spatial pattern
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2022 ◽  
Vol 135 ◽  
pp. 108552
Yi Tan ◽  
Min Chen ◽  
Linglei Zhang ◽  
Jia Li ◽  
Shuqing Nan ◽  

2022 ◽  
Vol 505 ◽  
pp. 119888
Jeffery B. Cannon ◽  
Seth W. Bigelow ◽  
J. Kevin Hiers ◽  
Steven B. Jack

Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 119
Guolei Zhou ◽  
Jing Zhang ◽  
Chenggu Li ◽  
Yanjun Liu

As populations continue to be concentrated in cities, the world will become entirely urbanized, and urban space is undergoing a drastic evolution. Understanding the spatial pattern of conversion and expansion of functional urban land, in the context of rapid urbanization, helps us to grasp the trajectories of urban spatial evolution in greater depth from a theoretical and practical level. Using the ESRI ArcGIS 9.3 software platform, methods, such as overlay analysis, transition matrix, and kernel density estimation, were used in order to analyze the spatiotemporal characteristics of different types of functional urban land conversion and expansion in the central city of Changchun. The results show that different types of functional urban land were often expanded and replaced, and the urban spatial structure was constantly evolving. The conversion and expansion of functional urban land show similar characteristics to concentric zone and sector modes and show dynamic changes in different concentric circles and directions at different periods. Our method can accurately identify the different types of functional urban land, and also explore the evolutionary trajectory of urban spatial structure. This study will help to coordinate the development of different functional urban spaces and to optimize the urban spatial structure in the future.

2022 ◽  
Vol 14 (2) ◽  
pp. 315
Julian Koch ◽  
Mehmet Cüneyd Demirel ◽  
Simon Stisen

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement has been supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often driven by underlying climate gradients, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated patterns. To address this, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as a modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. We apply the mesoscale Hydrological Model (mHM) to model the hydrological cycle of the Senegal River basin. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. As objective functions we applied the Kling-Gupta-Efficiency (KGE) for Q and the Spatial Efficiency (SPAEF) for ET. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial pattern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern, i.e., a 30% decrease in SPAEF. Both calibrations reached comparable performance of Q, i.e., KGE around 0.7. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.

2022 ◽  
Vol 14 (2) ◽  
pp. 291
Zhengyu Wang ◽  
Yaolin Liu ◽  
Yang Zhang ◽  
Yanfang Liu ◽  
Baoshun Wang ◽  

Land subsidence has become an increasing global concern over the past few decades due to natural and anthropogenic factors. However, although several studies have examined factors affecting land subsidence in recent years, few have focused on the spatial heterogeneity of relationships between land subsidence and urbanization. In this paper, we adopted the small baseline subset-synthetic aperture radar interferometry (SBAS-InSAR) method using Sentinel-1 radar satellite images to map land subsidence from 2015 to 2018 and characterized its spatial pattern in Wuhan. The bivariate Moran’s I index was used to test and visualize the spatial correlations between land subsidence and urbanization. A geographically weighted regression (GWR) model was employed to explore the strengths and directions of impacts of urbanization on land subsidence. Our findings showed that land subsidence was obvious and unevenly distributed in the study area, the annual deformation rate varied from −42.85 mm/year to +29.98 mm/year, and its average value was −1.0 mm/year. A clear spatial pattern for land subsidence in Wuhan was mapped, and several apparent subsidence funnels were primarily located in central urban areas. All urbanization indicators were found to be significantly spatially correlated with land subsidence at different scales. In addition, the GWR model results showed that all urbanization indicators were significantly associated with land subsidence across the whole study area in Wuhan. The results of bivariate Moran’s I and GWR results confirmed that the relationships between land subsidence and urbanization spatially varied in Wuhan at multiple spatial scales. Although scale dependence existed in both the bivariate Moran’s I and GWR models for land subsidence and urbanization indicators, a “best” spatial scale could not be confirmed because the disturbance of factors varied over different sampling scales. The results can advance the understanding of the relationships between land subsidence and urbanization, and they will provide guidance for subsidence control and sustainable urban planning.

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