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

文献类型：期刊论文

作者Zhang, Biyao; Ye, Huichun5; Lu, Wei1; Huang, Wenjiang5,6; Wu, Bo2,6; Hao, Zhuoqing6; Sun, Hong3
刊名REMOTE SENSING
出版日期2021
卷号13期号:11
关键词pine wilt disease high-resolution remote sensing spatiotemporal analysis complex landscape
DOI10.3390/rs13112083
文献子类Article
英文摘要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%).
学科主题Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
出版地BASEL
电子版国际标准刊号2072-4292
WOS关键词INDUCED TREE MORTALITY ; CHINA ; INDEX ; STAGE
WOS研究方向Science Citation Index Expanded (SCI-EXPANDED)
语种英语
WOS记录号WOS:000660603700001
出版者MDPI
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]
源URL[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.Key Lab Earth Observat Hainan Prov, Sanya 572029, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
4.Natl Forestry & Grassland Adm, Gen Stn Forest & Grassland Pest Management, Shenyang 110034, Peoples R China
5.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
6.Hebei Agr Univ, Coll Forestry, Baoding 071000, 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).

入库方式： OAI收割

来源：植物研究所

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