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Download MODIS and VIIRS active fire hotspot data from NASA FIRMS. Supports filtering by date range, bounding box, and instrument. Outputs CSV and GeoJSON fo...
---
name: fire-hotspot-download
display_name: 火点数据下载 (NASA FIRMS)
version: 0.1.0
author: ruiduobao
license: MIT-0
description: |
Download MODIS and VIIRS active fire hotspot data from NASA FIRMS.
Supports filtering by date range, bounding box, and instrument.
Outputs CSV and GeoJSON formats with near-real-time and standard products.
runtime: python>=3.8
tags: [gis, remote-sensing, fire, hotspot, modis, viirs, nasa, firms]
---
# Fire Hotspot Data Download
Download active fire hotspot data from NASA FIRMS (Fire Information for Resource Management System).
## Overview
NASA FIRMS distributes active fire data from MODIS and VIIRS instruments in near-real-time.
This data is essential for fire monitoring, air quality studies, and land management.
## Instruments
| Instrument | Product | Satellite | Resolution |
|------------|---------|-----------|------------|
| MODIS | MCD14DL | Terra+Aqua | 1km |
| VIIRS (NRT) | VNP14IMGTDL_NRT | Suomi-NPP | 375m |
| VIIRS (Standard) | VNP14IMGT | Suomi-NPP | 375m |
| VIIRS (NOAA-20) | VJ114IMGML | NOAA-20 (JPSS-1) | 375m |
## Features
- **MODIS and VIIRS**: both instruments supported
- **Near-real-time and standard products**: NRT for monitoring, standard for research
- **Date range and bbox filtering**: precise spatial-temporal queries
- **CSV and GeoJSON output**: tabular and geospatial formats
- **API key management**: secure key storage in config file
- **Progress tracking**: download progress with tqdm
## API Key
A free API key is required. Get one at: https://firms.modaps.eosdis.nasa.gov/api/map_key/
```bash
# Set your API key
python scripts/fire-hotspot-download.py set-key YOUR_API_KEY
# Or set via environment variable
export FIRMS_API_KEY="your_api_key"
```
## Usage
```bash
# Download fire hotspots for China (last 7 days)
python scripts/fire_hotspot_download.py download \
--instrument VIIRS --product NRT \
--bbox 73 18 135 54 \
--start 2024-01-01 --end 2024-01-07 \
--output china_fires.csv
# Download MODIS fires as GeoJSON
python scripts/fire_hotspot_download.py download \
--instrument MODIS --product STANDARD \
--bbox 116.0 39.5 116.8 40.2 \
--start 2024-03-01 --end 2024-03-31 \
--output beijing_fires.geojson --format geojson
# Download both instruments
python scripts/fire_hotspot_download.py download \
--instrument BOTH --product NRT \
--bbox -125 25 -66 50 \
--start 2024-06-01 --end 2024-06-07 \
--output us_fires.csv
# List available instruments
python scripts/fire_hotspot_download.py list-instruments
```
## Parameters
- `--instrument`: MODIS, VIIRS, or BOTH
- `--product`: NRT (near-real-time) or STANDARD
- `--bbox`: Bounding box as `west south east north`
- `--start/--end`: Date range (YYYY-MM-DD, max 7 days for NRT)
- `--output`: Output file path
- `--format`: `csv` or `geojson`
- `--confidence`: Minimum confidence threshold (0-100)
## Data Fields
| Field | Description |
|-------|-------------|
| latitude | Latitude of fire pixel |
| longitude | Longitude of fire pixel |
| brightness | Brightness temperature (K) |
| scan | Scan pixel size |
| track | Track pixel size |
| acq_date | Acquisition date |
| acq_time | Acquisition time (HHMM) |
| satellite | Satellite name |
| confidence | Detection confidence (0-100) |
| version | Product version |
