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Abstract:In light of the need for fine-grained, accurate, and timely urban land use information, a per-field classification approach was proposed in this paper to automatically map fine-grained urban land use in a study area within Haidian District, Beijing, China, in 2016. High-resolution remote sensing imagery and multi-source social sensing data were used to provide both physical and socioeconomic information. Four categories of attributes were derived from both data sources for urban land use parcels segmented by the OpenStreetMap road network, including spectral/texture attributes, landscape metrics, Baidu Point-Of-Interest (POI) attributes, and Weibo attributes. The random forests technique was adopted to conduct the classification. The importance of each attribute, attribute category, and data source was evaluated for the classification as a whole and the classification of individual land use types. The results showed that a testing accuracy of 77.83% can be achieved. The approach is relatively good at classifying open space and residential parcels, and poor at classifying institutional parcels. While using solely remote sensing data or social sensing data can achieve equally high overall accuracy, their importance varies in terms of the classification of individual classes. Landscape metrics are the most important for open space parcels. Spectral/texture attributes are more important in identifying institutional and residential parcels. The classification of business parcels relies more on landscape metrics and social sensing data, and less on spectral/texture attributes. The classification accuracy can be potentially improved upon the acquisition of purer parcels and the addition of new attributes. It is expected that the proposed approach will be useful for the routine update of urban land use information and large-scale urban land use mapping.

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We investigated the mercury (Hg) uptake by seedlings of rice (Oryza sativa L.) grown in solution and interactions between Hg and arsenate uptake. The results showed that increasing Hg 2+ concentrations in the nutrient solution decreased both root and shoot biomass. Hg 2+ at concentrations of 1.0 and 2.5 mg L −1 caused 50% reduction in root biomass. A 50% reduction in shoot biomass occurred at Hg 2+ concentrations of around 0.5 mg L −1 . Nevertheless, 0.5 mg As L −1 has no significant effect on plant yield. Hg accumulated in rice roots, and the Hg concentration factor in roots reached nearly 1900 at 2.5 mg Hg L −1 . The addition of As slightly increased the Hg concentration in the roots. However, As concentrations in the roots decreased significantly with increasing Hg concentration in the growth solution to 1.0 or 2.5 mg Hg L −1 . Shoot As concentrations decreased with increasing Hg concentrations in the growth solution, but increased again with further increase in Hg concentration to 2.5 mg L −1 . Possible mechanisms of Hg uptake and interactions between Hg and As in the uptake process are also discussed.

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