Spatial distribution of wetland vegetation biomass in the Poyang Lake National Nature Reserve, China

2015 ◽  
Vol 35 (2) ◽  
Author(s):  
吴桂平 WU Guiping ◽  
叶春 YE Chun ◽  
刘元波 LIU Yuanbo
Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2041
Author(s):  
Dandan Yan ◽  
Zhaoqing Luan ◽  
Dandan Xu ◽  
Yuanyuan Xue ◽  
Dan Shi

Water level fluctuations resulting from natural and anthropogenic factors have been projected to affect the functions and structures of wetland vegetation communities. Therefore, it is important to assess the impact of the hydrological gradient on wetland vegetation. This paper presents a case study on the Honghe National Nature Reserve (HNNR) in the Sanjiang Plain, located in Northeast China. In this study, 210 plots from 18 sampling line transects were sampled in 2011, 2012, and 2014 along the hydrological gradient. Using a Gaussian logistic regression model, we determined a relationship between three wetland plant species and a hydrologic indicator—a combination of the water level and soil moisture—and then applied that relationship to simulate the distribution of plants across a larger landscape by the geographic information system (GIS). The results show that the optimum ecological amplitude of Calamagrostis angustifolia to the hydrological gradient based on the probability of occurrence model was [0.09, 0.41], that of Carex lasiocarpa was [0.35, 0.57], and that of Carex pseudocuraica was [0.49, 0.77]. The optimum of Calamagrostis angustifolia was 0.25, Carex lasiocarpa was 0.46, and Carex pseudocuraica was 0.63. Spatial distribution probability maps were generated, as were maps detailing the distribution of the most suitable habitats for wetland vegetation species. Finally, the model simulation results were verified, showing that this approach can be employed to provide an accurate simulation of the spatial distribution pattern of wetland vegetation communities. Importantly, this study suggests that it may be possible to predict the spatial distribution of different species from the hydrological gradient.


2014 ◽  
Vol 26 (2) ◽  
pp. 253-259 ◽  
Author(s):  
YE Chun ◽  
◽  
WU Guiping ◽  
ZHAO Xiaosong ◽  
WANG Xiaolong ◽  
...  

2013 ◽  
Vol 25 (5) ◽  
pp. 707-714 ◽  
Author(s):  
YE Chun ◽  
◽  
ZHAO Xiaosong ◽  
WU Guiping ◽  
WANG Xiaolong ◽  
...  

2008 ◽  
Author(s):  
Xinghua Le ◽  
Zhewen Fan ◽  
Yu Fang ◽  
Yuping Yu ◽  
Yun Zhang

2014 ◽  
Vol 26 (2) ◽  
pp. 243-252 ◽  
Author(s):  
CHEN Bing ◽  
◽  
CUI Peng ◽  
LIU Guanhua ◽  
LI Fengshan ◽  
...  

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jun Wang ◽  
Qian He ◽  
Ping Zhou ◽  
Qinghua Gong

The main purposes of the study were to test the performance of the Revised Universal Soil Loss Equation (RUSLE) and to understand the key factors responsible for generating soil erosion in the Nanling National Nature Reserve (NNNR), South China, where soil erosion has become a very serious ecological and environmental problem. By combining the RUSLE and geographic information system (GIS) data, we first produced a map of soil erosion risk at 30 m-resolution pixel level with predicted factors. We then used consecutive Landsat 8 satellite images to obtain the spatial distribution of four types of soil erosion and carried out ground truth checking of the RUSLE. On this basis, we innovatively developed a probability model to explore the relationship between four types of soil erosion and the key influencing factors, identify high erosion area, and analyze the reason for the differences derived from the RUSLE. The results showed that the overall accuracy of image interpretation was acceptable, which could be used to represent the currently actual spatial distribution of soil erosion. Ground truth checking indicated some differences between the spatial distribution and class of soil erosion derived from the RUSLE and the actual situation. The performance of the RUSLE was unsatisfactory, producing differences and even some errors when used to estimate the ecological risks posed by soil erosion within the NNNR. We finally produced a probability table revealing the degree of influence of each factor on different types of soil erosion and quantitatively elucidated the reason for generating these differences. We suggested that soil erosion type and the key influencing factors should be identified prior to soil erosion risk assessment in a region.


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