152355 - Optimal and robust vegetation mapping.pdf (2.52 MB)
Optimal and robust vegetation mapping in complex environments using multiple satellite imagery: Application to mangroves in Southeast Asia
journal contribution
posted on 2023-05-21, 11:59 authored by Xiao, H, Su, F, Fu, D, Vincent LyneVincent Lyne, Liu, G, Pan, T, Teng, JA 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.
History
Publication title
International Journal of Applied Earth Observation and GeoinformationVolume
99Article number
102320Number
102320Pagination
1-13ISSN
1569-8432Department/School
Institute for Marine and Antarctic StudiesPublisher
Elsevier BVPlace of publication
NetherlandsRights 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/).Repository Status
- Open