Spectral mixture analysis to assess post-fire vegetation regeneration using Landsat Thematic Mapper imagery: Accounting for soil brightness variation

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• 作者：S. Veraverbeke^a ;   ; ^b ;   ; ^{Sander.S.Veraverbeke@jpl.nasa.gov} ; B. Somers^c ;   ; ^{ben.somers@vito.be} ; I. Gitas^d ;   ; ^{igitas@for.auth.gr} ; T. Katagis^d ;   ; ^{thkatag@for.auth.gr} ; A. Polychronaki^d ;   ; ^{anpolych@for.auth.gr} ; R. Goossens^a ;   ; ^{rudi.goossens@ugent.be}
• 关键词：Fire ; Vegetation recovery ; Landsat Thematic Mapper ; Spectral mixture analysis ; MESMA ; Segmentation
• 刊名：International Journal of Applied Earth Observation and Geoinformation
• 出版年：2012
• 出版时间：February 2012
• 年：2012
• 卷：14
• 期：1
• 页码：1-11
• 全文大小：1869 K

文摘

Post-fire vegetation cover is a crucial parameter in rangeland management. This study aims to assess the post-fire vegetation recovery 3 years after the large 2007 Peloponnese (Greece) wildfires. Post-fire recovery landscapes typically are mixed vegetation¨Csubstrate environments which makes spectral mixture analysis (SMA) a very effective tool to derive fractional vegetation cover maps. Using a combination of field and simulation techniques this study aimed to account for the impact of background brightness variability on SMA model performance. The field data consisted out of a spectral library of in situ measured reflectance signals of vegetation and substrate and 78 line transect plots. In addition, a Landsat Thematic Mapper (TM) scene was employed in the study. A simple SMA, in which each constituting terrain feature is represented by its mean spectral signature, a multiple endmember SMA (MESMA) and a segmented SMA, which accounts for soil brightness variations by forcing the substrate endmember choice based on ancillary data (lithological map), were applied. In the study area two main spectrally different lithological units were present: relatively bright limestone and relatively dark flysch (sand-siltstone). Although the simple SMA model resulted in reasonable regression fits for the flysch and limestones subsets separately (coefficient of determination R² of respectively 0.67 and 0.72 between field and TM data), the performance of the regression model on the pooled dataset was considerably weaker (R² = 0.65). Moreover, the regression lines significantly diverged among the different subsets leading to systematic over-or underestimations of the vegetative fraction depending on the substrate type. MESMA did not solve the endmember variability issue. The MESMA model did not manage to select the proper substrate spectrum on a reliable basis due to the lack of shape differences between the flysch and limestone spectra,. The segmented SMA model which accounts for soil brightness variations minimized the variability problems. Compared to the simple SMA and MESMA models, the segmented SMA resulted in a higher overall correlation (R² = 0.70), its regression slope and intercept were more similar among the different substrate types and its resulting regression lines more closely resembled the expected one-one line. This paper demonstrates the improvement of a segmented approach in accounting for soil brightness variations in estimating vegetative cover using SMA. However, further research is required to evaluate the model's performance for other soil types, with other image data and at different post-fire timings.