Live fuel moisture content estimation from MODIS: A deep learning approach

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., 2016. Tensorflow: a system for large-scale machine learning. In: 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), pp. 265–283. Arganaraz, 2016, Estimation of live fuel moisture content from MODIS images for fire danger assessment in Southern Gran Chaco, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9, 5339, 10.1109/JSTARS.2016.2575366 Bottou, 2012, 421 Caccamo, 2012, Monitoring live fuel moisture content of heathland, shrubland and sclerophyll forest in south-eastern Australia using MODIS data, Int. J. Wildland Fire, 21, 257, 10.1071/WF11024 Chollett, F., 2015. Keras. In. https://keras.io. Chuvieco, 2014, Integrating geospatial information into fire risk assessment, Int. J. Wildland Fire, 23, 606, 10.1071/WF12052 Daly, 1994, A statistical-topographic model for mapping climatological precipitation over mountainous terrain, J. Appl. Meteorol. Climatol., 33, 140, 10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2 Danson, 2004, Estimating live fuel moisture content from remotely sensed reflectance, Remote Sens. Environ., 92, 309, 10.1016/j.rse.2004.03.017 Dasgupta, 2007, Evaluating remotely sensed live fuel moisture estimations for fire behavior predictions in Georgia, USA, Remote Sens. Environ., 108, 138, 10.1016/j.rse.2006.06.023 Davis, 1997, Variation of snow cover ablation in the boreal forest: a sensitivity study on the effects of conifer canopy, J. Geophys. Res.: Atmos., 102, 29389, 10.1029/97JD01335 Dillon, 2011, Both topography and climate affected forest and woodland burn severity in two regions of the western US, 1984 to 2006, Ecosphere, 2, art130, 10.1890/ES11-00271.1 Fan, 2018, Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region, Remote Sens. Environ., 205, 210, 10.1016/j.rse.2017.11.020 García, 2008, Combining AVHRR and meteorological data for estimating live fuel moisture content, Remote Sens. Environ., 112, 3618, 10.1016/j.rse.2008.05.002 Hall, D., Riggs, G., 2016. MODIS/Terra Snow Cover 8-Day L3 Global 500 m SIN Grid, Version 6. In. Boulder, Colorado USA: NASA National Snow and Ice Data Center Distributed Active Archive Center. Hao, 2007, Retrieval of real-time live fuel moisture content using MODIS measurements, Remote Sens. Environ., 108, 130, 10.1016/j.rse.2006.09.033 Jia, 2019, Estimating live fuel moisture using SMAP L-band radiometer soil moisture for Southern California, USA, Remote Sensing, 11, 1575, 10.3390/rs11131575 Jurdao, 2012, Modelling fire ignition probability from satellite estimates of live fuel moisture content, Fire Ecol., 8, 77, 10.4996/fireecology.0801077 Jurdao, 2013, Regional estimation of woodland moisture content by inverting Radiative Transfer Models, Remote Sens. Environ., 132, 59, 10.1016/j.rse.2013.01.004 Kingma, D.P., Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Li, 2018, Comparison of fire radiative power estimates from VIIRS and MODIS observations, J. Geophys. Res.: Atmos., 123, 4545, 10.1029/2017JD027823 Li, 2016, Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping, Int. J. Remote Sens., 37, 5632, 10.1080/01431161.2016.1246775 McCandless, 2020, Enhancing wildfire spread modelling by building a gridded fuel moisture content product with machine learning, Machine Learn.: Sci. Technol., 1 Monsivais-Huertero, 2018, Phenology-based backscattering model for corn at L-band, IEEE Trans. Geosci. Remote Sens., 56, 4989, 10.1109/TGRS.2018.2803153 Mouillot, 2002, Simulating climate change impacts on fire frequency and vegetation dynamics in a Mediterranean-type ecosystem, Glob. Change Biol., 8, 423, 10.1046/j.1365-2486.2002.00494.x Nolan, 2016, Large-scale, dynamic transformations in fuel moisture drive wildfire activity across southeastern Australia, Geophys. Res. Lett., 43, 4229, 10.1002/2016GL068614 Pelletier, 2019, Temporal convolutional neural network for the classification of satellite image time series, Remote Sensing, 11, 523, 10.3390/rs11050523 Peterson, 2008, Mapping live fuel moisture with MODIS data: a multiple regression approach, Remote Sens. Environ., 112, 4272, 10.1016/j.rse.2008.07.012 Pimont, 2019, A cautionary note regarding