Distinct transpiration characteristics of black locust plantations acclimated to semiarid and subhumid sites in the Loess Plateau, China

2022 ◽  
Vol 262 ◽  
pp. 107402
Author(s):  
Jinlin Lyu ◽  
Qiu-Yue He ◽  
Qiu-Wen Chen ◽  
Ran-Ran Cheng ◽  
Guoqing Li ◽  
...  
Keyword(s):  
Loess Plateau ◽  
Black Locust ◽  
The Loess Plateau

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.

Spatial differences in the radial growth responses of black locust (Robinia pseudoacacia Linn.) to climate on the Loess Plateau, China

2021 ◽  
Vol 67 ◽  
pp. 125832
Author(s):  
Maierdang Keyimu ◽  
Zongshan Li ◽  
Bojie Fu ◽  
Weiliang Chen ◽  
Jingshu Wei ◽  
...  
Keyword(s):  
Loess Plateau ◽  
Radial Growth ◽  
Black Locust ◽  
Robinia Pseudoacacia ◽  
Growth Responses ◽  
The Loess Plateau ◽  
Spatial Differences
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