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

Research output: Contribution to journalArticlepeer-review

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Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia’s Mangroves Using Deep Learning. / Lomeo, Davide; Singh, Minerva.

In: REMOTE SENSING, Vol. 14, No. 10, 2291, 10.05.2022.

Research output: Contribution to journalArticlepeer-review

Harvard

Lomeo, D & Singh, M 2022, 'Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia’s Mangroves Using Deep Learning', REMOTE SENSING, vol. 14, no. 10, 2291. https://doi.org/10.3390/rs14102291

APA

Lomeo, D., & Singh, M. (2022). Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia’s Mangroves Using Deep Learning. REMOTE SENSING, 14(10), [2291]. https://doi.org/10.3390/rs14102291

Vancouver

Lomeo D, Singh M. Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia’s Mangroves Using Deep Learning. REMOTE SENSING. 2022 May 10;14(10). 2291. https://doi.org/10.3390/rs14102291

Author

Lomeo, Davide ; Singh, Minerva. / Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia’s Mangroves Using Deep Learning. In: REMOTE SENSING. 2022 ; Vol. 14, No. 10.

Bibtex Download

@article{dc857731370f417fa2db9b63eb9b4a5a,
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.",
keywords = "Mangrove, deforestation, Convolutional Neural Networks, Google Collaboratory, Google Earth Engine, monitoring framework",
author = "Davide Lomeo and Minerva Singh",
year = "2022",
month = may,
day = "10",
doi = "10.3390/rs14102291",
language = "English",
volume = "14",
journal = "REMOTE SENSING",
issn = "2072-4292",
publisher = "Multidisciplinary Digital Publishing Institute",
number = "10",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - Cloud-Based Monitoring and Evaluation of the Spatial-Temporal Distribution of Southeast Asia’s Mangroves Using Deep Learning

AU - Lomeo, Davide

AU - Singh, Minerva

PY - 2022/5/10

Y1 - 2022/5/10

N2 - 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.

AB - 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.

KW - Mangrove

KW - deforestation

KW - Convolutional Neural Networks

KW - Google Collaboratory

KW - Google Earth Engine

KW - monitoring framework

UR - https://www.mdpi.com/2072-4292/14/10/2291/htm

UR - http://www.scopus.com/inward/record.url?scp=85130535865&partnerID=8YFLogxK

U2 - 10.3390/rs14102291

DO - 10.3390/rs14102291

M3 - Article

VL - 14

JO - REMOTE SENSING

JF - REMOTE SENSING

SN - 2072-4292

IS - 10

M1 - 2291

ER -

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