The grey-green divide: multi-temporal analysis of greenness across 10,000 urban centres derived from the Global Human Settlement Layer (GHSL)

ABSTRACT The presence of green spaces within city centres has been recognized as a valuable component of the city landscape. Vegetation provides a variety of benefits including energy saving, improved air quality, reduced noise pollution, decreased ambient temperature and psychological restoration. Evidence also shows that the amount of vegetation, known as ‘greenness’, in densely populated areas, can also be an indicator of the relative wealth of a neighbourhood. The ‘grey-green divide’, the contrast between built-up areas with a dominant grey colour and green spaces, is taken as a proxy indicator of sustainable management of cities and planning of urban growth. Consistent and continuous assessment of greenness in cities is therefore essential for monitoring progress towards the United Nations Sustainable Development Goal 11. The availability of multi-temporal greenness information from Landsat data archives together with data derived from the city centres database of the Global Human Settlement Layer (GHSL) initiative, offers a unique perspective to quantify and analyse changes in greenness across 10,323 urban centres all around the globe. In this research, we assess differences between greenness within and outside the built-up area for all the urban centres described by the city centres database of the GHSL. We also analyse changes in the amount of green space over time considering changes in the built-up areas in the periods 1990, 2000 and 2014. The results show an overall trend of increased greenness between 1990 and 2014 in most cities. The effect of greening is observed also for most of the 32 world megacities. We conclude that using simple yet effective approaches exploiting open and free global data it is possible to provide quantitative information on the greenness of cities and its changes over time. This information is of direct interest for urban planners and decision-makers to mitigate urban related environmental and social impacts.

[1]  Martha C. Anderson,et al.  Landsat-8: Science and Product Vision for Terrestrial Global Change Research , 2014 .

[2]  C. Justice,et al.  Selecting the spatial resolution of satellite sensors required for global monitoring of land transformations , 1988 .

[3]  Martino Pesaresi,et al.  Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator - SDG 11.3.1 , 2018, ISPRS Int. J. Geo Inf..

[4]  B. Markham,et al.  Forty-year calibrated record of earth-reflected radiance from Landsat: A review , 2012 .

[5]  L. Dijkstra,et al.  Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping , 2017 .

[6]  Alan H. Strahler,et al.  Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments , 2005 .

[7]  C. Small High spatial resolution spectral mixture analysis of urban reflectance , 2003 .

[8]  Molly E. Brown,et al.  Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Wei Guo,et al.  2006–2015 mega-drought in the western USA and its monitoring from space data , 2015 .

[10]  Tim D. Fletcher,et al.  Urban Stormwater Runoff: A New Class of Environmental Flow Problem , 2012, PloS one.

[11]  Sérgio Freire,et al.  Combining GHSL and GPW to improve global population mapping , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[12]  K. Thomas,et al.  Atlas of the Human Planet - Mapping Human Presence on Earth with the Global Human Settlement Layer , 2016 .

[13]  Kevin J. Gaston,et al.  City-wide relationships between green spaces, urban land use and topography , 2008, Urban Ecosystems.

[14]  Karen C. Seto,et al.  Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing , 2014, Remote. Sens..

[15]  W. Leal Filho,et al.  An Evidence-Based Review of Impacts, Strategies and Tools to Mitigate Urban Heat Islands , 2017, International journal of environmental research and public health.

[16]  Pengyu Zhu,et al.  Demand for urban forests in United States cities , 2008 .

[17]  Conghong Huang,et al.  The temporal trend of urban green coverage in major Chinese cities between 1990 and 2010 , 2014 .

[18]  Jinsong Deng,et al.  Monitoring Urban Greenness Dynamics Using Multiple Endmember Spectral Mixture Analysis , 2014, PloS one.

[19]  Jonas Ardö,et al.  Disentangling the effects of climate and people on Sahel vegetation dynamics , 2008 .

[20]  Yi‐Chen Wang,et al.  Spatial–temporal dynamics of urban green space in response to rapid urbanization and greening policies , 2011 .

