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Excessive emissions of greenhouse gases — of which carbon dioxide is the most significant component, are regarded as the primary reason for increased concentration of atmospheric carbon dioxide and global warming. Terrestrial vegetation sequesters 112–169 PgC (1PgC = 1015g carbon) each year, which plays a vital role in global carbon recycling. Vegetation carbon sequestration varies under different land management practices. Here we propose an integrated method to assess how much more carbon can be sequestered by vegetation if optimal land management practices get implemented. The proposed method combines remotely sensed time-series of net primary productivity datasets, segmented landscape-vegetation-soil zones, and distance-constrained zonal analysis. We find that the global land vegetation can sequester an extra of 13.74 PgC per year if location-specific optimal land management practices are taken and half of the extra clusters in ~15% of vegetated areas. The finding suggests optimizing land management is a promising way to mitigate climate changes.

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We investigated nutritional characteristics and active components in the liver of Wagyu×Qinchuan cattle and Qinchuan cattle produced in Shaanxi (China). We observed significant differences (p<0.05) in the proximate composition of protein, fat, carbohydrate, total energy, and glycogen. Wagyu×Qinchuan cattle liver showed higher (p<0.05) sodium, iron, zinc, and selenium concentrations than Qinchuan cattle liver. The amino acid composition of Wagyu×Qinchuan cattle liver was richer (p<0.05) in 13 types of amino acids, with the exception of Asp (10.06%), Val (5.86%), and Met (1.72%). Total essential amino acids accounted for almost half the composition (39.69%) in Wagyu×Qinchuan cattle liver. Wagyu×Qinchuan cattle liver had lower (p<0.05) levels of monounsaturated fatty acids (18.2%), but higher (p<0.05) levels of polyunsaturated fatty acids (35.11%), compared with Qinchuan cattle liver (23.29% and 28.11%, respectively). The thrombogenic index was higher in Qinchuan cattle liver (0.86) than in Wagyu×Qinchuan cattle liver (0.70), and the glutathione (38.0 mg/100g) and L-carnitine (2.12 μM/g) content was higher (p<0.05) in Wagyu×Qinchuan cattle liver than in Qinchuan cattle liver (29.8 mg/100g and 1.41 μM/g, respectively). According to the results obtained, the liver of Wagyu×Qinchuan cattle, which is insufficiently used, should be increasingly utilized to improve its commercial value.

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Explicit information of tree species composition provides valuable materials for the management of forests and urban greenness. In recent years, scholars have employed multiple features in tree species classification, so as to identify them from different perspectives. Most studies use different features to classify the target tree species in a specific growth environment and evaluate the classification results. However, the data matching problems have not been discussed; besides, the contributions of different features and the performance of different classifiers have not been systematically compared. Remote sensing technology of the integrated sensors helps to realize the purpose with high time efficiency and low cost. Benefiting from an integrated system which simultaneously acquired the hyperspectral images, LiDAR waveform, and point clouds, this study made a systematic research on different features and classifiers in pixel-wised tree species classification. We extracted the crown height model (CHM) from the airborne LiDAR device and multiple features from the hyperspectral images, including Gabor textural features, gray-level co-occurrence matrix (GLCM) textural features, and vegetation indices. Different experimental schemes were tested at two study areas with different numbers and configurations of tree species. The experimental results demonstrated the effectiveness of Gabor textural features in specific tree species classification in both homogeneous and heterogeneous growing environments. The GLCM textural features did not improve the classification accuracy of tree species when being combined with spectral features. The CHM feature made more contributions to discriminating tree species than vegetation indices. Different classifiers exhibited similar performances, and support vector machine (SVM) produced the highest overall accuracy among all the classifiers.

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