Assessing the Impact of Urban Sprawl on Net Primary Productivity of Terrestrial Ecosystems Using a Process-Based Model—A Case Study in Nanjing, China

Urban sprawl/urbanization has large impacts on the structure and function of terrestrial ecosystems. Net primary production (NPP) is an important indicator for estimating the earth's ability to support life and aids the evaluation of sustainable development of the terrestrial ecosystem. In this study, the process-based boreal ecosystem productivity simulator (BEPS) model was used in conjunction with leaf area index (LAI) dataset, land cover, and meteorological and soil data to simulate daily NPP at spatial resolution 250 m in Nanjing, a representative region within the Yangtze Delta, for the period 2001-2010. Effects of urbanization on land-cover change and regional NPP are quantitatively evaluated. The results show that during this period, urbanization caused significant land-cover change. Compared with 2001, urbanized area and area covered by water bodies increased significantly, while vegetated area declined greatly. The greatest loss was cropland, followed by evergreen coniferous and closed deciduous forests. There were obvious spatial differences in NPP variations. The reduction rate of annual NPP in the major city of Nanjing, Jiangning District, and Gaochun County was much higher than that in Pukou and Luhe district, and Lishui County. These results indicate that a process-based model driven by remote sensing is useful in assessing the impact of urban sprawl on NPP, and urbanization, not climate factors, is a main factor for NPP reduction for an urbanizing region.

[1]  Shi Pei-jun,et al.  How does the conversion of land cover to urban use affect net primary productivity? A case study in Shenzhen city, China , 2009 .

[2]  Bernd Steingrobe,et al.  Root production and root mortality of winter wheat grown on sandy and loamy soils in different farming systems , 2001, Biology and Fertility of Soils.

[3]  Paige S. Warren,et al.  Future directions in urban ecology , 2006 .

[4]  J. Randerson,et al.  Primary production of the biosphere: integrating terrestrial and oceanic components , 1998, Science.

[5]  C. Tucker,et al.  Satellite estimates of productivity and light use efficiency in United States agriculture, 1982–98 , 2002 .

[6]  M. Alberti.  The Effects of Urban Patterns on Ecosystem Function , 2005 .

[7]  Jing M. Chen,et al.  Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications , 1999 .

[8]  Fang Jing,et al.  IMPLICATIONS AND ESTIMATIONS OF FOUR TERRESTRIAL PRODUCTIVITY PARAMETERS , 2001 .

[9]  Jan Pisek,et al.  Algorithm for global leaf area index retrieval using satellite imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[10]  ByRKMHAY.  Harvest index : a review of its use in plant breeding and crop physiology , 2012 .

[11]  Huiling Long,et al.  Monitoring the effects of land use and cover changes on net primary production: a case study in China's Yongding River basin. , 2009 .

[12]  Stephen V. Stehman,et al.  Model-assisted estimation as a unifying framework for estimating the area of land cover and land-cover change from remote sensing , 2009 .

[13]  W. Ju,et al.  Assessing the impact of restoration-induced land conversion and management alternatives on net primary productivity in Inner Mongolian grassland, China , 2013 .

[14]  Yanlian Zhou,et al.  Prediction of summer grain crop yield with a process-based ecosystem model and remote sensing data for the northern area of the Jiangsu Province, China , 2010 .

[15]  Y. Zhoua,et al.  Observation and simulation of net primary productivity in Qilian Mountain , western China , 2007 .

[16]  W. Zipperer,et al.  Ecosystem processes along an urban-to-rural gradient , 2005, Urban Ecosystems.

[17]  J. Pisek,et al.  Effects of foliage clumping on the estimation of global terrestrial gross primary productivity , 2012 .

[18]  Robert Costanza,et al.  Ecosystem Appropriation by Cities , 1996 .

[19]  D. Xie,et al.  LAI inversion algorithm based on directional reflectance kernels. , 2007, Journal of environmental management.

[20]  C. Woodcock,et al.  Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation , 2013 .

[21]  S. Running,et al.  A general model of forest ecosystem processes for regional applications I. Hydrologic balance, canopy gas exchange and primary production processes , 1988 .

[22]  D. Lu.  The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[23]  Jiyuan Liu,et al.  Assessing the consequence of land use change on agricultural productivity in China , 2009 .

[24]  J. Ross Quinlan,et al.  Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.

[25]  Zhi Qiao,et al.  Assessing the impact of the urbanization process on net primary productivity in China in 1989-2000. , 2014, Environmental pollution.

[26]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[27]  Fan Jing-yun,et al.  Seasonal Dynamics of Terrestrial Net Primary Production in Response to Climate Changes in China , 2003 .

