Dendrolimus Punctatus Walker Damage Detection Based on Fisher Discriminant Analysis and Random Forest
XU Zhang-hua1, 2, 3, 4, HUANG Xu-ying1, LIN Lu1, WANG Qian-feng1, LIU Jian2, CHEN Chong-cheng3, YU Kun-yong2, ZHOU Hua-kang5, ZHANG Hua-feng6
1. College of Environment and Resources, Fuzhou University, Fuzhou 350116, China
2. Fujian Provincial Key Laboratory of Resources and Environment Monitoring & Sustainable Management and Utilization, Sanming 365004, China
3. Key Lab of Spatial Data Mining & Information Sharing, Ministry of Education, Fuzhou 350116, China
4. Fujian Provinical Key Laboratory of Remote Sensing of Soil Erosion and Oisaster Protection, Fuzhou 350116, China
5. Yanping District Forestry Bureau，Nanping 353000, China
6. Xiamen Forest Pest Control and Quarantine Station, Xiamen 361004, China
Abstract：The construction of the pest detection algorithm is a process of coupling the “ground-space” features, which is an important guarantee to realize its remote sensing monitoring. Taking Sanming City, Jiangle County, Sha County and Yanping District in Nanping City in Fujian Province as the experimental areas, it gathered 182 samples of Dendrolimus punctatus Walker damage and randomly divided them into training set and validation set, and 5 repeated tests and 1 test of index screening were performed. According the host representations damaged by Dendrolimus punctatus Walker, 7 ground and remote sensing characteristic indices including pine forest leaf area index (LAI), standard deviation of LAI (SEL), normalized difference vegetation index (NDVI), wetness from tasseled cap transformation (WET), green band (B2), red band (B3), near infrared band (B4) were obtained, then the models of Fisher discriminant analysis and random forest for pest levels were constructed. The detection precision, Kappa coefficient and ROC curve were used to comprehensively compare the detection effects of these two algorithms, as well as the paired t-test. The results showed that all the 7 indices have the pest responsiveness, while SEL and NDVI are relatively weak; the average detection precision of Fisher discriminant analysis in 6 tests was 73.26%, Kappa coefficient was 0.631 9, and 79.30%, 0.715 1 of RF respectively, indicating RF is significantly better than the former one (p<0.05); for the 3 pest levels of non-damage, mild damage and moderate damage, the detection precision, Kappa coefficient and AUC of RF were all significantly higher than Fisher discriminant analysis (p<0.05), while for the severe damage, Fisher was better. On the whole, the Dendrolimus punctatus Walker damage detection effect of RF is better than Fisher discriminant analysis, but Fisher has more accurate for the severe damage and the mode is clear, easy to by promoted, so these two algorithms could be comprehensively utilized to put forward the pest monitoring work. The results can provide a technical reference for the effective detection of Dendrolimus punctatus Walker damage as well as other forest pests and diseases, and lay a foundation of the remote sensing monitoring.
Key words：Dendrolimus punctatus Walker damage; Fisher discriminant analysis; Random forest; Detection effect; “Ground-space” features
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