College of Life Sciences and Agricultural Engineering, Nanyang Normal University, Nanyang 473061, Henan, China.
School of Civil Engineering and Architecture, Baise University, Baise 533000, Guangxi, China.
Comput Intell Neurosci. 2022 May 30;2022:3901372. doi: 10.1155/2022/3901372. eCollection 2022.
Extracting vegetation cover information by combining multisource satellite images can improve the time scale of vegetation cover monitoring, realize encrypted observation in short period, and shorten the regional vegetation remote sensing monitoring cycle. The NDVI and RVI datasets from 2007-2019 were extracted using 9 phases of multisource satellite images (Landsat TM/OLI, Sentinel-2 MSI, and GF-1 PMS) covering Xiaxi, Sichuan. Three typical validation sites representing higher vegetation cover in mountains and no vegetation cover in water bodies in the region, respectively, were selected to extract NDVI and RVI at the corresponding locations. Linear regression and Spearman correlation coefficient () analysis were used to verify the correlation between NDVI and RVI from multisource images. The results showed that the vegetation indices fluctuated smoothly in the time series within the validation sites, and the vegetation indices of multisource satellite images were good measures of long-term vegetation cover in the region; the vegetation indices of the same satellite images showed significant correlations (both and exceeded 0.8), and the vegetation indices of different satellite images (PSM and MSI, PSM and OLI) showed more significant correlations (both and exceeded 0.7); the smaller the difference between the original resolutions of satellite images, the more significant the correlation between the extracted NDVI and RVI.
通过结合多源卫星图像提取植被覆盖信息，可以提高植被覆盖监测的时间尺度，实现短时间加密观测，缩短区域植被遥感监测周期。利用覆盖四川峡溪的 9 期多源卫星图像（Landsat TM/OLI、Sentinel-2 MSI 和 GF-1 PMS）提取了 2007-2019 年的 NDVI 和 RVI 数据集。选择了 3 个典型验证点，分别代表该区域山区高植被覆盖和水体无植被覆盖的情况，分别在相应位置提取 NDVI 和 RVI。采用线性回归和 Spearman 相关系数（）分析验证多源图像中 NDVI 和 RVI 的相关性。结果表明，验证点内时间序列中的植被指数波动平稳，多源卫星图像的植被指数是该区域长期植被覆盖的良好度量指标；同一卫星图像的植被指数具有显著相关性（和均超过 0.8），不同卫星图像（PSM 和 MSI、PSM 和 OLI）的植被指数具有更显著的相关性（和均超过 0.7）；卫星图像原始分辨率差异越小，提取的 NDVI 和 RVI 之间的相关性越显著。