[21]  Ranga B. Myneni,et al.  The interpretation of spectral vegetation indexes , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Shuguang Liu,et al.  Spatiotemporal trends of terrestrial vegetation activity along the urban development intensity gradient in China's 32 major cities. , 2014, The Science of the total environment.

[23]  Ferri Stefano,et al.  Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014 , 2016 .

[24]  J. Masek,et al.  The vegetation greenness trend in Canada and US Alaska from 1984–2012 Landsat data , 2016 .

[25]  Sérgio Freire,et al.  Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer , 2018, Remote. Sens..

[26]  G. Chander,et al.  Assessment of the NASA–USGS Global Land Survey (GLS) datasets , 2013 .

[27]  Hankui K. Zhang,et al.  Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. , 2016, Remote sensing of environment.

[28]  Jiangye Yuan,et al.  Learning Building Extraction in Aerial Scenes with Convolutional Networks , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[30]  Martino Pesaresi,et al.  A New Method for Earth Observation Data Analytics Based on Symbolic Machine Learning , 2016, Remote. Sens..

[31]  J. G. Conijn,et al.  Trends in Global Vegetation Activity and Climatic Drivers Indicate a Decoupled Response to Climate Change , 2015, PloS one.

[32]  Nicholas C. Coops,et al.  Regional assessment of pan-Pacific urban environments over 25 years using annual gap free Landsat data , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[33]  A. Sia,et al.  Perspectives on five decades of the urban greening of Singapore , 2013 .

[34]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[35]  Jayajit Chakraborty,et al.  Street Trees and Equity: Evaluating the Spatial Distribution of an Urban Amenity , 2009 .

[36]  Zalán Tobak,et al.  Spatiotemporal Assessment of Vegetation Indices and Land Cover for Erbil City and Its Surrounding Using Modis Imageries , 2017 .

[37]  Paul Brindley,et al.  Temporal changes in greenspace in a highly urbanized region , 2011, Biology Letters.

[38]  Sérgio Freire,et al.  GHS built-up grid, derived from Landsat, multitemporal (1975, 1990, 2000, 2014) , 2015 .

[39]  H. Jordan,et al.  Value of urban green spaces in promoting healthy living and wellbeing: prospects for planning , 2015, Risk management and healthcare policy.

[40]  K. Kumagai ANALYSIS OF VEGETATION DISTRIBUTION IN URBAN AREAS : SPATIAL ANALYSIS APPROACH ON A REGIONAL SCALE , 2008 .

[41]  Patricia Gober,et al.  Desert urbanization and the challenges of water sustainability , 2010 .

[42]  J. Canadell,et al.  Greening of the Earth and its drivers , 2016 .

[43]  Martha C. Anderson,et al.  Free Access to Landsat Imagery , 2008, Science.

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

[45]  Yin Ren,et al.  Temporal trend of green space coverage in China and its relationship with urbanization over the last two decades. , 2013, The Science of the total environment.

[46]  Martino Pesaresi,et al.  GHS settlement grid, following the REGIO model 2014 in application to GHSL Landsat and CIESIN GPW v4-multitemporal (1975-1990-2000-2015) , 2016 .

[47]  Le Yu,et al.  Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega-Cities , 2017, Remote. Sens..

[48]  S. Searle,et al.  Urban environment of New York City promotes growth in northern red oak seedlings. , 2012, Tree physiology.

[49]  Frédéric Baret,et al.  Intercalibration of vegetation indices from different sensor systems , 2003 .

[50]  Kevin J. Gaston,et al.  The scaling of green space coverage in European cities , 2009, Biology Letters.

[51]  J. Croft,et al.  Spatial Disparities in the Distribution of Parks and Green Spaces in the USA , 2013, Annals of behavioral medicine : a publication of the Society of Behavioral Medicine.

[52]  C.Y. Jim,et al.  Assessment and valuation of the ecosystem services provided by urban forests. , 2008 .

[53]  Jan Verbesselt,et al.  Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology , 2013, Remote. Sens..

[54]  Nicholas C. Coops,et al.  Estimating urban vegetation fraction across 25 cities in pan-Pacific using Landsat time series data , 2017 .