Monitoring Invasion Process of Spartina alterniflora by Seasonal Sentinel-2 Imagery and an Object-Based Random Forest Classification

Yanlin Tian,Chao Wang,Dehua Mao,Zongming Wang,Mingming Jia,Baojia Du

doi:10.3390/rs12091383

Abstract

In the late 1990s, the exotic plant Spartina alterniflora (S. alterniflora), was introduced to the Zhangjiang Estuary of China for tidal zone reclamation and protection. However, it invaded rapidly and has caused serious ecological problems. Accurate information on the seasonal invasion of S. alterniflora is vital to understand invasion pattern and mechanism, especially at a high temporal resolution. This study aimed to explore the S. alterniflora invasion process at a seasonal scale from 2016 to 2018. However, due to the uncertainties caused by periodic inundation of local tides, accurately monitoring the spatial extent of S. alterniflora is challenging. Thus, to achieve the goal and address the challenge, we firstly built a high-quality seasonal Sentinel-2 image collection by developing a new submerged S. alterniflora index (SAI) to reduce the errors caused by high tide fluctuations. Then, an object-based random forest (RF) classification method was applied to the image collection. Finally, seasonal extents of S. alterniflora were captured. Results showed that (1) the red edge bands (bands 5, 6, and 7) of Sentinel-2 imagery played critical roles in delineating submerged S. alterniflora; (2) during March 2016 to November 2018, the extent of S. alterniflora increased from 151.7 to 270.3 ha, with an annual invasion rate of 39.5 ha; (3) S. alterniflora invaded with a rate of 31.5 ha/season during growing season and 12.1 ha/season during dormant season. To our knowledge, this is the first study monitoring S. alterniflora invasion process at a seasonal scale during continuous years, discovering that S. alterniflora also expands during dormant seasons. This discovery is of great significance for understanding the invasion pattern and mechanism of S. alterniflora and will facilitate coastal biodiversity conservation efforts.

Highlights

Spartina alterniflora (S. alterniflora) was introduced to China from North America in 1979 for the purpose of stabilizing seashore, reclaiming tidal land, and improving soil quality [1]
Multiyear seasonal Setinel-2 imagery was combined with random forest (RF) algorithm and object-based image analysis (OBIA) classification method and used to monitor the S. alterniflora invasion process at a continuous seasonal scale during 2016 to 2018
This is the first study to extract submerged S. alterniflora from the water by developing an S. alterniflora index (SAI) derived from reflectance peaks between red edge bands, narrow NIR, and SWIR2 in Sentinel-2 images to remove tide influences

Summary

Introduction

Spartina alterniflora (S. alterniflora) was introduced to China from North America in 1979 for the purpose of stabilizing seashore, reclaiming tidal land, and improving soil quality [1]. During the past three decades, S. alterniflora has been aggressively invading native coastal vegetation with an invasion rate of 137 km per decade [2]. With increasing awareness of the negative impacts of S. alterniflora, local and central governments are paying close attention to managing S. alterniflora invasion. Comprehensive management relies on detailed continuous information of S. alterniflora distributions, especially at a high temporal resolution [6]. Obtaining such information is a great challenge due to the high spatiotemporal variation of S. alterniflora in complex coastal environments [1]

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Methods

Results

Discussion

Conclusion