Quantifying Impacts of Land-Use/Cover Change on Urban Vegetation Gross Primary Production: A Case Study of Wuhan, China

This study quantified the impacts of land-use/cover change (LUCC) on gross primary production (GPP) during 2000–2013 in a typical densely urbanized Chinese city, Wuhan. GPP was estimated at 30-m spatial resolution using annual land cover maps, meteorological data of the baseline year, and the normalized difference vegetation index (NDVI), which was generated with the spatial and temporal adaptive reflectance fusion model (STARFM) based on Landsat and MODIS images. The results showed that approximately 309.95 Gg C was lost over 13 years, which was mainly due to the conversion from cropland to built-up areas. The interannual variation of GPP was affected by the change of vegetation composition, especially the increasing relative fraction of forests. The loss of GPP due to the conversion from forest to cropland fluctuated through the study period, but showed a sharp decrease in 2007 and 2008. The gain of GPP due to the conversion from cropland to forest was low between 2001 and 2009, but increased dramatically between 2009 and 2013. The change rate map showed an increasing trend along the highways, and a decreasing trend around the metropolitan area and lakes. The results indicated that carbon consequences should be considered before land management policies are put forth.

[1]  Jianguo Wu,et al.  Urbanization alters spatiotemporal patterns of ecosystem primary production: a case study of the Phoenix metropolitan region, USA. , 2009 .

[2]  R. Myneni,et al.  On the relationship between FAPAR and NDVI , 1994 .

[3]  W. Cohen,et al.  Evaluation of MODIS NPP and GPP products across multiple biomes. , 2006 .

[4]  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 .

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

[6]  James C. Storey,et al.  Four years of Landsat-7 on-orbit geometric calibration and performance , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[7]  T. Dawson,et al.  Urbanization effects on tree growth in the vicinity of New York City , 2003, Nature.

[8]  Paul J. Crutzen,et al.  New Directions: The growing urban heat and pollution "island" effect - impact on chemistry and climate , 2004 .

[9]  D. Roy,et al.  Achieving sub-pixel geolocation accuracy in support of MODIS land science , 2002 .

[10]  Yi Huang,et al.  Mapping and Evaluation of NDVI Trends from Synthetic Time Series Obtained by Blending Landsat and MODIS Data around a Coalfield on the Loess Plateau , 2013, Remote. Sens..

[11]  Paul R. Lowe,et al.  The Computation of Saturation Vapor Pressure , 1974 .

[12]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

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

[14]  Devendra Singh,et al.  Generation and evaluation of gross primary productivity using Landsat data through blending with MODIS data , 2011, Int. J. Appl. Earth Obs. Geoinformation.

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

[16]  Yan-xu Liu,et al.  The influence of rapid urbanization and land use changes on terrestrial carbon sources/sinks in Guangzhou, China , 2016 .

[17]  Galina Churkina,et al.  Modeling the carbon cycle of urban systems , 2008 .

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

[19]  Wunian Yang,et al.  Spatial Pattern of Carbon Sequestration and Urban Sustainability: Analysis of Land-Use and Carbon Emission in Guang’an, China , 2017 .

[20]  L. Pearson,et al.  Sustainable urban agriculture: stocktake and opportunities , 2010 .

[21]  Kasturi Devi Kanniah,et al.  Evaluation of Collections 4 and 5 of the MODIS Gross Primary Productivity product and algorithm improvement at a tropical savanna site in northern Australia , 2009 .

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

[23]  Conghe Song,et al.  Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain , 2011 .

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

[25]  W. Oechel,et al.  A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS , 2008 .

[26]  Mingguo Ma,et al.  Estimation of gross primary production over the terrestrial ecosystems in China , 2013 .

[27]  D. Roy,et al.  Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data , 2008 .

[28]  Yaolin Liu,et al.  Urban growth and its determinants across the Wuhan urban agglomeration, central China , 2014 .

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

[30]  J. Monteith SOLAR RADIATION AND PRODUCTIVITY IN TROPICAL ECOSYSTEMS , 1972 .

[31]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[32]  N. Grimm,et al.  Global Change and the Ecology of Cities , 2008, Science.

[33]  A. Gitelson,et al.  Remote estimation of crop gross primary production with Landsat data , 2012 .

[34]  Steven W. Running,et al.  Assessing interannual variation in MODIS-based estimates of gross primary production , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[35]  Nicholas C. Coops,et al.  Comparison of MODIS gross primary production estimates for forests across the U.S.A. with those generated by a simple process model, 3-PGS , 2007 .

[36]  Feng Li,et al.  Comprehensive concept planning of urban greening based on ecological principles: a case study in Beijing, China , 2005 .

[37]  Joanne C. White,et al.  Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. , 2009 .

[38]  Wout Verhoef,et al.  Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images , 1993 .

[39]  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.

[40]  Yaolong Zhao,et al.  Assessment Impacts of Weather and Land Use/Land Cover (LULC) Change on Urban Vegetation Net Primary Productivity (NPP): A Case Study in Guangzhou, China , 2013, Remote. Sens..

[41]  F. Javier García-Haro,et al.  A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion , 2015 .

[42]  K. Beurs,et al.  Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology , 2012 .

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

[44]  Jan Verbesselt,et al.  Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia , 2015 .