Mapping the maximum extents of urban green spaces in 1039 cities using dense satellite images
Bibliographic Data
| ID | 15550268 |
|---|---|
| Authors | Conghong Huang (0000-0002-0180-0806, Tsinghua University, corresponding author), Jun Yang (0000-0001-6740-4358, Tsinghua University), Nicholas Clinton (0000-0002-1112-1006, Google (United States)), Le Yu (0000-0003-3115-2042, Tsinghua University), Huabing Huang (0000-0001-6253-8437, Sun Yat-sen University), Iryna Dronova (0000-0003-3339-3704, University of California, Berkeley), Jing Jin (0000-0001-9032-521X, Tsinghua University) |
| Year | 2021 |
| Volume | 16 |
| Issue | 6 |
| Pages | 064072-064072 |
| Publication date | 2021-05-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environmental Research Letters (JOURNAL) |
| Journal identifiers | ISSN: 1748-9326 • E-ISSN: 1748-9326 |
| Publisher | IOP Publishing (PUBLISHER • GB) |
| DOI | 10.1088/1748-9326/ac03dc |
| OpenAlex | W3164268217 |
| Language | EN |
| Citations received | 13 |
| References cited | 59 |
Spatial data of urban green spaces (UGS) are critical for cities worldwide to evaluate their progress towards achieving the urban sustainable development goals on UGS. However, UGS maps at the global scale with acceptable accuracies are not readily available. In this study, we mapped UGS of all 1039 mid- and large-sized cities across the globe in 2015 with dense remote sensing data (i.e. 51 494 Landsat images) and Google Earth Engine (GEE) platform. Also, we quantified the spatial distribution and accessibility of UGS within the cities. By combining the greenest pixel compositing method and the percentile-based image compositing method, we were able to obtain the maximum extent of UGS in cities while better differentiating UGS from other vegetation such as croplands. The mean overall classification accuracy reached 89.26% (SD = 3.26%), which was higher than existing global land cover products. Our maps showed that the mean UGS coverage in 1039 cities was 38.46% (SD = 20.27%), while the mean UGS accessibility was 82.67% (SD = 22.89%). However, there was a distinctive spatial equity issue as cities in high-income countries had higher coverage and better accessibility than cities in low-income countries. Besides developing a protocol for large-scale UGS mapping, our study results provide key baseline information to support international endeavors to fulfill the relevant urban sustainable development goals
Baseline (sea · Cartography · Compositing · Geography · Image (mathematics · Percentile · Remote sensing · Scale (ratio · Statistics · Sustainable development · Computer Science · Environmental Science · Land Use and Ecosystem Services · Mathematics · Urban Green Space and Health · Urban Heat Island Mitigation · Geology
Contrasting inequality in human exposure to greenspace between cities of Global North and Global South
Rapid Mapping and Annual Dynamic Evaluation of Quality of Urban Green Spaces on Google Earth Engine
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Use of normalized difference built-up index in automatically mapping urban areas from TM imagery
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The scaling of green space coverage in European cities
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Optimizing green space locations to reduce daytime and nighttime urban heat island effects in Phoenix, Arizona
A review of assessing the accuracy of classifications of remotely sensed data
Modis Collection 5 global land cover
High-Resolution Global Maps of 21st-Century Forest Cover Change
Google Earth Engine
Random Forests
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A Quantitative Review of Urban Ecosystem Service Assessments
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Climate and Plant Distribution
| Citations per year | 2.6 |
|---|---|
| Citation span | 2021 - 2026 (6) |
| Highly cited | No |