Ning L.,Nanjing Normal University |
Ning L.,University of Massachusetts Amherst |
Ning L.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application |
Bradley R.S.,University of Massachusetts Amherst
Climate Dynamics | Year: 2015
The historical and future relationships between two major patterns of large-scale climate variability, the North Atlantic Oscillation (NAO) and the Pacific/North America pattern (PNA), and the regional winter temperature and precipitation over the eastern United States were systemically evaluated by using 17 general circulation models (GCMs) from the Coupled Model Intercomparison Project phase 5. Empirical orthogonal function analysis was used to define the NAO and PNA. The observed spatial patterns of NAO and PNA can be reproduced by all the GCMs with slight differences in locations of the centers of action and their average magnitudes. For the correlations with regional winter temperature and precipitation over the eastern US, GCMs perform best in capturing the relationships between the NAO and winter temperature, and between the PNA and winter temperature and precipitation. The differences between the observed and simulated relationships are mainly due to displacements of the simulated NAO and PNA centers of action and differences in their magnitudes. In simulations of the future, both NAO and PNA magnitudes increase, with uncertainties related to the model response and emission scenarios. When assessing the influences of future NAO/PNA changes on regional winter temperature, it is found that the main factors are related to changes in the magnitude of the NAO Azores center and total NAO magnitude, and the longitude of the PNA center over northwestern North America, total PNA magnitude, and the magnitude of the PNA center over the southeastern US. © 2015 Springer-Verlag Berlin Heidelberg
Chen Y.,University of Chinese Academy of Sciences |
Ge Y.,University of Chinese Academy of Sciences |
Ge Y.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application |
Song D.,CAS Institute of Policy and Management
IEEE Geoscience and Remote Sensing Letters | Year: 2015
A new superresolution mapping (SRM) method based on high-accuracy surface modeling (HASM) is proposed to generate land-cover maps at the subpixel scale. HASM uses the fundamental theorem of surfaces to uniquely define a land surface, which can produce less errors in interpolation results than classic methods, and thus, the proposed SRM method first uses it to estimate the soft class values of subpixels according to the fraction images of soft classification. Then, it transforms the soft class values into a hard-classified land-cover map using class allocation under the constraints of fraction images. Experiments on a synthetic image and a real remote sensing image show that the proposed method produces more accurate SRM maps than four existing SRM methods. Hence, the proposed method provides a new option for superresolution land-cover mapping. © 2004-2012 IEEE.
Tang B.-H.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research |
Tang B.-H.,French National Center for Scientific Research |
Tang B.-H.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application |
Shao K.,Hefei University of Technology |
And 4 more authors.
International Journal of Remote Sensing | Year: 2015
This work estimated the land surface emissivities (LSEs) for MODIS thermal infrared channels 29 (8.4–8.7 μm), 31 (10.78–11.28 μm), and 32 (11.77–12.27 μm) using an improved normalized difference vegetation index (NDVI)-based threshold method. The channel LSEs are expressed as functions of atmospherically corrected reflectance from the MODIS visible and near-infrared channels with wavelengths ranging from 0.4 to 2.2 μm for bare soil. To retain the angular information, the vegetation LSEs were explicitly expressed in the NDVI function. The results exhibited a root mean square error (RMSE) among the estimated LSEs using the improved method, and those calculated using spectral data from Johns Hopkins University (JHU) are below 0.01 for channels 31 and 32. The MODIS land surface temperature/emissivity (LST/E) products, MOD11_L2 with LSE derived via the classification-based method with 1 km resolution and MOD11C1 with LSE retrieved via the day/night LST retrieval method at 0.05° resolution, were used to validate the proposed method. The resultant variances and entropies for the LSEs estimated using the proposed method were larger than those extracted from MOD11_L2, which indicates that the proposed method better described the spectral variation for different land covers. In addition, comparing the estimated LSEs to those from MOD11C1 yielded RMSEs of approximately 0.02 for the three channels; however, more than 70% of pixels exhibited LSE differences within 0.01 for channels 31 and 32, which indicates that the proposed method feasibly depicts LSE variation for different land covers. © 2015 Taylor & Francis.
Wu W.,Heriot - Watt University |
Wang J.,CAS Beijing Institute of Geographic Sciences and Nature Resources Research |
Wang J.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application |
Dai T.,Jinan University
Annals of the American Association of Geographers | Year: 2016
A largely unexplored big data application in urban contexts is how cultural ties affect human mobility patterns. This article explores China’s intercity human mobility patterns from social media data to contribute to our understanding of this question. Exposure to human mobility patterns is measured by big data computational strategy for identifying hundreds of millions of individuals’ space-time footprint trajectories. Linguistic data are coded as a proxy for cultural ties from a unique geographically coded atlas of dialect distributions. We find that cultural ties are associated with human mobility flows between city pairs, contingent on commuting costs and geographical distances. Such effects are not distributed evenly over time and space, however. These findings present useful insights in support of the cultural mechanism that can account for the rise, decline, and dynamics of human mobility between regions. © 2016 by American Association of Geographers.
Wang C.,Jiangsu University |
Pei X.,State Key Laboratory Cultivation Base of Geographical Environment Evolution Jiangsu Province |
Yue S.,Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application |
Wen Y.,Nanjing Normal University
Wetlands | Year: 2016
Vegetation and soil are important factors in coastal wetland landscape evolution. This paper investigates the relationship between the aboveground biomass of Spartina alterniflora and soil factors of varying settling ages in Yancheng, China using correlation analysis and principal component analysis. The results indicate the following: (1) Soil factors varied significantly with different settlement ages of S. alterniflora that expanded toward the land and sea. Soil bulk density decreased with settlement age and was lowest for growth period IV (10 – 16 year old sites) whereas an opposite trend was shown for soil moisture. Soil salinity and soil nutrients were highest for growth period III (6 – 10 year old sites) (2) Principal component analysis demonstrated that soil bulk density, moisture and salinity are the main soil factors that drive landscape evolution in S. alterniflora marshes. (3) There was a significant positive correlation between S. alterniflora biomass and the organic matter and bulk density of soil (p < 0.05). Results showed that the invasion and settlement of S. alterniflora in the coastal wetland of Yancheng are changing the physical and chemical properties of the coastal wetland soil. This study has contributed to an understanding of wetland succession in the coastal landscape. © 2016 Society of Wetland Scientists