Research progress on remote sensing of pine wilt disease based on phased prevention and control

doi:10.13466/j.cnki.lczyyj.2025.05.013

Abstract

Abstract:

Pine wilt disease(PWD),characterized by rapid onset and high mortality rate constitutes a severe threat to forest ecological security in China.Consequntly,the timely and accurate monitoring of infected trees is imperative for mitigating its spread..Centered on the physiological and morphological attributes of PWD-infected pine trees across varying disease stages(early,middle,late,and terminal),this paper systematically reviews the monitoring principles and research progress in remote sensing unmanned aerial vehicle(UAV)remote sensing,and multi-source remote sensing data fusion technologies.The analysis reveals that while remote sensing exhibits distinct advantages in large-scale macroscopic surveys of infected trees at the middle,late,and terminal stages,its efficacy in detecting faint early-stage signals is hampered by constraints in spatial,temporal,and spectral resolution.UAV-based visible-light and multispectral remote sensing combine high spatial resolution with relatively cost-effectiveness for middle- and late-stage monitoring,however,they frequently fail to capture early physiological stress signals.Conversely,UAV hyperspectral remote sensing,benefiting from its continuous narrow-band characteristics,serves as the most potent approach for discerning subtle spectral changes at the early stage,despite challenges associated with high cost and complex data processing.By integrating multi-dimensional spatiotemporal information,multi-source remote sensing data fusion can mitigate the limitations inherent in individual data sources,representing an important development direction for achieving fine-scale,full-cycle monitoring of infected trees.Finally,considering the limitations of existing studies,this paper outlines future perspective on the development of low-cost,high-sensitivity sensors and the construction of full-cycle intelligent early-warning platforms,providing theoretical support and technical references for the scientific prevention and control of pine wilt disease.

Key words: remote sensing, pine wilt disease, infected trees, application advances

CLC Number:

S771.8
S763

YANG Chen, LI Yichen, ZHANG Maobin, LI Xin, SHI Wenjie, ZE Sangzi, MA Yunqiang. Research progress on remote sensing of pine wilt disease based on phased prevention and control[J]. Forest and Grassland Resources Research, 2025, (5): 121-128.

Figures/Tables 2

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疫木阶段	颜色与特征	主要技术手段	应用特点
早期	针叶略微发黄,无明显形态变化	无人机高光谱、无人机多光谱、多源遥感数据融合	高灵敏度,捕捉细微光谱变化
中期	大多数针叶转变为黄褐色,局部针叶脱落	卫星遥感、无人机可见光、无人机多光谱、无人机高光谱、多源遥感数据融合	利用光谱差异进行识别
晚期	针叶变为红褐色,全株枯死	卫星遥感、无人机可见光、无人机多光谱、无人机高光谱、多源遥感数据融合	外部形态特征显著,易识别
末期	树冠稀疏、针叶脱落殆尽,枝干裸露呈灰白色	卫星遥感、无人机可见光、无人机多光谱、无人机高光谱、多源遥感数据融合	特征显著,可快速普查枯死木分布

传感器类型	技术特征	识别算法	适用阶段	评价指标	文献
可见光	分辨率高、使用成本低	PWDViTNet	早期、中期、晚期、末期	早期平均精度:83.4% 中期平均精度:92.3% 晚期平均精度:91.4% 末期平均精度:97.2%	[27]
		改进的YOLOv11	中期和晚期(感染木)、末期(枯死木)	平均精度(整体):97.7%	[28]
		U-Net	中期、晚期、末期	精确率:81.36%	[29]
		改进的YOLOv4	中期、晚期	平均精度:80.85% (异常变色木包含中晚期疫木)	[30]
多光谱	包含红边、近红外波段,能捕捉植被指数变化	改进的YOLOv5	早期、中期、晚期	早期平均精度:81.2%和87.2% 中期平均精度:92.9%和93.5% 晚期平均精度:86.2%和84.8%	[31]
		Faster R-CNN、 YOLOv4、支持向量机、随机森林	早期、中期、晚期	平均精度(早期):44.59%~48.88% 平均精度(中晚期):71.30%~78.63%	[32]
		Fast R-CNN	中期晚期(病树)、末期(枯死树)	中期晚期分类正确率:90%; 末期分类正确率:82%	[33]
		改进的YOLOv8	中期和晚期	杰弗西斯-马图西塔距离(Jeffesis-Matusita Distance,J-M距离)均>1.00,表明光谱可分性良好	[34]
高光谱	波段连续且丰富,光谱分辨率极高,数据量大	3D-Res CNN	早期、晚期	早期分类精度:72.86%; 晚期分类精度:96.51%	[35]
		改进的Mask R-CNN	早期、中期、晚期、末期	分类精度(早期):74.89% 分类精度(中期、晚期、末期):83.29%~87.86%	[36]
		支持向量机	晚期、末期	晚期阶段错分率:91% 晚期阶段漏分率:77% 末期阶段错分率:59% 末期阶段漏分率:18%	[37]