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

REMOTE SENSING OF ENVIRONMENT

卷:242

Article

Tian, Jinyan^1,7  Wang, Le²  Yin, Dameng²  Li, Xiaojuan^1,7  Diao, Chunyuan³  Gong, Huili^1,7  Shi, Chen¹  Menenti, Massimo^4,5  Ge, Yong⁶  Nie, Sheng⁸  Ou, Yang⁵  Song, Xiaonan¹  Liu, Xiaomeng¹ 

[1] 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

关键词: Cloudy coastal zone;    Invasive species;    Phenology;    Google earth engine;    Remote sensing big data;    Deep learning;   

DOI  :  10.1016/j.rse.2020.111745

来源: Elsevier

【 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.

【 授权许可】

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