Ndvi2Gif Tutorial#
Welcome to the comprehensive tutorial for Ndvi2Gif, a Python library for multi-seasonal remote sensing analysis with Google Earth Engine.
What is Ndvi2Gif?#
Ndvi2Gif is a powerful remote sensing analytics suite that simplifies access to global satellite data through Google Earth Engine. While its name highlights the ability to create seasonal GIF animations, the true power of this tool lies in its capability to compute and export pixel-wise statistics for any region on Earth, across any time span covered by supported remote sensing datasets.
Key Features#
🛰️ Multi-Sensor Support: Sentinel-1/2/3, Landsat 4-9, MODIS, ERA5-Land, CHIRPS (7 platforms)
📊 88 Variables: 40+ vegetation indices + 47 ERA5 climate variables + CHIRPS precipitation
🌡️ Climate Analysis: Temperature, precipitation, soil moisture, radiation, wind (1950-present)
🤖 Machine Learning: Supervised and unsupervised land cover classification (8 algorithms)
📈 Time Series Analysis: Trend detection, phenology metrics, climate statistics
🌍 Global Coverage: Process any region on Earth with intelligent data type handling
📤 Flexible Export: GeoTIFF, Google Drive, Earth Engine Assets
🎨 Visualization: Automated GIF generation and interactive dashboards
What You’ll Learn#
This tutorial will guide you through:
Getting Started: Installation, authentication, and your first analysis
Core Tutorials: Step-by-step guides for common workflows
Advanced Features: SAR processing, time series, classification
Use Cases: Real-world applications in agriculture, wetlands, drought assessment
Reference: Complete API documentation and indices catalog
Quick Example#
import ee
from ndvi2gif import NdviSeasonality
# Authenticate and initialize
ee.Initialize()
# Create seasonal NDVI composite
ndvi = NdviSeasonality(
roi='your_area.shp',
sat='S2',
periods=12,
start_year=2023,
end_year=2024,
index='ndvi'
)
# Generate animated GIF
ndvi.get_gif('ndvi_evolution.gif')
Who Should Use This Tutorial?#
🌾 Agricultural researchers monitoring crop phenology
🌊 Environmental scientists assessing water quality
🌳 Ecologists studying vegetation dynamics
🛰️ Remote sensing analysts working with multi-temporal data
🎓 Students learning satellite image analysis
Prerequisites#
Basic Python knowledge
Familiarity with geospatial concepts (optional but helpful)
Google Earth Engine account (free for research and education)
Support & Community#
Citation#
If you use Ndvi2Gif in your research, please cite:
@software{garcia_diaz_ndvi2gif_2020,
author = {García Díaz, Diego},
title = {ndvi2gif: Multi-Seasonal Remote Sensing Analysis Suite},
url = {https://github.com/Digdgeo/Ndvi2Gif},
version = {1.2.2},
year = {2020}
}
Let’s get started! 🚀
Getting Started
Core Tutorials
Advanced Features