Seasonal waterbird population changes in Lashihai Lake in northwest Yunnan, China

Jun-tao, Liao; Ye, Hui; Ting-fa, Huang; Guihong, Peng

doi:10.1007/s11629-016-4164-3

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…The crisscrossed mountain, basin, and lake form the complex landforms. The ecological elements of mountain, lake, shrub, grassland, and forest mainly become the discriminative morphologic characteristics (Y. C. Huang et al, ; Liao et al, ). YC wetland is the largest intertidal coastal saline wetland in the world. It is located in the east part of Jiangsu Province and the geographical coordinates are 32°34′–34°28′N, 119°48′–121°56′E, 0–4 m. With an area covers 453,000 hectares, it faces the south of the Yellow Sea.…”

Section: Experimental Data and Devicesmentioning

confidence: 99%

“…The crisscrossed mountain, basin, and lake form the complex landforms. The ecological elements of mountain, lake, shrub, grassland, and forest mainly become the discriminative morphologic characteristics (Y. C. Huang et al, 2012;Liao et al, 2017). principally of the typical ecological unit-tropical, intertidal mangrove forest.…”

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%

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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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Section: Experimental Data and Devicesmentioning

confidence: 99%

“…The crisscrossed mountain, basin, and lake form the complex landforms. The ecological elements of mountain, lake, shrub, grassland, and forest mainly become the discriminative morphologic characteristics (Y. C. Huang et al, 2012;Liao et al, 2017). principally of the typical ecological unit-tropical, intertidal mangrove forest.…”

Section: Characteristics Of Wetlandsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Lin

et al. 2019

Natural Resource Modeling

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“…They are the key links that connect breeding and wintering habitats, as well as areas where waterbirds refuel with food and gain energy during long-distance migration [ 12 , 13 ]. Studies have shown that the loss of stopovers is one of the major reasons for migratory birds’ population decline globally [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Mangcuo Lake in Hengduan Mountains: An Important Alpine Breeding and Stopover Site along Central Asian Flyway

Wang

Yang

Song

et al. 2023

Animals

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The stopovers provide food and habitat for migratory birds and therefore play an important role in facilitating the birds’ completion of their migration. The Hengduan Mountains, which have a complex topography, are located in a large corridor of the Central Asian migratory flyway, so the lakes along the Hengduan Mountains are important for waterbird migration. The existing research on lakes in the Hengduan Mountain area is mostly concentrated in the central and southern parts of the mountains, which proves that many lakes are wintering grounds for migrating birds. We wonder whether the ecological functions of lakes will change more with further elevation. With this question, we conducted four surveys for the seasonal bird survey in Mangcuo Lake, which is located in the northwest of the Hengduan Mountains, in Markam County of Qamdo City, between October 2019 and July 2020. We recorded a total of 6109 birds from 20 species of waterbirds, including 20 species of migratory waterbirds, accounting for 100% of all bird species. The diversity and richness of waterbirds in Mangtso Lake is shown as spring > autumn ≥, summer > winter, with no waterbirds in winter. The black-necked grebe (Podiceps nigricollis), great crested grebe (Podiceps cristatus), bar-headed goose (Anser indicus), and ruddy shelduck (Tadorna ferruginea) were the dominant species in the waterbird community. The highest number of waterbird species and total individuals were found in the transition zone between the marsh wetlands and lakes, and the number of waterbird species differed significantly among habitats (X2 = 14.405, p = 0.000), with habitat complexity being an important factor influencing waterfowl abundance and distribution. The IUCN Red-listed species recorded include the black-necked crane (Grus nigricollis), painted stork (Mycteria leucocephala), and common pochard (Aythya ferina). By comparing with other lakes in Hengduan Mountain, we found that the ecological functions of the plateau lakes in the Hengduan Mountains, to support the life histories of migrating waterbirds, are gradually transforming as the altitude rises, and can be divided into approximately three levels, with the first level of Qionghai, Chenghai, Erhai, and Jianhu at altitudes of 1500–2200 m being the most important ecological function in terms of providing wintering grounds for migrating birds. The second layer, at an altitude of 2400–3300 m, includes Lashihai, Lugu Lake, and Napahai, which are not only wintering wetlands for migratory birds but also important stopover sites. The third layer of Mangcuo Lake, which is above 4000 m above sea level, provides a breeding ground for some migratory waterbirds in summer and a migratory resting place for migratory waterbirds in spring and autumn. We advocate for the importance of Mangcuo Lake in the alpine region along the central Asian flyway, as well as emerging nature conservation action that was previously neglected.

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Waterbird Composition and Changes With Wetland Park Construction at Lake Dianchi, Yunnan–Guizhou Plateau

Wang

Yang

2021

Mountain Research and Development

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Seasonal waterbird population changes in Lashihai Lake in northwest Yunnan, China

Cited by 5 publications

References 8 publications

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

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

Mangcuo Lake in Hengduan Mountains: An Important Alpine Breeding and Stopover Site along Central Asian Flyway

Waterbird Composition and Changes With Wetland Park Construction at Lake Dianchi, Yunnan–Guizhou Plateau

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