wildfire-analyser 0.2.11

Python library for post-fire assessment and wildfire analysis using Google Earth Engine.

wildfire-analyser

Python project for post-fire assessment and burned area analysis using Sentinel-2 imagery and Google Earth Engine (GEE).

This project supports multiple spectral indices (dNBR, dNDVI, RBR), visual products, and paper-ready burned area statistics.

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Scientific Background

This project is based on the peer-reviewed study:

Spatial and statistical analysis of burned areas with Landsat-8/9 and Sentinel-2 satellites: 2023 Çanakkale forest fires Authors: Deniz Bitek, Fusun Balik Sanli, Ramazan Cuneyt Erenoglu Study area: Çanakkale Province, Turkey

The methodology implemented in wildfire-analyser follows the same analytical framework and burn severity thresholds described in the paper, particularly for the Sentinel-2–based analysis, including:

• dNBR, dNDVI and RBR indices
• Burn severity classification tables
• Area statistics in hectares and percentage

Minor numerical differences may occur due to cloud masking, spatial sampling, and Google Earth Engine implementation details.

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Installation and Usage

Follow the steps below to install and test wildfire-analyser inside an isolated environment:

mkdir test
cd test

python3 -m venv venv
source venv/bin/activate

pip install wildfire-analyser

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Required Files Before Running the Client

Before running the client, you must prepare the following items:

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1. Add a GeoJSON polygon (ROI)

Create a folder named polygons in the project root and place your ROI polygon file inside it:

/tmp/test/
├── polygons/
│   └── your_polygon.geojson
└── venv/

Example GeoJSON files are available in the repository (e.g. canakkale_aoi_1.geojson).

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2. Create the .env file with GEE credentials

In the project root, add a .env file containing your Google Earth Engine authentication variables.

A .env.template file is available in the repository.

/tmp/test/
├── .env
├── polygons/
└── venv/

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Running the Client (Standard Mode)

After adding the .env file and your GeoJSON polygon:

python3 -m wildfire_analyser.client \
  --roi polygons/canakkale_aoi_1.geojson \
  --start-date 2023-07-01 \
  --end-date 2023-07-21 \
  --deliverables \
    DNBR_VISUAL \
    DNDVI_VISUAL \
    RBR_VISUAL \
    DNBR_AREA_STATISTICS \
    DNDVI_AREA_STATISTICS \
    RBR_AREA_STATISTICS \
  --days-before-after 1

This will:

• Run the post-fire assessment pipeline
• Generate visual thumbnail URLs
• Generate scientific GeoTIFF outputs (when applicable)
• Compute burned area statistics
• Print all results to the terminal

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Deliverables

You may explicitly select deliverables using --deliverables.

Scientific products

• RGB_PRE_FIRE
• RGB_POST_FIRE
• NDVI_PRE_FIRE
• NDVI_POST_FIRE
• NBR_PRE_FIRE
• NBR_POST_FIRE
• DNDVI
• DNBR
• RBR

Visual products

• RGB_PRE_FIRE_VISUAL
• RGB_POST_FIRE_VISUAL
• DNDVI_VISUAL
• DNBR_VISUAL
• RBR_VISUAL

Severity maps and statistics

• DNBR_AREA_STATISTICS
• DNDVI_AREA_STATISTICS
• RBR_AREA_STATISTICS

Example:

python3 -m wildfire_analyser.client \
   --roi polygons/canakkale_aoi_1.geojson \
   --start-date 2023-07-01 \
   --end-date 2023-07-21 \
   --deliverables DNBR_VISUAL DNBR_AREA_STATISTICS \
   --days-before-after 1

If --deliverables is not provided, all available deliverables are generated.

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Paper Preset Mode (Reproducibility)

The client also supports paper presets, which are predefined experimental configurations designed to reproduce published results.

Example preset: PAPER_DENIZ_FUSUN_RAMAZAN

Run:

python3 -m wildfire_analyser.client \
  --deliverables PAPER_DENIZ_FUSUN_RAMAZAN

This preset:

• Executes the analysis for two distinct burned areas
• Uses paper-aligned temporal windows
• Generates only visual outputs and statistics
• Does not export scientific GeoTIFFs
• Prints results grouped by area

Internally, it runs:

Area	ROI	Pre-fire	Post-fire
Area 1	canakkale_aoi_1.geojson	2023-07-01	2023-07-21
Area 2	canakkale_aoi_2.geojson	2023-07-31	2023-08-30

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Help

For help and full usage information:

python3 -m wildfire_analyser.client --help

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Setup Instructions for Developers

1. Clone the repository

git clone [e-mail]:camargo-advanced/wildfire-analyser.git
cd wildfire-analyser

2. Create a virtual environment

python3 -m venv venv

3. Activate the virtual environment

source venv/bin/activate

4. Install dependencies

pip install -r requirements.txt

5. Configure environment variables

Copy your .env file to the project root. A .env.template file is provided.

6. Run the sample client

python3 -m wildfire_analyser.client \
   --roi polygons/canakkale_aoi_1.geojson \
   --start-date 2023-07-01 \
   --end-date 2023-07-21 \
   --days-before-after 1

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Useful Commands

Deactivate the virtual environment

deactivate

Build and publish a new PyPI release

rm -rf dist/*
python -m build
twine upload dist/*

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Citation

If you use this software for scientific work, please cite:

Spatial and statistical analysis of burned areas with Landsat-8/9 and Sentinel-2 satellites: 2023 Çanakkale forest fires Deniz Bitek, Fusun Balik Sanli, Ramazan Cuneyt Erenoglu.

And cite this repository as the reference implementation.