidly adopted in global change research. Here, we introduce the methodolo- gies, advantages and disadvantages of meta-analysis, and review its application in global climate change research, including the responses of ecosystems to global warming and rising CO2 and O3 concentrations, the effects of land use and management on climate change and the effects of distur- bances on biogeochemistry cycles of ecosystem. Despite limitation and potential misapplication, meta-analysis has been demonstrated to be a much better tool than traditional narrative review in synthesizing results from multiple studies. Several methodological developments for research synthe- sis have not yet been widely used in global climate change researches such as cumulative meta-analysis and sensitivity analysis. It is necessary to update the results of meta-analysis on a given topic at regular intervals by including newly published studies. Emphasis should be put on multi-factor interaction and long-term experiments. There is great potential to apply meta-analysis to global climate change research in China because research and observation networks have been established (e.g. ChinaFlux and CERN), which create the need for combining these data and results to provide support for governments’ decision making on climate change. It is expected that meta-analysis will be widely adopted in future climate change research.

关键词：全球变暖气候变化元分析生态系统 CO2浓度

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Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites

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Science China Earth Sciences 2016年第2期59卷 337-348页

作者： ZHANG TingLong SUN Rui peng changhui ZHOU GuoYi WANG ChunLing ZHU QiuAn YANG YanZheng College of Resources and Environmental Science Northwest A&F University Laboratory of Ecosystems Forecasting and Global Change College of Forestry Northwest A&F University State Key Laboratory of Remote Sensing Science Co-sponsored by Beijing Normal University and IRSA School of Geography and Remote Sensing Sciences Beijing Normal University Institute of Environment Sciences Department of Biological Sciences University of Quebec at Montreal South China Botanical Garden Chinese Academy of Sciences

Model simulation and in situ observations are often used to research water and carbon cycles in terrestrial ecosystems, but each of these methods has its own advantages and limitations. Combining these two methods could improve the accuracy of quantifying the dynamics of the water and carbon fluxes of an ecosystem. Data assimilation is an effective means of integrating modeling with in situ observation. In this study, the ensemble Kalman filter(En KF) and the unscented Kalman filter(UKF) algorithms were used to assimilate remotely sensed leaf area index(LAI) data with the Biome-BGC model to simulate water and carbon fluxes at the Harvard Forest Environmental Monitoring Site(EMS) and the Dinghushan site. After MODIS LAI data from 2000–2004 were assimilated into the improved Biome-BGC model using the En KF algorithm at the Harvard Forest site, the R2 between the simulated and observed results for NEE and evapotranspiration increased by 7.8% and 4.7%, respectively. In addition, the sum of the absolute error(SAE) and the root mean square error(RMSE) of NEE decreased by an average of 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration decreased by 24.5% and 25.5%, respectively. MODIS LAI data of 2003 were assimilated into the Biome-BGC model for the Dinghushan site, and the R2 values between the simulated and observed results for NEE and evapotranspiration were increased by 6.7% and 17.3%, respectively. In addition, the SAE values of NEE and ET were decreased by 11.3% and 30.7%, respectively, and the RMSE values of NEE and ET decreased by 10.1% and 30.9%, respectively. These results demonstrate that the accuracy of carbon and water flux simulations can be effectively improved when remotely sensed LAI data are properly integrated with ecosystem models through a data assimilation approach.

关键词： Net ecosystem exchange Ensemble Kalman filter Unscented Kalman filter Biome-BGC model Leaf area index

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Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China

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Forest Ecosystems 2021年第3期8卷 413-424页

作者： Peixin Ren Zelin Liu Xiaolu Zhou changhui peng Jingfeng Xiao Songhan Wang Xing Li peng Li College of Geographic Science Hunan Normal UniversityChangsha 410081China Department of Biology Sciences Institute of Environment SciencesUniversity of Quebec at MontrealC.P.8888Succ.Centre-VilleMontreal H3C 3P8Canada Earth Systems Research Center Institute for the Study of EarthOceansand SpaceUniversity of New HampshireDurhamNH 03824USA International Institute for Earth System Sciences Nanjing UniversityNanjing 210023China Jiangsu Provincial Key Laboratory of Geographic Information Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjing 210023China

Background:Vegetation phenology research has largely focused on temperate deciduous forests,thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical ***:Using satellite solar-induced chlorophyll fluorescence(SIF)and MODIS enhanced vegetation index(EVI)data,we applied two methods to evaluate temporal and spatial patterns of the end of the growing season(EGS)in subtropical vegetation in China,and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative *** results indicated that the averaged EGS derived from the SIF and EVI based on the two methods(dynamic threshold method and derivative method)was later than that derived from gross primary productivity(GPP)based on the eddy covariance technique,and the time-lag for EGSsif and EGSevi was approximately 2 weeks and 4 weeks,*** found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation(accounting for more than 73%and 62%of the study areas,respectively),but negatively correlated with preseason maximum temperature(accounting for more than 59%of the study areas).In addition,EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors,and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests,shrub and ***:Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of *** found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study *** findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China,and provide a new perspective for accurately evaluating the role played by evergreen vegetatio

关键词： Carbon cycle Evergreen vegetation Plant phenology Solar-induced Fluorescence Climate change MODIS Eddy covariance

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Integrating global socio-economic influences into a regional land use change model for China

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Frontiers of Earth Science 2014年第1期8卷 81-92页

作者： Xia XU Qiong GAO changhui peng Xuefeng CUI Yinghui LIU Li JIANG State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Beijing 100875 China Department of Biology Sciences institute of environment Science University of Quebec at Montreal Montreal H3C 3P8 Canada College of Global Change and Earth System Research Beijing Normal University Beijing 100875 China Academy of Resource Beijing Normal University Beijing 100875 China Academy of Disaster Reduction and Emergency Management Beijing Normal University Beijing 100875 China

