Early Detection of Dendroctonus valens Infestation with Machine Learning Algorithms Based on Hyperspectral Reflectance

Remote Sensing

Linfeng Yu¹  Zhongyi Zhan¹  Youqing Luo¹  Bingtao Gao¹  Lili Ren¹ 

[1] Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China;

关键词: red turpentine beetle (Dendroctonus valens LeConte);    early detection;    hyperspectral analysis;    spectral vegetation indices;    machine learning algorithms;   

DOI  :  10.3390/rs14061373

来源: DOAJ

【 摘 要 】

The red turpentine beetle (Dendroctonus valens LeConte) has caused severe ecological and economic losses since its invasion into China. It gradually spreads northeast, resulting in many Chinese pine (Pinus tabuliformis Carr.) deaths. Early detection of D. valens infestation (i.e., at the green attack stage) is the basis of control measures to prevent its outbreak and spread. This study examined the changes in spectral reflectance after initial attacking of D. valens. We also explored the possibility of detecting early D. valens infestation based on spectral vegetation indices and machine learning algorithms. The spectral reflectance of infested trees was significantly different from healthy trees (p < 0.05), and there was an obvious decrease in the near-infrared region (760–1386 nm; p < 0.01). Spectral vegetation indices were input into three machine learning classifiers; the classification accuracy was 72.5–80%, while the sensitivity was 65–85%. Several spectral vegetation indices (DID, CUR, TBSI, DDn₂, D₇₃₅, SR₁, NSMI, R_NIR•CRI₅₅₀ and RVSI) were sensitive indicators for the early detection of D. valens damage. Our results demonstrate that remote sensing technology could be successfully applied to early detect D. valens infestation and clarify the sensitive spectral regions and vegetation indices, which has important implications for early detection based on unmanned airborne vehicle and satellite data.

【 授权许可】

Unknown