Remote sensing of insect pests in larch forest based on physical model

A physical decision method was proposed here to monitor Larch forest insect pests at early stage. Three remote sensing indicators were defined, which are CWC (canopy water content), TVDI (Temperature/Vegetation Dryness Index) and LAI (Leaf Area Index). The Five-scale model and artificial neural network (ANN) were combined to inverse the three factors from Landsat data. Based on training samples of health or attacked pixels, a decision tree was built to classify pest-infected pixels. Field validation showed that the prediction of forest compartments with insect pest were highly consistent with the ground field data.