首页|Landsat spectral unmixing analysis for mapping herbaceous fractional cover in wildfire-prone Mediterranean-type ecosystems
Landsat spectral unmixing analysis for mapping herbaceous fractional cover in wildfire-prone Mediterranean-type ecosystems
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NETL
NSTL
Taylor & Francis
ABSTRACT Portions of Southern California’s native shrubland communities are being replaced by invasive herbaceous vegetation. These non-native species can increase the risk of wildfire ignition and spread. Expansion of these competitive invasive species in recently burned areas following a wildfire can lead to complete conversion and replacement of native shrubs and trees, which in turn increases the likelihood of future wildfire that spreads rapidly and widely through a positive feedback loop: the grass-fire cycle. Despite the association between herbaceous abundance and wildfire risk, image processing approaches for identification and quantification of fractional herbaceous cover in Southern California shrublands are not well established. The objective of this study is to comparatively assess the accuracy of herbaceous cover estimation and mapping based on three different unmixing models applied to Landsat multispectral data for San Diego County, U.S.A. during 2020. The models included: spectral mixture analysis (SMA) using a single set of spectral endmembers; multiple endmember SMA (MESMA); and temporal mixture model (TMM) analysis of year-long stacks of spectral indices computed from multiple Landsat acquisitions. Feature inputs included single date, multi-date, and spectral reflectance and spectral vegetation index (normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)) combinations. When compared to reference data generated from aerial imagery, results demonstrated that SMA applied to a date during the summer season (August) estimated unburned and intact herbaceous cover most accurately (mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) values of 8.85%, 12.02%, and 0.85, respectively). Therefore, Landsat unmixing model results suggest that mapping, reconstructing, and monitoring of herbaceous cover at the 10% accuracy level is appropriate. These methods will enable improved detection of sensitive habitats in Mediterranean-type ecosystems around the world by satellite for wildfire-prone communities and identify target areas for monitoring and mitigating the grass-fire cycle.
Spectral unmixinglandsatremote sensingwildfire
Krista R. Lee West、Douglas A. Stow、Daniel J. Sousa、Dar A. Roberts、John F. O’Leary
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San Diego State University||University of California
NETL
NSTL
Taylor & Francis
