Quantifying urban place centrality, defined as its relative importance in serving its peripheral areas, provides insights into urban structures, optimizes resource allocation, and supports strategic urban planning. Ce...
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Quantifying urban place centrality, defined as its relative importance in serving its peripheral areas, provides insights into urban structures, optimizes resource allocation, and supports strategic urban planning. Centrality is shaped by three aspects: service volume, spatial reach, and directional diversity. However, existing measures often assess these factors separately and few integrate them simultaneously. Additionally, centrality analyses often overlook local perspectives and intra-day dynamics. To this end, we propose a novel origin-destination flow-based centrality measure, namely Total Torque Work (TTW), that integrates these three aspects into a single value, conceptualized as “Torque work of flow”, where flow volume, length, and direction correspond to the force magnitude, lever arm, and angular displacement. The effectiveness of the TTW is validated by simulation experiments. We apply this measure to analyze macro- and micro-centralities in Beijing, using taxi and shared bike flow data. Macro-centrality shows a monocentric structure, with higher values near railway stations, airports, and business and commercial centers. Micro-centrality is more polycentric, with subway stations exhibiting higher centrality. Time series clustering identifies three temporal patterns in both macro- and micro-centralities: two “daytime-dominant” patterns linked to multifunctional activities and commuting, and a “nighttime-dominant” pattern in residential areas. The study concludes with several implications for urban planning, emphasizing the importance of incorporating multi-spatiotemporal scales.
Afforestation has been regarded as a potentially effective strategy for the mitigation of climate warming. China has the largest planted forest area worldwide benefited by a series of large-scale afforestation and ref...
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Afforestation has been regarded as a potentially effective strategy for the mitigation of climate warming. China has the largest planted forest area worldwide benefited by a series of large-scale afforestation and reforestation project since the 1970s. The planted forests (PF) have been widely reported as an enduring carbon sink. However, the climatic regulation effect of PF on climate through biophysical process remains unclear. To this end, we utilized satellite observations to quantify the impact of the conversion of grasslands (GRA), croplands (CRO) and natural forests (NF) to PF on land surface temperature (LST) between 2003 and 2012 and illustrated the underlying biophysical mechanisms based on a revised Intrinsic Biophysical Mechanism (IBM) method. The results indicated that afforestation of PF caused negligible daily cooling effect (−0.005 ± 0.002 K) compared with NF while induced obvious daily cooling effect compared with GRA (−0.253 ± 0.004 K) and CRO (−0.162 ± 0.002 K). We also demonstrated that the revised IBM attribution method is applicable in interpreting the biophysical mechanism of PF on LST. Attribution analysis revealed that the surface roughness and Bowen ratio dominated the cooling effect as the surface became rougher and more evaporative cooling after converting NF, GRA, or CRO to PF. The contributions of albedo were less stark compared with other components and presented a weak warming effect. Furthermore, the atmospheric feedback was proved that have a pronounced impact on LST and should be included in the attribution of LST change. This study focuses on the potential of PF afforestation to provide local climate adaptation benefits, filling the gaps of previous studies involved to biophysical effect failure in distinguishing NF and PF, and provides new guidance for local climate impact evaluation when implementing forestry projects.
Convolutional neural networks (CNNs) have achieved state-of-the-art results on many visual recognition tasks. However, current CNN models still exhibit a poor ability to be invariant to spatial transformations of imag...
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TOPSAR is an earth-imaging technique, which can provide wide swath coverage. The paper introduces a TOPSAR focusing and calibrating experiment based on the TOPSAR data acquired by Gaofen3(GF3). In this paper, we first...
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Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual recognition tasks. However, the combination of convolution and pooling operations only shows invariance to small local...
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Tracking the temporal dynamics of urban heat island (UHI) is critical for urban heat adaptation and mitigation strategies. However, whether UHI trends have shifted recently and their underlying drivers remain unknown....
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Tracking the temporal dynamics of urban heat island (UHI) is critical for urban heat adaptation and mitigation strategies. However, whether UHI trends have shifted recently and their underlying drivers remain unknown. Here we investigate the variabilities in surface UHI trends and their associated determinants in 2,104 cities worldwide from 2000 to 2022. Our findings reveal that approximately half of the world's cities have experienced notable shifts in surface UHI trends, predominantly characterized by UHI deceleration. These shifts can be primarily attributed to alterations in vegetation trends during the day and to modifications in surface albedo and local warming trends at night. Our study challenges the conventional linear models commonly employed to estimate surface UHI trends, suggesting potential biases in such estimates. Our findings underscore the need for nuanced policies to curtail UHI growth by considering changes in urban underlying surfaces and background climate, particularly from a nonlinear perspective. Satellite records reveal that almost half of the world's cities experienced changes in urban heat island (UHI) trends, mainly slowdowns The UHI deceleration occurs at a more rapid speed in larger cities and old urban area as compared to smaller cities and newly urbanized area The changes in UHI trends are intricately regulated by nonlinear changes in urbanization processes and background climate changes Cities worldwide have witnessed a pervasive upward trend in surface urban heat island (UHI). However, it remains unclear whether this trend has changed. Here we reveal approximately half of the world's cities experiencing significant shifts in surface UHI trends, primarily characterized by deceleration. These shifts are mainly regulated by local changes in vegetation, albedo and air temperature. Our research underscores the limited understanding of temporal dynamics of surface UHI, offering the first indication of global prevalence of nonlinear trend
Landslide disasters have caused incalculable losses to human. In China, 90% occurrence of landslides are directly induced by rainfall or indirectly related to rainfall. Because of its geographical location and the cli...
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ISBN:
(数字)9781728163741
ISBN:
(纸本)9781728163758
Landslide disasters have caused incalculable losses to human. In China, 90% occurrence of landslides are directly induced by rainfall or indirectly related to rainfall. Because of its geographical location and the climate that belongs to the subtropical monsoon humid climate, the proportion of rainfall-induced landslides accounts for more than 70% of the total geological disasters in Bazhou district. In this paper, based on the geographic informationsystem (GIS) technology, combined with the historical landslide hazard data, we conducted a study on landslide probabilistic quantitative model composed of landslide susceptibility evaluation and the rainfall intensity-duration threshold model. The research results showed that the prediction accuracy of rainfall model is 81.82%. And the landslide hazard prediction was carried out for rainfall-induced landslides and potential landslides, with a prediction accuracy of 90.91%. The research results showed that the meteorological early warning model results were consistent with the actual inspection results.
SAR image simulation plays a useful role in SAR target interpretation and recognition. The current SAR target simulation methods require high precision of models and simulation parameters, and are only forward process...
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For the preparation of any target Bell state under continuous quantum measurement, this paper proposes a method which achieves the control objective by switching between two different models or by switching between tw...
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For the preparation of any target Bell state under continuous quantum measurement, this paper proposes a method which achieves the control objective by switching between two different models or by switching between two control channels under one model. Proper control Hamiltonians are selected for the two system models, a switching strategy between the two models is designed, and the stability of the whole switching system is proved in theory. For a given target Bell state, the effectiveness of the proposed switching control strategy between different models is illustrated through simulation experiments.
Correlation filters (CF) have received considerable attention in visual tracking because of their computational efficiency. Leveraging deep features via off-the-shelf CNN models (e.g., VGG), CF trackers achieve state-...
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