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Optimal and robust vegetation mapping in complex environments using multiple satellite imagery: Application to mangroves in Southeast Asia

Citation

Xiao, H and Su, F and Fu, D and Lyne, V and Liu, G and Pan, T and Teng, J, Optimal and robust vegetation mapping in complex environments using multiple satellite imagery: Application to mangroves in Southeast Asia, International Journal of Applied Earth Observation and Geoinformation, 99 Article 102320. ISSN 1569-8432 (2021) [Refereed Article]


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Copyright Statement

2021 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. Published by Elsevier B.V. This is an open access article under the Creative Commons BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

DOI: doi:10.1016/j.jag.2021.102320

Abstract

A band selection model was described for efficient and accurate remotely-sensed vegetation mapping in cloudy mixed-vegetation areas, demonstrated with an application on mapping mangroves in Southeast Asia (SE Asia). We show how to use multi-source satellite imagery and Cloud Computing Platforms to improve mapping and computational efficiency in complex environments. A key element of the method relies upon field surveys to establish a detailed sample database that includes easily-confused land cover. The Maximal Separability and Information (MSI) model was developed to select key bands for target land cover classification from multiple satellite imagery based on two principles: 1. maximize separability of the target cover from other land cover; and 2. maximize and prioritize information from band combinations. Application of the MSI model to map mangroves in SE Asia using three optical and SAR data systems (Landsat OLI, Sentinel-2 and Sentinel-1) showed: 1. Sentinel-2 is better at classifying mangrove than Landsat and Sentinel-1; and 2. SWIR, NIR and Red bands (with SWIR in particular) are effective in separating mangrove from other vegetation. The MSI-mapped mangroves showed lower computation cost compared to using all bands from individual satellites, and higher accuracy (above 90%) when applied to SE Asia. It was robust in tolerating smaller sample sizes, thereby demonstrating computational feasibility and substantial improvements with the MSI model for large-scale land cover mapping in complex environments.

Item Details

Item Type:Refereed Article
Keywords:remote sensing band selection, large-scale mapping ,mangrove mapping, multi-source data
Research Division:Health Sciences
Research Group:Health services and systems
Research Field:Implementation science and evaluation
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the environmental sciences
UTAS Author:Lyne, V (Dr Vincent Lyne)
ID Code:152355
Year Published:2021
Web of Science® Times Cited:3
Deposited By:Fisheries and Aquaculture
Deposited On:2022-08-17
Last Modified:2022-09-20
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