Prof. Zhaocong Wu Profile

Prof. Zhaocong Wu

at Wuhan Univ

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SPIE Journal Paper | 5 October 2021

Forest classification using synthetic GF-1/WFV time series and phenological parameters

Lei Xu, Zhaocong Wu, Zhi Zhang, Xu Wang

JARS, Vol. 15, Issue 04, 042413, (October 2021) https://doi.org/10.1117/12.10.1117/1.JRS.15.042413

KEYWORDS: MODIS, Image fusion, Data fusion, Vegetation, Spatial resolution, Remote sensing, Earth observing sensors, Data modeling, Satellites, Reflectivity

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High-accuracy forest type mapping is crucial for forest resource inventory to support forest management, conservation biology, and ecological restoration. The Chinese GF-1 satellite can provide high-spatial resolution and low-cost data for forestry monitoring. However, it is difficult to obtain time-series GF-1 images with high spatial resolution and high temporal frequency because of technical constraints and cloud contamination, which affects the accuracy of forest type extraction. We proposed a method for mapping forest type using Chinese GF-1/widefield view (WFV) multi-spectral data, the fused GF-1/WFV normalized difference vegetation index (NDVI) time series, and the phenological parameters. We used the enhanced spatial and temporal adaptive reflectance fusion model algorithm to fuse Chinese GF-1/WFV and Moderate Resolution Imaging Spectroradiometer NDVI to generate high-spatiotemporal resolution NDVI time-series image data and extract phenological parameters in the northeastern part of Hubei Province, China. We then designed four different combinations of GF-1/WFV spectral features, the fused NDVI time-series features, and the phenological parameters. Finally, a random forest algorithm is used to classify forest types in the study area. The results showed that the proposed method can obtain satisfactory results. The overall accuracy based on the combination of GF-1/WFV spectral features, high-resolution time-series NDVI, and phenological features was 89.68%. Compared with the classification using only GF-1/WFV spectral data, the overall accuracy was improved by 9.60% points. These results demonstrate that the proposed method can support forest type survey and mapping at the regional or global scale.

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