A Review of Applying Drones and Remote Sensing Technology in Mangrove Ecology

Abstract

Mangrove forests are one of the ecosystems with the richest biodiversity and the highest functional value of ecosystem services in the world. For mangrove research, it is particularly important to facilitate mangrove mapping, plant species classification, biomass and carbon sink estimation using remote sensing technologies. Recently, more and more studies have combined unmanned aerial vehicles and remote sensing technology to estimate plant traits and the biomass of mangrove forests. Various multispectral and hyperspectral data are used to establish various vegetation indexes for plant classification, and data models for biomass estimation and carbon sink calculation. This study systematically reviews the use of remote sensing and unmanned aerial vehicles in mangrove studies during the past three decades based on 2,424 peer-reviewed papers. By synthesizing these studies, we identify the pros and cons of different indices and models developed from remote sensing technologies by sorting out past cases. Specifically, we review the use of remote sensing technologies in mapping the past and present area, plant species composition, biomass of mangrove forests and examines the threats to the degradation of mangrove forests. Our findings reveal that there is increasing integration of machine learning and remote sensing to facilitate mangrove mapping and species identification. Moreover, multiples sources of remote sensing data tend to be combined to improve species classification accuracy and enhance the precision of mangrove biomass estimates when integrated with field-based data.

Plant phenotyping relevance

マングローブの種分類、バイオマス推定、植物形質推定に用いるドローン・リモートセンシング手法を体系的にレビューしており、植物状態の取得・推定方法が中心である。

Code and data availability

This is a review article on drones and remote sensing in mangrove ecology. It contains no paper-specific phenotype datasets, imagery, code, or models; all tables summarize cited prior studies. The only availability statement is 'Data are available upon request sent to Xiaoguang Ouyang,' with no public URL or described,

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