The water index (WI) is designed to highlight inland water bodies in remotely sensed imagery. The application of WI for water body mapping is mainly based on the thresholding method. However, there are three primary difficulties with this method: (1) inefficient identification of mixed water pixels; (2) confusion of water bodies with background noise; and (3) variation in the threshold values according to the location and time of image acquisitions. Considering that mixed water pixels usually appear in narrow rivers or shallow water at the edge of lakes or wide rivers, an automated method is proposed for extracting rivers and lakes by combining the WI with digital image processing techniques to address the above issues. The data sources are the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) images for three representative areas in China. The results were compared with those from existing thresholding methods. The robustness of the new method in combination with different WIs is also assessed. Several metrics, which include the Kappa coefficient, omission and commission errors, edge position accuracy and completeness, were calculated to assess the method’s performance. The new method generally outperformed the thresholding methods, although the degree of improvement varied among WIs. The advantages and limitations of the proposed method are also discussed.

Floods and water resource management are major challenges for human in present and the near future, and snowmelt floods which usually break out in arid or semi-arid regions often cause tremendous social and economic losses, and integrated information system (IIS) is valuable to scientific and public decision-making. This paper presents an integrated approach to snowmelt floods early-warning based on geoinformatics (i.e. remote sensing (RS), geographical information systems (GIS) and global positioning systems (GPS)), Internet of Things (IoT) and cloud services. It consists of main components such as infrastructure and devices in IoT, cloud information warehouse, management tools, applications and services, the results from a case study shows that the effectiveness of flood prediction and decision-making can be improved by using the IIS. The prototype system implemented in this paper is valuable to the acquisition, management and sharing of multi-source information in snowmelt flood early-warning even in other tasks of water resource management. The contribution of this work includes developing a prototype IIS for snowmelt flood early-warning in water resource management with the combination of IoT, Geoinformatics and Cloud Service, with the IIS, everyone could be a sensor of IoT and a contributor of the information warehouse, professional users and public are both servers and clients for information management and services. Furthermore, the IIS provides a preliminary framework of e-Science in resources management and environment science. This study highlights the crucial significance of a systematic approach toward IISs for effective resource and environment management.

Global land cover mapping with high accuracy is essential to downstream researches. Five global land cover data sets derived from moderate-resolution satellites, i.e., Global Land Cover Characterization (GLCC), University of Maryland land cover product (UMd), Global Land Cover 2000 project data (GLC2000), MODIS Land Cover product (MODIS LC), and GLOBCOVER land cover product (GlobCover), have been widely used in many researches. However, these data sets were produced using different data sources and class definitions, which led to high uncertainty and inconsistency when using them. This study looked into the consistencies and discrepancies among the five data sets in China. All of the compared data sets were aggregated to consistent spatial resolution and extent, along with a 12-class thematic classification schema; intercomparisons among five datasets and each with reference data GLCD-2005 were performed. Results show reasonable agreement across the five data sets over China in terms of the dominating land cover types like Grassland and Cropland; while discrepancies of Forest classes, particularly Shrubland and Wetland among them are great. Additionally, GLC2000 has the highest agreement with GLCD-2005; MODIS LC gets the highest map-specific consistency compared with others; whereas UMd has the lowest agreement with GLCD-2005, but also has the lowest map-specific consistency.

Effective integration and wide sharing of geospatial data is an important and basic premise to facilitate the research and applications of geographic information science. However, the semantic heterogeneity of geospatial data is a major problem that significantly hinders geospatial data integration and sharing. Ontologies are regarded as a promising way to solve semantic problems by providing a formalized representation of geographic entities and relationships between them in a manner understandable to machines. Thus, many efforts have been made to explore ontology-based geospatial data integration and sharing. However, there is a lack of a specialized ontology that would provide a unified description for geospatial data. In this paper, with a focus on the characteristics of geospatial data, we propose a unified framework for geospatial data ontology, denoted GeoDataOnt, to establish a semantic foundation for geospatial data integration and sharing. First, we provide a characteristics hierarchy of geospatial data. Next, we analyze the semantic problems for each characteristic of geospatial data. Subsequently, we propose the general framework of GeoDataOnt, targeting these problems according to the characteristics of geospatial data. GeoDataOnt is then divided into multiple modules, and we show a detailed design and implementation for each module. Key limitations and challenges of GeoDataOnt are identified, and broad applications of GeoDataOnt are discussed.

Global warming, sea-level rise, and rapid urbanization all increase the risk of compound extreme weather events, presenting challenges for the operation of urban-related infrastructure, including transportation infrastructure. In this context, some questions become important. For example, what are the temporal and spatial distribution and development trends of transportation resilience when considering the impact of multilpe extreme weather events on the urban transportation system? What is the degree of loss of urban transportation resilience (UT resilience) under different extreme event intensities, and how long will it take for the entire system to restore balance? In the future, if extreme weather events become more frequent and intense, what trends will urban transportation resilience show? Considering these problems, the current monitoring methods for transportation resilience under the influence of extreme events are lacking, especially the monitoring of the temporal and spatial dynamic changes of transportation resilience under the influence of compined extreme events. The development of big data mining technology and deep learning methods for spatiotemporal predictions made the construction of spatiotemporal data sets for evaluating and predicting UT resilience-intensity indicators possible. Such data sets reveal the temporal and spatial features and evolution of UT resilience intensity under the influence of compound extreme weather events, as well as the related future change trends. They indicate the key research areas that should be focused on, namely, the transportation resilience under climate warming. This work is especially important in planning efforts to adapt to climate change and rising sea levels and is relevant to policymakers, traffic managers, civil protection managers, and the general public.

Landslide susceptibility assessment is an important means of helping to reduce and manage landslide risk. The existing studies, however, fail to examine the spatially varying relationships between landslide susceptibility and its explanatory factors. This paper investigates the spatial variation in such relationships in Liangshan, China, leveraging a spatially explicit model, namely, geographical random forest (GRF). By comparing with random forest (RF), we found that GRF achieves a higher performance with an AUC of 0.86 due to its consideration of the spatial heterogeneity among variables. GRF also provides a higher-quality landslide susceptibility map than RF by correctly placing 92.35% of the landslide points in high-susceptibility areas. The local feature importance derived from GRF allows us to understand that the impact of conditioning factors varies across space, which can provide implications for policy development by local governments to place different levels of attention on different conditioning factors in specific counties to prevent and mitigate landslides. To account for the spatial dependence among the data in the model performance assessment, we use spatial cross-validation (CV) to split the data into subsets spatially rather than randomly for model training and testing. The results show that spatial CV can effectively address the over-optimistic bias in model error evaluation.