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Spatio-temporal probability threshold method of remote sensing for mangroves mapping in China

  • role： First author第一作者
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

  • Email：2224174409@qq.com
  • Introduction：黄可，研究方向为遥感影像处理和信息提取。E-mail： 2224174409@qq.com
HUANG Ke,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

MENG Xiangzhen,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

  • Email：yanggang@nbu.edu.cn
  • Introduction：杨刚，研究方向为遥感影像数据质量改善与信息提取理论和方法、遥感滨海健康监测技术与应用。E-mail： yanggang@nbu.edu.cn
YANG Gang*,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

SUN Weiwei

résumé

As an appropriate forest community in tropical and subtropical coastal zone, mangrove has unique ecological function and great social and economic value. However, mangroves globally are decreasing at an average rate of 1% per year and are facing threats, such as the reduction of biodiversity and the degradation of ecosystem service functions. Mangroves in China have experienced repeated destruction and protection, and remote sensing monitoring can provide scientific support and decision-making reference suggestions for implementing large-scale mangrove ecosystem protection and restoration in China. Based on Google Earth Engine platform, this study proposed a Spatio-temporal Probability Threshold Method to extract mangrove extent in China, and it is conducive to analyzing the temporal and spatial changing trends of mangroves in China.In this study, we selected 516 images of Landsat 8 in 2015. We used unsupervised classification for land-water separation, and then generated the potential growth area of mangroves. A multi-feature decision tree classification method was constructed based on multiple indexes and spectral information to extract rough mangrove growth extent, and the mangrove growth probability was further calculated based on long time-series data. The probability threshold was determined through experiments to extract precise mangrove extent. In addition, we set up four comparative experiments for mangrove extraction, using two decision tree classification methods (based on spectral indices only and based on original bands only) and two supervised classification methods (CART and SVM).Results show that the best mangrove probability threshold is 0.5, and the producer’s accuracy for mangrove is 90.36%. CAS_Mangrove dataset has the highest producer’s accuracy for mangrove (91.73%), but the details of the edge are inaccurate; the producer’s accuracy for mangrove of GMW dataset is the lowest (64.64%), thereby ignoring the young and scattered mangroves. All methods of four comparative experiments overestimate the mangrove extent in varying degrees. The total area of mangroves in China in 2015 extracted by the proposed method is 21932 hectares.This study proposed a Spatio-temporal Probability Threshold Method for mangrove extraction, considering the impact of tidal inundation from a new perspective through the mangrove growth probability. This method has high accuracy (90.36%) of mangrove extraction, and it can extract young and scattered mangroves effectively. According to the study, the distribution of mangrove in China in 2015 was obtained, and the total area of mangroves in China is 21932 hectares. The mangroves are mainly distributed in Guangxi and Guangdong, accounting for 73.22 percent of the country’s area. Compared with the method of selecting images at low tide for mangrove extraction, Spatio-temporal Probability Threshold Method makes full use of Landsat data, which are simpler and faster, and avoids the high uncertainty in the artificial coastal area.

mots-clés

remote sensing;Google Earth Engine;Landsat;mangroves;long time-series;CMRI

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