Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine

Abstract

Mangroves are among the most productive ecosystems in existence, with many ecological benefits. Therefore, generating accurate thematic maps from mangrove ecosystems is crucial for protecting, conserving, and reforestation planning for these valuable natural resources. In this paper, Sentinel-1 and Sentinel-2 satellite images were used in synergy to produce a detailed mangrove ecosystem map of the Hara protected area, Qeshm, Iran, at 10 m spatial resolution within the Google Earth Engine (GEE) cloud computing platform. In this regard, 86 Sentinel-1 and 41 Sentinel-2 data, acquired in 2019, were employed to generate seasonal optical and synthetic aperture radar (SAR) features. Afterward, seasonal features were inserted into a pixel-based random forest (RF) classifier, resulting in an accurate mangrove ecosystem map with average overall accuracy (OA) and Kappa coefficient (KC) of 93.23% and 0.92, respectively, wherein all classes (except aerial roots) achieved high producer and user accuracies of over 90%. Furthermore, comprehensive quantitative and qualitative assessments were performed to investigate the robustness of the proposed approach, and the accurate and stable results achieved through cross-validation and consistency checks confirmed its robustness and applicability. It was revealed that seasonal features and the integration of multi-source remote sensing data contributed towards obtaining a more reliable mangrove ecosystem map. The proposed approach relies on a straightforward yet effective workflow for mangrove ecosystem mapping, with a high rate of automation that can be easily implemented for frequent and precise mapping in other parts of the world. Overall, the proposed workflow can further improve the conservation and sustainable management of these valuable natural resources.

Land Use/Land Cover Change and Their Driving Factors in the Yellow River Basin of Shandong Province Based on Google Earth Engine from 2000 to 2020

“…The basic principle of this algorithm is to construct a collection of decision tree classifiers, each decision tree would give a classification choice by using the mechanism of multiple decision tree voting to improve the problem of easy overfitting of decision trees as well as the use of majority voting mechanism strategy to obtain the final output [52,53]. Compared with other machine learning methods, RF classification algorithm has better robustness and can run effectively on large datasets [54][55][56]. Some scholars have carried out relevant research on LULC classification by using the RF algorithm on the GEE platform and achieved excellent research results [57][58][59].…”

Mapping Phragmites australis Aboveground Biomass in the Momoge Wetland Ramsar Site Based on Sentinel-1/2 Images

“…Studies have shown that the integration of Sentinel-1 and Sentinel-2 can improve the accuracy of mapping land cover [39,40]. Two sensitive vegetation indices, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), were selected based on Sentinel-2 data.…”

Unimodal and Multimodal Perception for Forest Management: Review and Dataset

“…Several works proposed this approach as a way of assessing quantitatively the forest yield, forest biomass, and carbon dynamics from high-resolution remote sensing or UAV-based imagery [15][16][17][18][19][20][21][22]. The inventory from a certain ecosystem can also be estimated by mapping it through satellite images, as was made in [23] for a mangrove ecosystem. The authors used a pixelbased random forest classifier that resulted in a mangrove map with an overall accuracy of 93%.…”