Sub-pixel mapping of flood inundation (SMFI) is one of the hotspots in remote sensing and relevant research and application fields. In this study, a novel method based on the integration of Bayesian regulation back-propagation neural network (BRBP) and particle swarm optimization (PSO), so-called IBRBPPSO, is proposed for SMFI in river basins. The IBRBPPSO-SMFI algorithm was developed and evaluated using Landsat images fromthe Changjiang river basin in China and the Murray-Darling basin in Australia. Compared with traditional SMFI methods, IBRBPPSO-SMFI consistently achieves the most accurate SMFI results in terms of visual and quantitative evaluations. IBRBPPSO-SMFI is superior to PSO-SMFI with not only an improved accuracy, but also an accelerated convergence speed of the algorithm. IBRBPPSO-SMFI reduces the uncertainty in mapping inundation in river basins by improving the accuracy of SMFI. The result of this study will also enrich the SMFI methodology, and thereby benefit the environmental studies of river basins.

Urban flooding is a serious natural hazard to many cities all over the world, which has dramatic impacts on the urban environment and human life. Urban flooding mapping has practical significance for the prevention and management of urban flood disasters. Remote sensing images with high temporal resolutions are widely used for urban flooding mapping, but have a limitation of relatively low spatial resolutions. In this study, a new method based on a generalized regression neural network (GRNN) is proposed to achieve improved accuracy in super-resolution mapping of urban flooding (SMUF) from remote sensing images. The GRNN-SMUF algorithm was proposed and then assessed using Landsat 5 and Landsat 8 images of Brisbane city in Australia and Wuhan city in China. Compared to three traditional methods, GRNN-SMUF mapped urban flooding more accurately according to both visual and quantitative assessments. The results of this study will improve the accuracy of urban flooding mapping using easily-available remote sensing images with medium-low spatial resolutions and will be propitious to the prevention and management of urban flood disasters.

The rapid development of global industrialization and urbanization has resulted in a great deal of electric power consumption (EPC), which is closely related to economic growth, carbon emissions, and the long-term stability of global climate. This study attempts to detect spatiotemporal dynamics of global EPC using the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime stable light (NSL) data. The global NSL data from 1992 to 2013 were intercalibrated via a modified invariant region (MIR) method. The global EPC at 1 km resolution was then modeled using the intercalibrated NSL data to assess spatiotemporal dynamics of EPC from a global scale down to continental and national scales. The results showed that the MIR method not only reduced the saturated lighted pixels, but also improved the continuity and comparability of the NSL data. An accuracy assessment was undertaken and confined that the intercalibrated NSL data were relatively suitable and accurate for estimating EPC in the world. Spatiotemporal variations of EPC were mainly identified in Europe, North America, and Asia. Special attention should be paid to China where the high grade and high-growth type of EPC covered 0.409% and 1.041% of the total country area during the study period, respectively. The results of this study greatly enhance the understanding of spatiotemporal dynamics of global EPC at the multiple scales. They will provide a scientific evidence base for tracking spatiotemporal dynamics of global EPC. (C) 2016 Elsevier Ltd. All rights reserved.

China's rapid industrialization and urbanization have resulted in a great deal of CO2 (carbon dioxide) emissions, which is closely related to its sustainable development and the long term stability of global climate. This study proposes panel data analysis to model spatiotemporal CO2 emission dynamics at a higher resolution in China by integrating the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime stable light (NSL) data with statistic data of CO2 emissions. Spatiotemporal CO2 emission dynamics were assessed from national scale down to regional and urban agglomeration scales. The evaluation showed that there was a true positive correlation between NSL data and statistic CO2 emissions in China at the provincial level from 1997 to 2012, which could be suitable for estimating CO2 emissions at 1 km resolution. The spatiotemporal CO2 emission dynamics between different regions varied greatly. The high-growth type and high-grade of CO2 emissions were mainly distributed in the Eastern region, Shandong Peninsula and Middle south of Liaoning, with clearly lower concentrations in the Western region, Central region and Sichuan-Chongqing. The results of this study will enhance the understanding of spatiotemporal variations of CO2 emissions in China. They will provide a scientific basis for policy-making on viable CO2 emission mitigation policies. (C) 2015 Elsevier Ltd. All rights reserved.

Super-resolution mapping of urban flood (SMUF) is one of the hotspots in remote sensing and urban environment research. In this letter, a new SMUF method based on the fusion of support vector machine and general regression neural network (FSVMGRNN) was proposed to achieve enhanced performance. An SVM-SMUF algorithm was developed and a fusion criterion was formulated. Then, the FSVMGRNN-SMUF algorithm was developed. The results of FSVMGRNN-SMUF were evaluated using Landsat 8 OLI imagery of two representative cities in China. FSVMGRNN-SMUF yielded the most accurate SMUF results among the five SMUF methods according to visual comparisons and quantitative comparisons. The mapping accuracy of FSVMGRNN-SMUF related to the kernel functions was also analyzed and discussed. The results of this letter will help to boost practical applications of median-low resolution remote sensing images in urban flooding mapping, and to strengthen the means for monitoring and assessing urban flooding disasters.

