Postfire burn-severity classification of the Hayman Fire, CO: based on hyperspectral data

Laes, D.; Maus, P.; Lewis, S.; Robichaud, P.; Kokaly, R. 2004. Postfire burn-severity classification of the Hayman Fire, CO: based on hyperspectral data. JFSP RFP 2001-2 Task 1. Remote Sensing Applications Center Project Report RSAC-0068-RPT1. Salt Lake City, UT: US Department of Agriculture Forest Service, Remote Sensing Application Center. 29 p.

Keywords: burn severity, Hayman fire

Links: PDF [2.8 MB] From RSAC site

Abstract: The objectives of this project were to assess the potential of hyperspectral data to determine burn severity after wildfires and evaluate if the high spectral information makes it possible to detect water-repellency conditions of the soil. A burn-severity map helps prioritize rehabilitation efforts after a fire, reducing subsequent soil erosion and other problems. Generating such a classification requires developing a methodology to analyze the data and compiling a library containing spectra of burn-related materials.

Existing burn-severity classifications based on multispectral broadband remote sensing data have a few drawbacks that using higher spectral and spatial data can help solve. To analyze this more detailed information, two types of field data were collected in selected locations: plot data related to water repellency and burn severity, as well as field spectrometer data about surface materials that could be related to the image spectra.

The imagery data were analyzed using two methods. One method selects two bands of the short-wave infrared part of the electromagnetic spectrum to generate a simple ratio. This method is analogous to the normalized burn ratio that currently classifies broadband multispectral data but makes use of the additional narrow bands available in hyperspectral imagery, making it easier to target the wavelength used in the analysis. The second method, based on the mixture-tuned, matched-filtering algorithm, is a more traditional hyperspectral technique. The spectra comprising the signal present in a pixel are unmixed based on .pure. end-member spectra generated from material present on the surface.

The ratio method has the advantage of delivering results fairly fast compared to the second method because there is less need to preprocess the data accurately. However, just as the multispectral ratio does, this method produces a result for every pixel in the image whether it is related to the burn or not. The unmixing method results in less false positives and relates the classification of the data to materials present on the ground. Unmixing the data is, however, time consuming and very sensitive to preprocessing procedures such as atmospheric correction.

Relating the spectra to soil water-repellency conditions needs additional research. The orthorectification of the imagery was too coarse for accurate correlation with field data. But even with better rectification, this problem may be hard to solve. Determining water-repellency conditions often requires access to the soil just below the surface. Since visible and near-infrared wavelengths cannot penetrate the soil particles, remote sensing may not be the right tool. In locations where the fire has not burned the canopy, soil conditions cannot be assessed at all.

Moscow FSL publication no. 2004p