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

Remote sensing (Basel, Switzerland), 2021-06, Vol.13 (13), p.2565 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs13132565

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Title:
Mangrove Ecosystem Mapping Using Sentinel-1 and Sentinel-2 Satellite Images and Random Forest Algorithm in Google Earth Engine
Author: Ghorbanian, Arsalan ; Zaghian, Soheil ; Asiyabi, Reza Mohammadi ; Amani, Meisam ; Mohammadzadeh, Ali ; Jamali, Sadegh
Subjects: aerial roots ; Algorithms ; Automation ; Classification ; Cloud computing ; Coasts ; Conservation ; Data acquisition ; Datasets ; Earth and Related Environmental Sciences ; Ecosystems ; Engineering and Technology ; Environmental Engineering ; Fjärranalysteknik ; Geovetenskap och miljövetenskap ; Google Earth Engine ; Google Earth Engine (GEE) ; mangrove ecosystem ; Mangroves ; Mapping ; Natural resource management ; Natural resources ; Natural Sciences ; Naturgeografi ; Naturresursteknik ; Naturvetenskap ; optical ; Physical Geography ; Polygons ; Protected areas ; Random Forest (RF) ; Reforestation ; Remote Sensing ; Robustness ; Satellite imagery ; Satellites ; Sentinel ; Spatial discrimination ; Spatial resolution ; Sustainability management ; Synthetic aperture radar ; synthetic aperture radar (SAR) ; Teknik ; Thematic mapping ; Unmanned aerial vehicles ; Workflow
Is Part Of: Remote sensing (Basel, Switzerland), 2021-06, Vol.13 (13), p.2565
Description: 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.
Publisher: Basel: MDPI AG
Language: English
Identifier: ISSN: 2072-4292
EISSN: 2072-4292
DOI: 10.3390/rs13132565
Source: DOAJ Directory of Open Access Journals
AUTh Library subscriptions: ProQuest Central
SWEPUB Freely available online
ROAD: Directory of Open Access Scholarly Resources

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

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