Data and code for "Landslide susceptibility peaks at intermediate vegetation density: a global satellite analysis of critical NDVI thresholds"
Authors/Creators
-
1.
Guangzhou Institute of Geography
-
2.
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
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8.
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
Additional details
Funding
- National Natural Science Foundation of China
- 42271091
Dates
- Submitted
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2026-03-10Date submitted to iScience
Software
- Repository URL
- https://github.com/v4-hub/LanSVe
- Programming language
- Python , R
- Development Status
- Active