Spatio-Temporal Evolution, Future Trend and Phenology Regularity of Net Primary Productivity of Forests in Northeast China

[1]  W. Keeton,et al.  Economics of integrated harvests with biomass for energy in non-industrial forests in the northeastern US forest , 2019 .

[2]  S. Goward,et al.  Global Primary Production: A Remote Sensing Approach , 1995 .

[3]  Trend Analysis of Annual and Monthly Rainfall in Erbil City, Kurdistan Region, Iraq , 2019, Polytechnic Journal.

[4]  Liyun Dai,et al.  Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China , 2016 .

[5]  Jingyun Fang,et al.  Forest biomass patterns across northeast China are strongly shaped by forest height , 2013 .

[6]  Hongtao Li,et al.  Ecological civilization: Interpreting the Chinese past, projecting the global future , 2018, Global Environmental Change.

[7]  J. Randerson,et al.  Terrestrial ecosystem production: A process model based on global satellite and surface data , 1993 .

[8]  Jing Wu,et al.  Index system of urban resource and environment carrying capacity based on ecological civilization , 2018 .

[9]  Sam Van Holsbeeck,et al.  Feasibility of locating biomass-to-bioenergy conversion facilities using spatial information technologies: A case study on forest biomass in Queensland, Australia , 2020 .

[10]  S. Berg,et al.  Energy efficiency and the environmental impact of harvesting stumps and logging residues , 2010, European Journal of Forest Research.

[11]  Emanuele Santi,et al.  The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas , 2017 .

[12]  J. Ardö,et al.  The supply and demand of net primary production in the Sahel , 2014 .

[13]  Jiyuan Liu,et al.  Estimation and analysis of net primary productivity by integrating MODIS remote sensing data with a light use efficiency model , 2013 .

[14]  J. Paruelo,et al.  Spatial and temporal variation of human appropriation of net primary production in the Rio de la Plata grasslands , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[15]  Shunlin Liang,et al.  An efficient algorithm for calculating photosynthetically active radiation with MODIS products , 2017 .

[16]  P. Ciais,et al.  Consistent Land- and Atmosphere-Based U.S. Carbon Sink Estimates , 2001, Science.

[17]  D. Forrester,et al.  Analysing the spatial and temporal dynamics of species interactions in mixed-species forests and the effects of stand density using the 3-PG model , 2016 .

[18]  B. He,et al.  Quantitative assessment of human appropriation of aboveground net primary production in China , 2015 .

[19]  A. Lin,et al.  Impacts of preseason drought on vegetation spring phenology across the Northeast China Transect. , 2020, The Science of the total environment.

[20]  J. Hicke,et al.  Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years , 2019, Remote Sensing of Environment.

[21]  R. Yin,et al.  An empirical analysis of the driving forces of forest cover change in northeast China , 2017 .

[22]  S. Running,et al.  Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System , 2000 .

[23]  Jingyun Fang,et al.  Biomass carbon accumulation by Japan's forests from 1947 to 1995 , 2005 .

[24]  Hongguang Li,et al.  An Approach to Dynamic Asymptotic Estimation for Hurst Index of Network Traffic , 2010, Int. J. Commun. Netw. Syst. Sci..

[25]  T. Ancev,et al.  Forest governance and economic values of forest ecosystem services in Vietnam , 2020 .

[26]  Evan H. DeLucia,et al.  Forest carbon use efficiency: is respiration a constant fraction of gross primary production? , 2007 .

[27]  P. Hostert,et al.  Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites , 2014 .

[28]  Zhongmin Hu,et al.  Comparison of four light use efficiency models for estimating terrestrial gross primary production , 2015 .

[29]  L. A. Hunt,et al.  Estimation of Missing Solar Radiation Data for use in Agricultural Modelling , 1997 .

[30]  Jianlong Li,et al.  Modeling the dynamics of distribution, extent, and NPP of global terrestrial ecosystems in response to future climate change , 2017 .

[31]  T. Knoke,et al.  The valuation of forest ecosystem services as a tool for management planning - A choice experiment. , 2020, Journal of environmental management.

[32]  Guangsheng Zhou,et al.  Modeling SOC and NPP responses of meadow steppe to different grazing intensities in Northeast China , 2008 .

[33]  Matthieu Garcin,et al.  Estimation of time-dependent Hurst exponents with variational smoothing and application to forecasting foreign exchange rates , 2017 .

[34]  Jinpei Ou,et al.  Assessing the impacts of urban sprawl on net primary productivity using fusion of Landsat and MODIS data. , 2018, The Science of the total environment.

[35]  Pang-Ning Tan,et al.  Continental-scale comparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982–1998 , 2003 .

[36]  Leif Gustavsson,et al.  Climate effects of bioenergy from forest residues in comparison to fossil energy , 2015 .

