Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia’s Mangroves Using Deep Learning

Davide Lomeo; Minerva Singh

doi:10.3390/rs14102291

Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia’s Mangroves Using Deep Learning

Davide Lomeo, Minerva Singh

Geography

Imperial College London

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

59 Downloads (Pure)

Abstract

This paper proposes a cloud-based mangrove monitoring framework that uses Google Collaboratory and Google Earth Engine to classify mangroves in Southeast Asia (SEA) using satellite remote sensing imagery (SRSI). Three multi-class classification convolutional neural network (CNN) models were generated, showing F1-score values as high as 0.9 in only six epochs of training. Mangrove forests are tropical and subtropical environments that provide essential ecosystem services to local biota and coastal communities and are considered the most efficient vegetative carbon stock globally. Despite their importance, mangrove forest cover continues to decline worldwide, especially in SEA. Scientists have produced monitoring tools based on SRSI and CNNs to identify deforestation hotspots and drive targeted interventions. Nevertheless, although CNNs excel in distinguishing between different landcover types, their greatest limitation remains the need for significant computing power to operate. This may not always be feasible, especially in developing countries. The proposed framework is believed to provide a robust, low-cost, cloud-based, near-real-time monitoring tool that could serve governments, environmental agencies, and researchers, to help map mangroves in SEA.

Original language	English
Article number	2291
Number of pages	20
Journal	REMOTE SENSING
Volume	14
Issue number	10
DOIs	https://doi.org/10.3390/rs14102291
Publication status	Published - 10 May 2022

Keywords

Mangrove
deforestation
Convolutional Neural Networks
Google Collaboratory
Google Earth Engine
monitoring framework

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10.3390/rs14102291Licence: CC BY

Cloud-Based Monitoring and_LOMEO_Publishedonline10May2022_GOLD VoR (CC BY) Final published version, 4.44 MBLicence: CC BY

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title = "Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia{\textquoteright}s Mangroves Using Deep Learning",

abstract = "This paper proposes a cloud-based mangrove monitoring framework that uses Google Collaboratory and Google Earth Engine to classify mangroves in Southeast Asia (SEA) using satellite remote sensing imagery (SRSI). Three multi-class classification convolutional neural network (CNN) models were generated, showing F1-score values as high as 0.9 in only six epochs of training. Mangrove forests are tropical and subtropical environments that provide essential ecosystem services to local biota and coastal communities and are considered the most efficient vegetative carbon stock globally. Despite their importance, mangrove forest cover continues to decline worldwide, especially in SEA. Scientists have produced monitoring tools based on SRSI and CNNs to identify deforestation hotspots and drive targeted interventions. Nevertheless, although CNNs excel in distinguishing between different landcover types, their greatest limitation remains the need for significant computing power to operate. This may not always be feasible, especially in developing countries. The proposed framework is believed to provide a robust, low-cost, cloud-based, near-real-time monitoring tool that could serve governments, environmental agencies, and researchers, to help map mangroves in SEA.",

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Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia’s Mangroves Using Deep Learning

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