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High-resolution urban vegetation coverage estimation based on multi-source remote sensing data fusion

  • role： First author第一作者
  • Affiliation：

    Department of Survey and Remote Sensing, School of Geosciences and Info-physics, Central South University, Changsha 410083, China

    Center for Geomatics and Regional Sustainable Development Research, Central South University, Changsha 410083, China

  • Email：pixinyu@csu.edu.cn
  • Introduction：皮新宇，1996年生，男，硕士研究生，研究方向为环境遥感与应用。E-mail： pixinyu@csu.edu.cn
PI Xinyu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Survey and Remote Sensing, School of Geosciences and Info-physics, Central South University, Changsha 410083, China

    Center for Geomatics and Regional Sustainable Development Research, Central South University, Changsha 410083, China

  • Email：ynzeng@mail.csu.edu.cn
  • Introduction：曾永年，1959年生，男，教授，研究方向为遥感及地理信息系统及其环境变化研究。E-mail： ynzeng@mail.csu.edu.cn
ZENG Yongnian12*,  
  • Affiliation：

    Department of Survey and Remote Sensing, School of Geosciences and Info-physics, Central South University, Changsha 410083, China

    Center for Geomatics and Regional Sustainable Development Research, Central South University, Changsha 410083, China

HE Chengqiang12

resumen

The accurate extraction of quantitative information on urban vegetation coverage is of great significance for urban ecological environment assessment, urban planning, and sustainable urban development. With the development of remote sensing technology, effective means for obtaining regional and global vegetation coverage information have emerged. At present, urban vegetation coverage estimation methods based on single-sensor and single-phase remote sensing data are widely used. However, due to the complexity of urban land cover and the diversity of vegetation types, the accuracy of urban vegetation cover information extraction is compromised. In this study, we propose an urban vegetation coverage estimation method based on multi-source remote sensing data and Temporal Mixture Analysis (TMA). First, the best time series GF-1 NDVI data are obtained by using STARFM and vegetation phenomenological analysis. Second, on the basis of time series GF-1 NDVI and Landsat8 SWIR1 and SWIR2 data, TMA is used to estimate the urban vegetation coverage in Changsha City. Results show that the method based on multi-source remote sensing data and TMA can obtain highly accurate urban vegetation coverage estimates (RMSE=0.2485, SE=0.1377, MAE=0.1889). Compared with traditional methods like single-time phase spectral hybrid analysis and dimidiate pixel model, our method is more stable, and can obtain higher estimation accuracy in low, medium, and high vegetation coverage areas. This study provides an effective method for quantitative estimation of urban vegetation coverage.

palabra clave

multi-source satellite remote sensing data;GF-1;spatiotemporal fusion;temporal mixture analysis;vegetation coverage;urban area

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