A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis

Abstract Accurate and timely information regarding the extent and spatial distribution of urban areas on regional and global scales is crucially important for both scientific and policy-making communities. Stable nighttime light (NTL) data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) provides a unique proxy of human settlement and activity, which has been used in the mapping and analysis of urban areas and urbanization dynamics. However, blooming and saturation effects of DMSP/OLS NTL data are two unresolved problems in regional urban area mapping and analysis. This study proposed a new urban index termed the Temperature and Vegetation Adjusted NTL Urban Index (TVANUI). It is intended to reduce blooming and saturation effects and to enhance urban features by combining DMSP/OLS NTL data with Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer onboard the Terra satellite. The proposed index was evaluated in two study areas by comparison with established urban indices. The results demonstrated the proposed TVANUI was effective in enhancing the variation of DMSP/OLS light in urban areas and in reducing blooming and saturation effects, showing better performance than three established urban indices. The TVANUI also significantly outperformed the established urban indices in urban area mapping using both the global-fixed threshold and the local-optimal threshold methods. Thus, the proposed TVANUI provides a useful variable for urban area mapping and analysis on regional scale, as well as for urbanization dynamics using time-series DMSP/OLS and related satellite data.

[1]  Eric F. Lambin,et al.  Combining vegetation indices and surface temperature for land-cover mapping at broad spatial scales , 1995 .

[2]  Ramakrishna R. Nemani,et al.  International Journal of Remote Sensing the Nightsat Mission Concept the Nightsat Mission Concept , 2022 .

[3]  Osamu Higashi,et al.  A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data , 2009 .

[4]  Michael P. Johnson.  Environmental Impacts of Urban Sprawl: A Survey of the Literature and Proposed Research Agenda , 2001 .

[5]  Suming Jin,et al.  Completion of the 2011 National Land Cover Database for the Conterminous United States – Representing a Decade of Land Cover Change Information , 2015 .

[6]  Jun Li,et al.  An improved method of night-time light saturation reduction based on EVI , 2015 .

[7]  Donald W. Hillger,et al.  First-Light Imagery from Suomi NPP VIIRS , 2013 .

[8]  M Tan.  Urbanization in China: Critical issues in an Era of Rapid Growth , 2009 .

[9]  K. Oleson,et al.  Strong contributions of local background climate to urban heat islands , 2014, Nature.

[10]  M. Friedl,et al.  Sources of variation in radiometric surface temperature over a tallgrass prairie , 1994 .

[11]  D. Roberts,et al.  Census from Heaven: An estimate of the global human population using night-time satellite imagery , 2001 .

[12]  D. Lu,et al.  Use of impervious surface in urban land-use classification , 2006 .

[13]  P. Sutton,et al.  Radiance Calibration of DMSP-OLS Low-Light Imaging Data of Human Settlements , 1999 .

[14]  W. Yue,et al.  The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data , 2007 .

[15]  J. Muller,et al.  Night-time Imagery as a Tool for Global Mapping of Socioeconomic Parameters and Greenhouse Gas Emissions , 2000 .

[16]  Toby N. Carlson,et al.  The impact of land use — land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective , 2000 .

[17]  Yang Liu,et al.  Integrating Multiple Source Data to Enhance Variation and Weaken the Blooming Effect of DMSP-OLS Light , 2015, Remote. Sens..

[18]  C. Elvidge,et al.  A Technique for Using Composite DMSP/OLS "City Lights"Satellite Data to Map Urban Area , 1997 .

[19]  T. Oke.  The energetic basis of the urban heat island , 1982 .

[20]  Chunyang He,et al.  Quantifying spatiotemporal patterns of urban impervious surfaces in China: An improved assessment using nighttime light data , 2014 .

[21]  P. Gong,et al.  Validation of urban boundaries derived from global night-time satellite imagery , 2003 .

[22]  Dengsheng Lu,et al.  Regional mapping of human settlements in southeastern China with multisensor remotely sensed data , 2008 .

[23]  Qihao Weng.  Fractal Analysis of Satellite-Detected Urban Heat Island Effect , 2003 .

[24]  Xia Li,et al.  A Normalized Urban Areas Composite Index (NUACI) Based on Combination of DMSP-OLS and MODIS for Mapping Impervious Surface Area , 2015, Remote. Sens..

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

[26]  R. Ewing,et al.  The impact of urban form on U.S. residential energy use , 2008 .

[27]  C. Elvidge,et al.  Spatial analysis of global urban extent from DMSP-OLS night lights , 2005 .

[28]  Stephen P. Mills,et al.  Suomi satellite brings to light a unique frontier of nighttime environmental sensing capabilities , 2012, Proceedings of the National Academy of Sciences.

[29]  E. Mcpherson,et al.  Urban ecosystems and the North American carbon cycle , 2006 .

[30]  C. Milesi,et al.  Multi-scale standardized spectral mixture models , 2013 .

[31]  Cristina Milesi,et al.  Assessing the environmental impacts of human settlements using satellite data , 2003 .

[32]  Allen S. Hope,et al.  The relationship between surface temperature and a spectral vegetation index of a tallgrass prairie : effects of burning and other landscape controls , 1992 .

[33]  C. Elvidge,et al.  Methods Used For the 2006 Radiance Lights , 2010 .

