• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
成果搜索

author:

Wu, L. (Wu, L..) [1] | Wu, W. (Wu, W..) [2] (Scholars:吴文挺)

Indexed by:

EI Scopus PKU CSCD

Abstract:

Ecological catastrophes occurred in China's coastal region as a result of rapid invasion of Spartina alterniflora. It's critical to monitor the spatiotemporal dynamics of Spartina alterniflora. Remote sensing has a superior ability to monitor the dynamics of intertidal wetland vegetation on a broad scale as compared to traditional surveying techniques. However, it is of great challenges to precisely determine the amount of Spartina alterniflora using a single-phase image due to the spectral similarities of vegetation with various species. The extensive remote sensing data still need to be further explored in order to better understand the temporal and geographical characteristics of vegetation. The phonological cycle of the plant can be tracked using the time series remote sensing data, which can then provide additional temporal features for vegetation classification. However, the impacts of cloudy weather and tides in intertidal zones make it challenging to extract the phenological parameters, even though previous phenology-based algorithms can reduce misclassification caused by spectral similarity. In this study, we suggested a method to reduce the impact of cloudy weather and tides on time series remote sensing data by integrating the Maximum-value composite algorithm with Savitzky-Golay (S-G) filter. Then, based on the NDVI time series derived from archived Sentinel-2 data, the two-term Fourier function is used to determine the optimal time window and phenological parameters for the classification of Spartina alterniflora. The landscape of Fujian's coastal zone and the distribution of Spartina alterniflora were finally mapped using a random forest classifier on the Google Earth Engine (GEE) platform. The results revealed that, with an overall accuracy of 89.81% and a Kappa coefficient of 0.88, respectively, the beginning of the growing season (June to July) is the optimum time window for classifying intertidal vegetation. It demonstrates how effectively this technology may be used to monitor Spartina alterniflora on a broad scale in coastal areas. According to the results, Spartina alterniflora had a total area of roughly 100.78 km2 in Fujian's coastal zone in 2020, with the most of it being concentrated in Ningde, Fuzhou, Quanzhou, and Zhangzhou. The largest patches of Spartina alterniflora, with a distribution of 37.79%, were found in Ningde. Due to the coastal geomorphology, Spartina alterniflora displayed a large diversity in its spatial distribution along the Fujian coast. The demonstration in Fujian Province shows that the suggested method can offer significant potential for long-term and extensive spatial scale monitoring of Spartina alterniflora dynamics, which could assist coastal high-quality and sustainable development. © 2023 Journal of Geo-Information Science. All rights reserved.

Keyword:

Characteristics of spatial distribution GEE Maximum-value Composite NDVI time series Optimum time window Phenology S-G filtering algorithm Spartina alterniflora

Community:

  • [ 1 ] [Wu L.]National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350108, China
  • [ 2 ] [Wu W.]National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, The Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350108, China

Reprint 's Address:

Email:

Show more details

Related Keywords:

Source :

Journal of Geo-Information Science

ISSN: 1560-8999

CN: 11-5809/P

Year: 2023

Issue: 3

Volume: 25

Page: 606-624

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 5

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 6

Online/Total:93/10015301
Address:FZU Library(No.2 Xuyuan Road, Fuzhou, Fujian, PRC Post Code:350116) Contact Us:0591-22865326
Copyright:FZU Library Technical Support:Beijing Aegean Software Co., Ltd. 闽ICP备05005463号-1