Remote sensing detection model of damaged forest by tomicus piniperda in Yunnan province, China

Remote sensing detection model of damaged forest by tomicus piniperda was studied. It analyzed different detection models using multiple types of remote sensing data, such as TM, CBERS-1, AVHRR and MODIS data. The spectral features of the above remote sensing data (March, 2001) were given. And two detection models were put forward according to the spectral changing characteristics. One was named Difference Rate (DR) model with NIR and VIR data, which applied for TM, CBERS-1, AVHRR and MODIS. If DR was bigger, the forest grew healthier. Based on the typical sample, the different guidelines distinguished healthy and damaged forests were obtained. The other model was named Disaster Index (DI) model with thermal and NIR data, only suitable for MODIS. The guidelines of healthy and damaged forest were determined too. Greater DI was, the forest was stricken more badly. In conclusion, it will help monitor and assess the vermin occurrence and impact by remote sensing detection model.EI