林业科学2012，Vol.48Issue(9)：108-114,7.
基于岭回归和人工神经网络估测森林可燃物负荷量
Estimation of Forest Fuel Load Based with Ridge Regression and Artificial Neural Networks
摘要
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.关键词
可燃物负荷量/遥感/岭回归分析/GIS/人工神经网络Key words
fuel load/ remote sensing/ GIS/ ridge trace analysis/ artificial neural network分类
农业科技引用本文复制引用
王强,胡海清..基于岭回归和人工神经网络估测森林可燃物负荷量[J].林业科学,2012,48(9):108-114,7.基金项目
"十二五"农村领域国家科技计划课题(2011BAD37B0104) （2011BAD37B0104）
林业公益性行业科研专项经费(201004003-6) （201004003-6）
林业公益性行业科研专项经费(200804002-3) （200804002-3）
黑龙江省科技计划(GA09B201-06). （GA09B201-06）
