First Molecular Detection ofRickettsia felis-Like Organism inEulaelaps stabularisfrom the Changbai Mountain Area of China

2015 ◽  
Vol 101 (5) ◽  
pp. 514-519 ◽  
Author(s):  
Wei Fang ◽  
Lisi Yao ◽  
Xiaomei Cao ◽  
Yi Sun ◽  
Xiaolong Zhang ◽  
...  
Keyword(s):  
Molecular Detection ◽  
Changbai Mountain ◽  
Mountain Area

scholarly journals The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 372 ◽  
Author(s):  
Zhongjun Ma ◽  
Shengwu Qin ◽  
Chen Cao ◽  
Jiangfeng Lv ◽  
Guangjie Li ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Northeast China ◽  
Susceptibility Mapping ◽  
Changbai Mountain ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Topographic Wetness Index ◽  
Mountain Area ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

Landslides are one of the most frequent geomorphic hazards, and they often result in the loss of property and human life in the Changbai Mountain area (CMA), Northeast China. The objective of this study was to produce and compare landslide susceptibility maps for the CMA using an information content model (ICM) with three knowledge-driven methods (the artificial hierarchy process with the ICM (AHP-ICM), the entropy weight method with the ICM (EWM-ICM), and the rough set with the ICM (RS-ICM)) and to explore the influence of different knowledge-driven methods for a series of parameters on the accuracy of landslide susceptibility mapping (LSM). In this research, the landslide inventory data (145 landslides) were randomly divided into a training dataset: 70% (81 landslides) were used for training the models and 30% (35 landslides) were used for validation. In addition, 13 layers of landslide conditioning factors, namely, altitude, slope gradient, slope aspect, lithology, distance to faults, distance to roads, distance to rivers, annual precipitation, land type, normalized difference vegetation index (NDVI), topographic wetness index (TWI), plan curvature, and profile curvature, were taken as independent, causal predictors. Landslide susceptibility maps were developed using the ICM, RS-ICM, AHP-ICM, and EWM-ICM, in which weights were assigned to every conditioning factor. The resultant susceptibility was validated using the area under the ROC curve (AUC) method. The success accuracies of the landslide susceptibility maps produced by the ICM, RS-ICM, AHP-ICM, and EWM-ICM methods were 0.931, 0.939, 0.912, and 0.883, respectively, with prediction accuracy rates of 0.926, 0.927, 0.917, and 0.878 for the ICM, RS-ICM, AHP-ICM, and EWM-ICM, respectively. Hence, it can be concluded that the four models used in this study gave close results, with the RS-ICM exhibiting the best performance in landslide susceptibility mapping.

Application of GIS in Ecological Land Type (ELT) mapping—A case in Changbai Mountain area

2002 ◽  
Vol 13 (1) ◽  
pp. 56-60
Author(s):  
Xiao Bao-ying ◽  
Dai Li-min ◽  
Chen Gao ◽  
Shao Guo-fan
Keyword(s):  
Changbai Mountain ◽  
Mountain Area ◽  
Land Type ◽  
Application Of Gis

scholarly journals Analyzing the Impact of Climate Change and Human Activities on Surface Runoff in the Changbai Mountain Area, Northeast China

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3473
Author(s):  
Shanjun Zhang ◽  
Jia Liu ◽  
Chuanzhe Li ◽  
Fuliang Yu ◽  
Lanshu Jing ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
River Basin ◽  
Human Activities ◽  
Surface Runoff ◽  
Northeast China ◽  
Changbai Mountain ◽  
Mountain Area ◽  
Mountainous Regions ◽  
Runoff Changes

Climate change and human activities are two important factors affecting surface runoff. In water resource management and planning, it is generally important to separate the contribution of these factors when assessing runoff changes. The Changbai Mountain area is rich in water resources and is an important hydropower energy base for Northeast China. This study used Sen’s slope estimator to explore trends in runoff precipitation and evapotranspiration from 1960 to 2016, and the results showed a downward trend in runoff and an upward trend in precipitation and evaporation in most areas. The mutation point of the annual time series for the observed runoff was estimated, and the time series was divided into the base period (1960–1975) and impact period (1976–2016). Based on the Budyko framework, we performed attribution analysis of the runoff changes, and analyzed the difference between the mountainous region and the whole basin. We determined that the impacts of climate change and human activities, on average, accounted for decreases in the runoff by 60.15% and 39.85%, respectively, for the Second Songhua River Basin; 73.74% and 26.26%, respectively, for the Tumen River Basin; 84.76% and 15.24%, respectively, for the Yalu River Basin; human activities were the main causes of runoff changes in the Changbai Mountain area; climate change was the main cause of runoff changes in mountainous regions. The results of this study show that the reasons for the change in runoff in mountainous regions and the whole basin in the same area are different, which has some illuminating significance for water resources management of different elevation areas.

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