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

ABSTRACT

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 (${\mathit{R}}^2$ 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.

KEYWORDS:

• Residual network
• graph attention network
• super-pixel segmentation
• habitat quality
• driving force analysis

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

This study analyzed publicly available datasets, which can be found here: Geospatial Data Cloud (https://www.gscloud.cn/) and the Resource and Environmental Science and Data Centre of the Chinese Academy of Sciences (https://www.resdc.cn/).

Additional information

Funding

This study was supported by the National Natural Science Foundation of China (NSFC) [grant number 42341206], Chunhui Program Cooperative Research Project of Chinese Ministry of Education [HZKY20220279], Henan Provincial Science and Technology Research Project (232102211019, 222102210131), the Key Research Project Fund of Institution of Higher Education in Henan Province (23A520029), and Japan Society for the Promotion of Science (JSPS) KAKENHI [grant number 20K12146].