A Study on Spatial Distribution Extraction of Tidal Inundated Mangroves Based on High and Low Tide Level Images

Author:

You Haotian12,You Qixu1,Tang Xu1ORCID,Liu Yao1,Chen Jianjun1,Wang Feng1

Affiliation:

1. College of Geomatics and Geoinformation, Guilin University of Technology, No. 12 Jian’gan Road, Guilin 541006, China

2. Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology, No. 12 Jian’gan Road, Guilin 541004, China

Abstract

A majority of mangroves are located in the coastal intertidal zone and are subject to tidal periodic inundation. However, the previous vegetation indices used for extracting the spatial distribution of mangroves were not able to effectively extract submerged mangroves, and the applicability of the vegetation indices used on different spatial resolution images obtained from different sensors was not verified. In this study, a new vegetation index, namely the intertidal mangrove identification indices (IMIIs), was proposed, based on GF-2 images of high and low tide levels. Meanwhile, other commonly used vegetation indices were also extracted. All the vegetation indices were used to extract the spatial distribution of mangroves under tidal inundation, and applicability tests of the vegetation indices were conducted on Sentinel-2 images in three different regions. It was found that the IMIIs proposed based on GF-2 images of high and low tide levels can extract submerged mangroves relatively well, and the spatial distribution extraction results of mangroves are better than those of other vegetation indices, with IMII2 outperforming IMII1. At the same time, IMIIs have good applicability in medium resolution Sentinel-2 images, and there are relatively large differences in the extraction results of mangrove spatial distribution among different vegetation indices in areas with significant impact of tidal inundation. Among all vegetation indices, the extraction results of IMIIs are relatively superior. In most cases, multi variables collaborative application can improve the accuracy of mangrove spatial distribution extraction results. Based on the results of this study, it was concluded that the IMIIs proposed in this study can accurately extract the spatial distribution of mangroves inundated by tides from both medium- and high-resolution images, providing accurate basic data for effective management and scientific protection of mangrove resources.

Funder

National Natural Science Foundation of China

Guangxi Natural Science Foundation

Guangxi Science and Technology Base and Talent Project

BaGuiScholars program of the provincial government of Guangxi

Publisher

MDPI AG

Subject

Forestry

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