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Analysis of landscape pattern on urban land use based on GF-5 hyperspectral data

  • role： First author第一作者
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

  • Email：jingwenyuan@whu.edu.cn
  • Introduction： 袁静文，1991年生，女，博士研究生，研究方向为高光谱、高分辨率遥感影像分类及应用。E-mail：jingwenyuan@whu.edu.cn
YUAN Jingwen1,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

WU Chen2,  
  • Affiliation：

    School of Computer Science, Wuhan University, Wuhan 430079, China

DU Bo3,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

  • Email：zlp62@whu.edu.cn
  • Introduction： 张良培，1962年生，男，教授，研究方向为测绘、遥感图像处理、人工智能、模式识别。E-mail：zlp62@whu.edu.cn
ZHANG Liangpei2*,  
  • Affiliation：

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

WANG Shugen1

resumen

With the increasing of population, the world has experienced unprecedented urbanization, especially in developing countries. Rapid urbanization inevitably brings serious ecological and environmental problems. In order to better monitor the urbanization process of mega-cities, this study, which focus on north of the Yangtze river of Wuhan metropolitan region, attempts aimed to analyze the urban landscape pattern characteristics and the trends of urbanization trends by using the north of the Yangtze River of Wuhan metropolitan region as the case study.In this study, we proposed a modified Spectral–Spatial Unified Network (SSUN-)-CRF method, which revised from a Spectral-Spatial Unified Networks classification (SSUN) method, based on deep-learning, to extract the urban land use classification on a fine scale and recover detailed structure with GF-5 hyperspectral data in 2018. Then, we assessed seven key landscape pattern indices to understand the landscape pattern features of seven regions in Wuhan, which are separated into main urban area and suburban area. Then, in this paper we built the urban expansion model, from class metrics to landscape metrics.The resulst show that: (1) The SSUN-CRF classification algorithm can achieve as little loss of spectral information as possible while taking into account spatial information. Our method can effectively achieve edge refinement and overcome the misclassification of semantic segmentation. The Overall Accuracy (OA) and Kappa were higher than 0.9048 and 0.8807, respectively. (2) According to the landscape analysis, on the class metrics level: the PLAND (Percentage of Landscape) indices of different classes in different districts demonstrate that Wuhan has abundant water resource, the main urban area of Wuhan is more urbanized than that of suburban area. Comparing three kinds of residential areas, Residential Two is the principal part with high-rise buildings. It indicate that Wuhan has a higher per capita living standard. However, Hanyang District develops slower than other main urban areas with the problem of “villages in the city”. Furthermore, the advantages of agriculture and water area in the suburban area are obvious, the ecological environment is good, but the urbanization degree is low. The the ecological virescence of suburban area needs to be improved. The PD (Patch Density) and AI (Aggregation Index) indices illustrate that Jianghan District has the most evenly distributed commercial area, and has the center of business in Wuhan. While, the commercial area in the suburban district is less and concentrated. (3) On the landscape metrics level: We choose 4 indices to analysis the landscape in Wuhan including PAFRAC (Perimeter Area Fractal Dimension), CONTAG (Contagion Index), SHDI (Shannon's Diversity Index) and SHEI (Shannon's Evenness Index). Wuhan shows the stable and diverse landscape ecological with balanced land use distribution. What’s more, after using moving window to evaluate the state of Wuhan’s urbanization, it is clear to see the tendency in line from the main urban area to the southeast and southwest of the suburban districts. The main urban area is more evenly distributed and urbanized than the suburban area.The results of this research of serve as guide for the overall development plan, optimizing land use optimization, and sustainable development construction in Wuhan. What’s more, this model can further provide decision-making reference for the development of the Yangtze River Economic Belt and the “Yangtze River Spindle.”

palabra clave

remote sensing;urbanization;landscape pattern;land use;deep learning;fully connected conditional random field;Fragstats;GF-5 hyperspectral image;Wuhan

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