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Artificial Intelligence Mangrove Monitoring System Based on Deep Learning and Sentinel-2 Satellite Data in the UAE (2017-2024)

Linlin Tan, Haishan Wu

TL;DR

The paper addresses the need for scalable, region-specific mangrove monitoring in the UAE by applying a UNet++-based semantic segmentation framework to Sentinel-2 time-series data. It demonstrates strong segmentation performance (mIoU 87.8%) and delivers a 10 m UAE mangrove dataset (2020) by fusing Global Mangrove Watch labels with high-resolution manual refinements. The study quantifies spatiotemporal changes from 2017 to 2024, documenting a UAE mangrove area increase to 9,142.21 ha and corresponding blue carbon sequestration of approximately 194,383.42 t C (~713,367.36 t CO2), with Abu Dhabi driving much of the growth. The approach provides a practical, transferable workflow for arid-coast mangrove monitoring and supports policy and restoration planning in the UAE and similar regions.

Abstract

Mangroves play a crucial role in maintaining coastal ecosystem health and protecting biodiversity. Therefore, continuous mapping of mangroves is essential for understanding their dynamics. Earth observation imagery typically provides a cost-effective way to monitor mangrove dynamics. However, there is a lack of regional studies on mangrove areas in the UAE. This study utilizes the UNet++ deep learning model combined with Sentinel-2 multispectral data and manually annotated labels to monitor the spatiotemporal dynamics of densely distributed mangroves (coverage greater than 70%) in the UAE from 2017 to 2024, achieving an mIoU of 87.8% on the validation set. Results show that the total mangrove area in the UAE in 2024 was approximately 9,142.21 hectares, an increase of 2,061.33 hectares compared to 2017, with carbon sequestration increasing by approximately 194,383.42 tons, equivalent to fixing about 713,367.36 tons of carbon dioxide. Abu Dhabi has the largest mangrove area and plays a dominant role in the UAE's mangrove growth, increasing by 1,855.6 hectares between 2017-2024, while other emirates have also contributed to mangrove expansion through stable and sustainable growth in mangrove areas. This comprehensive growth pattern reflects the collective efforts of all emirates in mangrove restoration.

Artificial Intelligence Mangrove Monitoring System Based on Deep Learning and Sentinel-2 Satellite Data in the UAE (2017-2024)

TL;DR

The paper addresses the need for scalable, region-specific mangrove monitoring in the UAE by applying a UNet++-based semantic segmentation framework to Sentinel-2 time-series data. It demonstrates strong segmentation performance (mIoU 87.8%) and delivers a 10 m UAE mangrove dataset (2020) by fusing Global Mangrove Watch labels with high-resolution manual refinements. The study quantifies spatiotemporal changes from 2017 to 2024, documenting a UAE mangrove area increase to 9,142.21 ha and corresponding blue carbon sequestration of approximately 194,383.42 t C (~713,367.36 t CO2), with Abu Dhabi driving much of the growth. The approach provides a practical, transferable workflow for arid-coast mangrove monitoring and supports policy and restoration planning in the UAE and similar regions.

Abstract

Mangroves play a crucial role in maintaining coastal ecosystem health and protecting biodiversity. Therefore, continuous mapping of mangroves is essential for understanding their dynamics. Earth observation imagery typically provides a cost-effective way to monitor mangrove dynamics. However, there is a lack of regional studies on mangrove areas in the UAE. This study utilizes the UNet++ deep learning model combined with Sentinel-2 multispectral data and manually annotated labels to monitor the spatiotemporal dynamics of densely distributed mangroves (coverage greater than 70%) in the UAE from 2017 to 2024, achieving an mIoU of 87.8% on the validation set. Results show that the total mangrove area in the UAE in 2024 was approximately 9,142.21 hectares, an increase of 2,061.33 hectares compared to 2017, with carbon sequestration increasing by approximately 194,383.42 tons, equivalent to fixing about 713,367.36 tons of carbon dioxide. Abu Dhabi has the largest mangrove area and plays a dominant role in the UAE's mangrove growth, increasing by 1,855.6 hectares between 2017-2024, while other emirates have also contributed to mangrove expansion through stable and sustainable growth in mangrove areas. This comprehensive growth pattern reflects the collective efforts of all emirates in mangrove restoration.

Paper Structure

This paper contains 18 sections, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Study Area and Materials
  3. Overview of the Study Area
  4. Data Sources and Preprocessing
  5. Remote Sensing Image Data
  6. Label Data Acquisition and Optimization
  7. Methodology
  8. UNet++ Model Architecture
  9. Model Implementation
  10. Training Strategy Optimization
  11. Results
  12. Accuracy Evaluation
  13. Analysis of Mangrove Changes in the UAE (2017-2024)
  14. Analysis of Changes in Mangrove Area in Each Emirate
  15. Analysis of Spatial Distribution Changes of Mangroves
  16. ...and 3 more sections

Figures (9)

  • Figure 1: UAE Mangrove Study Area
  • Figure 2: UNet++ model architecture
  • Figure 3: Validation set mIoU and training set loss curves during UNet++ model training
  • Figure 4: Mangrove Extraction Results of the UNet++ Model on the Test dataset ( optical satellite image on the left, the real mangrove area map in the middle, and the predicted mangrove area map on the right )
  • Figure 5: Changes in mangrove area in the UAE from 2017 to 2024
  • ...and 4 more figures