Research

Wildfire Remote Sensing

Drones, AI, and GIS for prescribed fire mapping, home wildfire risk, and forest recovery after fire.

GeoFly Lab, University of California, Santa Cruz · Funded by NASA through the FireSage program

GeoFly Lab uses drone remote sensing to study how fire moves through a landscape, how it threatens homes, and how forests recover afterward. Working with CAL FIRE and the Wildfire Interdisciplinary Research Center (WIRC), we fly thermal, multispectral, LiDAR, and RGB sensors before, during, and after fire events, capturing fire dynamics that are often invisible from the ground.

Drone view of smoke and flames during a prescribed burn
Figure 1. A prescribed burn recorded from a GeoFly Lab drone.

1. Prescribed fire mapping

During prescribed burns, thermal drones see through dense smoke and record the structure of active fire in real time. The georeferenced thermal video resolves flame intensity and rate of spread at fine spatial scales. Combined with measurements of fuel type, terrain, and wind, these observations feed GIS-based models that simulate fire progression and improve prediction.

Crews on the ground often cannot see the fire front through smoke. We therefore train computer vision models on thermal drone video to detect the head of the fire automatically, giving firefighters precise, real-time information on where the fire is moving. The same archive of thermal imagery serves as training data for automated analysis of fire behavior.

Thermal image of a burn next to matching RGB imagery, with sample areas outlined
Figure 2. Thermal imagery (left) compared with RGB imagery (right) of the same area during a burn.

LiDAR surveys map fuel volume and measure fuel change before and after each burn, quantifying vegetation structure and fire impact across the landscape. Multispectral sensors, including near-infrared bands, track vegetation condition, burn severity, and recovery over time. Together with weather-tower data and high-resolution RGB imagery, these sensors form a multi-sensor platform for prescribed fire management.

LiDAR-derived elevation and classification maps, and multispectral imagery before and after fire
Figure 3. LiDAR products and multispectral imagery before and after a prescribed burn.

Field data collection was led by Owen Hussey (M.A. Geography, 2025), and analysis by Dr. Xiangyu Ren, who applied AI and GIS methods to turn the imagery into fire-science results. The study is presented in the Canyon Fire Experiment story map, with an accompanying 3D scene of the study canyon.

2. Home Ignition Zone

The Home Ignition Zone (HIZ) project, a collaboration between GeoFly Lab and the Wilkin Fire Ecology Lab, extends earlier on-the-ground surveys of conditions around homes. Drones and remote sensing now allow wildfire risk to be assessed across whole communities, with methods that are faster, more consistent, and more scalable than traditional field evaluations. The work is integrated with WIRC, CAL FIRE, and industry mentors through the NSF Industry–University Cooperative Research Center (IUCRC) program, linking the science to insurance, utility management, and state wildfire planning.

Home Ignition Zone assessment map of a property, and slope-based defensible space guidance
Figure 4. Mapping a property’s Home Ignition Zone. Recommended defensible-space distances increase with slope.

Study sites include fire-affected and high-risk communities in Paradise, Tahoe Donner, and Santa Cruz. High-resolution drone imagery and thermal data map vegetation, building materials, and potential ember pathways, and are combined with NASA satellite time series and street-level imagery for a multi-scale view of risk. Pairing these products with curbside and full-property evaluations captures details that standard defensible-space inspections often miss, such as vent mesh size or gaps in construction materials.

Community engagement is central to the project. We speak with residents about wildfire risk, home hardening, and the barriers they face in carrying out mitigation, so that recommendations are practical and specific to each community. HIZ fieldwork was led by Henri Brillon (M.A. Geography, 2025), who coordinated site evaluations and connected community surveys with geospatial data collection.

3. Post-fire recovery at San Vicente Redwoods

San Vicente Redwoods is among the most ecologically important coastal mixed evergreen forests in Northern California. Since the 2020 CZU Lightning Complex fire, GeoFly Lab and partners at WIRC, NASA Ames, and San José State University have studied how fire severity shapes forest structure, aboveground biomass, and long-term recovery.

We combine satellite remote sensing, drone surveys, and Continuous Forest Inventory plot data. NASA GEDI LiDAR and Sentinel-2 imagery are used to map burn severity and to track aboveground biomass before the fire, one year after, and three years after, revealing clear links between fire severity and tree mortality. Drone multispectral and LiDAR surveys add fine-scale detail on canopy structure, allowing biomass loss to be quantified accurately and species-specific recovery to be followed: coast live oak and Douglas-fir showed high mortality under high burn severity, while tanoak and madrone resprouted differently in moderately burned areas.

Maps and charts of plant cover from field plots, grouped by burn severity
Figure 5. Plant cover measured in field plots, compared across burn-severity classes.

By linking field ecology, remote sensing, and fire science, this work gives land managers, conservation organizations, and policymakers the information they need for forest restoration and carbon management in the Santa Cruz Mountains. It was led by student researcher Melina Kompella.

Three-dimensional drone LiDAR point cloud of a forest
Figure 6. Drone LiDAR point cloud of the forest canopy at San Vicente Redwoods.

4. Training and funding

The research is supported by NASA through the FireSage program. Through FireSage, undergraduate interns and graduate researchers analyze the drone and satellite data collected at our study sites and gain hands-on experience with thermal, multispectral, and LiDAR analysis, preparing the next generation of wildfire scientists and practitioners.

← All research