Spatiotemporal evolution and multi-scenario simulation of blue–green spaces in a semi-arid valley-constrained industrial city: a case study of central Lanzhou, China
Abstract
Blue–Green Spaces (BGSs) are essential ecological infrastructure for maintaining ecological security, enhancing ecosystem services and supporting sustainable development. In semi-arid valley-constrained industrial cities, however, limited buildable land, river-oriented industrial agglomeration and ecological fragility place these spaces under persistent pressure. Using central Lanzhou, Northwest China, as a representative case, we integrated land-use transition analysis, landscape metrics, kernel density estimation and the geographical detector model to examine changes in blue–green spaces from 2000 to 2023, and applied a Markov–PLUS model to simulate land-use patterns under three 2035 scenarios. The Markov–PLUS model achieved a Kappa coefficient of 0.958, an overall accuracy (OA) of 0.971 and a Figure of Merit (FoM) of 0.791, indicating satisfactory performance in reproducing historical land-use transitions and spatial change patterns. Green spaces decreased from 133,480 ha to 111,308 ha, while blue spaces declined from 2072 ha to 1493 ha, indicating a significant reduction in blue–green spaces during the historical period. Landscape patterns shifted from relative stability to fragmented restructuring, with transformation hotspots increasingly concentrated along the Yellow River valley. Considering that the most intensive blue–green space changes occurred during the recent period, the geographical detector analysis revealed that natural environmental constraints, particularly river accessibility and pollution pressure, remained dominant, while socioeconomic factors became increasingly influential under rapid urbanization. Furthermore, the OPGD analysis demonstrated that blue–green space changes were shaped by synergistic interactions among environmental constraints and human activities, with dominant factors exhibiting significant interactive enhancement across spatial scales, indicating robust and multi-dimensional driving mechanisms. Under the natural development, new urbanization and ecological protection scenarios, total blue–green spaces reached 108,468.2, 106,919.5 and 113,437.2 ha, respectively. The ecological protection scenario retained blue–green spaces to the greatest extent, highlighting the importance of integrating multi-scenario land-use simulation with ecological planning to support resilient territorial spatial management in semi-arid valley-constrained industrial cities.
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BibTeX
@article{wang2026spatiotemporal,
title = {{Spatiotemporal evolution and multi-scenario simulation of blue–green spaces in a semi-arid valley-constrained industrial city: a case study of central Lanzhou, China}},
author = {Xiyun Wang and Jieming Dong and Xianglong Tang and Zhuanghui Duan},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-72905-0},
url = {https://www.nature.com/articles/s41598-026-72905-0},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Spatiotemporal evolution and multi-scenario simulation of blue–green spaces in a semi-arid valley-constrained industrial city: a case study of central Lanzhou, China
AU - Xiyun Wang
AU - Jieming Dong
AU - Xianglong Tang
AU - Zhuanghui Duan
PY - 2026
DA - 2026/10/06
JO - Scientific Reports
DO - 10.1038/s41598-026-72905-0
UR - https://www.nature.com/articles/s41598-026-72905-0
AB - Blue–Green Spaces (BGSs) are essential ecological infrastructure for maintaining ecological security, enhancing ecosystem services and supporting sustainable development. In semi-arid valley-constrained industrial cities, however, limited buildable land, river-oriented industrial agglomeration and ecological fragility place these spaces under persistent pressure. Using central Lanzhou, Northwest China, as a representative case, we integrated land-use transition analysis, landscape metrics, kernel density estimation and the geographical detector model to examine changes in blue–green spaces from 2000 to 2023, and applied a Markov–PLUS model to simulate land-use patterns under three 2035 scenarios. The Markov–PLUS model achieved a Kappa coefficient of 0.958, an overall accuracy (OA) of 0.971 and a Figure of Merit (FoM) of 0.791, indicating satisfactory performance in reproducing historical land-use transitions and spatial change patterns. Green spaces decreased from 133,480 ha to 111,308 ha, while blue spaces declined from 2072 ha to 1493 ha, indicating a significant reduction in blue–green spaces during the historical period. Landscape patterns shifted from relative stability to fragmented restructuring, with transformation hotspots increasingly concentrated along the Yellow River valley. Considering that the most intensive blue–green space changes occurred during the recent period, the geographical detector analysis revealed that natural environmental constraints, particularly river accessibility and pollution pressure, remained dominant, while socioeconomic factors became increasingly influential under rapid urbanization. Furthermore, the OPGD analysis demonstrated that blue–green space changes were shaped by synergistic interactions among environmental constraints and human activities, with dominant factors exhibiting significant interactive enhancement across spatial scales, indicating robust and multi-dimensional driving mechanisms. Under the natural development, new urbanization and ecological protection scenarios, total blue–green spaces reached 108,468.2, 106,919.5 and 113,437.2 ha, respectively. The ecological protection scenario retained blue–green spaces to the greatest extent, highlighting the importance of integrating multi-scenario land-use simulation with ecological planning to support resilient territorial spatial management in semi-arid valley-constrained industrial cities.
SN - 2045-2322
ER -