Search

Assessing the distribution and risks associated with the soil lead content in the Tieguanyin tea plantations of Anxi County is critical, given the county's significance as the primary Tieguanyin tea production area in Fujian Province. This study examined the distribution characteristics of soil lead in Anxi County's tea plantations according to the Kriging spatial interpolation of the parameters of the semivariance function of the exponential model. Moreover, the sources of lead content were analyzed, considering geological backgrounds and anthropogenic influences. Ecological risks and the issuance of early warnings were also assessed. The soil lead content in the rocks of the Tieguanyin tea plantations in Anxi County followed the order: andesite > dacite > rhyolite > granite. The soil lead content gradually decreased from the center toward the east and west, forming four distinct north–south parallel zones. High-lead-content areas were identified at the border of Jiandou, Bailai, and Hushang; in the central part of Lutian; and in the southern part of Huqiu. The high levels of soil lead in the tea plantations possibly originated from industrial and mining activities, automobile exhaust, and agricultural activities. The distribution of single-factor pollution indices and potential risk evaluation based on the Soil Environmental Quality Standard, Environmental Technical Conditions for Tea Production Area, and Environmental Technical Conditions for Organic Tea Production Area indicated that the soil in Tieguanyin tea plantations in Anxi County was clean and safe for tea cultivation. [ABSTRACT FROM AUTHOR]

The effects of root systems on soil detachment by overland flow are closely related to vegetation types. The objective of this study was to quantify the effects of two gramineous roots (Paspalum mandiocanum with shallow roots and Pennisetum giganteum with deep roots) on soil detachment capacity, rill erodibility, and critical shear stress on alluvial fans of benggang in south‐east China. A 4‐m‐long and 0.12‐m‐wide flume was used. Slope steepness ranged from 9% to 27%, and unit flow discharge ranged from 1.39 × 10−3 to 4.19 × 10−3 m2 s−1. The mean detachment capacities of P. mandiocanum and P. giganteum lands were 18% and 38% lower than that of bare land, respectively, and the effects of root on reducing soil detachment were mainly reflected in the 0‐ to 5‐cm soil layer. The most important factors in characterizing soil detachment capacity were root length density and soil cohesion, and soil detachment capacity of the two grass lands could be estimated using flow shear stress, soil cohesion, and root length density (NSE = 0.90). With the increase in soil depth, rill erodibility increased, whereas shear stress decreased. The mean rill erodibilities of P. mandiocanum and P. giganteum lands were 81% and 61% as much as that of bare land, respectively. Additionally, rill erodibilities of the two grass lands could be estimated as an exponential function by root length density and soil cohesion (NSE = 0.88). The mean critical shear stress of P. mandiocanum and P. giganteum lands was 1.29 and 1.39 times that of bare land, respectively, and it could be estimated with a linear function by root length density (NSE = 0.76). This study demonstrated that planting of the two grasses P. mandiocanum and P. giganteum could effectively reduce soil detachment and enhance soil resistance to erosion on alluvial fans, with the deep roots of P. giganteum being more effective than the shallow roots of P. mandiocanum. The results are helpful for understanding the influencing mechanism of root systems on soil detachment process. [ABSTRACT FROM AUTHOR]

The paper used the unmanned aerial vehicle (UAV) which can quickly obtain a wide range of vegetation images to capture the low-altitude aerial photograph)' and associated with the multivariate HoG features to recognize vegetation types. Firstly, the texture infonnation of the image were extracted by Gabor filter and using HSV and Lab color space transform to extract the color infonnation. Next, the image were divided into N cells, then computing the gradient of histogram of each cell based on the texture and color infonnation to fonn the multivariate HoG features. Finally, taking the cell as the classification unit, combining the random forest machine learning algorithm, vegetation types recognition model were established. Taking the Anxi County in Fujian Province as the test area, the results showed that it was feasible to identify the vegetation types by using the optical image of low-altitude aerial photography; the highest recognition accuracy in recognizing vegetation and non-vegetation was 96.04%; the recognition accuracy rate was 82.44% in recognizing vegetation types at 20 m and decreased with the height increase. Moreover, the vegetation images of aerial photography were further collected, which was captured at the Changting County of Fujian Province, as another data to prove the robustness of the model of vegetation type recognition in different areas, the highest recognition accuracy was 73.31%, the accuracy was similar compared with the result of Anxi County. In this paper, the vegetation images by using the unmanned aerial vehicle with optical camera were conveniently, econmically obtained; the proposed vegetation type recognition model can be easily applied to the field vegetation type monitoring and management as the high precision and its robustness in various regions. In addition, according to the recognition result at various aerial height, the proposed aerial height should not be too high, 20 m as the reference height of aerial photography was suitable. [ABSTRACT FROM AUTHOR]