Mapping Wildfire Burn Scars with NASA EMIT Hyperspectral Data: A WISER-Based Remote Sensing Lab

This lab is designed for lower-division undergraduates with some coursework in environmental science, GIS, or remote sensing (e.g., a GIS, Environmental Problem Solving, Observing Earth from Above, or Remote Sensing of the Environment course). The content is readily adaptable to an upper-level undergraduate or introductory graduate remote sensing course.

Students should have basic familiarity with raster/satellite imagery concepts (raster data, spectral bands, reflectance) and general navigation of GIS or remote sensing software. No experience with hyperspectral data is required. Prior exposure to vegetation indices (e.g., NDVI) is helpful but not required as the NBR index and band math process are introduced within the activity itself.

This is lab 3 in a sequence of hyperspectral-thermal tutorials. It builds on students' earlier experience opening and visualizing EMIT imagery in WISER, and can be used either as a stand-alone exercise for instructors wanting a self-contained introduction to creating indices from hyperspectral data, evaluating pre- and post-disturbance, research for wildfire and burn-scar mapping, or as part of a broader hyperspectral remote sensing module.

Students will understand how hyperspectral reflectance data can be used to detect and characterize wildfire burn scars; learn the physical basis of the Normalized Burn Ratio (NBR) index, including how it contrasts near-infrared reflectance from healthy, structured vegetation against shortwave-infrared reflectance associated with moisture loss, ash, and charcoal. Students will learn how to interpret NBR maps and interpret spectral signatures differences between burned and unburned/vegetated land cover.

This activity moves students through the Applying, Analyzing, and Evaluating levels of Bloom's Revised Taxonomy.

At the Applying level, students construct and compute a derived spectral index (NBR) from raw reflectance bands, using a band math formula on real, unprompted satellite data rather than a worked example.

At the Analyzing level, students compare, contrast, and differentiate mean spectral signatures across two time points (pre- and post-fire) and across three spectral regions (visible, NIR, SWIR), identifying which wavelength ranges drive the observed change and distinguishing genuine vegetation/moisture loss from other possible surface signals.

At the Evaluating level, students appraise and critique the reliability of NBR as a proxy for burn severity — judging where low-NBR pixel values may reflect bare soil or shadow rather than true burn scar — and justify their interpretation of the spectral evidence in their written paragraph rather than treating the pseudo-colored map as ground truth.

Students will gain hands-on proficiency with a hyperspectral image-processing software package WISER (Workbench for Imaging Spectroscopy Exploration and Research), including loading multi-temporal NetCDF-4 satellite imagery, running band math expressions, applying color maps, delineating a region of interest, generating and collecting mean spectra, and exporting publication-quality plots. The activity also reinforces following and troubleshooting a complex multi-step technical workflow and writing a clear, evidence-based interpretation of quantitative results.

This lab is a 30-45 minute in-class activity designed to follow a lecture on how shortwave-infrared wavelengths from hyperspectral data can help us detect chemical composition and water in terrestrial ecosystems. The lab directs students to explore this in wildfire mapping applications.

The lab has five sections, centered on the January 2025 Eaton Fire that burned through the San Gabriel Mountains north of Altadena, CA. The exercise is conducted in WISER (Workbench for Imaging Spectroscopy Exploration and Research), which is an open-source, cross-platform GUI for visualizing and analyzing hyperspectral imagery. Built in Python on Qt/PySide, it runs on macOS, Windows, and Linux with no commercial license required.

(1) Generating the Normalized Burn Ratio Index — Students open paired pre-fire and post-fire EMIT L2A reflectance scenes (delivered as orthorectified, clipped NetCDF-4 files obtained via NASA's AppEEARS platform) in WISER, then calculate NBR using the band math expression (NIR − SWIR)/(NIR + SWIR), assigning Band 64 (857.59 nm) as NIR and Band 233 (2115.59 nm) as SWIR on the post-fire layer. Students apply a color-blind-friendly color ramp (YlOrBr) to visualize burn severity in pseudo-color, where unburned vegetation appears brown/orange and burn scars appear light yellow.

(2)Defining a Region of Interest (ROI) — Students zoom to the burn scar area and delineate a polygon ROI around the light-yellow (low-NBR) pixels in the San Gabriel Mountains, avoiding bare soil/shadow confounds where possible.

