The NOAA IR serves as an archival repository of NOAA-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by NOAA or funded partners.
As a repository, the NOAA IR retains documents in their original published format to ensure public access to scientific information.
i
Global fire modelling and control attributions based on the ensemble machine learning and satellite observations
-
2023
-
-
Source: Science of Remote Sensing, 7, 100088
[PDF-13.04 MB]
Details:
-
Journal Title:Science of Remote Sensing
-
Personal Author:
-
NOAA Program & Office:
-
Description:Contemporary fire dynamics is one of the most complex and least understood land surface phenomena. Global fire controls related to climate, vegetation, and anthropogenic activity are usually intertwined, and difficult to disentangle in a quantitative way. Here, we leveraged an ensemble of five machine learning (ML) models and multiple satellite-based observations to conduct global fire modeling for three fire metrics (burned area, fire number, and fire size), and quantified driving mechanisms underlying annual fire changes in a spatially resolved manner for the period 2003–2019. Ensemble learning is a meta-approach that combines multiple ML predictions to improve accuracy, robustness, and generalization performance. We found that the optimized ensemble ML well reproduced annual dynamics of global burned area (R2 = 0.90, P < 0.001), total fire numbers (R2 = 0.86, P < 0.001), and averaged fire size (R2 = 0.70, P < 0.001). Additionally, the ensemble ML captured key spatial patterns of multi-year mean magnitudes, annual variabilities, anomalies, and trends for different fire metrics. Our ML-based fire attributions further highlighted the dominant role of enhanced anthropogenic activity in reducing global burned area (−1.9 Mha/yr, P < 0.01), followed by climate control (−1.3 Mha/yr, P < 0.01) and insignificant positive vegetation control (0.4 Mha/yr, P = 0.60). Spatially, climate dominated a much larger burned area (53.7%) than human (23.4%) or vegetation control (22.9%); however, the counteracting effects from regional wetting and drying trends weakened the net climate impacts on global burned area. The fire number and fire size exhibited similar spatial control patterns with burned area; globally, however, fire number tended to be more affected by climate while fire size more influenced by human activities. Overall, our study confirmed the feasibility and efficiency of ensemble ML in global fire modeling and subsequent control attributions, providing a better understanding of contemporary fire regimes and contributing to robust fire projections in a changing environment.
-
Keywords:
-
Source:Science of Remote Sensing, 7, 100088
-
DOI:
-
ISSN:2666-0172
-
Format:
-
Publisher:
-
Document Type:
-
License:
-
Rights Information:CC BY-NC-ND
-
Compliance:Library
-
Main Document Checksum:
-
Download URL:
-
File Type:
