Considering that a large area of Pinus tabulaeformis suffers from Dendrolimus tabulaeformis in West Liaoning

this study monitored Dendrolimus. tabulaeformis disaster promptly

efficiently

and precisely through remote sensing technology. Information on the influencing factors

such as geography and meteorology

was collected to provide a reference for future prediction. This study adopted the method of Thematic Mapper（TM）

Enhanced TM Plus data source

and Geographic Information System technology to examine the image gray histograms of injured Pinus tabulaeformis with various damage degrees and monitor the damage degrees by using the Ratio Vegetation Index（RVI） [near infrared（NIR）/red]. To analyze the imposing factors

the classification results from RVI analysis were overlapped with geography and meteorology statistics

with cross reference to previous studies on the biological characteristics of Dendrolimus tabulaeformis. By analyzing the image gray histograms

this study determined that in the Pinus tabulaeformis spectrum

the NIR band shows high sensitivity to mild infection

whereas the red band shows high sensitivity to severe infection. Therefore

using RVI

which combines the two bands

prompts the disaster monitoring. The monitoring results are in accordance with the biological characteristics of Dendrolimus tabulaeformis

which prefer dry and warm environments

thus proving the efficiency of the remote sensing monitoring. For the factors that influence the damage

Dendrolimus tabulaeformis prefers south gentle slopes. Areas with long sunshine time

minimal rain

and low accumulative temperature suffer from severe damage. This finding provides a basis to predict the probability of disaster outbreak. By applying a novel converse method to test the reliability and accuracy of monitoring results

this study showed that researchers could take advantage of the biological and ecological characteristics of remote sensing objects to assess the reliability of results when forestry damage field investigations are lacking and there isno access to fieldwork sample statistics to estimate the monitoring results. This procedure could reduce the load and difficulty of field investigations.