Wildfire Monitoring in Southeast Asia: Causes, Data, and Analytical Tools

Wildfires are a growing concern in Southeast Asia (SEA), where climate variability, land-use changes, and agricultural practices combine to create conditions conducive to widespread fires. In 2023, Indonesia experienced a significant surge in wildfires, with the total area burned reaching 1.16 million hectares (2.87 million acres), a fivefold increase from 2022. Countries such as Indonesia, Malaysia, Thailand, and Myanmar regularly experience seasonal wildfires, often exacerbated by drought and the practice of slash-and-burn farming. These fires contribute to transboundary haze, biodiversity loss, and massive greenhouse gas emissions.

As the impact of wildfires intensifies, so does the need for real-time monitoring, predictive analysis, and actionable insights. The intersection of satellite Earth observation (EO), geospatial analytics, and machine learning is reshaping how we detect, understand, and respond to fire events.

In this article, I’ll explore:

• The main causes of wildfires in Southeast Asia

• Where and how to access fire-related satellite data

• Tools and methods to analyze this data

• Software commonly used by wildfire analysts

• And how all this links to operational geospatial work, including that done by organizations like ICEYE, a leader in satellite-based disaster monitoring using Synthetic Aperture Radar (SAR)

What Causes Wildfires in Southeast Asia?

Between January and September 2023, the Ministry of Environment and Forestry identified 2,608 hotspots, nearly six times the 441 hotspots during the same period in 2022. Wildfires in SEA arise from a complex mix of environmental, climatic, and anthropogenic drivers. Unlike many temperate regions, fires here are rarely caused by lightning. Instead, most ignition sources are human-related, contributing to 98% of all fires while broader landscape and climate patterns determine fire spread and intensity. Let’s look at the following reasons why:

1. Slash-and-Burn Agriculture

Traditional land-clearing methods like slash-and-burn are a dominant driver of wildfires, especially in Indonesia. Farmers intentionally set fires to clear vegetation for crops such as oil palm, rice, and rubber. These fires often escape control, spreading across forests and degraded peatlands.

2. Peatland Vulnerability

Peatlands are terrestrial wetland ecosystems in which waterlogged conditions prevent plant material from fully decomposing. SEA contains some of the largest tropical peatlands in the world, particularly in Sumatra, Kalimantan, and Papua. When peat soils are drained or disturbed, they become extremely flammable. Peat fires can smolder underground for weeks, are hard to extinguish, and release vast amounts of carbon into the atmosphere.

3. Land Use Change & Deforestation

Fragmented landscapes — caused by roads, logging, and plantations — are more prone to fires. Edges of cleared forests dry out faster and create corridors for fire spread. Deforestation also reduces local humidity and wind resistance, further increasing flammability.

4. El Niño and Climate Variability

El Niño is a warming of the ocean surface, or above-average sea surface temperatures, in the central and eastern tropical Pacific Ocean. El Niño years bring prolonged drought to SEA, lowering vegetation moisture and increasing fire risk. Major fire outbreaks occurred during El Niño events in 1997-98, 2015, and 2019, often resulting in regional haze crises affecting millions.

5. Human Negligence & Illegal Activities

Fires are also started by:

• Unattended campfires or agricultural burns

• Arson for land grabbing

• Poorly managed fire suppression policies

Reducing the frequency of forest fires by just 1 percent could generate a net benefit of between US$17 million and $145 million. Even the lower estimate of health benefits would exceed the agricultural gains from continued forest fires.

Where to Get Wildfire and Environmental Data

Monitoring wildfires effectively requires access to reliable, frequent, and spatially detailed data. Here are some of the most valuable sources:

Fire Detection (Active Fires & Hotspots)

• NASA FIRMS

  • MODIS (Moderate Resolution Imaging Spectroradiometer)

  • VIIRS (Visible Infrared Imaging Radiometer Suite)

  • Real-time fire alerts, downloadable CSV/KML, web apps

  • 🔗 https://firms.modaps.eosdis.nasa.gov/

• Sentinel-2 MSI (Copernicus)

  • High-res (10m) optical imagery for visual fire/burn mapping

  • 🔗 https://scihub.copernicus.eu/

• Sentinel-1 SAR

  • All-weather, day/night radar imagery useful for cloudy regions or nighttime fires

