Spatiotemporal dynamics in assimilated-LAI phenology and its impact on subtropical bamboo forest productivity

Abstract Phenology has a significant effect on forest growth and directly affects the forest ecosystem carbon cycle. Bamboo forests possess strong carbon sequestration capacities. However, it is not clear whether variations in phenology increase or decrease carbon uptake and storage in subtropical bamboo forests. We first extracted the length of the growing season (LOS) by coupling a data assimilation algorithm and MODIS leaf area index (LAI) data, and then the LOS was used to drive the integrated terrestrial ecosystem carbon-budget (InTEC) model to simulate gross primary productivity (GPP) and net ecosystem productivity (NEP) in Zhejiang Province from 2001 to 2017. Our results showed that the LOS estimation using the assimilated LAI time series was more reliable than that of the MODIS LAI and enhanced vegetation index (EVI). The annual average LOS increased on average by 0.76 day yr−1 from 2001 to 2017. The GPP and NEP simulations based on the LAI assimilation-based phenology indicated that bamboo forest ecosystems possess strong carbon sequestration capacities and act as carbon sinks, with mean annual GPP and NEP values of 434.74 ± 257.93 g C m−2 yr−1 and 141.42 ± 82.54 g C m−2 yr−1, respectively, during 2001–2017. An increase of one day in the regional annual LOS increases the annual average GPP and NEP by 1.34 g C m−2 yr−1 and 0.75 g C m−2 yr−1, respectively. Moreover, the interannual variation of NEP was significantly correlated with precipitation and temperature, whereas GPP was not. Our results demonstrated that phenology extraction based on LAI data assimilation should play an important role in the simulation of bamboo forest productivity with ecological process models. The variation in phenology induced by climate change can strengthen the bamboo forest carbon sink, which is of great significance for subtropical forests coping with climate change in the future.

[1]  Russell K. Monson,et al.  Coupling between carbon cycling and climate in a high-elevation, subalpine forest: a model-data fusion analysis , 2007, Oecologia.

[2]  Z. Ouyang,et al.  [Regional and global estimates of carbon stocks and carbon sequestration capacity in forest ecosystems: A review]. , 2015, Ying yong sheng tai xue bao = The journal of applied ecology.

[3]  Weiliang Fan,et al.  Estimating bamboo forest aboveground biomass using EnKF-assimilated MODIS LAI spatiotemporal data and machine learning algorithms , 2018, Agricultural and Forest Meteorology.

[4]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[5]  國合會系統管理者.  Global Forest Resources Assessment , 2016 .

[6]  Josep Peñuelas,et al.  Land surface phenology from VEGETATION and PROBA-V data. Assessment over deciduous forests , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Jing M. Chen,et al.  Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[8]  V. Singh,et al.  Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982-2013) , 2018 .

[9]  Xuejian Li,et al.  Estimating and Analyzing the Spatiotemporal Pattern of Aboveground Carbon in Bamboo Forest by Combining Remote Sensing Data and Improved BIOME-BGC Model , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  Qun Du,et al.  The effect of phenology on the carbon exchange process in grassland and maize cropland ecosystems across a semiarid area of China. , 2019, The Science of the total environment.

[11]  P. Ciais,et al.  Influence of spring and autumn phenological transitions on forest ecosystem productivity , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[12]  C. Justice,et al.  Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation , 1997 .

[13]  Liang Chen,et al.  Comparison of Two Data Assimilation Methods for Improving MODIS LAI Time Series for Bamboo Forests , 2017, Remote. Sens..

[14]  Josef Cihlar,et al.  Approaches for reducing uncertainties in regional forest carbon balance , 2000 .

[15]  I. C. Prentice,et al.  Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect , 2002, Science.

[16]  H. Du,et al.  Spatiotemporal evolution and impacts of climate change on bamboo distribution in China. , 2019, Journal of environmental management.

[17]  Pingheng Li,et al.  [Simulating of carbon fluxes in bamboo forest ecosystem using BEPS model based on the LAI assimilated with Dual Ensemble Kalman Filter]. , 2016, Ying yong sheng tai xue bao = The journal of applied ecology.

[18]  P. Ciais,et al.  Net carbon dioxide losses of northern ecosystems in response to autumn warming , 2008, Nature.

[19]  Improvement of MODIS LAI Product in China , 2008 .

[20]  Josef Cihlar,et al.  An integrated terrestrial ecosystem carbon-budget model based on changes in disturbance, climate, and atmospheric chemistry , 2000 .

[21]  J. Berry,et al.  A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.

[22]  H. Du,et al.  Spatiotemporal Simulation of Net Ecosystem Productivity and Its Response to Climate Change in Subtropical Forests , 2019, Forests.

[23]  J. Peñuelas,et al.  European phenological response to climate change matches the warming pattern , 2006 .

[24]  Shunlin Liang,et al.  Time‐lag effects of global vegetation responses to climate change , 2015, Global change biology.

[25]  Xiaojun Xu,et al.  Implications of ice storm damages on the water and carbon cycle of bamboo forests in southeastern China , 2013 .

[26]  Christopher B. Field,et al.  Increases in early season ecosystem uptake explain recent changes in the seasonal cycle of atmospheric CO2 at high northern latitudes , 1999 .

[27]  I. Wing,et al.  Net carbon uptake has increased through warming-induced changes in temperate forest phenology , 2014 .

[28]  A. Ziegler,et al.  Carbon stocks in bamboo ecosystems worldwide: Estimates and uncertainties , 2017 .

[29]  Gang Bao,et al.  Dynamics of net primary productivity on the Mongolian Plateau: Joint regulations of phenology and drought , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[30]  Tiejun Wang,et al.  Phenological variation decreased carbon uptake in European forests during 1999–2013 , 2018, Forest Ecology and Management.

