Xu Li, Ouyang Xun-Zhi, Pan Ping, Zang Hao, Liu Jun, Yang Kai
Key Laboratory of National Forestry and Grassland Administration for the Protection and Restoration of Forest Ecosystem in Poyang Lake Basin, Nanchang 330045, China.
College of Forestry, Jiangxi Agricultural University, Nanchang 330045, China.
Ying Yong Sheng Tai Xue Bao. 2022 Jul;33(7):1948-1956. doi: 10.13287/j.1001-9332.202207.022.
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.
由于中国南方地形复杂且多云多雨天气频繁，难以获取长时间序列的高空间分辨率遥感影像。相比之下，时空融合能够同步获取具有高时空分辨率的遥感数据，这有利于提取森林植被类型信息。以江西省兴国县为研究区域，我们基于增强型时空自适应反射率融合模型（ESTARFM），将高空间分辨率的Landsat8 OLI和GF-1 WFV影像与具有高时间分辨率的MODIS09 A1影像进行融合，以8天为步长重建了增强植被指数（EVI）的ESTARFM_Landsat8 EVI和ESTARFM_GF-1 EVI时间序列数据，获取了物候（PH）特征，并利用随机森林分类模型识别森林植被类型。结果表明，ESTARFM_Landsat8 EVI和ESTARFM_GF-1 EVI的融合数据与真实影像之间的相关系数均大于0.7，且在空间分布上具有良好的一致性，可用于补充高空间分辨率的缺失数据。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融合数据的分类精度高于Landsat8。共选取43个变量作为分类的最优特征变量。总体精度和Kappa系数分别为95.6%和94.9%，其中包括37个连续的EVI值和6个物候特征信息。连续的EVI数据对森林植被类型的识别贡献更大，而物候特征信息有利于提高分类精度。ESTARFM融合算法适用于GF-1和MODIS数据，能够解决高空间分辨率影像长期序列不足的问题。GF-1时间融合影像在中国南方复杂地形和多云多雨天气频繁的情况下，对森林植被类型的识别具有较高的精度。
