Multi-modal temporal CNNs for live fuel moisture content estimation. (October 2022)

• Record Type: Journal Article
• Authors: Miller, Lynn; Zhu, Liujun; Yebra, Marta; Rüdiger, Christoph; Webb, Geoffrey I.
• Is Part Of: Environmental modelling & software. Volume 156(2022)
• Publication Date: 2022-10
• Subjects: Live fuel moisture content -- MODIS -- Convolutional neural network -- Time series analysis -- Fire risk -- Deep learning ensembles; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105467
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Live fuel moisture content (LFMC) is an important environmental indicator used to measure vegetation conditions and monitor for high fire risk conditions. However, LFMC is challenging to measure on a wide scale, thus reliable models for estimating LFMC are needed. Therefore, this paper proposes a new deep learning architecture for LFMC estimation. The architecture comprises an ensemble of temporal convolutional neural networks that learn from year-long time series of meteorological and reflectance data, and a few auxiliary inputs including the climate zone. LFMC estimation models are designed for two training and evaluation scenarios, one for sites where historical LFMC measurements are available (within-site), the other for sites without historical LFMC measurements (out-of-site). The models were trained and evaluated using a large database of LFMC samples measured in the field from 2001 to 2017 and achieved an RMSE of 20.87% for the within-site scenario and 25.36% for the out-of-site scenario. Highlights: A deep learning architecture for large-scale live fuel moisture content estimation. Multi-modal models which combine timeseries of meteorological and reflectance data. The models achieve higher accuracy than previous state of the art models. An ensemble of models is used to provide measures of estimation uncertainty. Provides a detailed analysis of the models' strengths and weaknesses.