It is difficult to obtain long time series of high spatial resolution remote sensing images in southern China because of the complex terrain and frequent cloudy and rainy weather. In contrast, the spatio-temporal fusion can sychonorously obtain remote sensing data with high spatial-temporal resolution, which is beneficial to extract forest vegetation type information. With Xingguo County of Jiangxi Province as the study area, we fused the Landsat8 OLI and GF-1 WFV images with high spatial resolution with high temporal resolution of MODIS09 A1 image on the basis of enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), reconstructed the time series data of ESTARFM_Landsat8 EVI and ESTARFM_GF-1 EVI with 8 d step of enhanced vegetation index (EVI), obtained the phenology (PH) characteristics, and identified the forest vegetation types by using random forest classification model. The results showed that the correlation coefficients between the fusion data of ESTARFM_Landsat8 EVI and ESTARFM_GF-1 EVI and the real images were all greater than 0.7, and had good consistency in spatial distribution, which could be used to supplement the missing data with high spatial resolution. The extraction accuracy of random forest classification with different combination modes was EVI+PH>EVI>PH and the classification accuracy of fusion data GF-1 was higher than that of Landsat8. A total of 43 variables were selected as the optimal feature variables for classification. The overall accuracy and Kappa coefficient were 95.6% and 94.9%, respectively, including 37 sequential EVI values and 6 phenological feature information. The sequential EVI data contributed more to the identification of forest vegetation types, while the phenological feature information was beneficial to improve the classification accuracy. The ESTARFM fusion algorithm was suitable for GF-1 and MODIS data, which could solve the problem of insufficient long-term sequence of high spatial resolution images. The GF-1 temporal fusion images had high accuracy in the identification of forest vegetation types in southern China under complex terrain and frequent cloudy and rainy weather.
我国南方地区地形条件较为复杂且多云雨,较难获取长时间序列高空间分辨率遥感影像,而时空融合技术能够同步获取高时空分辨率遥感数据,对提取其森林植被类型信息具有重要意义。以江西省兴国县为研究区,基于增强型时空自适应反射率融合模型(ESTARFM)分别将高空间分辨率的Landsat8 OLI、GF-1 WFV影像与高时间分辨率的MODIS09 A1影像进行融合,重构增强型植被指数(EVI)8 d步长的ESTARFM_Landsat8 EVI与ESTARFM_GF-1 EVI时序数据,获取物候(PH)特征信息,并利用随机森林分类模型识别森林植被类型。结果表明: 融合生成的ESTARFM_Landsat8 EVI、ESTARFM_GF-1 EVI与真实影像的相关系数均大于0.7,且在空间分布上具有较好的一致性,可用于补充缺失的高空间分辨率数据。不同组合方式的随机森林分类提取精度为EVI+PH>EVI>PH,且GF-1融合数据分类精度高于Landsat8融合数据。筛选出43个变量作为优选特征变量进行分类,总体精度与Kappa系数分别为95.6%和94.9%,其中,包括37个时序EVI值和6个物候特征信息,其时序EVI数据对森林植被类型识别的贡献程度更高,物候特征信息有利于分类精度的提高。ESTARFM融合算法适用于GF-1与MODIS数据,在一定程度上可解决长时间序列高空间分辨率影像不足的问题;GF-1时序融合影像在多云雨、地形条件复杂的南方地区森林植被类型识别中具有较高精度。.
Keywords: forest vegetation type; phenology; spatio-temporal fusion model; time series data.