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March 10, 2026Land Degradation and Development2 citations

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

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SASaif Ullah AkhterFSFaiza SarwarHGHamid Gulzar

Key Points

  • The research aims to assess the changes in mangrove forests in the Indus Delta and Sandspit region over a seven-year period using advanced remote sensing techniques.
  • Utilized multi-sensor remote sensing approach with data from Sentinel-1, Sentinel-2, Landsat-8, and ALOS.
  • Employed Google Earth Engine for data processing, including atmospheric correction and cloud masking.
  • Classified mangrove areas using a Random Forest classifier based on vegetation indices and environmental variables.
  • Validated classification accuracy with 89 field-verified mangrove points.
  • Achieved high classification accuracy across all years with overall accuracy > 90% and Kappa > 0.9.
  • Observed a decline in mangrove cover from 2016 to 2018, followed by a recovery peaking in 2023.
  • Found a correlation between improved vegetation index values, increased rainfall, and mangrove recovery.
  • Identified ongoing threats to mangroves including pollution and reduced freshwater.

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 > 90%, Kappa > 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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Cite This Study

Akhter et al. (2026) studied this question.

synapsesocial.com/papers/69af957570916d39fea4d1c1https://doi.org/10.1002/ldr.70497
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