With rapid economic development and urba- nization, land use in China has experienced huge changes in recent years; and this will probably continue in the future. Land use problems in China are urgent and need further study. Rapid land-use change and economic development make China an ideal region for integrated land use change studies, particularly the examination of multiple factors and global-regional interactions in the context of global economic integration. This paper presents an integrated modeling approach to examine the impact of global socio-economic processes on land use changes at a regional scale. We develop an integrated model system by coupling a simple global socio-economic model （GLOBFOOD） and regional spatial allocation model （CLUE）. The model system is illustrated with an application to land use in China. For a given climate change, population growth, and various socio-economic situations, a global socio-economic model simulates the impact of global market and economy on land use, and quantifies changes of different land use types. The land use spatial distribution model decides the type of land use most appropriate in each spatial grid by employing a weighted suitability index, derived from expert knowledge abot^t the ecosystem state and site conditions. A series of model simulations will be conducted and analyzed to demonstrate the ability of the integrated model to link global socio- economic factors with regional land use changes in China. The results allow an exploration of the future dynamics of land use and landscapes in China.

关键词： global socio-economic influence land use change model integrating China

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Re-estimating the changes and ranges of forest biomass carbon in China during the past 40 years

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Forest Ecosystems 2019年第4期6卷 396-413页

作者： Xiaolu Zhou Xiangdong Lei Caixia Liu Huabing Huang Carl Zhou changhui peng Research Center for Ecological Forecasting and Global Change Northwest A&F UniversityYangling 712100China Ecological Modeling and Carbon Science Department of Biology ScienceUniversity of Quebec at MontrealMontrealQC H3C 3P8Canada Institute of Forest Resource Information Techniques Chinese Academy of ForestryBeijing 100091China State Key Laboratory of Remote Sensing Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100101China Faculty of Health Sciences University of OttawaOttawaON K1N 6N5Canada.

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 ***: 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 ***: 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 ***: Presented methodological analysis highlights the possibility to determine a range that the true value could be located in. The two-ste

关键词： Allometric equation Forest carbon Stem-biomass proportion Volume-derived method Wood density

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Spatiotemporal analysis of the impact of urban landscape forms on PM_(2.5) in China from 2001 to 2020

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International Journal of Digital Earth 2023年第1期16卷 3417-3434页

作者： Shoutao Zhu Jiayi Tang Xiaolu Zhou peng Li Zelin Liu Cicheng Zhang Ziying Zou Tong Li changhui peng School of Geographic Sciences Hunan Normal UniversityChangshaPeople’s Republic of China Department of Biology Sciences Institute of Environment SciencesUniversity of Quebec at MontrealMontrealCanada

Urban landscape forms can be effective in reducing increasing PM_(2.5) concentrations due to urbanization in China,making it crucially important to accurately quantify the spatiotemporal impact of urban landscape forms on PM_(2.5) *** landscape indices and six control variables were selected to assess these impacts in 362 Chinese cities during different time scales from 2001 to 2020,using a spatiotemporal geographically weighted regression model,random forest models and partial dependence *** results show that there are spatiotemporal differences in the impacts of landscape indices on PM_(2.5).the proportion of urban green infrastructure(PLAND-UGI)and the fractal dimension of urban green infrastructure(FRACT-UGI)exacerbate PM_(2.5) concentrations in the northwest,the proportion of impervious surfaces(PLAND-Impervious)mitigates air pollution in northwest and southwest China,and shannon’s diversity index(SHDI)has seasonal differences in the ***-UGI is the landscape index with the largest contribution(30%)and interpretable *** relationship between FRACT and PM_(2.5) was more complex than for other landscape *** results of this study contribute to a deeper understanding of the spatial and temporal differences in the impact of urban landscape patterns on PM_(2.5),contributing to clean urban development and sustainable development.

关键词： Landscape index particulate matter spatiotemporal heterogeneity spatiotemporal geographically weighted regression model random forest

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Application of artificial neural networks in global climate change and ecological research:An overview

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Chinese Science Bulletin 2010年第34期55卷 3853-3863页

作者： LIU ZeLin peng changhui XIANG WenHua TIAN DaLun DENG XiangWen ZHAO MeiFang College of Life Science and Technology Central-South University of Forestry and Technology Changsha 410004 China Institute of Environment Sciences Department of Biology Sciences University of Quebec at Montreal Case postale 8888 succ Centre- Ville Montreal (QC) H3C 3P8 Canada

Fields that employ artificial neural networks(ANNs)have developed and expanded continuously in recent years with the ongoing development of computer technology and artificial *** has been adopted widely and put into practice by research-ers in light of increasing concerns over ecological issues such as global warming,frequent El Nio-Southern Oscillation(ENSO)events,and atmospheric circulation *** exist and there is a potential risk for misuse in that ANN model pa-rameters require typically higher overall sensitivity,and the chosen network structure is generally more dependent upon individ-ual ***,however,are relatively accurate when used for short-term predictions;despite global climate change re-search favoring the effects of interactions as the basis of study and the preference for long-term experimental *** remain a better choice than many traditional methods when dealing with nonlinear problems,and possesses great potential for the study of global climate change and ecological *** can resolve problems that other methods *** is especially true for situations in which measurements are difficult to conduct or when only incomplete data are *** is anticipated that ANNs will be widely adopted and then further developed for global climate change and ecological research.

关键词：人工神经网络全球气候变化生态问题应用大气环流异常短期预测计算机技术非线性问题

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