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Assessment of the urban habitat quality service functions and their drivers based on the fusion module of graph attention network and residual network

文献类型：期刊论文

作者Wang, Chunyang1,2; Yang, Kui2; Yang, Wei3; Li, Runkui4; Qiang, Haiyang5; Lu, Bibo1; Su, Baishun1; Yang, Zenan2
刊名INTERNATIONAL JOURNAL OF DIGITAL EARTH
出版日期2024-12-31
卷号17期号:1页码:29
ISSN号1753-8947
关键词Residual network graph attention network super-pixel segmentation habitat quality driving force analysis
DOI10.1080/17538947.2024.2306310
通讯作者Yang, Wei(yangwei@chiba-u.jp)
英文摘要Land use/cover change is a major cause of ecological degradation. Reliable LUCC data are essential for evaluating habitat quality. The current method of surface cover classification based on the convolutional neural networks (CNNs) is usually a local spatial operation using a regular convolutional kernel, which ignores the correlation between adjacent image elements. This paper proposes a combination network with two branches, branch 1 uses the K-nearest neighbor clustering algorithm to construct superpixels and then uses the data transformation module to construct a graph attention network (GAT); branch 2 constructs the CNN using attention and residual modules to obtain the spatial and higher-order semantic information of the images. Finally, the features are fused using weighted fusion, and a classification map with less point noise and greater consistency with the real surface coverage is obtained. The classification results of this network are better than those of the other competitive methods. In addition, the urbanization of Sanya has resulted in significant habitat degradation. A good fit (${\bi R}<^>2$R2 in 2020 = 0.639) between habitat quality (HQ) and natural and socioeconomic factors was observed in Sanya. Natural factors are more relevant to HQ than socioeconomic factors and vary spatially.
WOS关键词LAND-USE CHANGE ; HYPERSPECTRAL IMAGE CLASSIFICATION ; EXPANSION ; BIODIVERSITY ; TERRESTRIAL ; SUITABILITY ; INDICATORS ; MODELS ; IMPACT ; FOREST
资助项目National Natural Science Foundation of China (NSFC)[42341206] ; Chunhui Program Cooperative Research Project of Chinese Ministry of Education[HZKY20220279] ; Henan Provincial Science and Technology Research Project[232102211019] ; Henan Provincial Science and Technology Research Project[222102210131] ; Key Research Project Fund of Institution of Higher Education in Henan Province[23A520029] ; Japan Society for the Promotion of Science (JSPS) KAKENHI[20K12146]
WOS研究方向Physical Geography ; Remote Sensing
语种英语
出版者TAYLOR & FRANCIS LTD
WOS记录号WOS:001146711400001
资助机构National Natural Science Foundation of China (NSFC) ; Chunhui Program Cooperative Research Project of Chinese Ministry of Education ; Henan Provincial Science and Technology Research Project ; Key Research Project Fund of Institution of Higher Education in Henan Province ; Japan Society for the Promotion of Science (JSPS) KAKENHI
源URL[http://ir.igsnrr.ac.cn/handle/311030/202053]  
专题中国科学院地理科学与资源研究所
通讯作者Yang, Wei
作者单位1.Henan Polytech Univ, Sch Comp Sci & Technol, Jiaozuo, Peoples R China
2.Henan Polytech Univ, Sch Surveying & Land Informat Engn, Jiaozuo, Peoples R China
3.Chiba Univ, Ctr Environm Remote Sensing, Chiba 2638522, Japan
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
5.Chinese Acad Nat Resources Econ, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Chunyang,Yang, Kui,Yang, Wei,et al. Assessment of the urban habitat quality service functions and their drivers based on the fusion module of graph attention network and residual network[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2024,17(1):29.
APA Wang, Chunyang.,Yang, Kui.,Yang, Wei.,Li, Runkui.,Qiang, Haiyang.,...&Yang, Zenan.(2024).Assessment of the urban habitat quality service functions and their drivers based on the fusion module of graph attention network and residual network.INTERNATIONAL JOURNAL OF DIGITAL EARTH,17(1),29.
MLA Wang, Chunyang,et al."Assessment of the urban habitat quality service functions and their drivers based on the fusion module of graph attention network and residual network".INTERNATIONAL JOURNAL OF DIGITAL EARTH 17.1(2024):29.

入库方式： OAI收割

来源：地理科学与资源研究所

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