This study illustrated the growth of Scenedesmus obliquus and recycle of nutrients in wastewater combined with inorganic carbon under autotrophic conditions. Scenedesmus obliquus was cultivated under different conditions by adding sodium carbonate (Na 2 CO 3 ) at 15–40 mg l −1 separately in wastewater containing high nitrogen and phosphorus content. The growth characteristics of S. obliquus , pH and dissolved inorganic carbon (DIC) changes of microalgae liquid, the recycle rate of ammonia and phosphorus and lipid content were determined. The changes of pH and DIC showed that S. obliquus could use Na 2 CO 3 to grow, with lipid contents of 18–25%. Among all Na 2 CO 3 concentrations, 20 mg l −1 was the optimum, of which S. obliquus had the highest NH 3 -N recycle of 52% and P O 4 3 – P recycle of 67%. By the 14th day, its biomass production also reaches the maximum of 0.21 g l −1 . However, inorganic carbon fixation rate was inversely proportional to its concentration. Moreover, the biomass was in positive correlation with the Na 2 CO 3 concentration except 20 mg l −1 , which provided a possibility that S. obliquus could be acclimatized to adjust to high concentrations of inorganic carbon to promote biomass accumulation and recycle of nutrients.
To accurately identify slope hazards based on high-resolution remote sensing imagery, an improved watershed segmentation algorithm is proposed. The color difference of the Luv color space was used as the regional similarity measure for region merging. Furthermore, the area relative error for evaluating the image segmentation accuracy was improved and supplemented with the pixel quantity error to evaluate the segmentation accuracy. An unstable slope was identified to validate the algorithm on Chinese Gaofen-2 (GF-2) remote sensing imagery by a multiscale segmentation extraction experiment. The results show the following: (1) the optimal segmentation and merging scale parameters were, respectively, minimum threshold constant C for minimum area Amin of 500 and optimal threshold D for a color difference of 400. (2) The total processing time for segmentation and merging of unstable slopes was 39.702 s, much lower than the maximum likelihood classification method and a little more than the object-oriented classification method. The relative error of the slope hazard area was 4.92% and the pixel quantity error was 1.60%, which were superior to the two classification methods. (3) The evaluation criteria of segmentation accuracy were consistent with the results of visual interpretation and the confusion matrix, indicating that the criteria established in this study are reliable. By comparing the time efficiency, visual effect and classification accuracies, the proposed method has a good comprehensive extraction effect. It can provide a technical reference for promoting the rapid extraction of slope hazards based on remote sensing imagery. Meanwhile, it also provides a theoretical and practical experience reference for improving the watershed segmentation algorithm.
It is highly helpful and necessary to investigate and monitor the status of coal seam. Fortunately, remote sensing has facilitated the identification and dynamical monitoring of spontaneous combustion for a large area coal mining area, especially using the time series remotely-sensed datasets. In this paper, Datong Jurassic coal mining area is used as the study area, China, and an exclusion method and a multiplefactor analysis method are jointly used to identify the spontaneous combustion, including land surface temperature (LST), burnt rocks, and land use and land cover change (LUCC). The LST is firstly retrieved using a single-window algorithm due to a thermal infrared band of Landsat-5 TM (Thematic Mapper). Burnt rocks is then extracted using a decision-tree classification method based on a high-resolution SPOT-5 image. The thermal anomaly areas are identified and refined by the spatial overlay analysis of the above affecting factors. Three-period maps of coal fire areas are obtained and dynamically analyzed in 2007, 2009 and 2010. The results show that a total of 12 coal fire areas have been identified, which account for more than 1% of the total area of the study area. In general, there is an increasing trend yearly and a total of 771,970 m 2 is increased. The average annual increase is 257,320 m 2 , the average annual growth rate is 3.78%, and the dynamic degree is 11.29%.
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