Coupling of plant and soil C:N:P stoichiometry in black locust (Robinia pseudoacacia) plantations on the Loess Plateau, China

Seasonal variations in stoichiometry are a crucial regulatory mechanism for plant communities that respond to environmental changes. However, the seasonal characteristics of stoichiometry in plants, litter, and soil are poorly understood, especially in plantation ecosystems. Therefore, we explored the seasonal variations of C, N, and P contents and ratios between plants, litter, and soil of a Robinia pseudoacacia plantation on the Loess Plateau in China in 2017. The results indicate that the C, N, P contents and ratios in plants, litter and soil showed different seasonal patterns. The N and P contents of tree and shrub leaves substantially decreased over the growing season, while the C:N, C:P, and N:P ratios exhibited the opposite trend. The utilization efficiency of the N and P elements by trees and shrubs gradually increased with the change of the growing season. These results suggest that the C:N:P stoichiometry of plants was more sensitive to seasonal changes than the litter and soil; therefore, the potential impacts of time should be considered when using stoichiometry to explore the utilization of plant nutrients. Additionally, the P content between tree leaves and soil and the N content between herb leaves and soil were significantly positively correlated, indicating that the growth of the tree and herb layer in the R. pseudoacacia plantation in the area was restricted by P and N, respectively. Meanwhile, the N content in the leaves between trees and herbs showed a significant negative correlation, indicating that N competition existed between R. pseudoacacia and understory herbs, which was not conducive to the effective use of environmental resources by the R. pseudoacacia plantation ecosystem. This study contributes to vegetation restoration and plantation management on the Loess Plateau and provides basic information for global stoichiometric analyses.

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Estimating Forest Canopy Cover in Black Locust (Robinia pseudoacacia L.) Plantations on the Loess Plateau Using Random Forest

Forests ◽

10.3390/f9100623 ◽

2018 ◽

Vol 9 (10) ◽

pp. 623 ◽

Cited By ~ 6

Author(s):

Qingxia Zhao ◽

Fei Wang ◽

Jun Zhao ◽

Jingjing Zhou ◽

Shichuan Yu ◽

...

Keyword(s):

Random Forest ◽

Loess Plateau ◽

Forest Canopy ◽

Vegetation Index ◽

Black Locust ◽

Robinia Pseudoacacia ◽

Canopy Cover ◽

Window Size ◽

Important Variable ◽

The Loess Plateau

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.

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