Tribal Fire Science and Indigenous Data Sovereignty

Jupyter notebooks and shared Python modules for analyzing wildfire history, risk, and stewardship on and near Tribal lands in the United States, built with real data, modular code, and OCAP®-aligned data governance.

Author: Lilly Jones, PhD (Daear Consulting, LLC) Version: 1.0.0 Released February 2026
License: See LICENSE
Citation: See citation.cff or the citation section below

Why This Repo Exists

Tribal nations face disproportionate wildfire risk, yet most fire science tools and datasets are built without Tribal input and applied without Tribal oversight. This project exists to change that by providing open, reproducible, and sovereignty-conscious tools for Tribal-led fire research and land management.

All analysis uses real, documented public data sources. All code is modular and designed to be extended by Tribal colleges, land managers, and researchers working at the intersection of Indigenous data sovereignty and fire science.

Notebooks

Notebook	Description
historical_fast_fires_tribal.ipynb	Historical wildfire analysis (MTBS 1984–present) on and near Tribal lands (frequency, severity, and risk scores) by tribe
fast_fire_days_analysis.ipynb	Identifies and analyzes days with rapid fire growth using weather and fire occurrence data
community_assets_at_risk.ipynb	Maps Tribal community assets (infrastructure, housing, cultural sites) within fire risk zones
tribal_wui_pressure_index.ipynb	Quantifies Wildland-Urban Interface pressure on Tribal lands using WUI and fire perimeter data
tribal_fire_capacity_analysis.ipynb	Assesses fire suppression and response capacity across Tribal nations
jurisdictional_complexity_analysis.ipynb	Analyzes overlapping federal, state, and Tribal fire jurisdictions and their management implications
indigenous_fire_stewardship.ipynb	Documents and contextualizes Indigenous prescribed fire traditions and their relationship to modern fire management
fire_weather_monitoring_gaps.ipynb	Quantifies RAWS monitoring coverage relative to Tribal land, identifies monitoring dead zones, and provides a coverage gap score for federal investment in Tribal fire weather infrastructure
cross_tribal_collaboration.ipynb	Identifies opportunities for inter-tribal fire management coordination based on shared risk and geography
climate_projections_tribal_fire_weather.ipynb	Analyze fire history, capacity gaps, and structural risk based on current and historical conditions
postfire_watershed_vulnerability.ipynb	Burned watersheds are highly vulnerable to erosion, debris flows, and drinking water contamination. Tribal Nations in headwater areas face compounding risks when fire burns upstream of water supply infrastructure
smoke_air_quality_exposure.ipynb	Wildfire smoke measured as fine particulate matter (PM2.5) is a direct, measurable public health impact that connects fire science to community health outcomes

Getting Started

1. Clone the repo

git clone https://github.com/daearconsulting/tribal_fire_science.git
cd tribal_fire_science

2. Create the conda environment

conda env create -f environment.yaml
conda activate tribal-fire-science

3. Register the Jupyter kernel

python -m ipykernel install --user --name tribal-fire-science \
  --display-name "Python (tribal-fire-science)"

4. Open in VSCode

• File Open Folder tribal_fire_science
• Ctrl+Shift+P Python: Select Interpreter tribal-fire-science

5. Run your first notebook

Open notebooks/historical_fast_fires_tribal.ipynb and run all cells. The Census TIGER tribal boundaries will download and cache automatically on first run.

Data Setup

| Dataset | How to obtain | Loader | | Census TIGER AIANNH | Auto-downloads on first run | loaders.load_census_aian() | | NIFC Fire Perimeters | Auto-downloads on first run | loaders.load_nifc_perimeters() | | MTBS Burned Areas | Manual download required - see below | loaders.load_mtbs_perimeters() | | BIA Tribal Boundaries | Auto-downloads on first run | loaders.load_bia_tribal_boundaries() | | Native Land Digital | Auto-downloads on first run (CC BY-NC 4.0) | loaders.load_native_land_territories() | | FEMA National Risk Index | Auto-downloads on first run | loaders.load_fema_national_risk_index() | | WUI Dataset | Manual download required - see below | loaders.load_wui() | | NOAA Climate Data | Requires free API token - see below | loaders.load_noaa_climate_data() |

All cached data is stored in data/cache/ and data/raw/ and both are gitignored and never committed to the repo.

