An evolving marine environment and its driving forces of algal blooms in the Southern Yellow Sea of China.

Continuing global warming and intensification of human activities have substantially disturbed the balance of coastal marine ecosystems, potentially creating favorable conditions for algal blooms. Using serial remote sensing data and various national and provincial statistics, we investigated the spatial and temporal variations of the environmental driving forces for algal blooms in the Southern Yellow Sea between 2003 and 2017. The findings suggest that (1) Continual warming was observed in the Southern Yellow Sea. The study area evidenced more than three times the warming speed (0.41 °C/decade) of the global oceans (0.12 °C/decade) during the same period. There was an apparent warming zone in the region where macroalgal blooms tended to spread, with a heating of 1.0-1.5 °C (May-June). (2) Nutrient loadings have diverse patterns, characterized by fast-growing aquaculture activities and declining nutrients from land-based agriculture fertilizers and sewage discharge (based on published national and provincial statistics). (3) Growing expansion of algal blooms in the Southern Yellow Sea was confirmed by the relative increases in average May-June chlorophyll-a concentration of 46.7% and floating biomass area from 3.3% in 2003 to 13.4% in 2017. (4) While spatial correlation analysis showed a positive influence of the ocean surface temperature on chlorophyll-a, their relatively moderate (r = 0.40, p < 0.15) and declining correlations suggest that nutrient enrichment could be comparatively more influential on macroalgal blooms. Nutrient loading from the discharge of wastewater sourced from coastal aquaculture and organic residuals from land-sourcing sewage and industrial pollution, even though declining as reported, is still upholding a high level of nutrient enrichment in the study area. In addition, the fixed facilities for seaweed mariculture in the region provide vast breeding surfaces for algae. Consequently, the Southern Yellow Sea has become an ideal marine area for algal blooms.

Building climate change resilience by using a versatile toolkit for local governments and communities in rural Himalaya.

With the impacts of climate disruption becoming more evident there has been an increase in the uptake of climate change adaptation "toolkits" to assist local governments build community resilience and adapt to the impacts of climate change. There is increasing attention and call for practitioners to adopt proactive and participatory approaches to help in the adaptive response planning process. One such toolkit is the International Council for Local Environmental Initiatives (ICLEI) Asian Cities Climate Change Resilience Network (ACCRN) Process (IAP). This is a simple but rigorous toolkit developed to help local governments in Asian cities build resilience to the impacts of climate change. This paper outlines the application of the toolkit to determine its versatility in the rural context and was trialled in the Himalayan rural enclave of Ramgad in the Indian state of Uttarakhand. Given the differences between urban and rural environments, the outcomes highlighted the need for further investigation and analysis into the process to ensure that the methodology truly reflects the nature of rural systems and their level of vulnerability and adaptive capacity. Overall, the toolkit proved to be a simple but versatile toolkit to assess the vulnerability and adaptive capacity of communities in rural Himalaya. Over 40 resilience intervention strategies were developed for the Ramgad enclave and these were prioritized according to their technical, political, social and economic feasibility.

Long-term spatio-temporal precipitation variations in China with precipitation surface interpolated by ANUSPLIN.

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 in Yangtze Plain, which may affect spring ploughing and alteration of flood risk for this main rice-production areas of China. In north-western 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.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Integrating Entropy-Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard.

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 naïve 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.