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Zhao M.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Zhao M.,University of Chinese Academy of Sciences | Cheng W.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Cheng W.,Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application | And 4 more authors.
Journal of Geographical Sciences | Year: 2016

Urbanization is a complex process reflecting the growth, formation and development of cities and their systems. Measuring regional urbanization levels within a long time series may ensure healthy and harmonious urban development. Based on DMSP/OLS nighttime light data, a human—computer interactive boundary correction method was used to obtain information about built-up urban areas in the Bohai Rim region from 1992 to 2012. Consequently, a method was proposed and applied to measure urbanization levels using four measurement scale units: administrative division, land-sea location, terrain feature, and geomorphological types. Our conclusions are: 1) The extraction results based on DMSP/OLS nighttime light data showed substantial agreement with those obtained using Landsat TM/ETM+ data on spatial patterns. The overall accuracy was 97.70% on average, with an average Kappa of 0.79, indicating that the results extracted from DMSP/OLS nighttime light data were reliable and could well reflect the actual status of built-up urban areas. 2) Bohai Rim’s urbanization level has increased significantly, demonstrating a high annual growth rate from 1998 to 2006. Areas with high urbanization levels have relocated evidently from capital to coastal cities. 3) The distribution of built-up urban areas showed a certain degree of zonal variation. The urbanization level was negatively correlated with relief amplitude and altitude. A high level of urbanization was found in low altitude platforms and low altitude plains, with a gradual narrowing of the gap between these two geomorphological types. 4) The measurement method presented in this study is fast, convenient, and incorporates multiple perspectives. It would offer various directions for urban construction and provide reference values for measuring national-level urbanization. © 2016, Institute of Geographic Science and Natural Resources Research (IGSNRR), Science China Press and Springer-Verlag Berlin Heidelberg. Source


Zhao N.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Zhao N.,Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application | Chen C.-F.,Shandong University of Science and Technology | Zhou X.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Yue T.-X.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research
Environmental Earth Sciences | Year: 2015

In most cases, climate change projections from General Circulation Models (GCM) and Regional Climate Models cannot be directly applied to climate change impact studies, and downscaling is, therefore, needed. A large number of statistical downscaling methods exist, but no clear recommendations exist of which methods are more appropriate, depending on the application. This paper compares two different statistical downscaling methods, Presim1 and Presim2, using the Coupled Model Intercomparison Project Phase 5 (CMIP5) datasets and station observations. Both methods include two steps, but the major difference between them is how the CMIP5 dataset and the station data used. The downscaled precipitation data are validated with observations through China and Jiangxi province from 1976 to 2005. Results show that GCMs cannot be used directly in climate change impact studies. In China, the second method Presim2, which establishes regression model based on the station data, has a tendency to overestimate or underestimate the real values. The accuracy of Presim1 is much better than Presim2 based on mean absolute error, mean relative error and root mean square error. Presim1 fuses the mode data and station data effectively. Results also show the importance of the meteorological station data in the process of residual modification. © 2015, Springer-Verlag Berlin Heidelberg. Source


Zhao N.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Zhao N.,Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application | Yue T.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research | Yue T.,Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application | And 5 more authors.
Theoretical and Applied Climatology | Year: 2016

Downscaling precipitation is required in local scale climate impact studies. In this paper, a statistical downscaling scheme was presented with a combination of geographically weighted regression (GWR) model and a recently developed method, high accuracy surface modeling method (HASM). This proposed method was compared with another downscaling method using the Coupled Model Intercomparison Project Phase 5 (CMIP5) database and ground-based data from 732 stations across China for the period 1976–2005. The residual which was produced by GWR was modified by comparing different interpolators including HASM, Kriging, inverse distance weighted method (IDW), and Spline. The spatial downscaling from 1° to 1-km grids for period 1976–2005 and future scenarios was achieved by using the proposed downscaling method. The prediction accuracy was assessed at two separate validation sites throughout China and Jiangxi Province on both annual and seasonal scales, with the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE). The results indicate that the developed model in this study outperforms the method that builds transfer function using the gauge values. There is a large improvement in the results when using a residual correction with meteorological station observations. In comparison with other three classical interpolators, HASM shows better performance in modifying the residual produced by local regression method. The success of the developed technique lies in the effective use of the datasets and the modification process of the residual by using HASM. The results from the future climate scenarios show that precipitation exhibits overall increasing trend from T1 (2011–2040) to T2 (2041–2070) and T2 to T3 (2071–2100) in RCP2.6, RCP4.5, and RCP8.5 emission scenarios. The most significant increase occurs in RCP8.5 from T2 to T3, while the lowest increase is found in RCP2.6 from T2 to T3, increased by 47.11 and 2.12 mm, respectively. © 2016 Springer-Verlag Wien Source


Yang L.,Nanjing Normal University | Yang L.,Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application | Sheng Y.,Nanjing Normal University | Sheng Y.,Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application | Wang B.,Nanjing Normal University
Optik | Year: 2016

We describe a novel method of data fusion of terrestrial LiDAR and images for delineating and deriving features and 3D reconstruction of regular building facades. We discuss the registration of LiDAR and image based on the transferring parameters of coordinate systems. We conduct the initial features extraction of building from images based on the gradient direction, and the further processing of the features constrained by the integrity rules of building construction. And consequently we implement the converting of 2D image feature lines' to 3D point cloud space based on the former registration, and then the 3D point cloud data around the 3D feature lines are searched. Finally we obtain the more accurate 3D feature lines after a series of operations of "buffer area - plane fitting - feature line extraction" on the point cloud data around the feature lines, which is for better presentation of building model. At the end of the paper, the experiment of the data fusion of image and LiDAR was conducted. We argue that the method represent a fundamental and useful approach to improve and explore the accuracy and efficiency of 3D reconstruction of building façade based on terrestrial LiDAR data and optical image. © 2015 Elsevier GmbH. All rights reserved Source


Ma X.,CAS Institute of Geographical Sciences and Natural Resources Research | Ma X.,Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application | Pei T.,CAS Institute of Geographical Sciences and Natural Resources Research | Pei T.,Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application | And 3 more authors.
International Journal of Geographical Information Science | Year: 2016

In the high-speed urbanization process of China, the urban population has been increasing significantly, leading to a high-density aggregation of population. However, the sharp increase in population density has not produced commensurate improvements in the road networks. On the contrary, the population increase induced a serious evacuation vulnerability, which cities experience during various hazards and catastrophic events. Therefore, research on evacuation vulnerability is important to urban planning. To assess the evacuation vulnerability, the optimal and worst scenarios should be considered because all possible evacuation plans occur between these extremes. However, most previous evacuation vulnerability studies are based on the worst-case scenario, only providing an upper bound of a potential evacuation assessment. To provide a more comprehensive theoretical basis for decision-makers to understand the consequences caused by all possible evacuations, this paper proposes an optimal evacuation vulnerability assessment model that provides the lower bound on potential evacuation difficulties. The model is solved by a stepwise spreading algorithm based on Graph Theory. Subsequently, to evaluate the effectiveness of the model, the study adopts the model to assess the evacuation capability of different road network topologies. A comparison with previous research was performed. The model was demonstrated in an application to the South Luogu Alley of Beijing, China. The significance of this paper is that the combination of our model with previous research may provide a more complete theoretical basis for an evacuation vulnerability assessment. © 2016 Informa UK Limited, trading as Taylor & Francis Group Source

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