Remote sensing imaging: a useful method for assessing wetland vegetation evolution processes in the Nanjishan Wetland National Nature Reserve, Lake Poyang

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.

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Monitoring wetland of Poyang Lake National Nature Reserve zone by remote sensing

10.1117/12.813012 ◽

2008 ◽

Author(s):

Xinghua Le ◽

Zhewen Fan ◽

Yu Fang ◽

Yuping Yu ◽

Yun Zhang

Keyword(s):

Remote Sensing ◽

Nature Reserve ◽

Poyang Lake ◽

National Nature Reserve

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Responses of wetland vegetation to droughts and its impact factors in Poyang Lake National Nature Reserve

Journal of Lake Sciences ◽

10.18307/2014.0212 ◽

2014 ◽

Vol 26 (2) ◽

pp. 253-259 ◽

Cited By ~ 2

Author(s):

YE Chun ◽

◽

WU Guiping ◽

ZHAO Xiaosong ◽

WANG Xiaolong ◽

...

Keyword(s):

Nature Reserve ◽

Poyang Lake ◽

Wetland Vegetation ◽

Impact Factors ◽

National Nature Reserve

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Remote sensing image classification based on artificial neural network: A case study of Honghe Wetlands National Nature Reserve

2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering ◽

10.1109/cmce.2010.5610049 ◽

2010 ◽

Cited By ~ 2

Author(s):

Yu-guo Wang ◽

Hua-peng Li

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Image Classification ◽

Nature Reserve ◽

Remote Sensing Image ◽

National Nature Reserve ◽

Remote Sensing Image Classification ◽

Artificial Neural

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Distinguishing Original and Non-Original Stands at the Zhanjiang Mangrove National Nature Reserve (P.R. China): Remote Sensing and GIS for Conservation and Ecological Research

Remote Sensing ◽

10.3390/rs13142781 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2781

Author(s):

Juan Durango-Cordero ◽

Behara Satyanarayana ◽

Jonathan Cheung-Wai Chan ◽

Jan Bogaert ◽

Farid Dahdouh-Guebas

Keyword(s):

Remote Sensing ◽

Satellite Data ◽

Nature Reserve ◽

Structural Parameters ◽

Basal Area ◽

Principal Component ◽

Stem Density ◽

Ecological Research ◽

National Nature Reserve ◽

Mangrove Tree

The present research developed a novel methodological framework to differentiate natural mangrove stands (i.e., original), from stands which were planted and stands naturally established after interaction between planted and non-planted stands (e.g., through pollination, i.e., non-original). Ground-truth and remote sensing data were collected for Zhanjiang Mangrove National Nature Reserve (ZMNNR) in P.R. China. First, satellite images of Corona (1967) and GeoEye-1 (2009) were overlaid to identify original (1967) and non-original (2009) mangrove stands. Second, in both stands a total of 75 in situ plots (25 m2) were measured for ground-truthing of tree structural parameters including height, density, basal area and Complexity Index (CI). From temporal satellite data, we identify 236.12 ha of original mangrove and 567.88 ha of non-original mangrove in the reserve. Averaged measurements of the original mangrove stands, i.e., stem density (1164 nos. 0.1 ha−1), basal area (90.3 m2 0.1 ha−1) and CI (100.59), indicated that they were in a state of maturity and less disturbed compared to the non-original mangroves (density, 1241 nos. 0.1 ha−1; basal area, 4.92 m2 0.1 ha−1 and CI, 55.65). The Kruskal–Wallis test showed significant differentiation between the original and non-original mangrove tree structural parameters: Kandelia obovata’s density, X2 = 34.78, d.f. = 1, p = 0.001; basal area, X2 = 108.15, d.f. = 1, p = 0.001; Rizhopora stylosa’s density, X2 = 64.03, d.f. = 1, p = 0.001; basal area, X2 = 117.96, d.f. = 1, p = 0.001. The latter is also evident from the clustering plots generated from the Principal Component Analysis (PCA). Vegetation dynamics at the ZMNNR also enabled us to compare the species composition and distribution patterns with other Indo-West Pacific regions. Overall, the present study not only highlights the advantage of >50 years old satellite data but also provide a benchmark for future ecological research, conservation and management of the ZMNNR.

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