Ridge regression model for estimating pine wilt disease based on hyperspectral characteristics

Abstract

Pine wilt disease (PWD) caused by the pine wood nematode, Bursaphelenchus xylophilus, is considered as the most destructive forest-invasive alien species and may cause serious economic losses. A ridge regression model was proposed based on the hyperspectral characteristics to estimate the degrees of pine wilt disease for Pinus massoniana in Yongsheng forest of Chongqing, Southwest China. The spectral reflectance and quantitated pet levels for Pinus massoniana were measured from June to August 2017. And then the ridge trace analysis was operated on 14 spectral characteristics, which covered maximum and sum of reflectance ranging in green region (490~560 nm), yellow region (560~590 nm), red region (620~680 nm), red edge (680~780 nm), near-infrared region (780~1 100 nm), as well as the reflectance height of green peak (500~670 nm) and absorption depth of red valley (560~760 nm). Furthermore, the hyperspectral characteristic parameters with less collinearity were selected to construct the estimation model of PWD with ridge regression. The results demonstrated that ridge trace curves for the maximum of reflectance in red edge, near-infrared region, the sum of reflectance in the red edge, near-infrared region, as well as absorption depth of red valley were stable, which were not close to zero. Therefore, those five spectral characteristics could be considered in ridge regression modeling; when the ridge trace parameter k was 0.2, the ridge traces of the above five hyperspectral characteristic parameters became stable, and then the ridge regression coefficients were calculated. Finally, a regression estimation model of PWD was built with determination coefficient R of 0.868 6, root-mean-square error (RMSE) of 0.273 5, and average estimation accuracy of 87.15%. The research provided both scientific support and application reference for monitoring forest pet disease with remote sensing technology.