Detection of Discolored Trees Caused by Pine Wilt Disease Based on Vegetation Index Method Using Terrestrial Ecosystem Carbon Inventory Satellite Data

doi:10.13466/j.cnki.lyzygl.2023.04.015

Abstract

Abstract:

Pine wilt disease(PWD)is a forestry pest epidemic that poses a major threat to the survival ofpine trees.High-resolution satellite remote sensing is an effective approach for identifying PWD-infected pine trees.The Terrestrial Ecosystem Carbon Inventory Satellite(Goumang Satellite),which was launched on August 4,2022,has a four-band camera with a spatial resolution of more than 2m.The purpose of this paper is to investigate the vegetation index(VI)threshold approach for demonstrating PWD detection using Goumang Satellite multispectral data.Based on the multispectral characteristics of PWD discolored pine trees,an identification strategy based on two biomass-related and pigment-related VIs is proposed and a pine wilt disease index(PWDI)is designed.A test of PWD identification was conducted in Qixia City,Shandong Province.The results demonstrated that the double-VIs identification method outperformed the single-VI strategy,with the combination of PWDI and NDVI having the best PWD recognition capacity,with an overall accuracy of 84.5%,followed by the combinations of NWI-NDVI and NGRDI-NDVI.PWDI outperformed the other five examined VIs in terms of identification performance while using a single-VI method.This research demonstrates that the Goumang Satellite has a high potential for rapid and reliable detection of PWD discolored pine trees,and that the suggested double-VIs identification technique and the new PWDI can serve as a model for other comparable satellites used for PWD monitoring.

Key words: pine wilt disease, remote sensing, Terrestrial Ecosystem Carbon Inventory Satellite, Goumang, vegetation index

CLC Number:

S763
S771.8

JIAO Quanjun, ZHENG Yanfeng, HUANG Wenjiang, ZHANG Bing, ZHANG Heyi, SHI Yimeng, WU Fayun, FU Anmin. Detection of Discolored Trees Caused by Pine Wilt Disease Based on Vegetation Index Method Using Terrestrial Ecosystem Carbon Inventory Satellite Data[J]. FOREST RESOURCES WANAGEMENT, 2023, (4): 123-131.