Climate changes significantly impact environmental and hydrological processes. Precipitation is one of the most significant climatic parameters and its variability and trends have great influences on environmental and socioeconomic development. We investigate the spatio-temporal variability of precipitation occurrence frequency, mean precipitation depth, PVI and total precipitation in China based on long-term precipitation series from 1961 to 2015. As China's topography is diverse and precipitation is affected by topography strongly, ANUSPLIN can model the effect of topography on precipitation effectively is adopted to generate the precipitation interpolation surface. Mann-Kendall trend analysis and simple linear regression was adopted to examine long-term trend for these indicators. The results indicate ANUSPLIN precipitation surface is reliable and the precipitation variation show different regional and seasonal trend. For example, there is a sporadic with decreasing frequency precipitation trend in spring and a uniform with increasing frequency trend in summer inYangtze Plain, which may affect spring ploughing and alteration of flood risk for this main rice-production areas of China. In northwestern China, there is a uniform with increasing precipitation frequency and intensity trend, which is beneficial for this arid region. Our study could be helpful for other counties with similar climate types.

China's rapid urbanization has contributed to a massive agricultural land loss that could threaten its food security. Timely and accurate mapping of urban expansion and urbanization-related agricultural land loss can provide viable measures to be taken for urban planning and agricultural land protection. In this study, urban expansion in China from 2001 to 2013 was mapped using the nighttime stable light (NSL), normalized difference vegetation index (NDVI), and water body data. Urbanization-related agricultural land loss during this time period was then evaluated at national, regional, and metropolitan scales by integrating multiple sources of geographic data. The results revealed that China's total urban area increased from 31,076 km(2) in 2001 to 80,887 km(2) in 2013, with an average annual growth rate of 13.36%. This widespread urban expansion consumed 33,080 km(2) of agricultural land during this period. At a regional scale, the eastern region lost 18,542 km(2) or 1.2% of its total agricultural land area. At a metropolitan scale, the Shanghai-Nanjing-Hangzhou (SNH) and Pearl River Delta (PRD) areas underwent high levels of agricultural land loss with a decrease of 6.12% (4728 km(2)) and 6.05% (2702 km(2)) of their total agricultural land areas, respectively. Special attention should be paid to the PRD, with a decline of 13.30% (1843 km(2)) of its cropland. Effective policies and strategies should be implemented to mitigate urbanization-related agricultural land loss in the context of China's rapid urbanization.

Background: In the British Isles, Scots pine (Pinus sylvestris) is only thought to be native in the Scottish Highlands. However, there has been speculation that locally native specimens persist outside that region. Aims: This study addressed the question: is it bioclimatically plausible that locally native Scots pines could still persist in southern Scotland and northern England? Methods: The software package BIOCLIM, which has proved a useful tool for identifying possible locations of small populations and new species, was used to model current locations of Scots pine with climate surfaces. Based on this analysis, predictive maps were produced to identify where else in Scotland and northern England Scots pine might occur. Data were masked with soil types on which Scots pines naturally grow in Scotland to identify key areas where extant trees may still persist. Results: Results indicated that it is bioclimatically plausible that locally native Scots pines could persist in southern Scotland and northern England. However, further research is needed to confirm the natural origins of living Scots pines at particular locations. Conclusions: We propose investigations into the native status of Scots pine within the areas identified. If native Scots pines are verified outside the Scottish Highlands, this has significant implications for ecology and conservation.

With the rapid development of satellite imaging technology, large amounts of satellite images with high spatial resolutions are now available. High-resolution satellite imagery provides rich texture and structure information, which in the meantime poses a great challenge for automatic satellite scene recognition. In this study, a novel integration method of fuzzy theory and particle swarm optimization (IFTPSO) is proposed to achieve an increased accuracy of satellite scene recognition (SSR) in high-resolution satellite imagery. The particle encoding, fitness function and swarm search strategy are designed for IFTPSO-SSR. The IFTPSO-SSR method was evaluated using the satellite scenes from QuickBird, IKONOS and ZY-3. IFTPSO-SSR outperformed three traditional recognition methods with the highest recognition accuracy. The parameter sensitivity of IFTPSO-SSR was also discussed. The proposed method of this study can enhance the performance of satellite scene recognition in high-resolution satellite imagery, and thereby advance the research and applications of artificial intelligence and satellite image analysis.

