Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery.

A new type of copolymer was synthesized by ring-opening polymerization of 1,3-dioxan-2-one (TMC) and 2-hydro-2-oxo-1,3,2-dioxaphosphorinane (TMP) using (i-C₄H₉)₃Al as the initiator. The chemical structure was characterized by ¹H nuclear magnetic resonance and Fourier transform infrared. The respective reactivity ratios of TMC and TMP were determined using the Kelen–Tüdös method. Conversion studies showed that the composition of the copolymer agree with the feed composition at high conversions. © 1998 John Wiley & Sons, Ltd.

Accurate extraction of building vector contours from high-resolution remote sensing images is a fundamental task for urban mapping and geographic information systems. However, existing approaches often suffer from blurred boundaries and geometric distortions when dealing with buildings of complex shapes, limiting the accuracy and usability of the extracted building footprints. To address these challenges, this paper proposes a multi-level building contour refinement framework based on morphological perception. The proposed framework integrates a three-stage contour optimization strategy, including principal direction extraction, morphology-based contour reconstruction, and geometry-aware regularization, to progressively refine complex building contours under geometric constraints. In addition, a multi-dimensional contour complexity model and an adaptive threshold optimization network are introduced to dynamically adjust refinement parameters according to contour complexity. Experimental results on the WHU-Mix dataset demonstrate that the proposed method outperforms state-of-the-art approaches, achieving 87.52%, 77.43%, and 87.35% in boundary F1, vertex F1, and mIoU, respectively. These results indicate that the proposed framework provides an effective and robust solution for high-precision building vector contour extraction in complex remote sensing scenarios.

Forest types are associated with differences in soil properties and microbial communities, but the environmental correlates of microbial variation across soil depths remain poorly resolved. We examined whether PLFA-derived microbial community structure was more closely associated with bulk soil carbon (C) concentrations or with variation in soil C stoichiometry across five representative subtropical forest types in central China. Soil physicochemical properties and phospholipid fatty acid (PLFA) profiles were assessed in surface (0–10 cm) and subsurface (10–30 cm) soils. Forest types were associated with differences in soil moisture, pH, inorganic nitrogen, available phosphorus, total C (TC), total N (TN), and C ratio, accompanied by distinct patterns of PLFA-derived microbial characteristics. Oak forest had the highest TC and TN concentrations but the lowest total PLFA abundance, whereas dawn redwood forest had relatively high microbial biomass despite comparatively low TC and TN concentrations. RDA showed that soil C ratio was the strongest environmental variable associated with PLFA-based microbial community variation in both soil layers, followed by pH and NO₃⁻, while SWC showed an additional association in the surface soil. At the microbial-group level, soil C ratio was negatively associated with bacterial and actinomycete biomarkers, whereas soil pH showed positive associations with fungal and arbuscular mycorrhizal fungal PLFAs. These results indicate that variation in PLFA-derived microbial characteristics among the sampled forest types was more closely associated with bulk soil C stoichiometry and other soil environmental conditions than with TC concentration alone. Overall, bulk soil C stoichiometry provides an informative correlate of below-ground microbial variation across the sampled forest types, although these associations do not establish causal effects of forest type.