NRBR

Welcome to the practical hub for the Normalized Radar Burn Ratio (NRBR). This page hosts a collection of interactive tutorials and Jupyter Notebooks designed to help researchers, analysts, and students understand, compute, and apply the NRBR index with real satellite data.

Why These Tutorials?

• 🛠️ From Theory to Code: Bridge the gap between the scientific paper and operational use. Each notebook translates a key methodological step into executable code.
• 🌍 Work with Real Data: All tutorials are designed to work with real Sentinel-1 SAR data from actual wildfire events.
• ⚡ Accelerate Your Research: Use our pre-configured code examples as a template for your own analysis of fire-affected regions anywhere in the world.
• 🔍 Promote Transparency & Reproducibility: We believe in open science. These notebooks provide the complete computational workflow behind the NRBR index, allowing for full scrutiny and adaptation.

Tutorial Gallery

Notebook 1: Your First NRBR Map

• Deep Learning Burned area Segmentation Using SAR Sentinel-1.ipynb

Notebook 2: Comparing NRBR with Optical Indices (dNBR)

• Deep Learning Burned area Segmentation Using Optical Sentinel-2.ipynb