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Dryad

Spatiotemporal dynamics and machine learning-based prediction of aboveground biomass in the Indus delta mangroves

Naveed, Muhammad1; Satti, Zulqarnain2

Published Apr 03, 2026 on Dryad. https://doi.org/10.5061/dryad.h44j0zq1m

Data files

Apr 03, 2026 version files 6.61 MB

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Abstract

This dataset provides spatially explicit estimates of mangrove aboveground biomass (AGB) and associated environmental variables for the Indus Delta mangrove ecosystem. Field-based AGB spatial data were derived from the NASA CMS Global Mangrove Distribution, Aboveground Biomass, and Canopy Height dataset and used as reference data for model development. Multisource remote sensing data, including Sentinel-1 and Sentinel-2 optical imagery, were processed to extract predictor variables such as vegetation indices and surface characteristics. Additional environmental variables, including land surface temperature and land use/land cover, were incorporated to capture ecological controls on biomass distribution.

All satellite datasets underwent standard preprocessing steps, including atmospheric correction, radiometric calibration, cloud masking, and spatial resampling. The processed variables were then integrated into machine learning models (Random Forest, Gradient Boosted Regression Tree, Support Vector Regression, and Classification and Regression Trees) to estimate AGB across the study region.

The best-performing model (Gradient Boosted Regression Tree) was used to generate spatially explicit AGB maps and future projections for 2030, 2040, and 2050. Model outputs were exported as point-based datasets containing geographic coordinates and biomass values, along with corresponding spatial layers for mapping and analysis.