a b s t r a c tNitrogen (N) pollution is a global environmental problem that has greatly increased the risks of both the eutrophication of surface waters and contamination of ground waters. The majority of N pollution mainly comes from agricultural fields, in particular during rice growing seasons. In recent years, a gradual shift from the transplanting rice cultivation method to the direct seeding method has occurred, which results in different water and N losses from paddy fields and leads to distinct impacts on water environments. The N transport and transformations in an experimental direct-seeded-rice (DSR) field in the Taihu Lake Basin of east China were observed during two consecutive seasons, and simulated using Hydrus-1D model. The observed crop N uptake, ammonia volatilization (AV), N concentrations in soil, and N leaching were used to calibrate and validate the model parameters. The two most important inputs of N, i.e., fertilization and mineralization, were considered in the simulations with 220 and 145.5 kg ha −1 in 2008 and 220 and 147.8 kg ha −1 in 2009, respectively. Ammonia volatilization and nitrate denitrification were the two dominant pathways of N loss, accounting for about 16.0% and 38.8% of the total N input (TNI), respectively. Both nitrification and denitrification processes mainly occurred in the root zone. N leaching at 60 and 120 cm depths accounted for about 6.8% and 2.7% of TNI, respectively. The crop N uptake was 32.1% and 30.8% of TNI during the 2008 and 2009 seasons, respectively, and ammonium was the predominant form (74% of the total N uptake on average). Simulated N concentrations and fluxes in soil matched well with the corresponding observed data. Hydrus-1D could simulate the N transport and transformations in the DSR field, and could thus be a good tool for designing optimal fertilizer management practices in the future.

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Airborne light detection and ranging (lidar) has become a powerful support for acquiring geospatial data in numerous geospatial applications and analyses. However, the process of extracting ground points accurately and effectively from raw point clouds remains a big challenge. This study presents an improved top-hat filter with a sloped brim to enhance the robustness of ground point extraction for complex objects and terrains. The top-hat transformation is executed and the elevation change intensity of the transitions between the obtained top-hats and outer brims is inspected to suppress the omission error caused by protruding terrain features. Finally, the nonground objects of complex structures, such as multilayer buildings, are identified by the brim filter that is extended outward. The performance of the proposed filter in various environments is evaluated using diverse datasets with difficult cases. The comparison of the proposed filter with the commercial software Terrasolid TerraScan and other popular filtering algorithms demonstrates the applicability and effectiveness of this filter. Experimental results show that the proposed filter has great promise in terms of its application in various types of landscapes. Abrupt terrain features with dramatic elevation changes are well preserved, and diverse objects with complicated shapes are effectively removed. This filter has minimal omission and OPEN ACCESSRemote Sens. 2014, 6 12886 commission error oscillation for different test areas and thus demonstrates a stable and reliable performance in diverse landscapes. In addition, the proposed algorithm has high computational efficiency because of its simple and efficient data structure and implementation.

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Uncertainty remains over what properties of biochar and which groups of microorganisms are responsible for the direction and magnitude of observed biochar‐induced priming effects (PE). We selected maize straw, grass, peanut shells and sugar cane as feedstocks to produce biochar at 300, 400 and 500°C by slow pyrolysis, and carried out an 80‐day soil–biochar incubation experiment to investigate biochar‐induced soil PE by adopting isotopic techniques. Irrespective of pyrolysis temperature, grass‐derived biochar (Grass‐B) induced the largest PE (348 to 1214 mg C kg−1 soil), whereas peanut shell‐derived biochar (Peanut‐B) induced the smallest (−135 to 261 mg C kg−1 soil) PE. The intensity of PE was largely determined by the feedstock and was closely related to the proportion of cellulose and lignin in it. The bacterial and fungal communities at days 8 and 40 were investigated by high‐throughput sequencing of 16S rRNA and ITS genes. Biochar additions explained 54.0 and 52.9% of the total variation in bacterial and fungal community structure, respectively. The bacterial Actinobacteria and Firmicutes were dominant during the initial phase of the PE (at day 8), whereas the fungal Sordariomycetes and Tremellomycetes were abundant after the longer phase of incubation at day 40. A succession from bacterial community (used the available C fraction of biochar) to fungal community (used the recalcitrant C fraction of biochar and soil organic C) might occur during the PE, together with the alternation of apparent PE to real PE. Highlights Feedstock type determines biochar‐induced priming effects (PE). A succession from bacterial to fungal community occurred. Actinobacteria and Firmicutes used biochar available C during the early stage. Sordariomycetes and Tremellomycetes were associated with the later real PE.