Global mapping of artificial surfaces at 30-m resolution

Urbanization is expected to accelerate with population growth and economic development at the global scale. The artificial surface is the main land cover form of urbanization. On the one hand, urbanization provides spaces for industry, economic activities and residence. On the other hand, artificial surfaces change the earth surface to a large extent, thus significantly affecting natural processes such as the heat exchange, hydrological processes and ecological balance. Therefore, the global mapping of artificial surfaces is valuable for both natural science and social science. This study produced the global artificial surface maps at 30-m resolution for two base-years using the satellite images acquired around 2000 and 2010. First, we proposed a new definition of “artificial surface” based on patch level with consideration of its geographic meaning and image features at 30-m resolution. Second, pixel-based and object-based image processing techniques were combined for the extraction of artificial surface patches. Finally, human editing and a quality control system were employed to guarantee the quality of global mapping. Independent accuracy assessments show that the user’s accuracy of this product is higher than 80%. It can be concluded that the product is the most reliable one among all the available global datasets of artificial surfaces (or related types). The data can significantly contribute to various research fields, such as urbanization and ecosystem assessment.

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

[2]  Bernard Afiik Akanpabadai Akanbang,et al.  Spatio-temporal dynamics and livelihoods transformation in Wa, Ghana , 2018, Land Use Policy.

[3]  Pol Coppin,et al.  Endmember variability in Spectral Mixture Analysis: A review , 2011 .

[4]  Paul M. Mather,et al.  An assessment of the effectiveness of decision tree methods for land cover classification , 2003 .

[5]  Conghe Song,et al.  Long-term land cover dynamics by multi-temporal classification across the Landsat-5 record , 2013 .

[6]  Hassiba Nemmour,et al.  Multiple support vector machines for land cover change detection: An application for mapping urban extensions , 2006 .

[7]  Jiyuan Liu,et al.  Study on spatial pattern of land-use change in China during 1995–2000 , 2003, Science in China Series D Earth Sciences.

[8]  O. Dikshit,et al.  Improvement of classification in urban areas by the use of textural features: The case study of Lucknow city, Uttar Pradesh , 2001 .

[9]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

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

[11]  Pesaresi Martino,et al.  A methodology to quantify built-up structures from optical VHR imagery , 2009 .

[12]  James McCarthy,et al.  Rural geography: globalizing the countryside , 2008 .

[13]  Xuehong Chen,et al.  An automated approach for updating land cover maps based on integrated change detection and classification methods , 2012 .

[14]  X. H,et al.  A new index for delineating built-up land features in satellite imagery , 2008 .

[15]  R. Nemani,et al.  Global Distribution and Density of Constructed Impervious Surfaces , 2007, Sensors.

[16]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[17]  Ruiliang Pu,et al.  Advances in Environmental Remote Sensing : Sensors, Algorithms, and Applications , 2011 .

[18]  E. Kalnay,et al.  Impact of urbanization and land-use change on climate , 2003, Nature.

[19]  Hugo Carrão,et al.  Combining per-pixel and object-based classifications for mapping land cover over large areas , 2014 .

[20]  J. Qi,et al.  Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization , 2009 .

[21]  X. Tingdong,et al.  Creating and destroying vacancies in solids and non-equilibrium grain-boundary segregation , 2003 .

[22]  D. Lu,et al.  Urban classification using full spectral information of landsat ETM+ imagery in Marion County, Indiana , 2005 .

[23]  R. Nicholls,et al.  A global analysis of human settlement in coastal zones , 2003 .

[24]  D. Civco,et al.  Mapping urban areas on a global scale: which of the eight maps now available is more accurate? , 2009 .

[25]  Nandin-Erdene Tsendbazar,et al.  Global land cover mapping: current status and future trends , 2014 .

[26]  Antonio Di Gregorio,et al.  Land cover classification system (LCCS): classification concepts and user manual for software version 1.0 , 2000 .

[27]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[28]  M. Montgomery The Urban Transformation of the Developing World , 2008, Science.

[29]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .

