Marsh wetland degradation risk assessment and change analysis: A case study in the Zoige Plateau, China

Jiang, Weiguo; Lv, Jinxia; Wang, Cuicui; Chen, Zheng; Liu, Yinghui

doi:10.1016/j.ecolind.2017.06.059

Cited by 100 publications

(40 citation statements)

References 29 publications

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“…The annual average temperature range is 0-2 • C and the annual precipitation ranges from 600 to 800 mm [35]. There are rich and diverse types of vegetation, which are dominated by alpine grassland, swampland, shrubland, and forest [23]. The soil types include bog soil, peat soil, meadow soil and dark felt-like soil [22].…”

Section: Study Areamentioning

confidence: 99%

“…The method for constructing an ERA model, by starting with risk sources, habitats and ecological receptors based on certain stress factors known within the region, has been widely applied [17][18][19][20][21]. Jiang et al (2017) and Shen et al (2019) established a multi-index model based on different indicators to evaluate the degradation of marsh wetlands in the Zoige region [22,23]. However, their studies still have some limitations.…”

Section: Introductionmentioning

confidence: 99%

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Ecological Risk Assessment and Impact Factor Analysis of Alpine Wetland Ecosystem Based on LUCC and Boosted Regression Tree on the Zoige Plateau, China

Hou

Gao

et al. 2020

Remote Sensing

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The Zoige Plateau is typical of alpine wetland ecosystems worldwide, which play a key role in regulating global climate and ecological balance. Due to the influence of global climate change and intense human activities, the stability and sustainability of the ecosystems associated with the alpine marsh wetlands are facing enormous threats. It is important to establish a precise risk assessment method to evaluate the risks to alpine wetlands ecosystems, and then to understand the influencing factors of ecological risk. However, the multi-index evaluation method of ecological risk in the Zoige region is overly focused on marsh wetlands, and the smallest units of assessment are relatively large. Although recently developed landscape ecological risk assessment (ERA) methods can address the above limitations, the final directionality of the evaluation results is not clear. In this work, we used the landscape ERA method based on land use and land cover changes (LUCC) to evaluate the ecological risks to an alpine wetland ecosystem from a spatial pixel scale (5 km × 5 km). Furthermore, the boosted regression tree (BRT) model was adopted to quantitatively analyze the impact factors of ecological risk. The results show the following: (1) From 1990 to 2016, the land use and land cover (LULC) types in the study area changed markedly. In particular, the deep marshes and aeolian sediments, and whereas construction land areas changed dramatically, the alpine grassland changed relatively slowly. (2) The ecological risk in the study area increased and was dominated by regions with higher and moderate risk levels. Meanwhile, these areas showed notable spatio-temporal changes, significant spatial correlation, and a high degree of spatial aggregation. (3) The topographic distribution, climate changes and human activities influenced the stability of the study area. Elevation (23.4%) was the most important factor for ecological risk, followed by temperature (16.2%). Precipitation and GDP were also seen to be adverse factors affecting ecological risk, at levels of 13.0% and 12.1%, respectively. The aim of this study was to provide more precise and specific support for defining conservation objectives, and ecological management in alpine wetland ecosystems.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ecological Risk Assessment and Impact Factor Analysis of Alpine Wetland Ecosystem Based on LUCC and Boosted Regression Tree on the Zoige Plateau, China

Hou

Gao

et al. 2020

Remote Sensing

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“…It is distributed on flat and wide beaches, lakes and terraces. The mountain, river, lake, marsh, semimarsh, shrub, and grassland form the landscape patterns (Jiang et al, 2017;Li et al, 2014). The attributes of WVM are mainly influenced by the plant species and geographic characteristics and form the discriminated characteristics of the captured ecological vegetation images (Chen, Lin, He, & He, 2015;Junk et al, 2014;Marani, Da Lio, & D'Alpaos, 2013).…”

Section: Characteristics Of Wetlandsmentioning

confidence: 99%

“…To deal with these challenges, we introduced a deep-level cascade discriminative model based on convolutional neural networks (CNN) architecture (Esteva et al, 2017;LeCun, Bengio, & Hinton, 2015;Wang, Peng, Ma, & Xu, 2016) for identifying the WVM in the Ramsar sites list. There were four different classes of wetlands in our investigation, including DongZhai Harbor (DZH) intertidal mangrove wetland (Tang et al, 2014;Xi, Li, Xia, & Qu, 2016), Lashi Lake (LSL) alpine peat wetland (Y. C. Huang, Tian, Yue, Liu, & Lai, 2012;Liao, Ye, Huang, & Peng, 2017), Yancheng (YC) coastal saline wetland (L. Huang et al, 2015;Zang et al, 2017), and Zoige plateau freshwater lake wetland (Jiang, Lv, Wang, Chen, & Liu, 2017;Li et al, 2014) in China. We followed deep-level AlexNet CNN network structures (Krizhevsky, Sutskever, & Hinton, 2012;Srivastava, Hinton, Krizhevsky, Sutskever, & Salakhutdinov, 2014) to construct the WVM identification model.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence classification of wetland vegetation morphology based on deep convolutional neural network

