Luminescence dating of loessic sediments from the Loess plateau, China

1999 ◽  
Vol 87 (4) ◽  
pp. 675-684 ◽  
Author(s):  
M. Frechen
Keyword(s):  
Loess Plateau ◽  
Luminescence Dating ◽  
The Loess Plateau

Eco-economic regionalization of the Loess Plateau

2009 ◽  
Vol 17 (5) ◽  
pp. 1023-1028 ◽  
Author(s):  
Qing-Feng ZHANG ◽  
Fa-Qi WU
Keyword(s):  
Loess Plateau ◽  
The Loess Plateau ◽  
Economic Regionalization

scholarly journals Evaluation of Three Different Machine Learning Methods for Object-Based Artificial Terrace Mapping—A Case Study of the Loess Plateau, China

2021 ◽  
Vol 13 (5) ◽  
pp. 1021
Author(s):  
Hu Ding ◽  
Jiaming Na ◽  
Shangjing Jiang ◽  
Jie Zhu ◽  
Kai Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Loess Plateau ◽  
Water Conservation ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
The Loess Plateau ◽  
Object Based ◽  
Extreme Gradient Boosting

Artificial terraces are of great importance for agricultural production and soil and water conservation. Automatic high-accuracy mapping of artificial terraces is the basis of monitoring and related studies. Previous research achieved artificial terrace mapping based on high-resolution digital elevation models (DEMs) or imagery. As a result of the importance of the contextual information for terrace mapping, object-based image analysis (OBIA) combined with machine learning (ML) technologies are widely used. However, the selection of an appropriate classifier is of great importance for the terrace mapping task. In this study, the performance of an integrated framework using OBIA and ML for terrace mapping was tested. A catchment, Zhifanggou, in the Loess Plateau, China, was used as the study area. First, optimized image segmentation was conducted. Then, features from the DEMs and imagery were extracted, and the correlations between the features were analyzed and ranked for classification. Finally, three different commonly-used ML classifiers, namely, extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor (KNN), were used for terrace mapping. The comparison with the ground truth, as delineated by field survey, indicated that random forest performed best, with a 95.60% overall accuracy (followed by 94.16% and 92.33% for XGBoost and KNN, respectively). The influence of class imbalance and feature selection is discussed. This work provides a credible framework for mapping artificial terraces.

scholarly journals Age-related water use characteristics of Robinia pseudoacacia on the Loess Plateau

2021 ◽  
Vol 301-302 ◽  
pp. 108344
Author(s):  
Jian Wang ◽  
Bojie Fu ◽  
Lei Jiao ◽  
Nan Lu ◽  
Jianye Li ◽  
...  
Keyword(s):  
Water Use ◽  
Loess Plateau ◽  
Robinia Pseudoacacia ◽  
The Loess Plateau ◽  
Age Related
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