| bright_t31 | Channel 31 brightness (K) |
| frp | Fire Radiative Power (MW) |
| daynight | Day or Night |
## Installation
```bash
# Install dependencies
pip install requests>=2.28.0 tqdm
# Or install from requirements.txt
pip install -r scripts/requirements.txt
```
## Data Source
- **API**: https://firms.modaps.eosdis.nasa.gov/api/
- **Documentation**: https://firms.modaps.eosdis.nasa.gov/api/
- **License**: Public Domain (NASA open data)
- **Rate Limit**: No strict limit; recommended max 10 requests/minute
- **CRS**: WGS84 (EPSG:4326)
### Citation Format
```bibtex
@article{davies2009firms,
title={Fire Information for Resource Management System (FIRS): archiving and distributing MODIS active fire data},
author={Davies, D.K. and others},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2009},
doi={10.1109/TGRS.2009.2014067}
}
```
### NRT vs Standard Quality
| Product | Latency | Accuracy | Use Case |
|---------|---------|----------|----------|
| NRT (Near-Real-Time) | ~3 hours | Lower (automated QA) | Active fire monitoring, rapid response |
| Standard | ~2 weeks | Higher (manual QA) | Research, long-term analysis |
### Confidence Threshold
The `confidence` field ranges from 0–100%:
- **>75%**: High confidence — reliable fire detection
- **50–75%**: Medium confidence — possible fire, verify with other sources
- **<50%**: Low confidence — likely false positive
### VIIRS Satellites
| Satellite | Instrument ID | Product |
|-----------|--------------|---------|
| Suomi-NPP | VNP14IMGTDL_NRT / VNP14IMGT | 375m resolution |
| NOAA-20 (JPSS-1) | VJ114IMGML | 375m resolution |
Both provide similar spatial resolution; NOAA-20 offers improved nighttime detection.
### Long-Period Download Guidance
The API supports max 7 days per NRT request. For longer periods:
```bash
# Loop over weeks
for week in {0..3}; do
start=$(date -d "2024-01-01 +${week} weeks" +%Y-%m-%d)
end=$(date -d "2024-01-01 +${week} weeks + 6 days" +%Y-%m-%d)
python scripts/fire_hotspot_download.py download \
--instrument VIIRS --product NRT \
--bbox 73 18 135 54 \
--start $start --end $end \
--output "fires_week${week}.csv"
done
```
### Global Download
For global-scale analysis, split into regional tiles and download separately to avoid timeout:
- Americas, Europe-Africa, Asia-Pacific (4–6 regions recommended)
- Merge results after download.
### API Key Storage
- Keys set via `set-key` are stored in the local config file.
- Environment variable `FIRMS_API_KEY` overrides stored key.
- Config location: same directory as the script.
### Visualization
- **Point maps**: Load GeoJSON in QGIS, color by `confidence` (red = high, yellow = medium).
- **Time series**: Aggregate daily fire counts, plot as bar chart.
- **Heatmaps**: Use QGIS Heatmap renderer or `datashader` in Python.
## Troubleshooting
| Error | Cause | Solution |
|-------|-------|----------|
| `ConnectionError` | Network issue or API down | Check internet connection, retry in 1 minute |
| `HTTP 429` | Rate limit exceeded | Wait 60 seconds before retrying |
| `HTTP 404` | Invalid parameters or date range | Check parameter names and date format |
| `ValueError: bbox` | Invalid bounding box | Ensure format is `west,south,east,north` |
| Empty output | No data for query region/time | Try different date range or check coordinates |
| `ModuleNotFoundError` | Missing dependency | Run `pip install requests tqdm` |
| `403 Forbidden` | Invalid API key | Re-run `set-key` with valid key |
