Estimation of Leaf Loss Rate in Larch Infested ...

Based on the star ground combination model, the spectral reflectance is simulated from the Sentinel-2A image, and the spectral index (SI) and spectral derivative feature (SDF) are calculated. EO techniques based on optical data can provide useful indicators. While the risk assessment is probably best addressed at stand level for which suitable techniques and datasets exist (e.g. Sentinel-2A), the actual detection of infested trees. In this study, two typical conifer pests as Erannis Jacobsoni Djak.(EJD) and Pendrolimus Sibiricus Tschtv.(PST) are selected for forest area of Binder and Baruun buren in Mongolia. At the same time, the monitoring models of pest indicators were constructed, and the severity of pests was identified by Fuzzy C-Means(FCM) fuzzy clustering. The accuracy of the random forest (RF) model based on Sentinel-2A remote sensing simulation data is significantly improved. The spectral index and derivative spectral features of Sentinel-2A remote sensing simulation data have significant sensitivity to the two pest indicators. Using the spectral features of remote sensing simulation data, the indicators of conifer pest can be identified by RF and Partial Least Squares Regression(PLSR) algorithms. In the identification of conifer pests based on the non-simulated Sentinel-2A remote sensing data, the estimation accuracy of the two pests' leaf loss rate is the highest.