Identifying discrepant regions in urban mapping from historical and projected global urban extents

Mu, Haowei; Xu, Chen; Liu, Donglie; Li, Xuecao; Zhou, Yuyu; Gong, Peng; Huang, Jianxi; Du, Xiaoping; Guo, Jingchen; Cao, Wenting; Sun, Zhongchang

doi:10.1080/27669645.2022.2104990

Cited by 4 publications

(2 citation statements)

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“…We used the Land Use Harmonization 2 (LUH2) data to derive future urban growth pathways under eight scenarios determined 20 by Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). In the LUH2 dataset, the gridded-based scenarios (2015-2100) of multiple land-use types (e.g., urban) were developed by Integrated Assessment Models (IAMs), by jointly considering the socioeconomic development and climate change in the future (Mu et al, 2022;Hurtt et al, https://doi.org/10.5194/essd-2022-401 Preprint. Discussion started: 8 February 2023 c Author(s) 2023.…”

Section: Datamentioning

confidence: 99%

“…Spatially explicit global urban extents and dynamics play essential roles in assessing future urbanization-induced environmental and climate impacts (Acuto et al, 2018;Castán Broto and Bulkeley, 2013). The latest World Urbanization Prospects (Nations et al, 2019) reveal that the global urban population has grown rapidly from 750 million in 1950 to 4.5 billion in 2021, and it is anticipated to reach around 7 billion by 2050, with more than 2 billion people migrating from rural to 10 urban areas. Consequently, the urban expansion growth rates have been higher over past decades in rapidly developing regions (e.g., China and India), leading to a notable urban sprawl with rising concerns in the urban environment (Güneralp et al, 2017;Alberti et al, 2017), energy consumption (Li et al, 2019c), and human-environmental interactions.…”

Section: Introductionmentioning

confidence: 99%

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Global urban fractional changes at a 1 km resolution throughout 2100 under eight SSP-RCP scenarios

Zhou

et al. 2023

Preprint

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Abstract. The information of global spatially explicit urban extents under scenarios is important to mitigate future environmental risks caused by global urbanization and climate change. Although future dynamics of urban extent were commonly modelled with conversion from non-urban to urban using cellular automata (CA) based models, gradual changes of impervious surface area (ISA) at the pixel level were limitedly explored in previous studies. In this paper, we developed a global dataset of urban fractional changes at a 1 km resolution from 2020 to 2100 (5-year interval), under eight scenarios of socioeconomic pathways and climate change. First, to quantify the gradual change of ISA within the pixel, we characterized ISA growth patterns over past decades (i.e., 1985–2015) using a sigmoid growth model and annual global artificial impervious area (GAIA) data. Then, by incorporating the ISA-based growth mechanism with the CA model, we calibrated the state- specific urban CA model with quantitative evaluation at the global scale. Finally, we projected future urban fractional changes at 1km resolution under eight development pathways based on the harmonized urban growth demand from Land Use Harmonization2 (LUH2). The evaluation results show that the ISA-based urban CA model performs well globally, with an overall R2 of 0.9 and the Root Mean Square Error (RMSE) of 0.08 between modeled and observed ISAs in 2015. With the inclusion of temporal contexts of urban sprawl gained from GAIA, the dataset of global urban fractional change shows good agreement with 30-year historical observations from satellites. The dataset can capture spatially explicit variations of ISA and gradual ISA change within pixels. The dataset of global urban fractional change is of great use in supporting quantitative analysis of urbanization-induced ecological and environmental change at a fine scale, such as urban heat islands, energy consumption, and human-nature interactions in the urban system. The developed dataset of global urban fractional change is available at https://doi.org/10.6084/m9.figshare.20391117.v2 (He et al., 2022).

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Section: Datamentioning

confidence: 99%