MTBS Manual Download

1. Go to https://www.mtbs.gov/direct-download
2. Download the national fire perimeters shapefile
3. Extract and place at: data/raw/mtbs_perimeters/mtbs_perims_DD.shp

WUI Manual Download

1. Go to https://www.fs.usda.gov/rds/archive/catalog/RDS-2015-0047-2
2. Download and extract to: data/raw/wui/

NOAA API Token

1. Request a free token at https://www.ncei.noaa.gov/cdo-web/token
2. Create a .env file at repo root (never commit this):

NOAA_CDO_TOKEN=your_token_here

Repository Structure

tribal_fire_science/
├── notebooks/               # One notebook per analysis topic
├── src/                     # Shared Python modules
│   ├── __init__.py          # Exposes REPO_ROOT
│   ├── data/
│   │   ├── constants.py     # Paths, CRS, source URLs, thresholds
│   │   ├── loaders.py       # Fetch and cache functions for every dataset
│   │   └── validators.py    # Data integrity checks: no synthetic fallbacks
│   ├── viz/
│   │   ├── styles.py        # Color palettes, design tokens, mpl rcParams
│   │   ├── maps.py          # Folium base maps, tribal layers, choropleths
│   │   └── charts.py        # Timeline bars, heatmaps, scatter plots
│   ├── geo/
│   │   └── utils.py         # CRS helpers, spatial joins, area calculations
│   └── indigenous/
│       └── sovereignty.py   # OCAP® governance, attribution, TEK disclaimer
├── data/
│   ├── raw/                 # Downloaded source files (gitignored)
│   ├── interim/             # Intermediate processed files (gitignored)
│   ├── final/               # Analysis-ready files (gitignored)
│   └── cache/               # API response cache (gitignored)
├── outputs/                 # Figures and exported CSVs (gitignored)
├── environment.yaml         # Conda environment (Python 3.11)
├── workflows.md             # Tribal fire research workflow reference
└── citation.cff             # Citation metadata

Importing src in Notebooks

import sys
from pathlib import Path

REPO_ROOT = Path().resolve().parent
if str(REPO_ROOT) not in sys.path:
    sys.path.insert(0, str(REPO_ROOT))

from src.data import constants, loaders, validators
from src.viz import maps, charts, styles
from src.geo import utils as geo_utils
from src.indigenous.sovereignty import print_data_acknowledgment

Workflows

This repo supports the following Tribal fire research and management workflows. See workflows.md for full detail on inputs, steps, and tools for each.

Workflow	Purpose
Historical Fire Analysis and Risk Mapping	Understand past fire behavior to predict future risk
Fire Behavior Simulation	Model fire spread using FARSITE/FlamMap under varying conditions
Fire Impact on Cultural and Ecological Resources	Protect sacred sites, heritage areas, and ecocultural plants
Real-Time Fire Monitoring and Early Warning	Track active fires and generate dynamic risk alerts
Prescribed Burn Planning and Optimization	Support controlled burns for ecosystem restoration
Climate Change & Fire Regime Projection	Assess how shifting climate affects Tribal fire risk
Multi-Scale Decision Support Dashboard	Centralize fire data for Tribal land management decisions

Data Sovereignty

This project is guided by three complementary data governance frameworks:

OCAP® (First Nations Information Governance Centre): Tribal Nations own, control, access, and possess data about their own communities and territories. https://fnigc.ca/ocap-training/

CARE (Global Indigenous Data Alliance): Data use must deliver Collective Benefit to Indigenous peoples, respect their Authority to Control, uphold Responsibility to communities, and center Ethics across the full data lifecycle. https://www.gida-global.org/care

FAIR: Data is Findable, Accessible, Interoperable, and Reusable. FAIR governs technical data standards; CARE and OCAP® govern the ethical obligations to Tribal Nations that FAIR alone does not address. https://www.go-fair.org/fair-principles/

We recognize that:

• Tribal Nations are sovereign governments with the right to control data about their own communities and territories
• Federal and third-party boundaries (Census, BIA) are for analysis only and do not represent Tribal self-definition of territory
• Traditional Ecological Knowledge belongs to the communities that hold it and is not treated as extractable data
• This work is intended to support Tribal-led fire science

src/indigenous/sovereignty.py contains data attribution metadata, OCAP®/CARE/FAIR governance notes per dataset, and a TEK disclaimer. Call print_data_acknowledgment() at the top of every notebook that uses Tribal data sources.

Citation

If you use this software, please cite it as:

Jones, L., & Sanovia, J. (2026). Tribal Fire Science (Version 1.0.0).
https://doi.org/10.5281/zenodo.19265139

Or see citation.cff for machine-readable citation metadata.

Acknowledgments

This work adheres to Tribal data sovereignty principles. We gratefully acknowledge the nations whose lands and fire histories are represented in these analyses, and whose knowledge systems inform this work far beyond what any dataset can capture. We also acknowledge the Tribal data sovereignty work by Tribal members and thought leaders, including the authors of CARE, FAIR, OCAP®, Local Contexts, and IEEE 2890-2025.

Developed by Lilly Jones, PhD (Daear Consulting, LLC), in consultation with James Sanovia, MS (Daear Consulting, LLC), enrolled member of the Rosebud Sioux Tribe (Sicangu Lakota Oyate).