---
## Advanced Usage
### Automated Weekly Download
```bash
# Download last 7 days of fire data for China
python scripts/fire_hotspot_download.py download --bbox 73 18 135 54 --instrument BOTH --start $(date -d '7 days ago' +%Y-%m-%d) --end $(date +%Y-%m-%d) --output fires_weekly.csv
```
### CI/CD Integration (GitHub Actions)
```yaml
# .github/workflows/update-fires.yml
name: Fire Hotspot Monitor
on:
schedule:
- cron: '0 8 * * *' # Daily at 08:00 UTC
jobs:
download:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with:
python-version: '3.11'
- run: pip install requests
- env:
FIRMS_API_KEY: ${{ secrets.FIRMS_API_KEY }}
run: |
python scripts/fire_hotspot_download.py download \
--bbox 73 18 135 54 --instrument BOTH \
--start $(date -d '3 days ago' +%Y-%m-%d) \
--end $(date +%Y-%m-%d) \
--output data/fires_latest.csv
```
### PostgreSQL/PostGIS Import
```bash
python scripts/fire_hotspot_download.py download --bbox 73 18 135 54 --start 2024-01-01 --end 2024-01-31 --output fires.csv
psql -d gis_db -c "\COPY fire_hotspots(lat, lon, confidence, acq_date, instrument) FROM 'fires.csv' CSV HEADER"
```
### Performance Tips
- NRT API accepts max 7-day windows; split longer ranges into weekly chunks
- Use `--instrument VIIRS` for higher resolution (375m vs 1km)
- Add `sleep 1` between weekly requests for multi-week batches
---
## 中文说明
从 NASA FIRMS（资源管理火灾信息系统）下载 MODIS 和 VIIRS 活跃火点数据。
### 传感器
| 传感器 | 产品 | 卫星 | 分辨率 |
|--------|------|------|--------|
| MODIS | MCD14DL | Terra+Aqua | 1km |
| VIIRS (近实时) | VNP14IMGTDL_NRT | Suomi-NPP | 375m |
| VIIRS (标准) | VNP14IMGT | Suomi-NPP | 375m |
| VIIRS (NOAA-20) | VJ114IMGML | NOAA-20 (JPSS-1) | 375m |
### 核心功能
- **MODIS 和 VIIRS**：支持两种传感器
- **近实时和标准产品**：近实时用于监测，标准产品用于科研
- **日期范围和区域过滤**：精确的时空查询
- **CSV 和 GeoJSON 输出**：表格和地理空间格式
- **API 密钥管理**：安全存储在配置文件中
- **进度跟踪**：使用 tqdm 显示下载进度
### API 密钥
需要免费 API 密钥。获取地址：https://firms.modaps.eosdis.nasa.gov/api/map_key/
```bash
# 设置 API 密钥
python scripts/fire_hotspot_download.py set-key YOUR_API_KEY
# 或通过环境变量
set FIRMS_API_KEY=your_api_key
```
### 使用示例
```bash
# 下载中国区域火点（最近7天）
python scripts/fire_hotspot_download.py download \
--instrument VIIRS --product NRT \
--bbox 73 18 135 54 \
--start 2024-01-01 --end 2024-01-07 \
--output china_fires.csv
# 下载 MODIS 火点为 GeoJSON
python scripts/fire_hotspot_download.py download \
--instrument MODIS --product STANDARD \
--bbox 116.0 39.5 116.8 40.2 \
--start 2024-03-01 --end 2024-03-31 \
--output beijing_fires.geojson --format geojson
# 下载两种传感器数据
python scripts/fire_hotspot_download.py download \
--instrument BOTH --product NRT \
--bbox -125 25 -66 50 \
--start 2024-06-01 --end 2024-06-07 \
--output us_fires.csv
# 列出可用传感器
python scripts/fire_hotspot_download.py list-instruments
```
### 数据字段
| 字段 | 描述 |
|------|------|
| latitude | 火点纬度 |
| longitude | 火点经度 |
| brightness | 亮温 (K) |
| scan | 扫描像元大小 |
| track | 轨道像元大小 |
| acq_date | 获取日期 |
| acq_time | 获取时间 (HHMM) |
| satellite | 卫星名称 |
| confidence | 检测置信度 (0-100) |
| version | 产品版本 |
| bright_t31 | 31通道亮温 (K) |
| frp | 火辐射功率 (MW) |
| daynight | 白天或夜间 |
### 数据来源
- **API**: https://firms.modaps.eosdis.nasa.gov/api/
- **文档**: https://firms.modaps.eosdis.nasa.gov/api/
- **许可证**: 公共领域（NASA 开放数据）
- **引用**: Davies, D.K., et al., 2009. Fire Information for Resource Management System (FIRS): archiving and distributing MODIS active fire data. IEEE Transactions on Geoscience and Remote Sensing.
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