[28]  Tarin Paz-Kagan,et al.  Evaluation of ecosystem responses to land-use change using soil quality and primary productivity in a semi-arid area, Israel , 2014 .

[29]  W. Ju,et al.  Net primary productivity of China's terrestrial ecosystems from a process model driven by remote sensing. , 2007, Journal of environmental management.

[30]  Taylor H. Ricketts,et al.  The consequences of urban land transformation on net primary productivity in the United States , 2004 .

[31]  H. Odum,et al.  Primary Productivity of the Biosphere , 1978, Ecological Studies.

[32]  Mao Kun.  Research on ETM+ Image Restoration Method Based on Local Correlation Analysis , 2011 .

[33]  W. Schlesinger.  Biogeochemistry: An Analysis of Global Change , 1991 .

[34]  Ronald E. McRoberts,et al.  Probability- and model-based approaches to inference for proportion forest using satellite imagery as ancillary data , 2010 .

[35]  C. Elvidge,et al.  Using nighttime DMSP/OLS images of city lights to estimate the impact of urban land use on soil resources in the United States , 1997 .

[36]  Ronald E. McRoberts,et al.  Satellite image-based maps: Scientific inference or pretty pictures? , 2011 .

[37]  N. Grimm,et al.  Integrated Approaches to Long-TermStudies of Urban Ecological Systems , 2000 .

[38]  Cao Ming-kui,et al.  Spatial Pattern and Topographic Control of China's Agricultural Productivity Variability , 2007 .

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

[40]  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.

[41]  W. Ju,et al.  Impacts of droughts on carbon sequestration by China's terrestrial ecosystems from 2000 to 2011 , 2013 .

[42]  J. Chen,et al.  A process-based boreal ecosystem productivity simulator using remote sensing inputs , 1997 .

[43]  H. R. Sinclair,et al.  Assessing the Impact of Land Conversion to Urban Use on Soils with Different Productivity Levels in the USA , 2001 .

[44]  Taobo,et al.  The temporal and spatial patterns of terrestrial net primary productivity in China , 2003 .

[45]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[46]  Yanlian Zhou,et al.  Close relationship between spectral vegetation indices and Vcmax in deciduous and mixed forests , 2014 .

[47]  S. Running,et al.  Assessing the impact of urban land development on net primary productivity in the southeastern United States , 2003 .

[48]  R. DeFries,et al.  Classification trees: an alternative to traditional land cover classifiers , 1996 .

[49]  Yanlian Zhou,et al.  Development of a two-leaf light use efficiency model for improving the calculation of terrestrial gross primary productivity , 2013 .

[50]  Yao Cheng.  Remote Sensing Monitoring and Evaluation of Land Use Change in Nanjing Based on TM Images , 2010 .

[51]  Liu Chun-yang.  Study on Spatial-Temporal Changes of Nanjing Land Use Based on RS/GIS Technology , 2006 .

[52]  Maosheng Zhao,et al.  Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009 , 2010, Science.

[53]  Christopher B. Field,et al.  Combining satellite data and biogeochemical models to estimate global effects of human‐induced land cover change on carbon emissions and primary productivity , 1999 .

[54]  N. Grimm,et al.  Integrated Approaches to Long-TermStudies of Urban Ecological Systems , 2000 .

[55]  T. A. Black,et al.  Modelling multi-year coupled carbon and water fluxes in a boreal aspen forest , 2006 .

[56]  S. An,et al.  Assessing the impact of urbanization on regional net primary productivity in Jiangyin County, China. , 2007, Journal of environmental management.

[57]  Gérard Dedieu,et al.  Methodology for the estimation of terrestrial net primary production from remotely sensed data , 1994 .

[58]  C. Brodley,et al.  Decision tree classification of land cover from remotely sensed data , 1997 .

[59]  F. J. Gallego.  Remote sensing and land cover area estimation , 2004 .

[60]  Piao Shi,et al.  Seasonal Dynamics of Terrestrial Net Primary Production in Response to Climate Changes in China , 2003 .

[61]  Fengsong Pei,et al.  Assessing the differences in net primary productivity between pre- and post-urban land development in China , 2013 .

[62]  M. Carreiro,et al.  Forest Remnants Along Urban-Rural Gradients: Examining Their Potential for Global Change Research , 2005, Ecosystems.

[63]  Debra P. C. Peters,et al.  The changing landscape : ecosystem responses to urbanization and pollution across climatic and societal gradients , 2008 .

[64]  Yanhong Tang,et al.  Calibration of Terra/MODIS gross primary production over an irrigated cropland on the North China Plain and an alpine meadow on the Tibetan Plateau , 2008 .