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Long-term mangrove dynamics and safety in coastal areas: An emergency Remote Sensing-based analysis using Landsat and
Wenyang Xu1, Muhammad Haseeb2, Xiangtian Zheng1
1Nanjing Institute of Technology, Nanjing, China.
None:
Mangroves are essential to coastal ecosystem health, providing natural defenses against erosion and serving as key habitats. This study investigates long-term mangrove cover and fragmentation trends in Pakistan's Indus Delta and Sandspit regions using Landsat imagery (1988-2024) and Sentinel-2 datasets (2019, 2024). Classification results show significant mangrove expansion, with Sandspit increasing from 10.22 km2 in 1988 to 15.64 km2 in 2024, and the Indus Delta expanding from 305.54 km2 to 1002.82 km2 over the same period. Sentinel-2 data showed slightly higher cover estimates, with values of 15.24 km2 and 15.98 km2 at Sandspit and 895.18 km2 and 1009.76 km2 for the Indus Delta in 2019 and 2024, respectively. Canopy density analysis from 1988 to 2024 categorized mangroves based on Normalized difference vegetation index (NDVI) values into sparse, moderate, and thick-density classes. NDVI thresholds (sparse: 0.1-0.3; moderate: 0.3-0.5; thick: >0.5) were used to assess canopy density. Results indicated densification of mangrove forests and a shift from sparse to dense vegetation, reflecting ecological recovery. Classification accuracy exceeded 96 % with high kappa values (>0.93). Due to field access limitations, accuracy assessments were conducted using visual validation via high-resolution Google Earth imagery. Fragmentation and transition analyses further revealed dynamic patterns and resilience of these ecosystems under environmental stressors. Comparative analysis of Landsat and Sentinel-2 data revealed high classification accuracy, with overall accuracies exceeding 96 % and kappa values above 0.93. The findings also provide actionable insights for national and provincial forest departments, supporting policy recommendations aimed at enhancing mitigation and adaptation strategies in response to landform changes and climate variability.
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