气候变化领域集成服务门户(CCPortal): PCA-RF model for Dendrolimus punctatus Walker damage detection

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DOI	10.1007/s11069-021-04503-4
	PCA-RF model for Dendrolimus punctatus Walker damage detection
	Xu Z.; Shi W.; Lin L.; Huang X.; Wang Y.; Liu J.; Yu K.
发表日期	2021
ISSN	0921030X
起始页码	991
结束页码	1009
卷号	106 期号:1
英文摘要	At the present stage, the effective coupling information of “ground-space” is still a fundamental way to detect forest pest damage rapidly and accurately in remote sensing. It is of great significance to explore and construct a detection model that can comprehensively and effectively utilize ground microscopic features and remote sensing pixel information. Taking Dendrolimus punctatus Walker damage as the study object, the host characteristics and the differences with healthy pine forest are analyzed from the aspects of leaf volume, greenness, moisture, forest form and spectrum. Four experimental sites of Sanming City, Jiangle County, Sha County and Yanping District in Nanping City, Fujian, China are set as the experimental areas, and two forest stand features of LAI and SEL are measured, the remote sensing indicators of NDVI, WET and B2, B3, B4 are calculated or extracted. The PCA-RF detection model is constructed with pest levels of non-damage, mild damage, moderate damage and severe damage as dependent variables. This model reduces the dimensions by converting the initial variables into several principal components and making the principal components input to the random forest. The first three principal components of PCA can better replace the information of the original seven characteristic indicators, thereby reducing the seven-dimensional information to three dimensions. One hundred eighty-two samples are randomly divided into the training set and test set for the five repeats. The detection accuracy, Kappa coefficient, ROC are selected to evaluate the pest damage detection effects of PCA-RF model, and compare with Fisher discriminant analysis and BP neural network. The results show that the detection accuracy, Kappa coefficient and AUC of PCA-RF model in the training set are not as good as those of FDA and BPNN, but the detection accuracy and Kappa coefficient in the test set are significantly better than the other two algorithms, and the AUC value is also significantly higher than BNPP. This study proves that PCA-RF model simplifies the complex problem, inherits the advantages of random forest, has very strong generalization ability and robustness, and can be used as an effective solution for forest pest and disease detection. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.
关键词	BP neural networks (BPNN)Dendrolimus punctatus Walker damage Fisher discriminant analysis (FDA)PCA-RF Principal component analysis (PCA)Random forest (RF)
英文关键词	artificial neural network; coniferous forest; detection method; discriminant analysis; leaf; moisture; moth; pest damage; pixel; principal component analysis; China; Fujian; Fujian; Nanping; Sanming; Dendrolimus punctatus; Martes
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206683
作者单位	College of Environment and Resources, Fuzhou University, Fuzhou, Fujian 350108, China; Postdoctoral Research Station of Information and Communication Engineering, Fuzhou University, Fuzhou, Fujian 350108, China; Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Protection, Fuzhou, Fujian 350108, China; Key Lab of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou, Fujian 350108, China; Fujian Provincial Key Laboratory of Resources and Environment Monitoring and Sustainable Management and Utilization, Sanming, Fujian 365004, China; Zhicheng College, Fuzhou University, Fuzhou, Fujian 350002, China
推荐引用方式 GB/T 7714	Xu Z.,Shi W.,Lin L.,et al. PCA-RF model for Dendrolimus punctatus Walker damage detection[J],2021,106(1).
APA	Xu Z..,Shi W..,Lin L..,Huang X..,Wang Y..,...&Yu K..(2021).PCA-RF model for Dendrolimus punctatus Walker damage detection.Natural Hazards,106(1).
MLA	Xu Z.,et al."PCA-RF model for Dendrolimus punctatus Walker damage detection".Natural Hazards 106.1(2021).