[30]  H. Schellnhuber,et al.  Urbanised territories as a specific component of the Global Carbon Cycle , 2004 .

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

[32]  Bicheron Patrice,et al.  The Most Detailed Portrait of Earth , 2008 .

[33]  Shu Peng,et al.  A web-based system for supporting global land cover data production , 2015 .

[34]  C. Woodcock,et al.  Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data , 2012 .

[35]  Jungho Im,et al.  ISPRS Journal of Photogrammetry and Remote Sensing , 2022 .

[36]  M. Brockerhoff,et al.  World Urbanization Prospects: The 1996 Revision , 1998 .

[37]  D. Danko The digital chart of the world project , 1992 .

[38]  J. Fry,et al.  Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods , 2009 .

[39]  Deborah Balk,et al.  The Distribution of People and the Dimension of Place: Methodologies to Improve the Global Estimation of Urban Extents , 2004 .

[40]  M. Batty The Size, Scale, and Shape of Cities , 2008, Science.

[41]  K. K. Goldewijk Three Centuries of Global Population Growth: A Spatial Referenced Population (Density) Database for 1700–2000 , 2005 .

[42]  Qihao Weng,et al.  Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends , 2012 .

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

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

[45]  J. Wickham,et al.  Completion of the 2001 National Land Cover Database for the conterminous United States , 2007 .

[46]  Hankui K. Zhang,et al.  Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .

[47]  Matt Aitkenhead,et al.  Automating land cover mapping of Scotland using expert system and knowledge integration methods , 2011 .

[48]  C. Homer,et al.  Updating the 2001 National Land Cover Database Impervious Surface Products to 2006 using Landsat Imagery Change Detection Methods , 2010 .

[49]  R. G. Davies,et al.  Human impacts and the global distribution of extinction risk , 2006, Proceedings of the Royal Society B: Biological Sciences.

[50]  X. Bai,et al.  Society: Realizing China's urban dream , 2014, Nature.

[51]  Qihao Weng Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications , 2011 .

[52]  C. Woodcock,et al.  Compact, Dispersed, Fragmented, Extensive? A Comparison of Urban Growth in Twenty-five Global Cities using Remotely Sensed Data, Pattern Metrics and Census Information , 2008 .

[53]  Shu Peng,et al.  High-resolution remote sensing mapping of global land water , 2014, Science China Earth Sciences.

[54]  A. Schneider,et al.  A critical look at representations of urban areas in global maps , 2007 .

[55]  S. Angel,et al.  The dynamics of global urban expansion , 2005 .

[56]  J. Rogan,et al.  Remote sensing technology for mapping and monitoring land-cover and land-use change , 2004 .

[57]  P. Bocquier WORLD URBANIZATION PROSPECTS: AN ALTERNATIVE TO THE UN MODEL OF PROJECTION COMPATIBLE WITH URBAN TRANSITION THEORY 1 , 2005 .

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

[59]  Qihao Weng,et al.  Modeling Urban Growth Effects on Surface Runoff with the Integration of Remote Sensing and GIS , 2001, Environmental management.

[60]  Bert Guindon,et al.  Landsat urban mapping based on a combined spectral–spatial methodology , 2004 .

[61]  K. Seto,et al.  Comparing ARTMAP Neural Network with the Maximum-Likelihood Classifier for Detecting Urban Change , 2003 .

[62]  P. Ohadike Urbanization , 1968, Encyclopedia of the UN Sustainable Development Goals.

[63]  Hermann Kaufmann,et al.  Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data , 2007 .

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

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

[66]  Zifa Wang,et al.  Effect of urbanization on the winter precipitation distribution in Beijing area , 2009 .

[67]  Fei Chen,et al.  Analysis of observations on the urban surface energy balance in Beijing , 2012, Science China Earth Sciences.

[68]  B. Cohen Urban Growth in Developing Countries: A Review of Current Trends and a Caution Regarding Existing Forecasts , 2004 .

[69]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[70]  C. Ju,et al.  Concepts and Key Techniques for 30 m Global Land Cover Mapping , 2014 .