Abstract
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.
Highlights
The objectives of this study were to: (1) develop a new pixel-based phenological feature composite method (Ppf-CM) based on Google Earth Engine (GEE) with aims to minimize the cloud effects in the coastal zone; (2) investigate if the performance of the deep learning method can be enhanced with the proposed new pixel-based phenological feature; (3) understand how S. alterniflora has changed its spatial distribution in the Beibu Gulf of China from 1995 to 2017
This study presents that: (1) Ppf-CM accommodates the spatial phenology variability of S. alterniflora as well as enhances the spectral separability, and eases the problems caused by the scarcity of cloud-free Landsat scenes; (2) incorporation of the new phenological feature yielded from Ppf-CM as input data can improve the performance of the deep learning classifier; (3) This is the first time that invasion outbreak of the S. alterniflora was discovered at the regional scale
This paper proposed a new pixel-based cloud-minimizing phenological feature (Ppf-CM) for deep learning to understand Spartina alterniflora invasion in GEE
Summary
Invasive plants pose serious threats to biodiversity and environmental health by interfering with indigenous vegetation community, animal habitats, soil properties, water quality, and biogeochemical cycles (Dukes and Mooney, 2004; Hartig et al, 2002; Vitousek et al, 1997). Gulf of Mexico but has been deemed to be invasive species in other countries where it was introduced to (Callaway and Josselyn, 1992; Lu and Zhang, 2013). Among the countries that S. alterniflora has intruded, China turns out creating the severest impacts to local environments (Qiu, 2013). S. alterniflora was first introduced to China in 1979 from the United States for the primary sake of reducing soil erosions (Li and Zhang, 2008). It has caused serious damages to the natural environment and ecosystem, such as water and soil pollution, threatening the biodiversity, and causing economic losses (Wan et al, 2009)
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