Cloud-based monitoring and evaluation of the Spatial-temporal distribution of Southeast Asia’s man-groves using deep learning
File(s)
Published version
Author(s)
Davide, Lomeo
Singh, Minerva
Type
Journal Article
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.
Date Issued
2022-05-10
Date Acceptance
2022-05-08
Citation
Remote Sensing, 2022, 14 (10)
ISSN
2072-4292
Publisher
MDPI AG
Journal / Book Title
Remote Sensing
Volume
14
Issue
10
Copyright Statement
© 2022 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/).
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/).
License URL
Subjects
Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Technology
Environmental Sciences
Geosciences, Multidisciplinary
Remote Sensing
Imaging Science & Photographic Technology
Environmental Sciences & Ecology
Geology
mangrove
deforestation
convolutional neural networks
Google Collaboratory
Google Earth Engine
monitoring framework
CONVOLUTIONAL NEURAL-NETWORKS
BIG DATA APPLICATIONS
GOOGLE EARTH ENGINE
SCENE CLASSIFICATION
FORESTS
LEVEL
DEFORESTATION
0203 Classical Physics
0406 Physical Geography and Environmental Geoscience
0909 Geomatic Engineering
Publication Status
Published
Article Number
ARTN 2291