Queimadas INPE

Overview

Queimadas INPE, operated by Brazil's National Institute for Space Research (INPE), is a public service dedicated to monitoring and disseminating data on vegetation fires. Established in 1961, INPE has a long history of remote sensing and space science research. The Queimadas system specifically focuses on providing comprehensive information on fire occurrences, primarily across South America, utilizing satellite imagery and advanced geospatial analysis. The platform serves a diverse audience, including environmental researchers, governmental agencies, academic institutions, and the general public concerned with deforestation and climate change impacts.

The system excels in situations requiring broad-scale, historical, and near real-time fire data for large geographical areas. It is particularly valuable for tracking seasonal fire patterns, assessing the extent of burned areas, and supporting early warning systems for environmental disasters. For example, researchers might use Queimadas data to correlate fire incidents with land use changes or meteorological conditions, contributing to studies on climate change and biodiversity loss. Public data analysis for deforestation efforts frequently incorporates information from Queimadas to quantify impacts and inform conservation strategies.

While Queimadas INPE provides critical environmental intelligence, its primary mode of data dissemination is through web interfaces and downloadable geospatial datasets, such as shapefiles and KMLs. Direct programmatic access via a dedicated API is not a core offering; developers typically integrate Queimadas data by parsing publicly available reports or automating the download and processing of these datasets. This approach requires custom development for real-time applications or direct platform-to-platform integrations. The strength of Queimadas lies in its authoritative data sources and comprehensive coverage, making it an essential tool for non-commercial applications in environmental science and public policy.

Key features

• Satellite-based fire detection: Utilizes data from multiple satellites (e.g., NOAA, Aqua, Terra, METOP) to detect active fire fronts and hot spots across continental South America and beyond.
• Historical data archives: Provides access to extensive historical records of fire occurrences, enabling trend analysis and long-term environmental monitoring.
• Near real-time monitoring: Offers frequently updated fire alerts and mapping, supporting rapid response and situational awareness for environmental agencies.
• Burned area mapping: Generates maps delineating areas affected by fires, crucial for assessing ecological damage and recovery efforts.
• Geospatial data downloads: Facilitates the download of fire data in various formats (e.g., shapefiles, KML, CSV) for integration into GIS software and custom analytical tools.
• Customizable data visualization: Allows users to visualize fire data on interactive maps with filters for time periods, biomes, and administrative regions.
• Statistical reports: Publishes daily, weekly, and monthly statistical summaries of fire activity, aiding in policy formulation and public information campaigns.

Pricing

Queimadas INPE provides its data and services free of charge for public and academic use, aligning with its mission as a publicly funded research institution.

For further details on data usage policies, refer to the official Queimadas INPE documentation.

Common integrations

While Queimadas INPE does not offer a direct API, integration typically involves processing its publicly available data:

• Geographic Information Systems (GIS): Data in shapefile or KML format can be imported into GIS software like ArcGIS Pro (ArcGIS Developers) or QGIS for advanced spatial analysis and mapping.
• Custom data pipelines: Developers often build scripts (e.g., in Python) to automate the download and parsing of daily reports, CSV files, or shapefiles from the Queimadas website.
• Environmental modeling platforms: Integrated as input data for models simulating climate change impacts, biodiversity, or carbon emissions.
• Public dashboards and visualization tools: Raw data can be processed and displayed on custom dashboards using tools like Tableau or Power BI to present fire statistics to a broader audience.

Alternatives

• Google Maps Platform: Offers extensive mapping, geocoding, and location-based services, including satellite imagery, though not specifically focused on fire data.
• OpenCage Geocoding API: Provides global geocoding services for converting addresses to coordinates and vice versa, useful for location-based applications.
• LocationIQ: Offers geocoding, routing, and mapping APIs based on OpenStreetMap data, suitable for a wide range of location-aware applications.

Getting started

Since Queimadas INPE primarily offers data through its web interface and downloadable files rather than a direct API, programmatic access often involves web scraping or automated data retrieval. The following Python example demonstrates how one might programmatically download a daily CSV file of fire detections, assuming a predictable URL structure for daily data (this URL is illustrative and would need to be verified against the current Queimadas INPE website structure).

This example uses the requests library to download a CSV file and pandas to read and display its first few rows. You would need to adapt the URL to the exact format provided by Queimadas INPE for the specific dataset you wish to access.

import requests
import pandas as pd
from datetime import datetime

# Construct the URL for today's fire data (illustrative URL structure)
# You would need to verify the exact URL format from the Queimadas INPE documentation or website
today = datetime.now().strftime("%Y%m%d")
data_url = f"https://queimadas.dgi.inpe.br/queimadas/export/csv/{today}_active_fires.csv"

try:
    response = requests.get(data_url)
    response.raise_for_status() # Raise an HTTPError for bad responses (4xx or 5xx)

    # Save the content to a local CSV file
    file_name = f"inpe_fires_{today}.csv"
    with open(file_name, 'wb') as f:
        f.write(response.content)
    print(f"Successfully downloaded data to {file_name}")

    # Read the CSV into a pandas DataFrame and display the first 5 rows
    df = pd.read_csv(file_name)
    print("\nFirst 5 rows of the downloaded data:")
    print(df.head())

except requests.exceptions.HTTPError as errh:
    print(f"Http Error: {errh}")
except requests.exceptions.ConnectionError as errc:
    print(f"Error Connecting: {errc}")
except requests.exceptions.Timeout as errt:
    print(f"Timeout Error: {errt}")
except requests.exceptions.RequestException as err:
    print(f"Something went wrong: {err}")
except FileNotFoundError:
    print(f"Could not open or write to {file_name}")

Before running this code, ensure you have the requests and pandas libraries installed (pip install requests pandas). Always refer to the official Queimadas INPE documentation for the most current data access methods and file formats.