[34]  J. C. Price.  Using spatial context in satellite data to infer regional scale evapotranspiration , 1990 .

[35]  C. Elvidge,et al.  Mapping City Lights With Nighttime Data from the DMSP Operational Linescan System , 1997 .

[36]  Xiangzheng Deng,et al.  Growth, population and industrialization, and urban land expansion of China , 2008 .

[37]  Lin Ma,et al.  Evaluating Saturation Correction Methods for DMSP/OLS Nighttime Light Data: A Case Study from China's Cities , 2014, Remote. Sens..

[38]  T. Pei,et al.  Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities , 2012 .

[39]  Chong Liu,et al.  The Integrated Use of DMSP-OLS Nighttime Light and MODIS Data for Monitoring Large-Scale Impervious Surface Dynamics: A Case Study in the Yangtze River Delta , 2014, Remote. Sens..

[40]  Xiya Zhang,et al.  Regional Urban Extent Extraction Using Multi-Sensor Data and One-Class Classification , 2015, Remote. Sens..

[41]  B. Holben.  Characteristics of maximum-value composite images from temporal AVHRR data , 1986 .

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

[43]  Christopher D. Elvidge,et al.  VIIRS Nightfire: Satellite Pyrometry at Night , 2013, Remote. Sens..

[44]  Christopher D. Elvidge,et al.  Satellite inventory of human settlements using nocturnal radiation emissions: a contribution for the global toolchest , 1997 .

[45]  P. Ciais,et al.  Response to Comment on ``Surface Urban Heat Island Across 419 Global Big Cities'' , 2012 .

[46]  Zhifeng Liu,et al.  Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008 , 2012 .

[47]  Eric F. Lambin,et al.  Land-cover changes in sub-saharan Africa (1982–1991): Application of a change index based on remotely sensed surface temperature and vegetation indices at a continental scale , 1997 .

[48]  Zhang Xiaolei,et al.  Urbanization in Western China , 2013 .

[49]  A. Huete,et al.  A feedback based modification of the NDVI to minimize canopy background and atmospheric noise , 1995 .

[50]  S. Maithani,et al.  Monitoring growth of built-up areas in indo-gangetic plain using multi-sensor remote sensing data , 2010 .

[51]  S. Grimmond.  Urbanization and global environmental change: local effects of urban warming , 2007 .

[52]  C. Elvidge,et al.  Night-time lights of the world: 1994–1995 , 2001 .

[53]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[54]  Julia Boike,et al.  Spatio-temporal sensitivity of MODIS land surface temperature anomalies indicates high potential for large-scale land cover change detection in Arctic permafrost landscapes , 2014 .

[55]  Weiping Zhu,et al.  WRF simulations of urban heat island under hot-weather synoptic conditions: The case study of Hangzhou City, China , 2014 .

[56]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

[57]  K. Parikh,et al.  THE ENVIRONMENTAL CONSEQUENCES OF URBAN GROWTH: CROSS-NATIONAL PERSPECTIVES ON ECONOMIC DEVELOPMENT, AIR POLLUTION, AND CITY SIZE , 1992 .

[58]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[59]  J. Rodgers,et al.  Thirteen ways to look at the correlation coefficient , 1988 .

[60]  M. McKinney,et al.  Effects of urbanization on species richness: A review of plants and animals , 2008, Urban Ecosystems.

[61]  D. Lu,et al.  Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies , 2004 .

[62]  Bin Li,et al.  Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery , 2015, Remote. Sens..

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

[64]  N. T. Hoan,et al.  Global urban characterization using population density, DMSP and MODIS data , 2009, 2009 Joint Urban Remote Sensing Event.

[65]  U. Tappeiner,et al.  Determinants of urban–rural land surface temperature differences – A landscape scale perspective , 2015 .

[66]  D. Roy,et al.  An overview of MODIS Land data processing and product status , 2002 .

[67]  Eric F. Lambin,et al.  The surface temperature-vegetation index space for land cover and land-cover change analysis , 1996 .

[68]  Jonathan D. Jordan,et al.  Spatially Distributed Watershed Mapping and Modeling: Thermal Maps and Vegetation Indices to Enhance Land Cover and Surface Microclimate mapping (part 1) , 2003 .

[69]  K. Seto,et al.  The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity , 2013 .

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

[71]  T. Carlson,et al.  A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover , 1994 .

[72]  M. Ridd.  Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities , 1995 .

[73]  José A. Sobrino,et al.  Toward remote sensing methods for land cover dynamic monitoring: Application to Morocco , 2000 .

[74]  A Comparative Study on the Alteration of Industrial Structure in Chinese and American Urbanizations , 2014 .

[75]  C. Woodcock,et al.  Mapping Urban Areas by Fusing Multiple Sources of Coarse Resolution Remotely Sensed Data , 2003 .

[76]  Masanao Hara,et al.  A Saturated Light Correction Method for DMSP/OLS Nighttime Satellite Imagery , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[77]  Simon J. Hook,et al.  Intercomparison of versions 4, 4.1 and 5 of the MODIS Land Surface Temperature and Emissivity products and validation with laboratory measurements of sand samples from the Namib desert, Namibia , 2009 .