(3) Generating a Mean Spectrum Plot — Students generate and collect the mean reflectance spectrum of the ROI from both the pre-fire and post-fire layers on a single plot, labeling each by date, and interpret differences across the visible, NIR, and SWIR portions of the spectrum.
Exporting the Spectrum Plot — Students export the spectrum plot as a high-resolution image (EPS or PNG, 300 DPI) for use in their write-up.

(4) Assignment — Students submit a screenshot of their spectrum plot, a screenshot of their NBR index map, and a written paragraph, in their own words, describing the observed differences in reflectance between the pre-fire and post-fire spectra.

Materials needed:

(1) WISER software installed (MacOS or Windows) (https://ehlmann-research-group.github.io/WISER/#download-wiser). WISER is developed and maintained by the Ehlmann Research Group at Caltech & CU Boulder. For any questions on WISER, contact wiser_AT_lists.lasp.colorado.edu.

(2) Paired pre-fire and post-fire EMIT L2A reflectance scenes (NetCDF-4/.nc), orthorectified and clipped to the study area via NASA's AppEEARS tool. Images are provided as NetCDFs here.

(3) Lab instructions provided as a PDF.

Additional online availability:
This is the third lab in a series from a hyperspectral-thermal training series supported by NASA SMD Bridge Program Seed Funding (BPSF; 80NSSC24K1617) and NASA ECOSTRESS Science and Applications Team (ESAT; 80NSSC23K0309). This work contributes to "Observing Earth From Above" coursework available online at https://observingearthfromabove.com/.

Lab activity PDF (Acrobat (PDF) 66.2MB Aug10 26) 
Pre-wildfire EMIT NetCDF ( 176.7MB Aug10 26) 
Post-wildfire EMIT NetCDF ( 206.1MB Aug10 26) 

Data access: Instructors may provide students with already-clipped .nc files as the AppEEARS data request/processing step is not part of this lab and can take time to complete. This lab can be adapted to project-based learning or higher skill levels by asking students to request and clip alternative pre-fire and post-fire EMIT L2A scenes via NASA's AppEEARS tool.

Software setup: Install and test WISER on student or lab computers ahead of time. Band math calculations can take up to 20+ seconds (longer for larger granules), and generating a mean ROI spectrum can take ~30 seconds to 10+ minutes depending on the extend of the polygon created. This is worth telling students in advance so they don't assume the software has frozen.

Common confusion points: Students sometimes select the wrong image (pre- vs. post-fire) or the wrong band numbers in the Variable Bindings window when setting up the band math expression. Reviewing the correct Post-Fire/Band 64 (NIR)/Band 233 (SWIR) assignments as a class before releasing students to work independently can save troubleshooting time. The MacOS "Errno30" export error (noted in the tutorial) is also worth mentioning proactively to MacOS users.

Interpretation guidance: Remind students that near-zero NBR pixels can include bare soil or shadow, not just true burn scar. This is a good moment to reinforce the higher-order thinking goal of critically evaluating remote sensing output rather than treating the index as ground truth.

Student understanding is assessed through the Section 5 assignment, in which students submit:
(a) 1 screenshot of their spectrum plot, 
(b) 1 screenshot of their NBR index map, and 
(c) 1 written paragraph explaining, in their own words, the differences in reflectance between the pre-fire and post-fire spectra.

Suggested grading criteria:

Technical execution — correct band selections and band math expression; a pseudo-colored NBR map that clearly distinguishes burned from unburned areas; a correctly labeled, exported spectrum plot with both pre- and post-fire spectra present.

Interpretation accuracy — the written paragraph correctly connects observed spectral differences (lower visible reflectance and higher NIR reflectance pre-fire; the reverse post-fire; higher SWIR reflectance post-fire) to the underlying physical causes (loss of vegetation structure and chlorophyll absorption, reduced moisture, presence of ash/char).

Critical evaluation (optional, for higher-level courses) — whether students note limitations of the NBR index or ROI delineation (e.g., possible inclusion of bare soil/shadow pixels) rather than treating the map as unambiguous ground truth.

NASA EMIT mission overview: https://earth.jpl.nasa.gov/emit/mission/about/

NASA AppEEARS (used to obtain and clip EMIT scenes): https://appeears.earthdatacloud.nasa.gov/

WISER software: https://ehlmann-research-group.github.io/WISER/#download-wiser

Coursework and additional lectures: https://observingearthfromabove.com/