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Received：30 April 2021，
Published：07 December 2022
移动端阅览
防尘防护绿网（防尘绿网）作为抑尘的主要措施被建筑工地广泛使用。快速获取防尘绿网覆盖及时空变化信息，对防尘抑尘、生态环境保护措施的制定具有重要指导意义。本文基于Sentinel-2时间序列遥感影像，使用DeepLabv3+深度学习语义分割模型生成了济南市中心城区2016年—2020年逐年防尘绿网数据，随后利用景观格局、重心-标准差椭圆等方法分析了其空间分布特征和时空扩张趋势。研究结果表明：（1）本文方法的分割精准度、召回率、F1值、IoU分别为84.05%、80.09%、0.82、69.72%，可快速准确地提取城市防尘绿网进而实现大范围、长时序的防尘绿网动态监测。（2）相较于传统的遥感分类方法和其他语义分割模型，本方法的精度最优，防尘绿网的提取结果更精细；另外对北京和天津市的提取实验也证实了本模型的迁移能力。（3）2016年—2020年济南市防尘绿网铺设范围明显扩张，斑块面积和数量不断增多，破碎程度和形状复杂度不断增强，平均斑块面积增大，斑块间凝聚度和聚集度呈波动状态，景观格局复杂且不稳定；绿网铺设范围的扩张呈现出明显的方向性。防尘绿网的分布形态与动态扩张变化受到城市规划、项目进程等人为因素影响，城市规划决定了防尘绿网的分布，而防尘绿网的使用现状在一定程度上又反映了城市建设进程。因此，利用遥感手段对城市防尘绿网进行动态监测与管理，可为城市规划、生态环境建设和城市精细管理提供数据和技术支持，对城市扩张模式与城市重建工作管理具有重要意义。
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.
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