Non-structural carbon, nitrogen, and phosphorus between black locust and chinese pine plantations along a precipitation gradient on the Loess Plateau, China

Trees ◽  
2018 ◽  
Vol 32 (3) ◽  
pp. 835-846 ◽  
Author(s):  
Yang Cao ◽  
Yanan Li ◽  
Yunming Chen
Keyword(s):  
Loess Plateau ◽  
Black Locust ◽  
Pine Plantations ◽  
Precipitation Gradient ◽  
The Loess Plateau ◽  
Nitrogen And Phosphorus ◽  
Chinese Pine ◽  
Structural Carbon

Effects of Different Vegetation Types on Soil Microbiological Properties on the Loess Plateau

2013 ◽  
Vol 726-731 ◽  
pp. 4893-4899
Author(s):  
Feng Li Zhou ◽  
Sha Xue ◽  
Bing Wang ◽  
Guo Bin Liu
Keyword(s):  
Microbial Biomass ◽  
Loess Plateau ◽  
Black Locust ◽  
Ph Value ◽  
Vegetation Types ◽  
The Loess Plateau ◽  
Nutritional Properties ◽  
Chinese Pine ◽  
Soil Urease ◽  
Microbiological Properties

The re-establishment of natural species-rich health lands on abandoned farmland is one of the main measures in soil erosion control in the Loess Plateau of China. This study was conducted to understand how enzyme activities changed with nutritional properties and microbial biomass in different vegetation types in the loessial gully region of the Loess Plateau. Soil samples were collected in different vegetations which had planted for almost 30 years. For the collected soils, nutritional, microbial and enzymatic properties were determined. The result showed that soil nutritional properties and microbial biomass were enhanced in black locust-amorpha, compared with black locust, but weakened in Chinese pine-amorpha compared with Chinese pine. Besides, soil urease, α-amylase, alkaline phosphatase, catalase, saccharase and cellulase activities in creased with restoration, but decreased polyphenol oxidase. Moreover, urease activity was obviously high in korshinsk peashrub and black locust for the nitrogen fixation of them, and α-amylase was high in Chinese pine for low pH value. However, there still was a certain gap to Chinese arborvitae which was considered to be the climax community in the region. In general, the distance of vegetations to Chinese arborvitae was grassland > black locust > Chinese pine> korshinsk peashrub > Chinese pine-amorpha > black locust-amorpha.

scholarly journals Aboveground litter contribution to soil respiration in a black locust plantation in the Loess Plateau

2012 ◽  
Vol 32 (7) ◽  
pp. 2150-2157 ◽  
Author(s):  
周小刚 ZHOU Xiaogang ◽  
郭胜利 GUO Shengli ◽  
车升国 CHE Shengguo ◽  
张芳 ZHANG Fang ◽  
邹俊亮 ZOU Junliang ◽  
...  
Keyword(s):  
Soil Respiration ◽  
Loess Plateau ◽  
Black Locust ◽  
The Loess Plateau ◽  
Aboveground Litter

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

Forests ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 623 ◽  
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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