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Spatiotemporal Modeling of Mangrove Carbon Stock Along Pakistan's Coast Using Multi-Sensor Sentinel and Landsat Data
Junaid Ahmad Qadri1, Asif Sajjad1, Aqib Hassan Ali Khan2
1Department of Environmental Sciences, Faculty of Biological Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan.
Sensors (Basel, Switzerland)
|July 15, 2026
Summary
Coastal mangrove carbon stocks in Pakistan were quantified using satellite data and a light use efficiency (LUE) model. Significant interannual variability and a decline in 2021 were observed, highlighting the need for ongoing monitoring of these vital ecosystems.
Area of Science:
- Environmental Science
- Remote Sensing
- Ecology
Background:
- Coastal mangroves are critical carbon sinks, but their carbon stocks and dynamics are poorly quantified in many regions, especially in arid environments like Pakistan.
- Persistent cloud cover and the need for high-cadence monitoring pose challenges for traditional remote sensing approaches in the Indus Delta region.
Purpose of the Study:
- To quantify coastal mangrove carbon stocks and their interannual variability along the Pakistan coastline.
- To develop and apply a multi-sensor fusion framework integrated with a process-based light use efficiency (LUE) modeling approach for high-cadence monitoring.
- To establish a spatially resolved, multi-year baseline for coastal carbon assessment and ecosystem monitoring in arid tidal environments.
Main Methods:
- Utilized multi-sensor satellite data (Landsat 8/9, Sentinel-2) within Google Earth Engine for high-cadence monitoring and to overcome cloud cover issues.
- Employed a process-based light use efficiency (LUE) model incorporating environmental stress factors (temperature, VPD, salinity, PAR) to estimate gross primary productivity and biomass.
- Delineated mangrove extent using land use and land cover (LULC) classification and conducted field-based validation of biomass estimates with georeferenced sampling points.
Main Results:
- Estimated a mean coastal mangrove carbon stock of 31.95 Mg C ha-1 (117.3 Mg CO2 ha-1) with significant interannual variation (19.8% coefficient of variation).
- Observed a significant decline in carbon stocks in 2021 (-11.11%), correlating with reduced Normalized Difference Vegetation Index (NDVI) values.
- Revealed substantial spatial heterogeneity in carbon distribution (20.51 to 55.93 Mg C ha-1), influenced by localized salinity and water stress.
Conclusions:
- The developed multi-sensor fusion and LUE modeling framework effectively quantifies mangrove carbon stocks and their variability in challenging environments.
- Results provide crucial insights into the dynamics of mangrove carbon sequestration along Pakistan's coast, essential for conservation and climate change mitigation strategies.
- This study establishes a vital multi-year baseline for monitoring these critical coastal ecosystems in arid tidal regions.