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Soil radon anomaly is a common phenomenon in karst areas. In this paper, the geochemical behavior of radium (Ra) in the process of carbonate rock weathering and soil formation was used to reveal the accumulation mechanism of soil radon. In the karst area and non-karst area, 24 soil points were collected to measure radon and radium, and a carbonate weathering profile was collected in the karst area. The research showed that: (1) the theoretical radon value obtained from Ra is far greater than the radon value measured on site, indicating that soil Ra can provide a potential source for the source of soil Rn; (2) through the study of the geochemical behavior of Ra during the weathering process, it was found that Ra was significantly enriched during carbonate weathering. This paper deepened the understanding of the geochemical behavior of radionuclides (Ra, Rn) in karst environments. [ABSTRACT FROM AUTHOR]

The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of deep learning. Based on our experiments, we drew three main conclusions: First, the OHS-1 hyperspectral images used in this study have high spatial resolution (10 m), which reduces the influence of mixed pixel effect and greatly improves the classification accuracy. Second, limited by the amount of sample data, Conv1D-based classifier does not need too many layers to achieve high classification accuracy. In addition, the size of the convolution kernel has a great influence on the classification accuracy. Finally, the accuracy of Conv1D (85.04%) is higher than that of RF model (80.61%). Especially for broadleaf species with similar spectral characteristics, such as Manchurian walnut and aspen, the accuracy of Conv1D-based classifier is significantly higher than RF classifier (87.15% and 71.77%, respectively). Thus, the Conv1D-based deep learning framework combined with hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects in the future. [ABSTRACT FROM AUTHOR]