Figures/Tables 9

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References 27

[1]	叶建仁. 材线虫病在中国的流行现状,防治技术与对策分析[J]. 林业科学, 2019, 55(9):1-10.
[2]	国家林业和草原局. 全国松材线虫病疫情防控五年攻坚行动计划(2021—2025)[EB/OL].(2021-07-07)[2023-06-10]. http://www.forestry.gov.cn.
[3]	陶欢, 李存军, 程成, 等. 松材线虫病变色松树遥感监测研究进展[J]. 林业科学研究, 2020, 33(3):172-183.
[4]	方国飞, 黄文江, 牟晓伟, 等. 松材线虫病疫情精准监测实践与展望[J]. 中国森林病虫, 2022, 41(4):16-23.
[5]	武红敢, 牟晓伟, 杨清钰, 等. 无人机遥感技术在重庆市沙坪坝区松材线虫病监测中的应用[J]. 林业资源管理, 2019(2):109-115.
[6]	陶欢, 李存军, 谢春春, 等. 基于 HSV 阈值法的无人机影像变色松树识别[J]. 南京林业大学学报:自然科学版, 2019, 62(3):99-106.
[7]	刘顺利, 刘昌华, 张雷, 等. 基于改进SSD的无人机影像松材线虫病变色木检测[J]. 林业资源管理, 2022(3):135-141.
[8]	杨国峰, 何勇, 冯旭萍, 等. 无人机遥感监测作物病虫害胁迫方法与最新研究进展[J]. 智慧农业, 2022, 4(1):1-16.
[9]	亓兴兰, 曹祖宁, 刘健, 等. 基于卫星遥感影像的森林病虫害监测研究进展[J]. 林业资源管理, 2020(2):181-186.
[10]	乔睿, 唐娉, 石进, 等. WorldView-2影像的红叶松树识别研究[J]. 北京林业大学学报, 2015, 37(11):33-40.
[11]	秦琳, 孟先进, 张水花, 等. 北京三号卫星数据在松材线虫病遥感监测中的应用评价[J]. 林业资源管理, 2022(4):126-133.
[12]	覃先林, 李增元, 易浩若. 高空间分辨率卫星遥感影像树冠信息提取方法研究[J]. 遥感技术与应用, 2005, 20(2):228-232.
[13]	刘玉锋, 潘英, 李虎. 基于高空间分辨率遥感数据的天山云杉树冠信息提取研究[J]. 国土资源遥感, 2019, 31(4):112-119.
[14]	张晓东, 杨皓博, 蔡佩华, 等. 松材线虫病遥感监测研究进展及方法述评[J]. 农业工程学报, 2022, 38(18):184-194.
[15]	张新伟, 黄缙, 贺涛, 等. 陆地生态系统碳监测卫星总体设计[J]. 航天返回与遥感, 2022, 43(6):16-26.
[16]	徐华潮, 骆有庆, 张廷廷, 等. 松材线虫自然侵染后松树不同感病阶段针叶光谱特征变化[J]. 光谱学与光谱分析, 2011, 31(5):1352-1355.
[17]	黄明祥, 龚建华, 李顺, 等. 松材线虫病害高光谱时序与敏感特征研究[J]. 遥感技术与应用, 2012, 27(6):954-960.
[18]	山东省自然资源厅. 山东省松材线虫病防治技术规范(2022年版)[EB/OL].(2022-02-18)[2023-06-10]. http://dnr.shandong.gov.cn/zwgk_324/xxgkml/fzjz/lyyhswgl/202202/t20220218_3860246.html.
[19]	Wu Dewei, Yu Linfeng, Yu Run, et al. Detection of the monitoring window for pine wilt disease using multi-temporal UAV-based multispectral imagery and machine learning algorithms[J]. Remote Sensing, 2023, 15(2):444. doi: 10.3390/rs15020444
[20]	Rouse J W, Haas R H, Schell J A, et al. Monitoring vegetation systems in the Great Plains with ERTS[C]. Third ERTS-1 Symposium NASA,NASA SP-351,Washington DC,USA: 1974.
[21]	于泉洲, 王绍强, 黄昆, 等. 基于Hyperion高光谱数据的温带森林不同冠层结构的光谱特征分析[J]. 光谱学与光谱分析, 2015, 35(7):1980-1985.
[22]	Brown L, Chen J M, Leblanc S G, et al. A shortwave infrared modification to the simple ratio for LAI retrieval in boreal forests:An image and model analysis[J]. Remote sensing of environment, 2000, 71(1):16-25. doi: 10.1016/S0034-4257(99)00035-8
[23]	Jain A K. Fundamentals of digital image processing[M]. New Jersey: Prentice Hall, 1989.
[24]	Hunt E R, Doraiswamy P C, Mcmurtrey J E, et al. A visible band index for remote sensing leaf chlorophyll content at the canopy scale[J]. International Journal of Applied Earth Observations & Geoinformation, 2013, 21(1):103-112.
[25]	Pearson R L, Miller L D. Remote mapping of standing crop biomass for estimation of the productivity of the short grass prairie[C]. Eighth International Symposium on Remote Sensing of Environment,University of Michigan,Ann Arbor,MI,USA: 1972.
[26]	Gitelson A A, Gritz Y, Merzlyak M N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves[J]. Journal of Plant Physiology, 2003, 160(3):271-282. doi: 10.1078/0176-1617-00887 pmid: 12749084
[27]	Uto K, Kosugi Y, Ogata T. Hyperspectral analysis of Japanese oak wilt to determine normalized wilt index[C]. Geoscience and Remote Sensing Symposium(IGARSS 2008),Boston,MA,USA: 2008.

波段号	波段名称	中心波长/nm	波长范围/nm
B1	蓝	485	450~520
B2	绿	555	520~590
B3	红	660	630~690
B4	近红外	830	770~890

指数名称	缩写	计算公式	参考文献
Normalized difference vegetation index	NDVI	NDVI=(B4-B3)/(B4+B3)	[20]
Triangular greenness index	TGI	TGI=-0.5×((660-485)×(B3-B2)-(660-555)×(B3-B1))	[24]
Normalized green-red difference index	NGRDI	NGRDI=(B2-B3)/(B2+B3)	[25]
Green chlorophyll index	CI_green	CI_green=B4/B2-1	[26]
Normalized wilt index	NWI	NWI=-NGRDI×(NDVI+NGRDI)	[27]
Pine wilt disease index	PWDI	PWDI=-NGRDI×NDVI/B2	本研究

色素相关植被指数	单一植被指数策略			与NDVI指数组合策略
色素相关植被指数	召回率	准确率	总体精度	召回率	准确率	总体精度
H	83.6	62.3	71.4	83.6	70.5	76.5
TGI	77.6	66.5	71.6	77.6	74.4	76.0
NGRDI	85.5	62.3	72.1	85.5	69.9	76.9
CI_green	87.5	8.0	14.7	87.5	7.8	14.4
NWI	84.9	69.5	76.4	84.9	72.8	78.4
PWDI	81.6	85.8	83.6	81.6	87.6	84.5