Published June 24, 2026 | Version 1.0

Data and code for "Landslide susceptibility peaks at intermediate vegetation density: a global satellite analysis of critical NDVI thresholds"

  • 1. ROR icon Guangzhou Institute of Geography
  • 2. ROR icon Guangdong Academy of Medical Sciences
  • 3. Guangdong Emergency Technology Research Center for Geological Disasters
  • 4. Faculty of Agriculture, Shizuoka University, Japan
  • 5. School of Architecture and Urban Planning, Guangdong University of Technology
  • 6. Guangzhou Institute of Geography / Guangdong Emergency Technology Research Center for Geological Disasters, Guangdong Academy of Sciences, Guangzhou 510070, China
  • 7. College of Ecology and Environment, Nanjing Forestry University, China
  • 8. ROR icon Guangdong Academy of Sciences

Description

Curated data and reproducible analysis code accompanying the manuscript "Landslide susceptibility peaks at intermediate vegetation density: a global satellite analysis of critical NDVI thresholds" (iScience, manuscript ISCIENCE-D-26-03863). The archive contains a curated global inventory of 18,028 rainfall-triggered landslides (2000–2024) with pre-failure MODIS/Sentinel-2 vegetation indices, multi-source land cover, climate zone, and SRTM-derived terrain; the R and Python scripts that reproduce the segmented-regression breakpoint detection, Random Forest validation, all sensitivity analyses, and every figure and table; and the supplementary figures and tables. The analysis demonstrates a unimodal, non-monotonic ("vegetation paradox") relationship in which landslide susceptibility peaks within a Critical Transition Zone at NDVI 0.769–0.868. Note: an apparent pre-failure NDVI "early-warning" decline reported in earlier drafts is shown here (rev_B_contamination.R) to be an artefact of the 16-day MODIS composite straddling the failure date; we make no early-warning claim, and the central non-monotonic result is unaffected.

Files

LanSVe_zenodo_deposit.zip

Files (13.4 MB)

Name Size
md5:f96d6cb777a23c8607b0f4cf064b693e
13.4 MB Preview Download

Additional details

Funding

National Natural Science Foundation of China
42271091

Dates

Submitted
2026-03-10
Date submitted to iScience

Software

Repository URL
https://github.com/v4-hub/LanSVe
Programming language
Python , R
Development Status
Active