Version 1
: Received: 28 March 2018 / Approved: 29 March 2018 / Online: 29 March 2018 (06:06:32 CEST)
How to cite:
Ariza, A.; Salas Rey, J.F.; Merino de Miguel, S. Comparison of Maximum Likelihood Estimators and Regression Models in Mediterranean Forests Fires for Severity Mapping Using Landsat TM and ETM+ Data. Preprints2018, 2018030245. https://doi.org/10.20944/preprints201803.0245.v1
Ariza, A.; Salas Rey, J.F.; Merino de Miguel, S. Comparison of Maximum Likelihood Estimators and Regression Models in Mediterranean Forests Fires for Severity Mapping Using Landsat TM and ETM+ Data. Preprints 2018, 2018030245. https://doi.org/10.20944/preprints201803.0245.v1
Ariza, A.; Salas Rey, J.F.; Merino de Miguel, S. Comparison of Maximum Likelihood Estimators and Regression Models in Mediterranean Forests Fires for Severity Mapping Using Landsat TM and ETM+ Data. Preprints2018, 2018030245. https://doi.org/10.20944/preprints201803.0245.v1
APA Style
Ariza, A., Salas Rey, J.F., & Merino de Miguel, S. (2018). Comparison of Maximum Likelihood Estimators and Regression Models in Mediterranean Forests Fires for Severity Mapping Using Landsat TM and ETM+ Data. Preprints. https://doi.org/10.20944/preprints201803.0245.v1
Chicago/Turabian Style
Ariza, A., Javier F. Salas Rey and Silvia Merino de Miguel. 2018 "Comparison of Maximum Likelihood Estimators and Regression Models in Mediterranean Forests Fires for Severity Mapping Using Landsat TM and ETM+ Data" Preprints. https://doi.org/10.20944/preprints201803.0245.v1
Abstract
The severity of forest fires derived from remote sensing data for research and management has become increasingly widespread in the last decade, where these data typically quantify the pre- and post-fire spectral change between satellite images on multi-spectral sensors. However, there is an active discussion about which of the main indices (dNBR, RdNBR or RBR) is the most adequate to estimate the severity of the fire, as well about the adjustment model used in the classification of severity levels. This study proposes and evaluates a new technique for mapping severity as an alternative to regression models, based on the use of the maximum likelihood estimation (MLE) automatic learning algorithm, from GeoCBI field data and spectral indices dNBR, RdNBR and RBR applied to Landsat TM, ETM+ Images, for two fires in central Spain. We compare the severity discrimination capability on dNBR, RdNBR and RBR, through a spectral separability index (M) and then evaluated the concordance of these metrics with field data based on GeoCBI measurements. Specifically, we evaluated the correspondence (R2) between each metric and the continuous measurement of fire severity (GeoCBI) and the general precision of the regression and MLE models, for the four categorized levels of severity (Unburned, Low, Moderate, and High). The results show that the RBR has more spectral separability (average between two fires M = 2.00) that the dNBR (M = 1.82) and the RdNBR (M=1.80), additionally the GeoCBI has a better adjustment with the RBR of (R2 = 0.73), than the RdNBR (R2 = 0.72), and dNBR (R2 = 0.71). Finally, the overall classification accuracy achieved with the MLE (Kappa = 0.65) has a better result than regression models (Kappa = 0.58) and higher accuracy of individual classes.
Keywords
severity mapping; regression models; maximum likelihood; GeoCBI; dNBR; RdNBR; RBR
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