  • 🔗 https://scihub.copernicus.eu/

• ICEYE (SAR Constellation)

  • Commercial, rapid revisit radar imagery for persistent fire and flood monitoring

  • Used for actionable, high-urgency analysis

Land Cover, Vegetation, and Risk Context

• ESA WorldCover (Global 10m land cover)

• MODIS Land Cover Type (MCD12Q1)

• Global Peatland Database (Greifswald Moor Centrum)

• Wetlands International Peat Data

Climate & Weather

• NOAA GFS (Global Forecast System)

• ECMWF ERA5 (Reanalysis climate data for temperature, humidity, wind)

• TRMM / GPM for precipitation

• TROPOMI / MODIS AOD for smoke and haze detection

How to Analyze Wildfire Data

Once you've gathered the data, here’s how analysts typically process and extract insights from it.

1. Hotspot Time Series Analysis

Using fire detection data (MODIS/VIIRS), you can:

• Count daily/weekly fire incidents

• Visualize spatial clustering

• Compare across years or administrative regions

Tools: Python (pandas, matplotlib, geopandas), Google Earth Engine

Example: Plotting monthly hotspot density in Sumatra from 2015–2024.

2. SAR-Based Fire Monitoring (for Peat and Cloudy Areas)

SAR sensors like Sentinel-1 or ICEYE are invaluable where optical sensors fail. Analysts use:

• Pre/post event comparison of backscatter

• Time series change detection

• Thresholding or ML classification to detect burn scars

Tools: ESA SNAP, Python (rasterio, numpy), QGIS, ICEYE platform

3. Burned Area Mapping

Burn severity can be estimated using vegetation indices such as:

• NDVI (Normalized Difference Vegetation Index)

• NBR (Normalized Burn Ratio)

• dNBR (difference NBR pre- and post-fire)

Tools: Google Earth Engine, Sentinel Hub, QGIS/SNAP

4. Fire Risk Mapping

By integrating multiple layers — elevation, vegetation, land use, and climate — you can build fire risk maps using:

• Weighted overlays

• Machine learning models (Random Forest, SVM)

Tools: QGIS, ArcGIS Pro, Python (scikit-learn), Google Earth Engine

Tools & Software Used in Wildfire Monitoring

Final Thoughts: The Role of Geospatial Analysts in Wildfire Intelligence

Wildfires are no longer just an environmental issue—they're an operational, political, and humanitarian one. Analysts must combine EO data, field reports, and real-time alerts to provide actionable insights to decision-makers.

Platforms like ICEYE, with their SAR constellation and rapid-delivery products, are enabling the kind of near-real-time monitoring needed for disaster response and mitigation. A GIS Operational Analyst in this field should be fluent in:

• Remote sensing workflows

• Data fusion (optical + SAR + weather)

• Risk modeling and map production

• Communicating clearly with both technical and non-technical teams

Resources & Links

• 🔥 NASA FIRMS Fire Data Portal

• 🛰️ Sentinel Open Access Hub

• 📊 Google Earth Engine

• 🌍 ICEYE Website

• 🗺️ Global Forest Watch

• 🐍 geemap on GitHub

References

International Peatland Society (2019). What are peatlands? - International Peatland Society. [online] International Peatland Society. Available at: https://peatlands.org/peatlands/what-are-peatlands/.

‌USGS (2024). What is ‘El Niño’ and what are its effects? | U.S. Geological Survey. [online] www.usgs.gov. Available at: https://www.usgs.gov/faqs/what-el-nino-and-what-are-its-effects.

‌UNDRR (2024). Indonesia wildfires, 2023 - Forensic analysis. [online] Undrr.org. Available at: https://www.undrr.org/resource/indonesia-wildfires-2023-forensic-analysis.

💥 Into Wildfire Monitoring & Earth Observation?

If this post helped you understand how we monitor and analyze wildfires using satellite data and geospatial tools, follow me here on Hashnode.

I share practical, beginner-friendly tutorials on wildfire mapping, remote sensing, and tools like Google Earth Engine, QGIS, and Python—focused on real-world environmental challenges.

Let’s harness geospatial tech to understand, monitor, and protect our planet 🔥🌍🛰️
~ Aishwarya

**Disclaimer: The ideas and research presented in this article are my own. I used ChatGPT to help structure, clarify, and format the content to make it more accessible and understandable for readers.