[37]  M. Siraj Forest carbon stocks in woody plants of Chilimo-Gaji Forest, Ethiopia: Implications of managing forests for climate change mitigation , 2019 .

[38]  D. Guan,et al.  Forest biomass-carbon variation affected by the climatic and topographic factors in Pearl River Delta, South China. , 2019, Journal of environmental management.

[39]  S. Barr,et al.  Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations , 2019, Remote Sensing of Environment.

[40]  C. Hopkinson,et al.  Monitoring boreal forest biomass and carbon storage change by integrating airborne laser scanning, biometry and eddy covariance data , 2016 .

[41]  Yoon-Seok Chang,et al.  Large rate of uptake of atmospheric carbon dioxide by planted forest biomass in Korea , 2002 .

[42]  Ziming Li,et al.  Evaluations and comparisons of rule-based and machine-learning-based methods to retrieve satellite-based vegetation phenology using MODIS and USA National Phenology Network data , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[43]  E. Davidson,et al.  Satellite-based modeling of gross primary production in an evergreen needleleaf forest , 2004 .

[44]  Shilong Piao,et al.  Quantifying the response of forest carbon balance to future climate change in Northeastern China: Model validation and prediction , 2009 .

[45]  Wang Xiaoying,et al.  Impacts of Climate Change on Forest Ecosystems in Northeast China , 2013 .

[46]  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 .

[47]  B. Engel,et al.  Analysis on net primary productivity change of forests and its multi–level driving mechanism – A case study in Changbai Mountains in Northeast China , 2020 .

[48]  Shafiqur Rehman,et al.  Study of Saudi Arabian climatic conditions using Hurst exponent and climatic predictability index , 2009 .

[49]  Gregory P. Asner,et al.  Modeling regional variation in net primary production of pinyon–juniper ecosystems , 2012 .

[50]  J. Ni,et al.  Synthesis and analysis of biomass and net primary productivity in Chinese forests , 2001 .

[51]  R. Cesar Izaurralde,et al.  Estimating crop net primary production using national inventory data and MODIS-derived parameters , 2013 .

[52]  J. Corte-Real,et al.  Rainfall and river flow trends using Mann–Kendall and Sen’s slope estimator statistical tests in the Cobres River basin , 2015, Natural Hazards.

[53]  S. Wofsy,et al.  Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data , 2004 .

[54]  Alemu Gonsamo,et al.  The potential of the greenness and radiation (GR) model to interpret 8-day gross primary production of vegetation , 2014 .

[55]  Christopher B. Field,et al.  Increasing net primary production in China from 1982 to 1999 , 2003 .

[56]  Ming Li,et al.  Estimating daily global solar radiation during the growing season in Northeast China using the Ångström–Prescott model , 2012, Theoretical and Applied Climatology.

[57]  Haiyan Wang,et al.  Simulation of climate change and thinning effects on productivity of Larix olgensis plantations in northeast China using 3-PGmix model. , 2020, Journal of environmental management.

[58]  Guillermo P Podesta,et al.  Estimating daily solar radiation in the Argentine Pampas , 2004 .

[59]  H. Xie,et al.  Grassland dynamics in response to climate change and human activities in Xinjiang from 2000 to 2014 , 2018, Scientific Reports.

[60]  T. Yue,et al.  Estimation of China’s forest stand biomass carbon sequestration based on the continuous biomass expansion factor model and seven forest inventories from 1977 to 2013 , 2019, Forest Ecology and Management.

[61]  Chaoyang Wu,et al.  Modeling net primary production of a fast-growing forest using a light use efficiency model , 2010 .

[62]  Xianguo Lu,et al.  Spatiotemporal variation in vegetation spring phenology and its response to climate change in freshwater marshes of Northeast China. , 2019, The Science of the total environment.

[63]  M. Ishikawa,et al.  Interannual and seasonal variations in energy and carbon exchanges over the larch forests on the permafrost in northeastern Mongolia , 2014 .

[64]  Yanan Cao,et al.  Temperature sensitivity of soil heterotrophic respiration is altered by carbon substrate along the development of Quercus Mongolica forest in northeast China , 2019, Applied Soil Ecology.

[65]  Martin Mayfield,et al.  An ecological-thermodynamic approach to urban metabolism: Measuring resource utilization with open system network effectiveness analysis , 2019, Applied Energy.

[66]  Shashi Shekhar,et al.  Understanding global teleconnections of climate to regional model estimates of Amazon ecosystem carbon fluxes , 2004 .

[67]  Kaishan Song,et al.  Remote estimation of K-d (PAR) using MODIS and Landsat imagery for turbid inland waters in Northeast China , 2017 .

[68]  Jennifer Pontius,et al.  Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty , 2014 .