17 January 2020 Combination of Google Earth imagery and Sentinel-2 data for mangrove species mapping
Hongzhong Li, Yu Han, Jinsong Chen
Author Affiliations +
Abstract

Knowledge gained about mangrove species mapping is essential to understand mangrove species’ development and to better estimate their ecological service value. Spectral bands and spatial resolution of remote sensing data are two important factors for accurate discrimination of mangrove species. Mangrove species classification in Shenzhen Bay, China, was performed using Sentinel-2 (S2) multispectral instrument (MSI) data and Google Earth (GE) high-resolution imagery as data sources, and their suitability in mapping mangrove forest at a species level was examined. In the classification feature groups, the spectral bands were from the S2 MSI data and the textural features were based on GE imagery. The support vector machine classifier was used in mangrove species classification processing with eight groups of features, which were based on different S2 spectral bands and different GE spatial resolution textural features. The highest overall accuracy of our mapping results was 78.57% and the kappa coefficient was 0.74, which indicated great potential for using the combination of S2 MSI and GE imagery for distinguishing and mapping mangrove species.

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2020/$28.00 © 2020 SPIE
Hongzhong Li, Yu Han, and Jinsong Chen "Combination of Google Earth imagery and Sentinel-2 data for mangrove species mapping," Journal of Applied Remote Sensing 14(1), 010501 (17 January 2020). https://doi.org/10.1117/1.JRS.14.010501
Received: 4 July 2019; Accepted: 16 December 2019; Published: 17 January 2020
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Cited by 5 scholarly publications.
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KEYWORDS
Associative arrays

Spatial resolution

Multispectral imaging

Image classification

Feature extraction

Remote sensing

Agriculture

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