In this paper, multi-spectral satellite images, Light Detection and Ranging (LiDAR) point clouds and thematic data are used for recognition and extraction of interference areas, and based on high resolution digital orthophoto map, use hue-saturation-value (HSV) or...
Zhao, B.G.: Pine wilt disease in China. In: Zhao, B.G., Futai, K., Sutherland, J.R., Takeuchi, Y. (eds.) Pine Wilt Disease. Springer, Cham (2008). https://doi.org/10.1007/978-4-431-75655-2_4
Chi, H.B., Zhang, R.J.: Discussion of the prevention of pine wilt disease. Sci. Technol. Vis. 032, 189 (2012)
Google Scholar
Vollenweider, P., Günthardt-Goerg, M.S.: Erratum: Diagnosis of abiotic and biotic stress factors using the visible symptoms in foliage. Environ. Pollut. 137, 455–465 (2006). https://doi.org/10.1016/j.envpol.2005.01.032
CrossRef Google Scholar
Radeloff, V.C., Mladenoff, D.J., et al.: Effects of interacting disturbances on landscape pat-terns: budworm defoliation and salvage logging. Ecol. Appl. 10(1), 233–247 (2000)
CrossRef Google Scholar
Meddens, A., Hicke, J.A., Vierling, L.A., et al.: Evaluating methods to detect bark beetle-caused tree mortality using single-date and multi-date Landsat imagery. Remote Sens. Environ. 132(10), 49–58 (2013)
CrossRef Google Scholar
Meddens, A., Hicke, J.A.: Spatial and temporal patterns of Landsat-based detection of tree mortality caused by a mountain pine beetle outbreak in Colorado, USA. For. Ecol. Manage. 322(3), 78–88 (2014)
CrossRef Google Scholar
Sun, F., Wang, J., Fu, W., et al.: Development and application of settlement index of forest pests and diseases for large areas through using MODIS-NDVI data. For. Resour. Manag. 6, 149 (2017)
Google Scholar
Qi, X., Xiao, F., Liu, J., et al.: Study on monitoring Dendrolimus punctatus damage based on SPOT-5 remote sensing image. J. Central South Univ. For. Technol. 039(004), 59–65 (2019)
Google Scholar
You, C., Pan, J., Liu, Y.: System for Bursaphelenchus xylophilus infected pines identification and positioning and video meeting based on remote sensing. J. Zhejiang Sci. Technol. 41(01), 51–58 (2021)
Google Scholar
Huang, B.: Monitoring Bursaphelenchus xylophilus with multispectrum camera in UAV. Guangxi For. Sci. 49(03), 380–384 (2020)
Google Scholar
Li, H., Xu, H., Zheng, H., et al.: Research on pine wood nematode surveillance technology based on unmanned aerial vehicle remote sensing image. J. Chin. Agric. Mech. 41(9), 170–175 (2020)
Google Scholar
Syifa, M., Park, S.J., Lee, C.W.: Detection of the pine wilt disease tree candidates for drone remote sensing using artificial intelligence techniques. Engineering 6(08), 919–935 (2020)
CrossRef Google Scholar
Liu, J., Wang, C., Chang, Y.: Monitoring method of Bursaphelenchus xylophilus based on multi-feature CRF by UAV image. Bull. Surv. Mapp. (07), 78–82 (2019)
Google Scholar
Wu, H., Wang, C., Yuan, C.: Unmanned aerial survey of discolored trees. Chin. Acad. For. 38(04), 29–32+37 (2019)
Google Scholar
Tao, H., Li, C., Xie, C., et al.: Recognition of red-attack pine trees from UAV imagery based on the HSV threshold method. J. Nanjing For. Univ. (Nat. Sci. Ed.) 43(03), 99–106 (2019)
Google Scholar
Wulder, M.A., Dymond, C.C., White, J.C., et al.: Detection and monitoring of the mountain pine beetle (2004)
Google Scholar
Tao, H., Li, C., Cheng, C., et al.: Progress in remote sensing monitoring for pine wilt disease induced tree mortality: a review. For. Res. 33(03), 175–186 (2020)
Google Scholar