the use of cumulative burnt areas for the determination of fire danger index breakpoints, Int. J. Wildland Fire, 28, 254, 10.1071/WF18056 Pimont, 2019, Why is the effect of live fuel moisture content on fire rate of spread underestimated in field experiments in shrublands?, Int. J. Wildland Fire, 28, 127, 10.1071/WF18091 Qi, 2012, Monitoring live fuel moisture using soil moisture and remote sensing proxies, Fire Ecol., 8, 71, 10.4996/fireecology.0803071 Quan, 2016, Retrieval of grassland live fuel moisture content by parameterizing radiative transfer model with interval estimated LAI, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 9, 910, 10.1109/JSTARS.2015.2472415 Quan, 2021, Global fuel moisture content mapping from MODIS, Int. J. Appl. Earth Obs. Geoinf., 101, 102354, 10.1016/j.jag.2021.102354 Rao, 2020, SAR-enhanced mapping of live fuel moisture content, Remote Sens. Environ., 245, 111797, 10.1016/j.rse.2020.111797 Reichstein, 2019, Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195, 10.1038/s41586-019-0912-1 Riggs, G.A., Hall, D.K., Román, M.O., 2015. MODIS snow products collection 6 user guide. National Snow and Ice Data Center, Boulder, CO, USA, 66. Ruffault, 2018, How well do meteorological drought indices predict live fuel moisture content (LFMC)? An assessment for wildfire research and operations in Mediterranean ecosystems, Agric. For. Meteorol., 262, 391, 10.1016/j.agrformet.2018.07.031 Safran, I., Shamir, O., 2017. Depth-width tradeoffs in approximating natural functions with neural networks. In: International Conference on Machine Learning. PMLR, pp. 2979–2987. Schaaf, C., Wang, Z., 2015. MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted Ref Daily L3 Global - 500m. In: NASA EOSDIS Land Processes DAAC. Schlerf, 2006, Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data, Remote Sens. Environ., 100, 281, 10.1016/j.rse.2005.10.006 Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Machine Learn. Res., 15, 1929 Stow, 2007, Stability, normalization and accuracy of MODIS-derived estimates of live fuel moisture for southern California chaparral, Int. J. Remote Sens., 28, 5175, 10.1080/01431160701616129 Taylor, 2001, Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res.: Atmos., 106, 7183, 10.1029/2000JD900719 Trombetti, 2008, Multi-temporal vegetation canopy water content retrieval and interpretation using artificial neural networks for the continental USA, Remote Sens. Environ., 112, 203, 10.1016/j.rse.2007.04.013 Ulaby, 2014 Viegas, 2001, Estimating live fine fuels moisture content using meteorologically-based indices, Int. J. Wildland Fire, 10, 223, 10.1071/WF01022 Wang, 2019, Assessment of the dual polarimetric sentinel-1A data for forest fuel moisture content estimation, Remote Sensing, 11, 1568, 10.3390/rs11131568 Williams, 2016, Recent advances and remaining uncertainties in resolving past and future climate effects on global fire activity, Curr. Climate Change Rep., 2, 1, 10.1007/s40641-016-0031-0 Yebra, 2009, Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: solving the ill-posed inverse problem, Remote Sens. Environ., 113, 2403, 10.1016/j.rse.2009.07.001 Yebra, 2008, Estimation of live fuel moisture content from MODIS images for fire risk assessment, Agric. For. Meteorol., 148, 523, 10.1016/j.agrformet.2007.12.005 Yebra, 2013, A global review of remote sensing of live fuel moisture content for fire danger assessment: moving towards operational products, Remote Sens. Environ., 136, 455, 10.1016/j.rse.2013.05.029 Yebra, 2018, A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing, Remote Sens. Environ., 212, 260, 10.1016/j.rse.2018.04.053 Yebra, 2019, Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications, Sci. Data, 6, 1 Yuan, 2020, Deep learning in environmental remote sensing: achievements and challenges, Remote Sens. Environ., 241, 111716, 10.1016/j.rse.2020.111716 Zhu, 2019, A multi-frequency framework for soil moisture retrieval from time series radar data, Remote Sens. Environ., 235, 111433, 10.1016/j.rse.2019.111433 Zhu, 2019, Soil moisture retrieval from time series multi-angular radar data using a dry down constraint, Remote Sens. Environ., 231, 111237, 10.1016/j.rse.2019.111237