2020, Volume 6, Issue 8
Engineering >> 2020, Volume 6, Issue 8 doi: 10.1016/j.eng.2020.07.001
Detection of the Pine Wilt Disease Tree Candidates for Drone Remote Sensing Using Artificial Intelligence Techniques
Division of Science Education, Kangwon National University, Gangwon-do 24341, Republic of Korea
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Abstract
Pine wilt disease (PWD) has recently caused substantial pine tree losses in Republic of Korea. PWD is considered a severe problem due to the importance of pine trees to Korean people, so this problem must be handled appropriately. Previously, we examined the history of PWD and found that it had already spread to some regions of Republic of Korea; these became our study area. Early detection of PWD is required. We used drone remote sensing techniques to detect trees with similar symptoms to trees infected with PWD. Drone remote sensing was employed because it yields high-quality images and can easily reach the locations of pine trees. To differentiate healthy pine trees from those with PWD, we produced a land cover (LC) map from drone images collected from the villages of Anbi and Wonchang by classifying them using two classifier methods, i.e., artificial neural network (ANN) and support vector machine (SVM). Furthermore, compared the accuracy of two types of Global Positioning System (GPS) data, collected using drone and hand-held devices, for identifying the locations of trees with PWD. We then divided the drone images into six LC classes for each study area and found that the SVM was more accurate than the ANN at classifying trees with PWD. In Anbi, the SVM had an overall accuracy of 94.13%, which is 6.7% higher than the overall accuracy of the ANN, which was 87.43%. We obtained similar results in Wonchang, for which the accuracy of the SVM and ANN was 86.59% and 79.33%, respectively. In terms of the GPS data, we used two type of hand-held GPS device. GPS device 1 is corrected by referring to the benchmarks sited on both locations, while the GPS device 2 is uncorrected device which used the default setting of the GPS only. The data collected from hand-held GPS device 1 was better than those collected using hand-held GPS device 2 in Wonchang. However, in Anbi, we obtained better results from GPS device 2 than from GPS device 1. In Anbi, the error in the data from GPS device 1 was 7.08 m, while that of the GPS device 2 data was 0.14 m. In conclusion, both classifiers can distinguish between healthy trees and those with PWD based on LC data. LC data can also be used for other types of classification. There were some differences between the hand-held and drone GPS datasets from both areas.
Keywords
Pine wilt disease ; Drone remote sensing ; Artificial neural network ; Support vector machine ; Global positioning system
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References
[ 1 ] Shin SC. Pine wilt disease in Korea. In: Zhao BG, Futai K, Sutherland JR, Takeuchi Y, editors. Pine wilt disease. Tokyo: Springer; 2008. p. 26–32. link1
[ 2 ] Kwon SD. Changes in Korean pine forests. Monthly Inf For Sci 2006;181:16–7. Korean
[ 3 ] Kwon TS, Shin JH, Lim JH, Kim YK, Lee EJ. Management of pine wilt disease in Korea through preventative silvicultural control. For Ecol Manage 2011;261 (3):562–9. link1
[ 4 ] Han H, Chung YJ, Shin SC. First report of pine wilt disease on Pinus koraiensis in Korea. Plant Dis 2008;92(8):1251. link1
[ 5 ] Mamiya Y. History of pine wilt disease in Japan. J Nematol 1988;20(2):219–26. link1
[ 6 ] Mota MM, Vieira P. editors. Pine wilt disease: a worldwide threat to forest ecosystems. Dordrecht: Springer; 2008. p. 405. link1
[ 7 ] Webster J, Mota M. Pine wilt disease: global issues, trade and economic impact. In: Mota MM, Vieira PR, editors. Pine wilt disease: a worldwide threat to forest ecosystems. Dordrecht: Springer; 2008. link1
[ 8 ] Ikegami M, Jenkins TAR. Estimate global risks of a forest disease under current and future climates using species distribution model and simple thermal model–pine wilt disease as a model case. For Ecol Manage 2018;409:343–52. link1
[ 9 ] Futai K. Pine wilt in Japan: from first incidence to the present. In: Zhao BG, Futai K, Sutherland JR, Takeuchi Y, editors. Pine wilt disease. Tokyo: Springer; 2008. p. 5–12. link1
[10] Zhao BG. Pine wilt disease in China. In: Zhao BG, Futai K, Sutherland JR, Takeuchi Y, editors. Pine wilt disease. Tokyo: Springer; 2008. p. 18–25. link1
[11] Mamiya Y. Pathology of the pine wilt disease caused by Bursaphelenchus xylophilus. Annu Rev Phytopathol 1983;21(1):201–20. link1
[12] Kim JB, Jo MH, Oh JS, Lee KJ, Park SJ, Um HH. Temporal and spatial correlation analysis of Bursaphelenchus xylophilus damage area. In: Proceedings of the Korean Society of Agricultural and Forest Meteorology 2001 Spring Conference; 2001 Jun; Seoul, Korea; 2001. p. 49–52.
