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Particle-beam-driven plasma wakefield acceleration (PWFA) enables various novel high-gradient techniques for powering future compact light-source and high-energy physics applications. Here, a driving particle bunch excites a wakefield response in a plasma medium, which may rapidly accelerate a trailing witness beam. In this Letter, we present the measurement of ratios of acceleration of the witness bunch to deceleration of the driver bunch, the so-called transformer ratio, significantly exceeding the fundamental theoretical and thus far experimental limit of 2 in a PWFA. An electron bunch with ramped current profile was utilized to accelerate a witness bunch with a transformer ratio of 4.6_{-0.7}^{+2.2} in a plasma with length ∼10  cm, also demonstrating stable transport of driver bunches with lengths on the order of the plasma wavelength.

In arid and semiarid areas, the importance of soil inorganic carbon (SIC) is at least as high as that of soil organic carbon (SOC) in affecting the regional carbon budget following vegetation rehabilitation. However, variations in SIC have been uncertain, and few studies have analyzed the interactions between the SOC and SIC pools. We measured SIC, SOC, δ13C‐SIC, and δ13C‐SOC after planting Mongolian pine (MP) and Artemisia ordosica (AO) on shifting sand land (SL) over 10 years in the Mu Us Desert, northwest China. The results showed that, compared to SL, SIC stocks at 0–100 cm in MP and AO lands significantly increased by 12.6 and 25.8 Mg ha−1, respectively; SOC stocks in MP and AO lands significantly increased by 24.0 and 38.4 Mg ha−1, respectively. Both δ13C‐SIC and δ13C‐SOC in the 2 plantation lands were significantly lower than those in SL were. All 315 samples exhibited a negatively linear relationship between SIC content and δ13C‐SIC (R2 = .70, p < .01) and showed positively linear relationships between SIC content and SOC content (R2 = .69, p < .01) and between δ13C‐SIC and δ13C‐SOC (R2 = .61, p < .01). The results demonstrated that vegetation rehabilitation on SL has a high potential to sequester SIC and SOC in semiarid deserts. The reduction in δ13C‐SIC and the relationship of SIC with δ13C‐SIC following vegetation rehabilitation suggested that SIC sequestration is likely caused by the formation of pedogenic inorganic carbon. The relationships between SIC and SOC and between δ13C‐SIC and δ13C‐SOC implied that the pedogenic inorganic carbon formation may be closely related to the SOC accumulation.

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Precise growing stock volume (GSV) estimation is essential for monitoring forest carbon dynamics, determining forest productivity, assessing ecosystem forest services, and evaluating forest quality. We evaluated four machine learning methods: classification and regression trees (CART), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF), for their reliability in the estimation of the GSV of Pinus massoniana plantations in China’s northern subtropical regions, using remote sensing data. For all four methods, models were generated using data derived from a SPOT6 image, namely the spectral vegetation indices (SVIs), texture parameters, or both. In addition, the effects of varying the size of the moving window on estimation precision were investigated. RF almost always yielded the greatest precision independently of the choice of input. ANN had the best performance when SVIs were used alone to estimate GSV. When using texture indices alone with window sizes of 3 × 5 × 5 or 9 × 9, RF achieved the best results. For CART, SVM, and RF, R2 decreased as the moving window size increased: the highest R2 values were achieved with 3 × 3 or 5 × 5 windows. When using textural parameters together with SVIs as the model input, RF achieved the highest precision, followed by SVM and CART. Models using both SVI and textural parameters as inputs had better estimating precision than those using spectral data alone but did not appreciably outperform those using textural parameters alone.

The forest canopy is the medium for energy and mass exchange between forest ecosystems and the atmosphere. Remote sensing techniques are more efficient and appropriate for estimating forest canopy cover (CC) than traditional methods, especially at large scales. In this study, we evaluated the CC of black locust plantations on the Loess Plateau using random forest (RF) regression models. The models were established using the relationships between digital hemispherical photograph (DHP) field data and variables that were calculated from satellite images. Three types of variables were calculated from the satellite data: spectral variables calculated from a multispectral image, textural variables calculated from a panchromatic image (Tpan) with a 15 × 15 window size, and textural variables calculated from spectral variables (TB+VIs) with a 9 × 9 window size. We compared different mtry and ntree values to find the most suitable parameters for the RF models. The results indicated that the RF model of spectral variables explained 57% (root mean square error (RMSE) = 0.06) of the variability in the field CC data. The soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) were more important than other spectral variables. The RF model of Tpan obtained higher accuracy (R2 = 0.69, RMSE = 0.05) than the spectral variables, and the grey level co-occurrence matrix-based texture measure—Correlation (COR) was the most important variable for Tpan. The most accurate model was obtained from the TB+VIs (R2 = 0.79, RMSE = 0.05), which combined spectral and textural information, thus providing a significant improvement in estimating CC. This model provided an effective approach for detecting the CC of black locust plantations on the Loess Plateau.