Three-dimensional green quantity (3DGQ) mapping and integrated 2D–3D assessment reveal hidden urban green deficits and inequities
7 affiliations
- 1School of Landscape Architecture, Beijing Forestry University, Beijing, 100083, PR China; Key Laboratory of Urban and Rural Landscape Construction, National Forestry and Grassland Administration, Beijing, 100083, PR China. Electronic address: [email protected]
- 2School of Landscape Architecture, Beijing Forestry University, Beijing, 100083, PR China. Electronic address: [email protected]
- 3School of Landscape Architecture, Beijing Forestry University, Beijing, 100083, PR China; Key Laboratory of Urban and Rural Landscape Construction, National Forestry and Grassland Administration, Beijing, 100083, PR China. Electronic address: [email protected]
- 4School of Landscape Architecture, Beijing Forestry University, Beijing, 100083, PR China. Electronic address: [email protected]
- 5School of Landscape Architecture, Beijing Forestry University, Beijing, 100083, PR China; Key Laboratory of Urban and Rural Landscape Construction, National Forestry and Grassland Administration, Beijing, 100083, PR China. Electronic address: [email protected]
- 6School of Landscape Architecture, Beijing Forestry University, Beijing, 100083, PR China; Key Laboratory of Urban and Rural Landscape Construction, National Forestry and Grassland Administration, Beijing, 100083, PR China. Electronic address: [email protected]
- 7School of Landscape Architecture, Beijing Forestry University, Beijing, 100083, PR China; Key Laboratory of Urban and Rural Landscape Construction, National Forestry and Grassland Administration, Beijing, 100083, PR China. Electronic address: [email protected]
Abstract
Sustainable Development Goal 11 emphasizes inclusive, resilient, and sustainable cities, in which high-quality urban green space (UGS) is essential. However, traditional two-dimensional (2D) metrics, such as Green Coverage Ratio (GCR), fail to capture the vertical complexity of UGS, leading to incomplete assessments in planning practice. With accelerating densification and land scarcity restricting horizontal expansion, sustainable UGS growth faces critical challenges. To address this gap, we developed a Three-Dimensional Green Quantity (3DGQ) mapping framework and an integrated 2D–3D assessment approach for urban green-space evaluation. We leveraged global open-source satellite data—including spaceborne LiDAR, canopy height, canopy coverage, and optical vegetation information—to retrieve growing-season Leaf Area Index (LAI) using a machine-learning model, and introduced the Three-Dimensional Green Coverage Ratio (3DGCR) to enhance 3DGQ performance assessment. Applying this framework to Beijing's central urban area, the tuned Random Forest model achieved an R 2 of 0.76, RMSE of 0.51, and MAE of 0.30 m 2 /m 2 for LAI retrieval, supporting 3DGQ mapping at resolutions as fine as 1 m. Results revealed a spatial divergence between 2D and 3D metrics: 3DGCR showed a “low-south, high-north” gradient, contrasting with the 2D “low-center, high-periphery” pattern. At the subdistrict level, 38.4% of units were classified as 2D–3D green imbalance under the coupling coordination framework. We identified four “2D–3D matching patterns” to provide spatial guidance for targeted UGS regeneration. Moreover, the equity assessment unveiled a “double inversion” between spatially and population-weighted results; specifically, 66.7% of subdistricts had lower population-weighted Gini coefficients for 3DGCR than for GCR. Our results further suggest that vertical UGS structural optimization in high-density cores may function as an “equity amplifier.” These findings demonstrate that integrated 2D–3D assessment can uncover hidden urban green deficits and inequities overlooked by planar indicators, providing a diagnostic basis for structural optimization, equity-oriented planning, and sustainable urban green-space governance.
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BibTeX
@article{huang2026threedimensional,
title = {{Three-dimensional green quantity (3DGQ) mapping and integrated 2D–3D assessment reveal hidden urban green deficits and inequities}},
author = {Shoubang Huang and Kai Zhou and Jingyi Han and Haochun Hu and Xicheng Zhang and Qing Lin and X G Wang},
journal = {Journal of Environmental Management},
year = {2026},
month = jun,
doi = {10.1016/j.jenvman.2026.130082},
url = {https://doi.org/10.1016/j.jenvman.2026.130082},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Three-dimensional green quantity (3DGQ) mapping and integrated 2D–3D assessment reveal hidden urban green deficits and inequities
AU - Shoubang Huang
AU - Kai Zhou
AU - Jingyi Han
AU - Haochun Hu
AU - Xicheng Zhang
AU - Qing Lin
AU - X G Wang
PY - 2026
DA - 2026/06/01
JO - Journal of Environmental Management
DO - 10.1016/j.jenvman.2026.130082
UR - https://doi.org/10.1016/j.jenvman.2026.130082
AB - Sustainable Development Goal 11 emphasizes inclusive, resilient, and sustainable cities, in which high-quality urban green space (UGS) is essential. However, traditional two-dimensional (2D) metrics, such as Green Coverage Ratio (GCR), fail to capture the vertical complexity of UGS, leading to incomplete assessments in planning practice. With accelerating densification and land scarcity restricting horizontal expansion, sustainable UGS growth faces critical challenges. To address this gap, we developed a Three-Dimensional Green Quantity (3DGQ) mapping framework and an integrated 2D–3D assessment approach for urban green-space evaluation. We leveraged global open-source satellite data—including spaceborne LiDAR, canopy height, canopy coverage, and optical vegetation information—to retrieve growing-season Leaf Area Index (LAI) using a machine-learning model, and introduced the Three-Dimensional Green Coverage Ratio (3DGCR) to enhance 3DGQ performance assessment. Applying this framework to Beijing's central urban area, the tuned Random Forest model achieved an R 2 of 0.76, RMSE of 0.51, and MAE of 0.30 m 2 /m 2 for LAI retrieval, supporting 3DGQ mapping at resolutions as fine as 1 m. Results revealed a spatial divergence between 2D and 3D metrics: 3DGCR showed a “low-south, high-north” gradient, contrasting with the 2D “low-center, high-periphery” pattern. At the subdistrict level, 38.4% of units were classified as 2D–3D green imbalance under the coupling coordination framework. We identified four “2D–3D matching patterns” to provide spatial guidance for targeted UGS regeneration. Moreover, the equity assessment unveiled a “double inversion” between spatially and population-weighted results; specifically, 66.7% of subdistricts had lower population-weighted Gini coefficients for 3DGCR than for GCR. Our results further suggest that vertical UGS structural optimization in high-density cores may function as an “equity amplifier.” These findings demonstrate that integrated 2D–3D assessment can uncover hidden urban green deficits and inequities overlooked by planar indicators, providing a diagnostic basis for structural optimization, equity-oriented planning, and sustainable urban green-space governance.
SN - 0301-4797
SN - 1095-8630
ER -