基于GF-1/WFV与MODIS时空融合的森林覆盖定量提取

Quantitative Extraction of Forest Cover Based on Fusing of GF-1/WFV and MODIS Data

森林作为陆地生态系统的重要组成部分,因其巨大的碳储量和固碳能力而备受关注,利用高分1号卫星的NDVI数据(GF-1 NDVI)可实现森林覆盖的定量提取。然而,由于受阴雨天气、运行成本等因素的影响,难以形成GF-1 NDVI时间序列数据,无法满足森林覆盖高精度提取的需求,为此,以河南省嵩山部分地区为实验区,应用STAVFM算法融合GF-1/WFV NDVI与MODIS NDVI,生成8 d步长的GF-1/WFV NDVI时间序列数据,在此基础上,提取NDVI特征并与GF-1/WFV的光谱特征进行组合,最后,采用SVM分类方法实现研究区森林覆盖的定量提取。研究结果表明,利用STAVFM算法生成的GF-1/WFV NDVI时序数据效果理想,很好地解决了GF-1 NDVI时序数据的缺失问题,由其NDVI特征与GF-1/WFV光谱特征构成的组合能够实现森林覆盖的有效提取,基于SVM分类后的总体分类精度为94.72%,与未融入NDVI特征的GF-1/WFV原始影像的分类结果相比,提高了4.90个百分点。

As an important part of terrestrial ecosystem, forest is concerned by its huge carbon storage and carbon sequestration capacity. With the successful launch of China＇ s high score 1 （GF - 1 ） satellite, it is possible to use NDVI data to realize the quantitative extraction of forest cover. However, due to the impact of rainy weather, operating costs and other factors, it is difficult to form NDVI GF- 1 time series data, which cannot meet the demand for high precision extraction of forest cover. With the aim to solve this problem, Songshan was taken as part of the Henan GF - 1/WFV NDVI and MODIS NDVI experimentation area, application of STAVFM algorithm was integrated, and GF- 1/WFV NDVI time series data was used to generate the 8 d step, then from the time series data in NDVI feature extraction and spectral features were combined with GF- 1/WFV. Finally, SVM classification method was used to realize quantitative forest coverage extraction. The research results showed that the NDVI GF- 1/WFV sequence data generated by the STAVFM algorithm was ideal, which can solve the problem of the NDVI GF-1 time series data. The overall classification accuracy based on the SVM classification was 94.72% , which was improved by 4.90 percentage points compared with the classification results of the original GF- 1/WFV image without fusing the characters of NDVI. This method provided a new way for high precision extraction of forest cover.