A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery

doi:10.3390/rs13112083

	A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery
	Zhang, Biyao ; Ye, Huichun 1; Lu, Wei 2; Huang, Wenjiang 1,3; Wu, Bo 3,4; Hao, Zhuoqing 3; Sun, Hong 5
	2021
发表期刊	REMOTE SENSING
卷号	13 期号:11
摘要	Using high-resolution remote sensing data to identify infected trees is an important method for controlling pine wilt disease (PWD). Currently, single-date image classification methods are widely used for PWD detection in pure stands of pine. However, they often yield false detections caused by deciduous trees, brown herbaceous, and sparsely vegetated regions in complex landscapes, resulting in low user accuracies. Due to the limitations on the bands of the high-resolution imagery, it is difficult to distinguish wilted pine trees from such easily confused objects when only using the optical spectral characteristics. This paper proposes a spatiotemporal change detection method to reduce false detections in tree-scale PWD monitoring under a complex landscape. The framework consisted of three parts, which represent the capture of spectral, temporal, and spatial features: (1) the Normalized Green-Red Difference Index (NGRDI) was calculated as a descriptor of canopy greenness; (2) two NGRDI images with similar dates in adjacent years were contrasted to obtain a bitemporal change index that represents the temporal behaviors of typical cover types; and (3) a spatial enhancement was performed on the change index using a convolution kernel matching the spatial patterns of PWD. Finally, a set of criteria based on the above features were established to extract the wilted pine trees. The results showed that the proposed method effectively distinguishes wilted pine trees from other easily confused objects. Compared with single-date image classification, the proposed method significantly improved user's accuracy (81.2% vs. 67.7%) while maintaining the same level of producer's accuracy (84.7% vs. 82.6%).
关键词	pine wilt disease high-resolution remote sensing spatiotemporal analysis complex landscape
学科领域	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
DOI	10.3390/rs13112083
收录类别	SCI
语种	英语
WOS关键词	INDUCED TREE MORTALITY ; CHINA ; INDEX ; STAGE
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000660603700001
出版者	MDPI
文献子类	Article
出版地	BASEL
EISSN	2072-4292
资助机构	Strategic Priority Research Program of the Chinese Academy of Sciences [XDA19080304] ; Major Emergency Science and Technology Project of National Forestry and Grassland Administration [ZD202001] ; National Natural Science Foundation of China [42071320]
作者邮箱	zhangby@aircas.ac.cn ; yehc@aircas.ac.cn ; sanpangzi1228@126.com ; huangwj@aircas.ac.cn ; wubo@ibcas.ac.cn ; haozhuoqing20@mails.ucas.ac.cn ; sunhongcaf@163.com
作品OA属性	Green Published, gold
引用统计	被引频次：43[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/26540
专题	植被与环境变化国家重点实验室
作者单位	1.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China 2.Key Lab Earth Observat Hainan Prov, Sanya 572029, Peoples R China 3.Hebei Agr Univ, Coll Forestry, Baoding 071000, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 6.Natl Forestry & Grassland Adm, Gen Stn Forest & Grassland Pest Management, Shenyang 110034, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Biyao,Ye, Huichun,Lu, Wei,et al. A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery[J]. REMOTE SENSING,2021,13(11).
APA	Zhang, Biyao.,Ye, Huichun.,Lu, Wei.,Huang, Wenjiang.,Wu, Bo.,...&Sun, Hong.(2021).A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery.REMOTE SENSING,13(11).
MLA	Zhang, Biyao,et al."A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery".REMOTE SENSING 13.11(2021).

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