Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion

doi:10.1016/j.rse.2020.111745

	Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion
	Tian, Jinyan 1,3; Wang, Le 8; Yin, Dameng 8; Li, Xiaojuan 1,3; Diao, Chunyuan 7; Gong, Huili 1,3; Shi, Chen 1; Menenti, Massimo 5,6; Ge, Yong 4; Nie, Sheng 2
刊名	REMOTE SENSING OF ENVIRONMENT
	2020-06-01
卷号	242 页码:15
关键词	Cloudy coastal zone Invasive species Phenology Google earth engine Remote sensing big data Deep learning
ISSN号	0034-4257
DOI	10.1016/j.rse.2020.111745
通讯作者	Wang, Le(lewang@buffalo.edu)
英文摘要	Invasive 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.
资助项目	National Natural Science Foundation of China[41801331] ; National Natural Science Foundation of China[41828102]
WOS关键词	LANDSAT DATA ; SALT MARSHES ; IMAGE CLASSIFICATION ; LEAF SENESCENCE ; EXOTIC SPARTINA ; INDEX ; RESOLUTION ; SCENE ; LIDAR ; FIELD
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000523965600013
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/133918]
专题	中国科学院地理科学与资源研究所
通讯作者	Wang, Le
作者单位	1.Capital Normal Univ, Beijing Adv Innovat Ctr Imaging Technol, Beijing, Peoples R China 2.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing, Peoples R China 3.Capital Normal Univ, Beijing Lab Water Resources Secur, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 5.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing, Peoples R China 6.Delft Univ Technol, Delft, Netherlands 7.Univ Illinois, Dept Geog & Geog Informat Sci, Urbana, IL USA 8.SUNY Buffalo, Dept Geog, Buffalo, NY 14260 USA
推荐引用方式 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.