Mapping coastal wetlands in the Yellow River Delta, China during 2008–2019: impacts of valid observations, harmonic regression, and critical months. Issue 20 (18th October 2021)

• Record Type: Journal Article
• Authors: Wang, Zhanpeng; Ke, Yinghai; Chen, Mengmeng; Zhou, Demin; Zhu, Lin; Bai, Junhong
• Is Part Of: International journal of remote sensing. Volume 42:Issue 20(2021)
• Page Start: 7880
• Page End: 7906
• Publication Date: 2021-10-18
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2021.1966852
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Spartina alterniflora has become one of the top exotic invasive plants in coastal China. Accurate and timely mapping of coastal wetlands is critical to understand the spatiotemporal dynamics of S. alterniflora invasion and native species degradation. Due to the humid weather and frequent cloud cover in coastal wetlands, pixel-based classifications based on time-series satellite observations have been increasingly utilized to mitigate this problem. However, the spatial and temporal variations in valid observations might affect pixel-based classification accuracies, while this issue has been paid little attention in previous research. In this paper, we conducted annual mapping of the Yellow River Delta (YRD) wetland from 2008 to 2019 based on time-series Landsat 5/7/8 images using harmonic regression analysis and random forest classification on Google Earth Engine. We further analysed how valid observations and temporal distribution of observations affected the classification accuracy. Our results showed that the overall classification accuracies ranged from 87.25% in 2012 to 92.36% in 2018. The mapping results showed that S. alterniflora expanded from 39.91 ha to 4672.38 ha from 2008 to 2019, encroaching seagrass beds with 902.32 ha and encroaching S.salsa and bare flat with 3730.15 ha. This is the first time that maps of coastal wetlands of YRD are produced at an annual time step. Our analysis showed that the number of valid observations affected classification accuracies both temporally and spatially. Years with higher density of observations witnessed higher overall accuracies compared to the years with fewer observations. Pixels with more observations had a higher chance to be correctly classified compared to the pixels with fewer observations. Harmonic regression features helped improve classification accuracies (increase in overall accuracies from 0.15% to 3.32%), especially for vegetation types (increase in F -score from 0.19% to 3.86%). Greater number of valid observations enhances the importance of harmonic regression features in classification. The critical months identified were March, July, and October, and the combinations of these months achieved better accuracies (91.24%) than using all other observations (89.68%) in 2018, suggesting that temporal distributions of observations could be more important than the number of observations for harmonic-based classification of coastal wetlands such as YRD.