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

Jinyan Tian, Le Wang*, Dameng Yin, Xiaojuan Li, Chunyuan Diao, Huili Gong, Chen Shi, Massimo Menenti, Yong Ge, More Authors

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

41 Citations (Scopus)
107 Downloads (Pure)


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.

Original languageEnglish
Article number111745
Number of pages15
JournalRemote Sensing of Environment
Publication statusPublished - 2020


  • Cloudy coastal zone
  • Deep learning
  • Google earth engine
  • Invasive species
  • Phenology
  • Remote sensing big data


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