Regional flood risk caused by intensive rainfall under extreme climate conditions has increasingly attracted global attention. Mapping and evaluation of flood hazard are vital parts in flood risk assessment. This study develops an integrated framework for estimating spatial likelihood of flood hazard by coupling weighted naive Bayes (WNB), geographic information system, and remote sensing. The north part of Fitzroy River Basin in Queensland, Australia, was selected as a case study site. The environmental indices, including extreme rainfall, evapotranspiration, net-water index, soil water retention, elevation, slope, drainage proximity, and density, were generated from spatial data representing climate, soil, vegetation, hydrology, and topography. These indices were weighted using the statistics-based entropy method. The weighted indices were input into the WNB-based model to delineate a regional flood risk map that indicates the likelihood of flood occurrence. The resultant map was validated by the maximum inundation extent extracted from moderate resolution imaging spectroradiometer (MODIS) imagery. The evaluation results, including mapping and evaluation of the distribution of flood hazard, are helpful in guiding flood inundation disaster responses for the region. The novel approach presented consists of weighted grid data, image-based sampling and validation, cell-by-cell probability inferring and spatial mapping. It is superior to an existing spatial naive Bayes (NB) method for regional flood hazard assessment. It can also be extended to other likelihood-related environmental hazard studies.

Flooding hazard evaluation is the basis of flooding risk assessment which has significances to natural environment, human life and social economy. This study develops a spatial framework integrating na < ve Bayes (NB) and geographic information system (GIS) to assess flooding hazard at regional scale. The methodology was demonstrated in the Bowen Basin in Australia as a case study. The inputs into the framework are five indices: elevation, slope, soil water retention, drainage proximity and density. They were derived from spatial data processed in ArcGIS. NB as a simplified and efficient type of Bayesian methods was used, with the assistance of remotely sensed flood inundation extent in the sampling process, to infer flooding probability on a cell-by-cell basis over the study area. A likelihood-based flooding hazard map was output from the GIS-based framework. The results reveal elevation and slope have more significant impacts on evaluation than other input indices. Area of high likelihood of flooding hazard is mainly located in the west and the southwest where there is a high water channel density, and along the water channels in the east of the study area. High likelihood of flooding hazard covers 45 % of the total area, medium likelihood accounts for about 12 %, low and very low likelihood represents 19 and 24 %, respectively. The results provide baseline information to identify and assess flooding hazard when making adaptation strategies and implementing mitigation measures in future. The framework and methodology developed in the study offer an integrated approach in evaluation of flooding hazard with spatial distributions and indicative uncertainties. It can also be applied to other hazard assessments.

Reclamation of abandoned farmland is crucial to a sustainable agriculture in arid regions. This study aims to evaluate the impact of different reclamation treatments on abandoned salinized farmland. We investigated four artificial reclamation treatments, continuous cotton (CC), continuous alfalfa (CA), tree-wheat intercropping (TW) and trees (TS), which were conducted in 2011-2012 in the Manasi River Basin of Xinjiang Province, China. Soil nutrient, microorganism and enzyme activity were examined in comparison with natural succession (CK) in an integrated analysis on soil fertility improvement and soil salinization control with these reclamations. Results indicate that the four artificial reclamation treatments are more effective approaches than natural restoration to reclaim abandoned farmland. TW and CA significantly increased soil nutrient content compared to CK. CC reduced soil salinity to the lowest level among all treatments. TW significantly enhanced soil enzyme activity. All four artificial reclamations increased soil microbial populations and soil microbial biomass carbon. TW and CA had the greatest overall optimal effects among the four treatments in terms of the ecological outcomes. If both economic benefits and ecological effects are considered, TW would be the best reclamation mode. The findings from this study will assist in selecting a feasible method for reclamation of abandoned farmland for sustainable agriculture in arid regions.

Rapid urbanization with land use and cover change (LUCC) is making a substantially increasing contribution to global carbon emissions. Understanding the spatial processes and transitions mechanism of urban carbon metabolism system by LUCC could help local governments in regional spatial planning. Taking 13 cities in the Yangtze River Delta of China as examples, we quantitatively analyzed and mapped the spatial processes of urban carbon metabolism by LUCC from 1995 to 2015 in the region and investigated the relationships between urban size growth and urban carbon metabolism rate by LUCC (M-LUCC) using panel data regression analysis. A higher M-LUCC showed a larger negative impact on the urban carbon metabolism system by per unit area of land use change. We found that the highest negative carbon transitions were shifted from Shanghai to cities in the South Jiangsu Province. And the dominant negative carbon transitions and positive ones came from land use transfer into and out the industrial land and transportation land. The results of the panel model regression analysis showed the growths of urban population and land both correlated positively with M-LUCC. Further, we controlled the economic growth and urban form changes on the relationship between urban size growth and M-LUCC, and the results suggested both the benefits from compromising economic growth and optimizing urban form were overshadowed by the negative impact of urban size growth. The study provided a robust methodology for assessing urban carbon metabolism and provided new insights into land use controls to develop low carbon cities.