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Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion
Tian, Jinyan1,7; Wang, Le2; Yin, Dameng2; Li, Xiaojuan1,7; Diao, Chunyuan3; Gong, Huili1,7; Shi, Chen1; Menenti, Massimo4,5; Ge, Yong6; Nie, Sheng8; Ou, Yang5; Song, Xiaonan1; Liu, Xiaomeng1
2020-06-01
Source PublicationREMOTE SENSING OF ENVIRONMENT
ISSN0034-4257
Volume242Pages:15
Corresponding AuthorWang, Le(lewang@buffalo.edu)
AbstractInvasive Spartina alterniflora (S. alterniflora), a native riparian species in the U.S. Gulf of Mexico, has led to serious degradation to the ecosystem and biodiversity as well as economic losses since it was introduced to China in 1979. Although multi-temporal remote sensing offers unique capability to monitor S. alterniflora over large areas and long time periods, three major hurdle exist: (1) in the coastal zone where S. alterniflora occupies, frequent cloud coverage reduces the number of available images that can be used; (2) prominent spectral variations exist within the S. alterniflora due to phonological variations; (3) poor spectral separability between S. alterniflora and its co-dominant native species is often presented in the territories where S. alterniflora intruded in. To articulate these questions, we proposed a new pixel-based phenological feature composite method (PpfCM) based on Google Earth Engine. The Ppf-CM method was brainstormed to battle the aforementioned three hurdles as the basic unit for extracting phonological feature is individual pixel in lieu of an entire image scene. With the Ppf-CM-derived phenological feature as inputs, we took a step further to investigate the performance of the latest deep learning method as opposed to that of the conventional support vector machine (SVM); Lastly, we strive to understand how S. alterniflora has changed its spatial distribution in the Beibu Gulf of China from 1995 to 2017. As a result, we found (1) the developed Ppf-CM method can mitigate the phonological variation and augment the spectral separability between S. alterniflora and the background species regardless of the significant cloud coverage in the study area; (2) deep learning, compared to SVM, presented better potentials for incorporating the new phenological features generated from the Ppf-CM method; and (3) for the first time, we discovered a S. alterniflora invasion outbreak occurred during 1996-2001.
KeywordCloudy coastal zone Invasive species Phenology Google earth engine Remote sensing big data Deep learning
DOI10.1016/j.rse.2020.111745
WOS KeywordLANDSAT DATA ; SALT MARSHES ; IMAGE CLASSIFICATION ; LEAF SENESCENCE ; EXOTIC SPARTINA ; INDEX ; RESOLUTION ; SCENE ; LIDAR ; FIELD
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41801331] ; National Natural Science Foundation of China[41828102]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaEnvironmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000523965600013
PublisherELSEVIER SCIENCE INC
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/133919
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWang, Le
Affiliation1.Capital Normal Univ, Beijing Adv Innovat Ctr Imaging Technol, Beijing, Peoples R China
2.SUNY Buffalo, Dept Geog, Buffalo, NY 14260 USA
3.Univ Illinois, Dept Geog & Geog Informat Sci, Urbana, IL USA
4.Delft Univ Technol, Delft, Netherlands
5.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
7.Capital Normal Univ, Beijing Lab Water Resources Secur, Beijing, Peoples R China
8.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Tian, Jinyan,Wang, Le,Yin, Dameng,et al. Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion[J]. REMOTE SENSING OF ENVIRONMENT,2020,242:15.
APA Tian, Jinyan.,Wang, Le.,Yin, Dameng.,Li, Xiaojuan.,Diao, Chunyuan.,...&Liu, Xiaomeng.(2020).Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion.REMOTE SENSING OF ENVIRONMENT,242,15.
MLA Tian, Jinyan,et al."Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion".REMOTE SENSING OF ENVIRONMENT 242(2020):15.
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