Evaluating Endmember and Band Selection Techniques for Multiple Endmember Spectral Mixture Analysis using Post-Fire Imaging Spectroscopy

Zachary Tane,Sander Veraverbeke,Susan Ustin,Dar Roberts,Ángeles Casas,Carlos Ramirez

doi:10.3390/rs10030389

Abstract

Fire impacts many vegetated ecosystems across the world. The severity of a fire is major component in determining post-fire effects, including soil erosion, trace gas emissions, and the trajectory of recovery. In this study, we used imaging spectroscopy data combined with Multiple Endmember Spectral Mixture Analysis (MESMA), a form of spectral mixture analysis that accounts for endmember variability, to map fire severity of the 2013 Rim Fire. We evaluated four endmember selection approaches: Iterative Endmember Selection (IES), count-based within endmember class (In-CoB), Endmember Average Root Mean Squared Error (EAR), and Minimum Average Spectral Angle (MASA). To reduce the dimensionality of the imaging spectroscopy data we used uncorrelated Stable Zone Unmixing (uSZU). Fractional cover maps derived from MESMA were validated using two approaches: (1) manual interpretation of fine spatial resolution WorldView-2 imagery; and (2) ground plots measuring the Geo Composite Burn Index (GeoCBI) and the percentage of co-dominant and dominant trees with green, brown, and black needles. Comparison to reference data demonstrated fairly high correlation for green vegetation and char fractions (r2 values as high as 0.741 for the MESMA ash fractions compared to classified WorldView-2 imagery and as high as 0.841 for green vegetation fractions). The combination of uSZU band selection and In-CoB endmember selection had the best trade-off between accuracy and computational efficiency. This study demonstrated that detailed fire severity retrievals based on imaging spectroscopy can be optimized using techniques that would be viable also in a satellite-based imaging spectrometer.

Highlights

Fire behavior, size, and severity are changing in the western United States [1,2,3]
The balance of library complexity with accuracy must be considered if Multiple Endmember Spectral Mixture Analysis (MESMA) is to be used on spaceborne imaging spectroscopy at regional to global scales for ecological monitoring [58]
Using uncorrelated Stable Zone Unmixing (uSZU) In-CoB to generate MESMA cover fractions produced relatively high r2 values when compared to WorldView-2, even though the number of endmembers used and processing times were significantly less

Summary

Introduction

Size, and severity are changing in the western United States [1,2,3]. To fully comprehend these changes, techniques to reliably map fire effects over large areas are required. Variation in fire severity can effectively be broken down into detectable differences in the relative abundance of char, green vegetation, dead vegetation, and bare soil; remotely sensed fire severity assessments are essentially based on mixtures composed of these four constituents. Under this paradigm, Spectral Mixture Analysis (SMA), in which reflectance is assumed to a be a linear combination of components or endmembers at a subpixel level [11,12], represents a viable alternative to NBR-based analysis, potentially overcoming NBR’s sensitivity to different cover type and soil brightness variation [13,14]. SMA has been used previously to characterize tree mortality and soil char cover [10,15,16,17,18]

Methods

Results

Conclusion