What are the best sources of remote sensing data for wildfire detection and monitoring?

1 Optical data

Optical data refers to images captured by sensors that record visible and near-infrared light reflected by the earth's surface. Optical data can be useful for detecting active fires, mapping fire perimeters, assessing fire severity, and estimating burned area. Some of the advantages of optical data are its high spatial resolution, wide availability, and low cost. Some of the disadvantages are its susceptibility to cloud cover, smoke, and atmospheric interference, and its limited temporal resolution. Some of the best optical data sources for wildfire detection and monitoring are Landsat, Sentinel-2, MODIS, VIIRS, and Planet.

2 Thermal data

Thermal data refers to images captured by sensors that record infrared radiation emitted by the earth's surface and the fire itself. Thermal data can be useful for detecting active fires, especially at night or under cloudy conditions, and for measuring fire temperature and radiative power. Some of the advantages of thermal data are its high sensitivity to fire, its ability to penetrate clouds and smoke, and its frequent temporal resolution. Some of the disadvantages are its low spatial resolution, its saturation at high fire temperatures, and its difficulty in distinguishing fire from other heat sources. Some of the best thermal data sources for wildfire detection and monitoring are MODIS, VIIRS, GOES, Landsat, Sentinel-3, and ASTER.

3 Radar data

Radar data refers to images captured by sensors that emit and receive microwave signals that interact with the earth's surface and vegetation. Radar data can be useful for mapping fire scars, detecting changes in vegetation structure and biomass, and estimating soil moisture and fuel moisture. Some of the advantages of radar data are its ability to penetrate clouds, smoke, and canopy, its independence from sunlight, and its high spatial resolution. Some of the disadvantages are its complexity, its sensitivity to surface roughness and topography, and its limited availability and accessibility. Some of the best radar data sources for wildfire detection and monitoring are Sentinel-1, ALOS-2, RADARSAT-2, and UAVSAR.

4 Lidar data

Lidar data refers to images captured by sensors that emit and receive laser pulses that measure the distance and reflectance of objects. Lidar data can be useful for mapping fire effects, detecting changes in vegetation height and density, and estimating fuel load and carbon emissions. Some of the advantages of lidar data are its high spatial resolution, its ability to capture vertical structure and canopy gaps, and its accuracy and precision. Some of the disadvantages are its high cost, its limited coverage and availability, and its difficulty in processing and interpretation. Some of the best lidar data sources for wildfire detection and monitoring are airborne lidar, ICESat-2, GEDI, and GLAS.

5 GIS tools

GIS tools are software applications that allow you to store, manipulate, analyze, and visualize geospatial data. GIS tools can help you integrate and compare different remote sensing data sources, perform spatial and temporal analysis, create maps and charts, and communicate your findings. Some of the advantages of GIS tools are their versatility, functionality, interoperability, and user-friendliness. Some of the disadvantages are their learning curve, their hardware and software requirements, and their data quality and compatibility issues. Some of the best GIS tools for wildfire detection and monitoring are ArcGIS, QGIS, Google Earth Engine, R, and Python.

6 Here’s what else to consider

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