ndvi2gif 1.3.0

Multi-seasonal remote sensing analysis suite: composites, SAR preprocessing, time series analytics, and land cover classification with Google Earth Engine

Ndvi2Gif: Multi-Seasonal Remote Sensing Index Composites

Richter's stained glass in Cologne Cathedral. Inspiration for this library.

Ndvi2Gif is a Python library for multi-temporal remote sensing analysis with Google Earth Engine. It provides seasonal compositing, 40+ vegetation and environmental indices, SAR preprocessing, time series analytics, land cover classification, and hydroperiod analysis — all server-side, without downloading raw data.

Built on top of Google Earth Engine and geemap. Adapted and extended through its use in the eLTER and SUMHAL projects.

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📚 Documentation

https://digdgeo.github.io/Ndvi2Gif/

• Getting Started
• API Reference
• Indices Catalog
• Example Notebooks

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✨ What's New in v1.3.0

New module: SpatialPhenologyAnalyzer — GEE-native, per-pixel phenology rasters (Start/Peak/End of Season and derived metrics) for the whole ROI, entirely server-side. Three methods (threshold, derivative, and harmonic Fourier regression — the Earth Engine-native replacement for double-logistic fitting), per-year or multi-year aggregate outputs, and export to local GeoTIFF or Google Drive with named bands. See CHANGELOG for details.

Previously, in v1.2.0: HydroperiodAnalyzer (GEE-native flood duration analysis), SCL-based cloud masking for Sentinel-2, and numpy 2.x support.

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Modules

Module	Description
NdviSeasonality	Core engine: seasonal compositing, 40+ indices, 7 sensors, flexible ROI input
HydroperiodAnalyzer	Wetland flood duration analysis (days/year) with multi-year anomaly detection
TimeSeriesAnalyzer	Trend detection (Mann-Kendall, Sen's slope), phenology metrics, dashboards
SpatialPhenologyAnalyzer	Per-pixel phenology rasters (SOS/POS/EOS) via threshold, derivative or harmonic methods
S1ARDProcessor	Sentinel-1 SAR preprocessing: terrain correction, speckle filtering
LandCoverClassifier	Supervised (RF, SVM, CART) and unsupervised (K-means, LDA) classification

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Installation

pip install ndvi2gif

conda install -c conda-forge ndvi2gif

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Quick Start

import ee
from ndvi2gif import NdviSeasonality

ee.Authenticate()
ee.Initialize()

# Monthly NDVI composites from Sentinel-2 (2018–2024)
ndvi = NdviSeasonality(
    roi=your_roi, sat='S2', periods=12,
    start_year=2018, end_year=2024,
    key='percentile', percentile=85, index='ndvi'
)

ndvi.get_gif(name='ndvi_evolution.gif')

Yes, it makes nice GIFs — but it's much more than that.

Crop pattern dance around Los Palacios y Villafranca (SW Spain)

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What you can do with it

• Compute pixel-wise statistics over any region and time span — seasonal medians, percentiles, multi-year aggregations
• Monitor 40+ indices across Sentinel-1/2/3, Landsat (4–9), MODIS, ERA5-Land, and CHIRPS
• Analyse wetland hydroperiod and multi-year flood anomalies with HydroperiodAnalyzer
• Detect trends and phenology (SOS, EOS, POS, Length of Season) with TimeSeriesAnalyzer
• Classify land cover with multi-temporal feature stacks and Random Forest, SVM, or K-means
• Preprocess Sentinel-1 SAR with terrain correction and speckle filtering
• Export to GeoTIFF, Google Drive, or Earth Engine Assets
• Use any ROI: shapefile, GeoJSON, drawn geometry, eLTER DEIMS ID, Sentinel-2 tile, or Landsat path/row

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Supported Sensors

Sentinel-1 (SAR) · Sentinel-2 SR · Sentinel-3 OLCI · Landsat 4–9 SR · MODIS MOD09A1 · ERA5-Land · CHIRPS

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Contributing

Bug reports and feature requests: GitHub Issues

Pull requests are welcome. See CONTRIBUTING.md for guidelines — it includes step-by-step instructions for adding new indices and datasets.

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Citation

@software{garcia_diaz_ndvi2gif_2020,
  author  = {García Díaz, Diego},
  title   = {ndvi2gif: Multi-Seasonal Remote Sensing Analysis Suite},
  url     = {https://github.com/Digdgeo/Ndvi2Gif},
  version = {1.3.0},
  year    = {2020}
}

Acknowledgments

Special thanks to Qiusheng Wu for his invaluable work in developing and promoting open-source geospatial software, to the Google Earth Engine team, and to the broader open-source geospatial community.

License

MIT — see LICENSE.txt