Lin

et al. 2019

Natural Resource Modeling

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In real-world wetland vegetation morphology (WVM) detection, large scene variations such as those due to landform, vegetation, sunlight, weather, and sky, as well as camera parameter settings such as focal length and shooting angle, require systematic and complicated artificial intelligence technology to accurately discriminate inter and intra-class wetland objections. To deal with these challenges, we introduced a deep-level discriminative model based on convolutional neural networks (CNN) for classifying the images of DongZhai Harbor intertidal, Lashi Lake alpine, Yancheng coastal and Zoige plateau wetlands in China. A 96-dimensional convolution operation with kernel sizes of 11 × 11 × 3 first applied to the resized 227 × 227 WVM input pictures to acquire the effective morphologic features. The perceptron layers of the rectified linear unit and the batch normalization were used in the middle layer to achieve the better gradient propagation property during the training process. The WVM features were down-sampled by the pooling networks to reduce the neuron dimensions. The fully connected layer linked to the output of the convolutional and pooling layers to obtain the high-level WVM species information for the final WVM classification purpose. The deep-level CNN-based method was compared with the traditional shallow-level feature-designed algorithms of conditional maximum entropy regression, multilayer perceptron and support vector machine. The deep-level algorithm showed the superior performance for detecting the WVM species, which provided a superior alternative routine for accurate artificial intelligence classification of WVM in the ecological engineering application. Recommendations for Resource Managers• This proposed artificial intelligence model can be used to effectively help people understand the macroscopic scene changes of ecological elements from the wetland vegetation morphology images. • The detection results show the robustness of the discriminative model, for it is not affected by the complex natural factors such as landform, vegetation, sunlight, weather, and sky, as well as human factors such as camera parameter settings of focal length and shooting angle to some extent. • The deep-level model based on the cascade convolution framework can be used to extract more distinguishing detailed ecological features from the original wetland vegetation morphology images compared with the shallow-level algorithms using a single convolutional layer. K E Y W O R D S classification, convolutional neural network, ecological engineering, vegetation morphology, wetland

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“…(Wang et al, 2006;Zhao and Gao, 2007;Zheng et al, 2012). The lakemarsh wetland system degradation also can lead to frequent occurrences of global warming, reduced biodiversity, and floods 5 disasters (Costanza, 2006;Engelhardt and Ritchie, 2002;Jiang et al, 2017;Woodward and Wui, 2001). The increasing population and rapid developing economy cause the contradiction between human and lake-marsh wetland system for water and land.…”

mentioning

confidence: 99%

Determination of the Optimal Lake-Marsh Pattern in the Lake-Marsh Wetland System based on Ecological Land Use and Ecological Water Use

Weng

Yan

et al. 2019

Preprint

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Abstract. The land use and water use competitions appear more seriously between lake-marsh wetland system and its surrounding socio-economic system, also inside the lake-marsh wetland system. However, few studies focus on solving the contradictions mentioned above. While this study proposed an optimal lake-marsh pattern determination method based on the joint regulation of ecological land and ecological water, on considering the ecological services values and water shortage amount in the lake-marsh wetland system. The optimal lake-marsh pattern determination should explore the land and water demand of the protection objects, also the water supply and water demand (especially evapotranspiration) in annual and monthly time-scale. Calculation and analysis were performed for the optimal pattern of the Wolonghu Wetlands as an example. The results mainly showed that the optimal lake-marsh pattern of the Wolonghu Wetlands can be obtained with the area ratio of lake-marsh equaling to 0.651 : 0.349 and the corresponding water level of 87.4 m in annual scale. In monthly scale, except July and August, the optimal area ratio of lake and marsh varied from 0.634 : 0.366 to 0.738 : 0.262. This study could provide references for the Wolonghu Wetlands management, also for similar lake-marsh wetland system and other ecological system.

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Marsh wetland degradation risk assessment and change analysis: A case study in the Zoige Plateau, China

Cited by 100 publications

References 29 publications

Ecological Risk Assessment and Impact Factor Analysis of Alpine Wetland Ecosystem Based on LUCC and Boosted Regression Tree on the Zoige Plateau, China

Ecological Risk Assessment and Impact Factor Analysis of Alpine Wetland Ecosystem Based on LUCC and Boosted Regression Tree on the Zoige Plateau, China

Artificial intelligence classification of wetland vegetation morphology based on deep convolutional neural network

Determination of the Optimal Lake-Marsh Pattern in the Lake-Marsh Wetland System based on Ecological Land Use and Ecological Water Use

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