Published February 3, 2026 | Version v1
Dataset Open

Data and code for "Functional composition and structural diversity enhance mangrove forest resilience in the Sundarbans"

Authors/Creators

  • 1. The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China
  • 2. Leibniz Centre for Tropical Marine Research & University of Bremen, Bremen, Germany
  • 3. Department of Geography, Durham University, South Road, Durham DH1 3LE, United Kingdom
  • 4. School of Environmental and Forest Sciences, University of Washington, Seattle, Washington, USA
  • 5. ROR icon Purdue University West Lafayette
  • 6. Forestry and Wood Technology Discipline, Khulna University, Khulna-9208, Bangladesh

Description

SRF_ETH_CanopyHeight_StableLand_2020_250m.tif– Canopy height at 250m spatial resolution derived from ETH’s GEDI LiDAR based global canopy height.

SRF_kNDVI_2000_2024.tif– 25 years kNDVI time series data derived from 16 days NDVI of MODIS 

Data_Mangrove_resilience_Sundarbans.xlsx – Plot‑level biodiversity, environmental variables, sediment properties, and remote‑sensing–derived resilience indicators and disturbance metrics used for SEM and statistical analyses.

 SEM_analysis.R – R script for structural equation modelling, including data preparation, model specification, diagnostics, and extraction of standardized path coefficients.

 Sundarbans boundary– KMZ file of the entire Sundarbans boundary, digitized in Google Earth Pro.

 Python_code_resilience_biodiversity_Sundarbans.ipynb – Jupyter Notebook containing the full Python workflow for generating Figures 1–5: MODIS kNDVI processing, deseasoning/detrending, perturbation detection, resilience indicator calculation, and spatial mapping.

Files

Python_code_resilience_biodiversity_Sundarbans.ipynb

Files (368.2 MB)

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Additional details

Dates

Submitted
2026-01-21

Software

Programming language
Python , R
Development Status
Active