سابِق|تال

Urban green plastic cover extraction and spatial pattern changes in Jinan city based on DeepLabv3+ semantic segmentation model

  • role： First author第一作者
  • Affiliation：

    School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China

  • Email：ctliu96@163.com
  • Introduction：刘春亭，研究方向为遥感信息提取及应用。E-mail: ctliu96@163.com
LIU Chunting1,  
  • Affiliation：

    College of Land Science and Technology, China Agricultural University, Beijing 100193, China

    State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China

FENG Quanlong23,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China

  • Email：liujiantao18@sdjzu.edu.cn
  • Introduction：刘建涛，研究方向为机器学习、遥感信息提取、城市遥感。E-mail: liujiantao18@sdjzu.edu.cn
LIU Jiantao1*,  
  • Affiliation：

    Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

WANG Ying4,  
  • Affiliation：

    School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China

SHI Tongguang1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

LI Yi5,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

GONG Jianhua5,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

ZHAO Huihui5

ملخص

Green plastic covers have been widely used as the primary method of dust prevention in construction sites. The rapid acquisition of green plastic cover and spatial–temporal change information has an important guiding significance for the formulation of dust prevention and ecological environmental protection measures. This work extracted the covered area of green plastic cover by using DeepLabv3+ semantic segmentation model based on Sentinel-2 remote sensing data and realized the annual green plastic cover segmentation and extraction in Jinan from 2016 to 2020. The spatial distribution characteristics and spatial–temporal expansion trend of green plastic cover were analyzed by area statistics, landscape pattern analysis, and mean center-standard deviation ellipse. Results show that: (1) According to the accuracy evaluation, the proposed architecture reaches an acceptable accuracy, with 84.05% precision, 80.09% recall, 0.82 F1 score, and 69.72 IoU, which could quickly and accurately extract the urban green plastic cover and realize the large-scale and time series dynamic monitoring and management. (2) The accuracy of DeepLabv3+ model used in this work is the best compared with the traditional remote sensing classification method and other sematic segmentation models, and the extraction results of green plastic cover are more precise. In addition, the extraction experiments in Beijing and Tianjin also confirmed the migration ability of the model. (3) The laying range of green plastic cover significantly expanded from 2016 to 2020. The area and number of green plastic cover patches, fragmentation degree, and shape complexity increased. The average patch area increased, the cohesion and aggregation between patches fluctuated, and the landscape pattern was complex and unstable. The expansion has an obvious direction. The distribution and dynamic expansion of green plastic cover are affected by human factors, such as urban planning and project process. Urban planning determines the distribution of green plastic cover, and the current use of green plastic cover reflects the process of urban construction to a certain extent. Therefore, the dynamic monitoring and management of urban green plastic cover by remote sensing means can provide data and technical support for urban planning, ecological environment construction, and urban accurate management, which are of great significance to the management of urban expansion mode and reconstruction.

مفهوم

green plastic cover;remote sensing;semantic segmentation;DeepLabv3+;Sentinel-2;temporal and spatial variation

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