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皮新宇, 曾永年, 贺城墙. 融合多源遥感数据的高分辨率城市植被覆盖度估算[J]. 遥感学报, 2021,25(6):1216-1226.
Xinyu PI, Yongnian ZENG, Chengqiang HE. High-resolution urban vegetation coverage estimation based on multi-source remote sensing data fusion[J]. National Remote Sensing Bulletin, 2021,25(6):1216-1226.
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.
多源遥感数据GF-1时空融合时间混合分析植被覆盖度城市
multi-source satellite remote sensing dataGF-1spatiotemporal fusiontemporal mixture analysisvegetation coverageurban area
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