Publications

Miller, L; Zhu, LJ; Yebra, M; Rudiger, C; Webb, GI (2022). Multi-modal temporal CNNs for live fuel moisture content estimation. ENVIRONMENTAL MODELLING & SOFTWARE, 156, 105467.

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.

DOI:
10.1016/j.envsoft.2022.105467

ISSN:
1873-6726