Characteristics of pine wood nematode disease in Nankang District, Ganzhou, Jiangxi Province, China

doi:10.13287/j.1001-9332.202402.030

Abstract

Abstract: Pine wood nematode (PWN) disease is one of the major disasters in forests of southern China, causing substantial forest resources and ecological and economic losses. Based on field surveys and WFV image data from the GF-1 satellite, we constructed a spatial identification model of PWN disease with the random forest model to explore the relative influences of topography, human activities and stand factors on the occurrence of diseases and predict their spatial distribution. We then used the spatial autocorrelation analysis to assess the distribution characteristics of PWN disease at the regional scale. The results showed that the random forest model constructed in this study was effective in identifying pine nematode diseases (AUC value=0.99, overall accuracy=0.96). The norma-lized difference greenness index (NDGI), the distance to the highway, and normalized vegetation index (NDVI) were important factors in explaining the spatial variations of PWN disease occurrence. There was a positive spatial correlation in the occurrence of PWN disease (not randomly distributed but with obvious spatial aggregation characteristics). The high occurrence areas of pine wood nematode disease concentrated in Chitu Township, Zhufang Township and Shibatang Township, low occurrence areas concentrated in the vicinity of Rongjiang Street. The areas far away from the highway, low in elevation, and close to county roads were suffered to PWN disease. The results could serve the regional monitoring of pine nematode disease occurrence and provide practical guidance for PWN disease management.

Key words: pine wood nematode disease, monitoring model, random forest, multispectral data, GF-1 WFV

YUAN Jiayu, Xiong Li, WU Zhiwei, ZHU Shihao, KANG Ping, LI Shun. Characteristics of pine wood nematode disease in Nankang District, Ganzhou, Jiangxi Province, China[J]. Chinese Journal of Applied Ecology, 2024, 35(2): 507-515.

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