Impacts of future urban expansion on natural habitats will intensify in China: scenario analysis with the improved LUSD-urban model

Lu, Wenlu; Zhang, Da; Ren, Qiang; Qi, Tao; He, Chunyang

doi:10.1007/s10980-023-01740-9

Cited by 3 publications

(4 citation statements)

References 36 publications

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“…Previous studies have suggested that both SUHII and NSUHII can be used to characterize human comfort, but the NSUHII is a more accurate representation (Yao et al, 2021). In addition, the continuous increase in thermal environments exerts pressure on human health and energy consumption (Zhang et al, 2023), which thus should be given enough attention. Therefore, more attention should be paid to the study of NSUHII and driving factors so as to find applicable mitigation strategies.…”

Section: Discussionmentioning

confidence: 99%

“…Primarily triggered by climate change and the intricate interplay between land surface and atmospheric systems, the heat island effect in urban landscapes takes shape (Krayenhoff et al, 2018;Lu et al, 2023). Climate change has led to an imbalanced distribution of water and heat, leading to a surge in extreme high-temperature occurrences in certain regions.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, these alterations reshape meteorological variables like temperature and humidity within the lower atmospheric stratum. Ultimately, this gives rise to a remarkable phenomenon where urban temperatures significantly outstrip those in neighboring rural areas, thus giving birth to the urban heat island effect (UHI) (Manley, 1959;Parry& Chandler, 1966;Oke, 1982;Zhang et al, 2023). Featured by significant spatio-temporal variations and substantial side effects, the UHI effect imposes an obvious impact on regional climate, energy consumption, air pollution and human health (Shahmohamadi et al, 2011;Zhang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Ultimately, this gives rise to a remarkable phenomenon where urban temperatures significantly outstrip those in neighboring rural areas, thus giving birth to the urban heat island effect (UHI) (Manley, 1959;Parry& Chandler, 1966;Oke, 1982;Zhang et al, 2023). Featured by significant spatio-temporal variations and substantial side effects, the UHI effect imposes an obvious impact on regional climate, energy consumption, air pollution and human health (Shahmohamadi et al, 2011;Zhang et al, 2023). In addition, extreme weather events have become increasingly frequent under the context of climate change, which further exacerbate the UHI effect (Shi et al, 2021;Lou et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta region

Shi,

Sun,

Luo

2024

Front. Environ. Sci.

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Compared with surface temperature, the near-surface temperature is more related with human health. However, extensive researches have been conducted on the UHI effect globally using surface temperature considering its accessibility. In this study, a comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta Region is investigated. This study first proposed a spatialization method suitable for air temperature in highly urbanized areas with complex land cover. Based on this method, a dataset of 1-km gridded air temperature is developed, and an in-depth analysis of the changes of near-surface and surface heat island is further carried out. Results show that both the near-surface urban heat island intensity (NSUHII) and surface urban heat island intensity (SUHII) are rather strong over the past 20 years, presenting similar spatial distributions as well. However, in the rapidly expanding urban areas especially during summer and winter seasons, the difference in magnitude and time variations (R) between NSUHII and SUHII are pronounced. Hence, adaptions and mitigation strategies on NSUHI and SUHI should be developed and implemented separately in such occasions, which is especially important for developed areas such as Yangtze River Delta Region.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta region

Shi,

Sun,

Luo

2024

Front. Environ. Sci.

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Simulating land use change for sustainable land management in China's coal resource-based cities under different scenarios

Li,

Cao,

Liu

et al. 2024

Science of The Total Environment

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Multiple Land-Use Simulations and Driving Factor Analysis by Integrating a Deep Cascade Forest Model and Cellular Automata: A Case Study in the Pearl River Delta, China

Zhuang,

Liu,

Yan

et al. 2024

Remote Sensing

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Cellular automata (CA) models have been extensively employed to predict and understand the spatiotemporal dynamics of land use. Driving factors play a significant role in shaping and driving land-use changes. Mining land-use transition rules from driving factors and quantifying the contribution of driving factors to land-use dynamics are fundamental aspects of CA simulation. However, existing CA models have limitations in obtaining accurate transition rules and reliable interpretations simultaneously for multiple land-use simulations. In this study, we constructed a CA model based on a tree-based deep learning algorithm, deep cascade forest (DCF), to improve multiple land-use simulations and driving factors analysis. The DCF algorithm was utilized to mine accurate multiple land-use transition rules without overfitting to improve CA simulation accuracy. Additionally, a novel ensemble mean decrease of impurity (MDI) factor importance analysis method (DCF-MDI), which leverages the cascade structure of the DCF model, was proposed to qualify the contribution of each driving factor to land-use dynamics stably and efficiently. To evaluate the effectiveness of the proposed DCF-CA, we applied the model to simulate land-use distributions and explore the driving mechanisms of land-use dynamics in the Pearl River Delta (PRD), China, from 2000 to 2010. Compared to existing models, the proposed DCF-CA model exhibits the highest accuracy (FoM = 23.79%, PA = 39.77%, UA = 36.35%, OA = 91.50%), which demonstrates its superiority in mining accurate transition rules for capturing multiple land-use dynamics. Factor importance analysis reveals that the proposed DCF-MDI method yields more stable ranking orders and lower standard deviation of contribution weights (<0.10%) compared to the traditional method, indicating its robustness to random disturbances and effectiveness in elucidating the driving mechanisms of land-use dynamics. The DCF-CA model proposed in this study, demonstrating high simulation accuracy and reliable interpretability simultaneously, can provide substantial support for sustainable land use management.

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Impacts of future urban expansion on natural habitats will intensify in China: scenario analysis with the improved LUSD-urban model

Cited by 3 publications

References 36 publications

Comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta region

Comparative analysis of near-surface and surface urban heat islands in the Yangtze River Delta region

Simulating land use change for sustainable land management in China's coal resource-based cities under different scenarios

Multiple Land-Use Simulations and Driving Factor Analysis by Integrating a Deep Cascade Forest Model and Cellular Automata: A Case Study in the Pearl River Delta, China

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