A Framework for Multi-Dimensional Assessment of Wildfire Disturbance Severity from Remotely Sensed Ecosystem Functioning Attributes
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Ecological disturbanceEcosystem functioningEcosystem functioning attributes (EFAs)Fire severitySatellite image time-seriesWildfire
Marcos, B.; Gonçalves, J.; Alcaraz-Segura, D.; Cunha, M.; Honrado, J.P. A Framework for Multi-Dimensional Assessment of Wildfire Disturbance Severity from Remotely Sensed Ecosystem Functioning Attributes. Remote Sens. 2021, 13, 780. https://doi.org/ 10.3390/rs13040780
SponsorshipPortuguese national funds through FCT-Foundation for Science and Technology, I.P., under the GreenRehab project PCIF/RPG/0077/2017; Junta de Andalucia P18-RT-1927; Project DETECTOR A-RNM-256-UGR18; European Union Funds for Regional Development; Portuguese Foundation for Science and Technology European Commission; Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) European Commission; European Social Fund, within the 2014-2020 EU Strategic Framework, through FCT SFRH/BD/99469/2014; Individual Scientific Employment Stimulus Program (2017), through FCT CEECIND/02331/2017
Wildfire disturbances can cause modifications in different dimensions of ecosystem functioning, i.e., the flows of matter and energy. There is an increasing need for methods to assess such changes, as functional approaches offer advantages over those focused solely on structural or compositional attributes. In this regard, remote sensing can support indicators for estimating a wide variety of effects of fire on ecosystem functioning, beyond burn severity assessment. These indicators can be described using intra-annual metrics of quantity, seasonality, and timing, called Ecosystem Functioning Attributes (EFAs). Here, we propose a satellite-based framework to evaluate the impacts, at short to medium term (i.e., from the year of fire to the second year after), of wildfires on four dimensions of ecosystem functioning: (i) primary productivity, (ii) vegetation water content, (iii) albedo, and (iv) sensible heat. We illustrated our approach by comparing inter-annual anomalies in satellite-based EFAs in the northwest of the Iberian Peninsula, from 2000 to 2018. Random Forest models were used to assess the ability of EFAs to discriminate burned vs. unburned areas and to rank the predictive importance of EFAs. Together with effect sizes, this ranking was used to select a parsimonious set of indicators for analyzing the main effects of wildfire disturbances on ecosystem functioning, for both the whole study area (i.e., regional scale), as well as for four selected burned patches with different environmental conditions (i.e., local scale). With both high accuracies (area under the receiver operating characteristic curve (AUC) > 0.98) and effect sizes (Cohen’s |d| > 0.8), we found important effects on all four dimensions, especially on primary productivity and sensible heat, with the best performance for quantity metrics. Different spatiotemporal patterns of wildfire severity across the selected burned patches for different dimensions further highlighted the importance of considering the multi-dimensional effects of wildfire disturbances on key aspects of ecosystem functioning at different timeframes, which allowed us to diagnose both abrupt and lagged effects. Finally, we discuss the applicability as well as the potential advantages of the proposed approach for more comprehensive assessments of fire severity.