Retrieval of Live Fuel Moisture Content Based on Multi-Source Remote Sensing Data and Ensemble Deep Learning Model

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 17; p. 4378
• Main Authors: Xie, Jiangjian, Qi, Tao, Hu, Wanjun, Huang, Huaguo, Chen, Beibei, Zhang, Junguo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Biomass; canopy height; Deep learning; ensemble learning; Environmental risk; fire spread; Forest & brush fires; fuel moisture index; Fuels; Land cover; Landsat; Landsat satellites; live fuel moisture content; Machine learning; Model accuracy; MODIS; Moisture content; Moisture effects; multi-source remote sensing; Remote sensing; Retrieval; risk; Risk assessment; Stacking; Vegetation; Water content; Wildfires; United States > US
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14174378

Live fuel moisture content (LFMC) is an important index used to evaluate the wildfire risk and fire spread rate. In order to further improve the retrieval accuracy, two ensemble models combining deep learning models were proposed. One is a stacking ensemble model based on LSTM, TCN and LSTM-TCN models, and the other is an Adaboost ensemble model based on the LSTM-TCN model. Measured LFMC data, MODIS, Landsat-8, Sentinel-1 remote sensing data and auxiliary data such as canopy height and land cover of the forest-fire-prone areas in the Western United States, were selected for our study, and the retrieval results of different models with different groups of remote sensing data were compared. The results show that using multi-source data can integrate the advantages of different types of remote sensing data, resulting in higher accuracy of LFMC retrieval than that of single-source remote sensing data. The ensemble models can better extract the nonlinear relationship between LFMC and remote sensing data, and the stacking ensemble model with all the MODIS, Landsat-8 and Sentinel-1 remote sensing data achieved the best LFMC retrieval results, with R2  = 0.85, RMSE = 18.88 and ubRMSE = 17.99. The proposed stacking ensemble model is more suitable for LFMC retrieval than the existing method.