Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.envsoft.2022.105467 | Environmental Modelling & Software |
Keywords | DocType | Volume |
Live fuel moisture content,MODIS,Convolutional neural network,Time series analysis,Fire risk,Deep learning ensembles | Journal | 156 |
ISSN | Citations | PageRank |
1364-8152 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lynn Miller | 1 | 0 | 0.34 |
| Liujun Zhu | 2 | 0 | 0.34 |
| Marta Yebra | 3 | 0 | 0.34 |
| Christoph Rüdiger | 4 | 190 | 23.56 |
| Geoffrey I. Webb | 5 | 3130 | 234.10 |