气候变化领域集成服务门户(CCPortal): Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion

CCPortal

DOI	10.1016/j.rse.2020.111745
	Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion
	Tian J.; Wang L.; Yin D.; Li X.; Diao C.; Gong H.; Shi C.; Menenti M.; Ge Y.; Nie S.; Ou Y.; Song X.; Liu X.
发表日期	2020
ISSN	00344257
卷号	242
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 (Ppf-CM) 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. © 2020 The Authors
英文关键词	Cloudy coastal zone; Deep learning; Google earth engine; Invasive species; Phenology; Remote sensing big data
语种	英语
scopus关键词	Biodiversity; Coastal zones; Engines; Learning systems; Losses; Pixels; Remote sensing; Speech; Support vector machines; Google earths; Invasive species; Multi-temporal remote sensing; Phenology; Phonological features; Phonological variation; Spartina alterniflora; Spectral separability; Deep learning; biodiversity; biological invasion; coastal zone; composite; environmental degradation; native species; phenology; remote sensing; spatial distribution; spectral analysis; support vector machine; Atlantic Ocean; Gulf of Mexico; Gulf of Tonkin; Pacific Ocean; South China Sea; Spartina alterniflora
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179349
作者单位	Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, China; Department of Geography, The State University of New York at Buffalo, Buffalo, NY, United States; Department of Geography and Geographic Information Science, University of Illinois at Urbana-ChampaignIL, United States; Delft University of Technology, Delft, Netherlands; State Key Laboratory of Remote Sensing Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Beijing Laboratory of Water Resources Security, Capital Normal University, Beijing, China; Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Tian J.,Wang L.,Yin D.,et al. Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion[J],2020,242.
APA	Tian J..,Wang L..,Yin D..,Li X..,Diao C..,...&Liu X..(2020).Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion.Remote Sensing of Environment,242.
MLA	Tian J.,et al."Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion".Remote Sensing of Environment 242(2020).