Using Web-enabled Landsat Data time series to analyze the impacts of urban areas on remotely sensed vegetation dynamics

Earth is currently experiencing rapid urban growth with >50% of global population living in urban areas. Urbanization occurs as cities expand to meet the demands of increasing populations and socioeconomic growth. Consequently, there is a need for remote sensing research to detect, quantify, and monitor urbanization and subsequent impacts on the environment. Here we used Normalized Difference Vegetation Index (NDVI) data products derived from the Web-enabled Landsat Data (WELD) project to (1) characterize the response of vegetation to urban land cover change and (2) analyze the impacts of urban areas on land surface phenology across rural to urban gradients for two cities located on the United States Great Plains. Here we fit the decade (2003-2012) of NDVI observations as a quadratic function of thermal time to calculate land surface phenology (LSP) metrics and characterize vegetation dynamics on an urban-rural gradient. We found croplands to exhibit greater variation in NDVI at half thermal time to peak compared to forest and developed land cover types. We found a linear relationship between modeled peak height NDVI and NDVI at half thermal time to peak in forest and developed pixels, as well as pixels that experienced a land cover change from cropland to developed. In general, duration of season decreased with distance from the city center in deciduous forest pixels for both cities. Developed pixels had lower modeled peak height NDVI, longer duration of season and greater variation compared to forest pixels.

[1]  M. Duggin,et al.  A temporal analysis of urban forest carbon storage using remote sensing , 2006 .

[2]  M. Friedl,et al.  Mapping global urban areas using MODIS 500-m data: new methods and datasets based on 'urban ecoregions'. , 2010 .

[3]  M. Friedl,et al.  A new map of global urban extent from MODIS satellite data , 2009 .

[4]  D. Roy,et al.  Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States , 2010 .

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

[6]  K. Seto,et al.  Global urban land-use trends and climate impacts , 2009 .

[7]  D. Civco,et al.  THE DIMENSIONS OF GLOBAL URBAN EXPANSION: ESTIMATES AND PROJECTIONS FOR ALL COUNTRIES, 2000–2050 , 2011 .

[8]  D. Hollinger,et al.  Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. , 2009, Tree physiology.

[9]  Geoffrey M. Henebry,et al.  Dual scale trend analysis for evaluating climatic and anthropogenic effects on the vegetated land surface in Russia and Kazakhstan , 2009 .

[10]  M. Friedl,et al.  Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data , 2013 .

[11]  C. Woodcock,et al.  Continuous change detection and classification of land cover using all available Landsat data , 2014 .

[12]  L. Hutyra,et al.  Mapping carbon storage in urban trees with multi-source remote sensing data: relationships between biomass, land use, and demographics in Boston neighborhoods. , 2014, The Science of the total environment.

[13]  K. Seto,et al.  Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools , 2012, Proceedings of the National Academy of Sciences.

[14]  Martin Herold,et al.  Some Recommendations for Global Efforts in Urban Monitoring and Assessments from Remote Sensing , 2009 .

[15]  Nazmul Hossain,et al.  Change of impervious surface area between 2001 and 2006 in the conterminous United States , 2011 .

[16]  G. Henebry,et al.  Land surface phenology, climatic variation, and institutional change: Analyzing agricultural land cover change in Kazakhstan , 2004 .

[17]  Suming Jin,et al.  A comprehensive change detection method for updating the National Land Cover Database to circa 2011 , 2013 .