收稿：2019-06-05，
纸质出版：2021-06-07
移动端阅览
准确获取城市植被覆盖定量信息对城市生态环境评价，城市规划及可持续城市发展具有重要意义。遥感技术的发展为获取区域及全球植被覆盖信息提供了有效手段，目前基于单传感器、单时相遥感数据的城市植被覆盖度估算方法得到较为广泛的应用。然而，由于城市地表覆盖的复杂性、植被类型的多样性，在一定程度上影响了城市植被覆盖信息提取的精度。为此，本文提出一种基于多源遥感数据与时间混合分析的城市植被覆盖度估算方法。首先，通过时空融合、植被物候特征分析获得最佳时序的GF-1 NDVI数据；其次，基于时间序列的GF-1 NDVI及Landsat 8 SWIR1、SWIR2数据，采用时间混合分析方法以长沙市为例估算城市植被覆盖度。实验研究表明，基于多源遥感数据与时间混合分析方法获得了较高精度的城市植被覆盖度估算（RMSE为0.2485，SE为0.1377，MAE为0.1889），相对于单时相光谱混合分析、传统的像元二分法，本文提出的方法更为稳定，在低、中、高不同植被覆盖区均能获得较高的估算精度，为城市植被覆盖度定量估算提供了有效方法。
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.
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