[13] Kim SR, Lee WK, Lim CH, Kim M, Kafatos MC, Lee SH, et al. Hyperspectral analysis of pine wilt disease to determine an optimal detection index. Forests 2018;9(3):115. link1
[14] Kim MI, Lee WK, Kwon TH, Kwak DA, Kim YS, Lee SH. Early detecting damaged trees by pine wilt disease using DI (detection index) from portable near infrared camera. J Korean Soc For Sci 2011;100(3):374–81. link1
[15] Tang L, Shao G. Drone remote sensing for forestry research and practices. J For Res 2015;26(4):791–7. link1
[16] Yuan H, Yang G, Li C, Wang Y, Liu J, Yu H, et al. Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: analysis of RF, ANN, and SVM regression models. Remote Sens 2017;9(4):309. link1
[17] Aasen H, Burkart A, Bolten A, Bareth G. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: from camera calibration to quality assurance. ISPRS J Photogramm Remote Sens 2015;108:245–59. link1
[18] Feng Q, Liu J, Gong J. UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sens 2015;7(1):1074–94. link1
[19] Lei T, Zhang Y, Lu J, Pang Z, Fu J, Kan G, et al. The application of UAV remote sensing in mapping of damaged buildings after earthquakes. In: Proceedings of the 10th International Conference on Digital Image Processing; 2018 May 11– 14; Shanghai, China; 2018. p. 1080651.
[20] Yamazaki F, Liu W. Remote sensing technologies for post-earthquake damage assessment: a case study on the 2016 Kumamoto earthquake. In: Proceedings of the 6th Asia Conference on Earthquake Engineering; 2016 Sept 22–24; At Cebu City, Philippines; 2016. p. 8.
[21] Niethammer U, James MR, Rothmund S, Travelletti J, Joswig M. UAV-based remote sensing of the Super-Sauze landslide: evaluation and results. Eng Geol 2012;128:2–11. link1
[22] Giordan D, Manconi A, Tannant DD, Allasia P. UAV: low-cost remote sensing for high-resolution investigation of landslides. In: Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium; 2015 Jul 26– 31; Milan, Italy; 2015. p. 5344–7.
[23] Casagli N, Frodella W, Morelli S, Tofani V, Ciampalini A, Intrieri E, et al. Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning. Geoenviron Disasters 2017;4(1):9. link1
[24] Favalli M, Fornaciai A, Nannipieri L, Harris A, Calvari S, Lormand C. UAV-based remote sensing surveys of lava flow fields: a case study from Etna’s 1974 channel-fed lava flows. Bull Volcanol 2018;80(3):29. link1
[25] Rüdiger J, Lukas T, Bobrowski N, Gutmann A, Liotta M, de Moor M, et al. Compositional variation in aging volcanic plumes-analysis of gaseous SO2, CO2 and halogen species in volcanic emissions using an unmanned aerial vehicle (UAV). In: Proceedings of the EGU General Assembly Conference; 2017 Apr 23–28; Vienna, Austria; 2017. p. 892.
[26] Xiong Y, Zhang Z, Chen F. Comparison of artificial neural network and support vector machine methods for urban land use/cover classifications from remote sensing images a case study of Guangzhou, South China. In: Proceedings of the 2010 International Conference on Computer Application and System Modeling; 2010 Oct 22–24; Taiyuan, China; 2010. p. V13–52.
[27] Liang D, Guan Q, Huang W, Huang L, Yang G. Remote sensing inversion of leaf area index based on support vector machine regression in winter wheat. Nongye Gongcheng Xuebao (Beijing) 2013;29(7):117–23. link1
[28] Yoon H, Kim Y, Ha K, Lee SH, Kim GP. Comparative evaluation of ANN- and SVM-time series models for predicting freshwater-saltwater interface fluctuations. Water 2017;9(5):323. link1
[29] Murmu S, Biswas S. Application of fuzzy logic and neural network in crop classification: a review. Aquat Procedia 2015;4:1203–10. link1
[30] Yuan H, van Der Wiele CF, Khorram S. An automated artificial neural network system for land use/land cover classification from Landsat TM imagery. Remote Sens 2009;1(3):243–65. link1
[31] Maier HR, Dandy GC. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Modell Software 2000;15(1):101–24. link1
[32] Kadavi PR, Lee CW. Land cover classification analysis of volcanic island in Aleutian Arc using an artificial neural network (ANN) and a support vector machine (SVM) from Landsat imagery. Geosci J 2018;22(4):653–65. link1
[33] Kavzoglu T, Colkesen I. A kernel functions analysis for support vector machines for land cover classification. Int J Appl Earth Obs Geoinf 2009;11 (5):352–9. link1
[34] Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20(3):273–97. link1
[35] Lu D, Batistella M, Li G, Moran E, Hetrick S, Freitas CDC, et al. Land use/cover classification in the Brazilian Amazon using satellite images. Pesqui Agropecu Bras 2012;47(9):1185–208. link1
[36] Verrelst J, Muñoz J, Alonso L, Delegido J, Rivera JP, Camps-Valls G, et al. Machine learning regression algorithms for biophysical parameter retrieval: opportunities for Sentinel-2 and -3. Remote Sens Environ 2012;118:127–39. link1
[37] Liaw A, Wiener M. Classification and regression by randomForest. R News 2002;2(3):18–22. link1
[38] Zhai S, Jiang T. A novel particle swarm optimization trained support vector machine for automatic sense-through-foliage target recognition system. Knowl Based Syst 2014;65:50–9. link1
[39] Congalton RG. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 1991;37(1):35–46. link1
[40] Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas 1960;20(1):37–46. link1
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