Studying structural and functional connectivities of human cerebral cortex has drawn significant interest and effort recently. A fundamental and challenging problem arises when attempting to measure the structural and/or functional connectivities of specific cortical networks: how to identify and localize the best possible regions of interests (ROIs) on the cortex? In our view, the major challenges come from uncertainties in ROI boundary definition, the remarkable structural and functional variability across individuals and high nonlinearities within and around ROIs. In this paper, we present a novel ROI prediction framework that localizes ROIs in individual brains based on their learned fiber shape models from multimodal task-based functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data. In the training stage, shape models of white matter fibers are learnt from those emanating from the functional ROIs, which are activated brain regions detected from task-based fMRI data. In the prediction stage, functional ROIs are predicted in individual brains based only on DTI data. Our experiment results show that the average ROI prediction error is around 3.94 mm, in comparison with benchmark data provided by working memory and visual task-based fMRI. Our work demonstrated that fiber bundle shape models derived from DTI data are good predictors of functional cortical ROIs.

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Geospatial dashboards have attracted increasing attention from both user communities and academic researchers since the late 1990s. Dashboards can gather, visualize, analyze and advise on urban performance to support sustainable development of smart cities. We conducted a critical review of the research and development of geospatial dashboards, including the integration of maps, spatial data analytics, and geographic visualization for decision support and real-time monitoring of smart city performance. The research about this kind of system has mainly focused on indicators, information models including statistical models and geospatial models, and other related issues. This paper presents an overview of dashboard history and key technologies and applications in smart cities, and summarizes major research progress and representative developments by analyzing their key technical issues. Based on the review, we discuss the visualization model and validity of models for decision support and real-time monitoring that need to be further researched, and recommend some future research directions.

Digital Elevation Models (DEMs) have been successfully used in a large range of environmental issues. Several methods such as digital contour interpolation and remote sensing have allowed the generation of DEMs, some of which are now freely available for almost the entire globe. The Soil and Water Assessment Tool (SWAT) is a widely used semi-distributed model operating at the watershed level and has previously been shown to be very sensitive to the quality of the input topographic information. The objective of this study was to evaluate the impact of DEMs generated from different data sources, respectively DLG5m (local Digital Line Graph, 5 m interval), ASTER30m (1 arc-s ASTER Global DEM Version 1, approximately 30 m resolution), and SRTM90m (3 arc-s SRTM Version 4, approximately 90 m resolution), on SWAT predictions for runoff, sediment, total phosphor (TP) and total nitrogen (TN). Eleven resolutions, from 5 m to 140 m, were considered in this study. Results indicate that the predictions of TPs and TNs decreased substantially with coarser resampled resolution. Slightly decreased trends could be found in the predicted sediments when DEMs were resampled to coarser resolutions. Predicted runoffs were not sensitive to resampled resolutions. The predicted outputs based on DLG5m were more sensitive to resampled resolutions than those based on ASTER30m and SRTM90m. At original resolutions, the predicted outputs based on ASTER30m and SRTM90m were similar, but the predicted TNs and TPs based on ASTER30m and SRTM90m were much lower than the one based on DLG5m. For the predicted TNs and TPs, which were substantially sensitive to DEM resolutions, the output accuracies of SWAT derived from ASTER30m and SRTM90m could be improved by down-scaled resampling, but they could not improve on finer DEM (DLG5m) at the same resolution. This study helps GIS environmental model users to understand the sensitivities of SWAT to DEM resolution, and choose feasible DEM data for environmental models

The exotic plant Spartina alterniflora was introduced to Yueqing Bay more than 20 years ago for tidal land reclamation and as a defense against typhoons, but it has rapidly expanded and caused enormous ecological consequences. Mapping the spread and distribution of S. alterniflora is the first step toward understanding the factors that determine the population expansion patterns. Remote sensing is a promising tool to monitor the expansion of S. alterniflora. Twelve Landsat TM images and Support Vector Machine (SVM) were used to delineate the invasion of S. alterniflora from 1993 to 2009, and SPOT 6 images and Object-Based Image Analysis (OBIA) were used to map the distribution of S. alterniflora in 2014. In situ data and Unmanned Aerial Vehicle (UAV) images were used as supplementary data. S. alterniflora spread rapidly in Yueqing Bay over the past 21 years. Between 1993 and 2009, the area of S. alterniflora increased by 608 times (from 4 to 2432 ha). The rapid expansion of S. alterniflora covered almost all of the bare mudflats around the mangrove forests and the cultivated mudflats. However, from 2009 to 2014, the rate of expansion of S. alterniflora began to slow down in Yueqing Bay, and the total area of S. alterniflora in Yantian decreased by 275 ha. These phenomena can be explained by the landscape changes and ecological niches. Through the expansion of S. alterniflora, it was found that the ecological significance and environmental impact of S. alterniflora was different in different regions in Yueqing Bay. The conservation plans for Yueqing Bay should consider both the positive and negative effects of S. alterniflora, and the governmental policy should be based on the different circumstances of the regions.

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Soil erosion, as a serious environmental problem worldwide, poses a great threat to human sustainability. Spatiotemporal information on soil erosion is of vital importance to finding a solution for this problem. A case study was conducted to characterize the dynamics of soil erosion risk in

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