刚竹毒蛾危害下的毛竹叶片光谱特征及虫害等级检测研究

doi:10.3964/j.issn.1000-0593(2021)04-1253-07

Abstract
References
Related Citation (15)

Download: PDF (3235 KB)
Export: BibTeX | EndNote (RIS)

Abstract Understanding the spectral characteristics of moso bamboo leaves damaged by Pantana phyllostachysae Chao can provide theoretical guidance for developing applicable and effective technologies to monitor the ecological safety of the bamboo forest. Compared with the traditional multispectral data, hyperspectral remote sensing can sense the subtle changes of host spectrum among different severity of Pantana phyllostachysae Chao. However, the related researches were still rare, and the spectral change mechanism of the host needs to be further summarized. Therefore, this study analyzed the spectral differences among healthy, damaged and off-yearmoso bamboo leaves based on 552 field measured spectrums. The characteristic variables that can act as indicators of leaves health status were selected. Finally, the model for monitoring the damage of leaves caused by Pantana phyllostachysae Chao was established using the XGBoost algorithm. The results show that: (1) with the increase of pest damage, the reflectance of damaged leaves gradually appeared “green low and red high” in visible-band, while the reflectance noticeably decreased in near-infrared band, and the reflectance of damaged leaves in shortwave infrared band was significantly higher than that of healthy leaves, especially in the two typical water vapor absorption bands (1 450 and 1 940 nm); (2) the reflectance of off-year leaves in visible and near infrared bands was significantly higher than healthy and damaged leaves; (3) the spectral characteristics of indentation-only leaves only slightly changed in comparison with healthy leaves, while the red band reflectance of leaves with red-brown disease spots increased to some extent, andthe leaves with gray-white disease spots completely lost the basic spectral characteristics of vegetation; (4) according to the feature importance score determined by the XGBoost algorithm, the contribution of each characteristic variable was PRI＞FDVI_{576, 717}＞NPCI＞DSWI＞VOG 1＞RVSI＞NDWI; (5) the overall average accuracy of the model to detect the damage by the pest was 74.39%, and the accuracy for healthy, mild damaged, severe damaged, off-year, and moderate damaged leaves was 94.55%, 74.93%, 84.12%, 71.10%, and 33.48%, respectively.

Key words：Forest pest; Remote sensing; Spectral characteristics; Pantana phyllostachysae Chao

Received: 2020-03-03 Accepted: 2020-07-09

ZTFLH:

S433

Corresponding Authors: JU Wei-min E-mail: juweimin@nju.edu.cn

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)04-1253-07 OR https://www.gpxygpfx.com/EN/Y2021/V41/I04/1253

[1] CHEN De-liang, ZHAO Ren-you, QU Qiao-wen(陈德良, 赵仁友, 瞿巧文). Forest Pest and Disease(中国森林病虫), 2006, 25(6): 17.
[2] HONG Yi-cong(洪宜聪). Journal of Fujian Forestry Science and Technology(福建林业科技), 2013，(2): 42.
[3] Meng R, Dennison P E, Zhao F, et al. Remote Sensing of Environment, 2018, 215: 170.
[4] Zhang N, Zhang X L, Yang G J, et al. Remote Sensing of Environment, 2018, 217: 323.
[5] XU Zhang-hua, LIU Jian, CHEN Chong-cheng, et al(许章华, 刘健, 陈崇成, 等). Remote Sensing for Land and Resources(国土资源遥感), 2016, 28(2): 41.
[6] State Forestry Administration(国家林业局). LY-T 1681—2006. Standard of Forest Pests Occurrence and Disaster(林业有害生物发生及成灾标准). Beijing: Standards Press of China(北京: 中国标准出版社), 2006.
[7] Oumar Z, Mutanga O, Ismail R. International Journal of Applied Earth Observation and Geoinformation, 2013, 21: 113.
[8] ZHANG Su-lan, HUANG Jin-long, QIN Lin, et al(张素兰, 黄金龙, 秦林, 等). Transactions of The Chinese Society of Agricultural Machinery(农业机械学报), 2019, 50(4): 203.
[9] Nestola E, Scartazza A, Baccio D, et al. The Science of the Total Environment, 2018, 612: 1030.
[10] Ranjan R, Chopra U K, Sahoo R N, et al. International Journal of Remote Sensing, 2012, 33(20): 6342.
[11] Vogelmann J E, Rock B N, Moss D M. International Journal of Remote Sensing, 1993, 14(8): 1563.
[12] Merton R, Huntington J. In Summaries of the Eight JPL Airborne Earth Science Workshop. Pasadena: JPL Publication, 1999. 299.
[13] Gamon J A, Pen( u~ )elas J, Field C B. Remote Sensing of Environment, 1992, 41(1): 35.
[14] Xiao Y F, Zhao W J, Zhou D M, et al. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7): 4014.
[15] Daughtry T, Walthall L, Kim S, et al. Remote Sensing of Environment, 2000, 74: 229.
[16] Serrano L, Peñuelas J, Ustin S L. Remote Sensing of Environment, 2002, 81(2/3): 355.
[17] Chen T Q, Guestrin C. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco: ACM, 2016. 785.
[18] Yi Z P, Chen Z S, Pan J C, et al. Astrophysical Journal, 2019, 887(2): 241.
[19] Li Y C, Li C, Li M Y, et al. Forests, 2019, 10(12): 1073.