Abstract:

Based on data of a field positioning survey and the corresponding remote sensing and GIS, the forest fuel load and the influence factors were researched by using ridge trace analysis and artificial neural networks in Maoershan experimental forest station of Northeast Forestry University. Ridge regression method can overcome the negative impact imposed the undetermined parameters there exist in the multicollinearity relationship solution between variables which include ten main influence factors, i.e., TM3, TM(4×3) /7, TM4/3 and altitude. A model was established for estimating forest fuel load with the unit of pixel, and Ridge Regression and Artificial Neural Networks MAPE. The deviation of estimation by the two models was 17.6% and 11.7%. The result indicated that the quantitative estimation of forest fuel load for regional forests could be achieved.

Key words: fuel load, remote sensing, GIS, ridge trace analysis, artificial neural network