Remote sensing of urban greenspace exposure and equality: Scaling effects from greenspace and population mapping

Shengbiao WU, Wenbo YU, Jiafu AN, Chen LIN, Bin CHEN*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review


Satellite observations are increasingly used to characterize greenspace coverage, exposure, and equality assessment for environmental and health studies. Given the difference in spatial resolutions (namely, scale effect), different satellite datasets capture distinct levels of landscape details in urban green environments. However, existing studies on measuring scale effects are limited to the greenness mapping in a few sampled cities regardless of the scale effects from population mapping and the associated controls from greenspace landscape configurations. To close this knowledge gap, we conducted a comprehensive inventory of the scale effects, using widely used satellite-based greenness (i.e., 10-m Sentinel-2, 30-m Landsat-8, and 500-m MODIS) and population (i.e., 30-m HRPD, 100-m WorldPop, and 1-km GPW) mapping datasets over 679 major cities (urban area > 50 km2) in the United States. Results show that (1) compared with high-resolution Sentinel-2, Landsat-8 and MODIS overestimate greenspace coverage and human exposure but underestimate the inequality of human exposure to greenspace; (2) the differences in greenspace coverage and exposure across satellite sensors are linearly correlated with the greenspace provision magnitude; (3) landscape configuration explains the greenspace coverage differences across different satellite sensors. Aggregated and fragmented landscape metrics correlate positively and negatively with greenspace coverage differences, respectively; and (4) the spatial resolution of greenspace mapping shows a decreasing control while population data has tiny impacts on the inequality measurement of human exposure to greenspace. These findings answer how varying-scale satellite datasets cause a discrepancy in the measurement of greenspace coverage, human exposure, and inequality assessment. We advocate that researchers should select appropriate satellite-based greenness datasets by accounting for trade-offs between specific research benefits and costs to better position future greenspace-related environment and health outcome studies.
Original languageEnglish
Article number128136
Number of pages17
JournalUrban Forestry and Urban Greening
Publication statusE-pub ahead of print - 4 Nov 2023

Bibliographical note

Acknowledgements: This study is supported by National Key Research and Development Program of China (2022YFB3903703), the Research Grants Council of Hong Kong Early Career Scheme (HKU27600222) and General Research Fund (HKU17601423), National Natural Science Foundation of China Young Scientists Fund (42201373), NSFC/RGC Joint Research Scheme (N_HKU722/23), the International Research Center of Big Data for Sustainable Development Goals (CBAS2022GSP04), the Croucher Foundation (CAS22902/CAS22HU01), The University of Hong Kong HKU-100 Scholars Fund and Seed Fund for Strategic Interdisciplinary Research Scheme Fund, and The University of Hong Kong Faculty of Business and Economics and Shenzhen Research Institutes (SZRI2023- CRF-04).


  • Satellite greenspace mapping
  • Population-weighted exposure
  • Greenspace exposure inequality
  • Landscape configuration
  • Scale effect


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