Assessment and Evaluation of Spatio‐Temporal Dynamics of Mangrove Ecosystem Change Through Fusion of Optical, <scp>SAR</scp> , and Topographic Data

ABSTRACT This study examines the spatiotemporal dynamics of mangrove forests in the Indus Delta and Sandspit region of Sindh, Pakistan, from 2016 to 2023, utilizing a multi‐sensor remote sensing approach. Leveraging the capabilities of Google Earth Engine (GEE), satellite datasets from Sentinel‐1, Sentinel‐2, Landsat‐8, and ALOS were processed to monitor changes in mangrove extent, structure, and health. Annual composites were generated after applying rigorous pre‐processing techniques, including atmospheric correction, cloud masking, and spectral index calculations. The integration of radar data and elevation constraints enabled enhanced discrimination of vegetation, particularly in cloud‐prone zones. Vegetation indices (NDVI, EVI, MMRI, SAVI, URI) and environmental variables were used to classify mangrove areas using a Random Forest classifier. High classification accuracy was achieved across all years (overall accuracy &gt; 90%, Kappa &gt; 0.9), validated through 89 field‐verified mangrove points collected from Keti Bunder. Area estimations revealed a noticeable decline in mangrove cover from 2016 to 2018, followed by a gradual recovery, which peaked in 2023. This trend aligns with improved vegetation index values and increased rainfall, highlighting the ecological response of mangroves to precipitation variability. A comparative analysis with past studies revealed minor discrepancies, primarily due to differences in sensor resolution, classification methods, and temporal coverage. Notably, this study's integration of SAR, optical, and elevation data yielded more consistent and reliable annual estimates. While this study showed a similar rise in mangrove area to earlier works, some differences in numbers likely resulted from stricter data processing and validation. It also highlights ongoing threats, such as pollution and reduced freshwater, alongside positive impacts from efforts like the Ten Billion Tree Tsunami Programme. These insights can help guide future mangrove conservation in Pakistan.

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Journal
Land Degradation and Development
Published
2026-03-08
DOI
https://doi.org/10.1002/ldr.70497
Primary Topic
Coastal wetland ecosystem dynamics
Type
article
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article

Assessment and Evaluation of Spatio‐Temporal Dynamics of Mangrove Ecosystem Change Through Fusion of Optical, SAR , and Topographic Data

Faiza Sarwar, Muhammad Tayyab Sohail, Aleksandra O. Utkina, Eman Rafi Alamery et al.
Land Degradation and Development
Coastal wetland ecosystem dynamics
article

Assessment and Evaluation of Spatio‐Temporal Dynamics of Mangrove Ecosystem Change Through Fusion of Optical, SAR , and Topographic Data

Faiza Sarwar, Muhammad Tayyab Sohail, Aleksandra O. Utkina, Eman Rafi Alamery, Somayah Moshrif Alamri, Syed Amer Mahmood, Saif Ullah Akhter, Aqil Tariq, Hamid Gulzar, Khadeijah Yahya Faqeih
article en

Abstract

ABSTRACT This study examines the spatiotemporal dynamics of mangrove forests in the Indus Delta and Sandspit region of Sindh, Pakistan, from 2016 to 2023, utilizing a multi‐sensor remote sensing approach. Leveraging the capabilities of Google Earth Engine (GEE), satellite datasets from Sentinel‐1, Sentinel‐2, Landsat‐8, and ALOS were processed to monitor changes in mangrove extent, structure, and health. Annual composites were generated after applying rigorous pre‐processing techniques, including atmospheric correction, cloud masking, and spectral index calculations. The integration of radar data and elevation constraints enabled enhanced discrimination of vegetation, particularly in cloud‐prone zones. Vegetation indices (NDVI, EVI, MMRI, SAVI, URI) and environmental variables were used to classify mangrove areas using a Random Forest classifier. High classification accuracy was achieved across all years (overall accuracy &gt; 90%, Kappa &gt; 0.9), validated through 89 field‐verified mangrove points collected from Keti Bunder. Area estimations revealed a noticeable decline in mangrove cover from 2016 to 2018, followed by a gradual recovery, which peaked in 2023. This trend aligns with improved vegetation index values and increased rainfall, highlighting the ecological response of mangroves to precipitation variability. A comparative analysis with past studies revealed minor discrepancies, primarily due to differences in sensor resolution, classification methods, and temporal coverage. Notably, this study's integration of SAR, optical, and elevation data yielded more consistent and reliable annual estimates. While this study showed a similar rise in mangrove area to earlier works, some differences in numbers likely resulted from stricter data processing and validation. It also highlights ongoing threats, such as pollution and reduced freshwater, alongside positive impacts from efforts like the Ten Billion Tree Tsunami Programme. These insights can help guide future mangrove conservation in Pakistan.

Land Degradation and Development
Princess Nourah bint Abdulrahman University (SA), University of the Punjab (PK), Russian New University (RU), Government College University, Lahore (PK), Government College of Science (PK), Mississippi State University (US)
Openalex Percentile: Top 39%
Coastal wetland ecosystem dynamics
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