[31]  Li Xin.  A Bayesian Filter Framework for Sequential Data Assimilation , 2010 .

[32]  T. A. Black,et al.  Assessing eddy-covariance flux tower location bias across the Fluxnet-Canada Research Network based on remote sensing and footprint modelling , 2011 .

[33]  Annette Menzel,et al.  Growing season extended in Europe , 1999, Nature.

[34]  Weimin Ju,et al.  Spatial and temporal variations of forest LAI in China during 2000–2010 , 2012 .

[35]  Andrew E. Suyker,et al.  Interannual and spatial impacts of phenological transitions, growing season length, and spring and autumn temperatures on carbon sequestration: A North America flux data synthesis , 2012 .

[36]  Liu Liang-yun,et al.  Effects of phenological change on ecosystem productivity of temperate deciduous broad- leaved forests in North America , 2012 .

[37]  Ramakrishna R. Nemani,et al.  Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Ning Han,et al.  Mapping Global Bamboo Forest Distribution Using Multisource Remote Sensing Data , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[39]  Dailiang Peng,et al.  Improved modeling of land surface phenology using MODIS land surface reflectance and temperature at evergreen needleleaf forests of central North America , 2016 .

[40]  Paul V. Bolstad,et al.  An approach to spatially distributed modeling of net primary production (NPP) at the landscape scale and its application in validation of EOS NPP products , 1999 .

[41]  T. A. Black,et al.  Predicting the onset of net carbon uptake by deciduous forests with soil temperature and climate data: a synthesis of FLUXNET data , 2005, International journal of biometeorology.

[42]  Benjamin W. Heumann,et al.  Spring photosynthesis in a cool temperate bog , 2006 .

[43]  C. Tucker,et al.  Increased plant growth in the northern high latitudes from 1981 to 1991 , 1997, Nature.

[44]  Cheng Sun,et al.  [Variation characteristics of CO2 flux in Phyllostachys edulis forest ecosystem in subtropical region of China]. , 2013, Ying yong sheng tai xue bao = The journal of applied ecology.

[45]  Weimin Ju,et al.  Improved modeling of gross primary productivity (GPP) by better representation of plant phenological indicators from remote sensing using a process model , 2018 .

[46]  J. Chen,et al.  Relationship between Net Primary Productivity and Forest Stand Age under Different Site Conditions and Its Implications for Regional Carbon Cycle Study , 2018 .

[47]  O. Skre,et al.  Regional trends for bud burst and flowering of woody plants in Norway as related to climate change , 2008, International journal of biometeorology.

[48]  Shilong Piao,et al.  Variation in leaf flushing date influences autumnal senescence and next year’s flushing date in two temperate tree species , 2014, Proceedings of the National Academy of Sciences.

[49]  Guomo Zhou,et al.  Review of Carbon Fixation in Bamboo Forests in China , 2011, The Botanical Review.

[50]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[51]  Franz-W. Badeck,et al.  Plant phenology in Germany over the 20th century , 2005 .

[52]  Xiaojun Xu,et al.  Assimilating spatiotemporal MODIS LAI data with a particle filter algorithm for improving carbon cycle simulations for bamboo forest ecosystems. , 2019, The Science of the total environment.

[53]  A. Kostianoy,et al.  Inter-annual variability and interaction of remote-sensed vegetation index and atmospheric precipitation in the Aral Sea region , 2005 .

[54]  Xiaojun Xu,et al.  Coupled LAI assimilation and BEPS model for analyzing the spatiotemporal pattern and heterogeneity of carbon fluxes of the bamboo forest in Zhejiang Province, China , 2017 .

[55]  Lufeng Mo,et al.  Development of the BIOME-BGC model for the simulation of managed Moso bamboo forest ecosystems. , 2016, Journal of environmental management.

[56]  Bunkei Matsushita,et al.  Estimation of regional net primary productivity (NPP) using a process-based ecosystem model: How important is the accuracy of climate data? , 2004 .

[57]  W. Parton,et al.  Analysis of factors controlling soil organic matter levels in Great Plains grasslands , 1987 .

[58]  Maosheng Zhao,et al.  Improvements of the MODIS terrestrial gross and net primary production global data set , 2005 .

[59]  H. Mooney,et al.  Shifting plant phenology in response to global change. , 2007, Trends in ecology & evolution.

[60]  Dailiang Peng,et al.  Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites , 2017 .

[61]  F. Berninger,et al.  Response of carbon uptake to abiotic and biotic drivers in an intensively managed Lei bamboo forest. , 2018, Journal of environmental management.

[62]  J. Townshend,et al.  Spatially and temporally continuous LAI data sets based on an integrated filtering method: Examples from North America , 2008 .

[63]  José A. Sobrino,et al.  Global land surface phenology trends from GIMMS database , 2009 .

[64]  Liang-Yun Liu,et al.  Effects of phenological change on ecosystem productivity of temperate deciduous broad-leaved forests in North America: Effects of phenological change on ecosystem productivity of temperate deciduous broad-leaved forests in North America , 2013 .

[65]  H. Du,et al.  Spatiotemporal dynamics of bamboo forest net primary productivity with climate variations in Southeast China , 2020 .

[66]  Frédéric Baret,et al.  Vegetation baseline phenology from kilometric global LAI satellite products , 2016 .

[67]  Jing M. Chen,et al.  Modeling growing season phenology in North American forests using seasonal mean vegetation indices from MODIS , 2014 .

[68]  Chen Xin-an.  Discussion on the Growth Regulation of On-Year and off-Year Moso Bamboo , 2010 .