International ID for Author Rights and protection Intellectual Property


Why Publish Your Article in IJSURP


Public Research


Articles from over
100 Countries

Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning

Authore(s) : Yifang Ban || Division of GeoinformaticsKTH Royal Institute of Technology10044StockholmSweden

Volume : (16), Issue : 210, January - 2020

Abstract : In recent years, the world witnessed many devastating wildfires that resulted in destructive human and environmental impacts across the globe. Emergency response and rapid response for mitigation calls for effective approaches for near real-time wildfire monitoring. Capable of penetrating clouds and smoke, and imaging day and night, Synthetic Aperture Radar (SAR) can play a critical role in wildfire monitoring. In this communication, we investigated and demonstrated the potential of Sentinel-1 SAR time series with a deep learning framework for near real-time wildfire progression monitoring. The deep learning framework, based on a Convolutional Neural Network (CNN), is developed to detect burnt areas automatically using every new SAR image acquired during the wildfires and by exploiting all available pre-fire SAR time series to characterize the temporal backscatter variations. The results show that Sentinel-1 SAR backscatter can detect wildfires and capture their temporal progression as demonstrated for three large and impactful wildfires: the 2017 Elephant Hill Fire in British Columbia, Canada, the 2018 Camp Fire in California, USA, and the 2019 Chuckegg Creek Fire in northern Alberta, Canada. Compared to the traditional log-ratio operator, CNN-based deep learning framework can better distinguish burnt areas with higher accuracy. These findings demonstrate that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals with the launches of RADARSAT Constellation Missions in 2019, and SAR CubeSat constellations.

Keywords :Yifang Ban would like to KTH Royal Institute of Technology for the sabbatical funding support that enabled her to expand her and her team’s research into SAR-based wildfire monitoring. She would also like to thank Michael A. Wulder at Canadian Forest Service (Pacific Forestry Centre) for hosting her sabbatical. Puzhao Zhang is grateful to the Chinese Scholarship Council for the scholarship to study in KTH Royal Institute of Technology.

Article: Download PDF Journal DOI : 311/714

Cite This Article:

Near Real-Time Wildfire Progression Monitoring

Vol.I (16), Issue.I 210

Article No : 10040

Number of Downloads : 112

References :
Yue, X., Mickley, L. J., Logan, J. A. & Kaplan, J. O. Ensemble projections of wildfire activity and carbonaceous aerosol concentrations over the western United States in the mid-21st century. Atmos. Environ. 77, 767–780, https://doi.org/10.1016/j.atmosenv.2013.06.003 (2013). Schroeder, W. et al. Validation of GOES and MODIS active fire detection products using ASTER and ETM+... More
  • Yue, X., Mickley, L. J., Logan, J. A. & Kaplan, J. O. Ensemble projections of wildfire activity and carbonaceous aerosol concentrations over the western United States in the mid-21st century. Atmos. Environ. 77, 767–780, https://doi.org/10.1016/j.atmosenv.2013.06.003 (2013).
  • Schroeder, W. et al. Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data. Remote. Sens. Environ., https://doi.org/10.1016/j.rse.2008.01.005 (2008).
  • Schroeder, W., Oliva, P., Giglio, L. & Csiszar, I. A. The New VIIRS 375 m active fire detection data product: Algorithm description and initial assessment. Remote. Sens. Environ. 143, 85_ 96, https://doi.org/10.1016/j.rse.2013.12.008 (2014).
  • Wulder, M. A. et al. Characterizing boreal forest wildfire with multi-temporal Landsat and LIDAR data. Remote. Sens. Environ. 113, 1540–1555, https://doi.org/10.1016/j.rse.2009.03.004 (2009).
  • Quintano, C., Fernández-Manso, A. & Fernández-Manso, O. Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity. Int. J. Appl. Earth Obs. Geoinf. 64, 221–225, https://doi.org/10.1016/j.jag.2017.09.014 (2018).
  • Crowley, M. A., Cardille, J. A., White, J. C. & Wulder, M. A. Multi-sensor, multi-scale, Bayesian data synthesis for mapping within-year wildfire progression. Remote. Sens. Lett. 10, 302–311, https://doi.org/10.1080/2150704X.2018.1536300 (2019).
  • Chuvieco, E. et al. Historical background and current developments for mapping burned area from satellite Earth observation. Remote. Sens. Environ. 225, 45–64, https://doi.org/10.1016/j.rse.2019.02.013 (2019).
  • Engelbrecht, J., Theron, A., Vhengani, L. & Kemp, J. A Simple Normalized Difference Approach to Burnt Area Mapping Using Multi-Polarisation C-Band SAR. Remote. Sens. 9, 764, https://doi.org/10.3390/rs9080764 (2017).
  • French, N. H., Bourgeau-Chavez, L. L., Wang, Y. & Kasischke, E. S. Initial observations of Radarsat imagery at fire-disturbed sites in interior Alaska. Remote. Sens. Environ. 68, 89–94, https://doi.org/10.1016/S0034-4257(98)00094-7 (1999).
  • Goodenough, D. G. et al. Mapping fire scars using Radarsat-2 polarimetric SAR data. Can. J. Remote. Sens. 37, 500–509, https://doi.org/10.5589/m11-060 (2012).
  • Imperatore, P. et al. Effect of the Vegetation Fire on Backscattering: An Investigation Based on Sentinel-1 Observations. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 10, 4478–4492, https://doi.org/10.1109/JSTARS.2017.2717039 (2017).
  • Mouillot, F. et al. Ten years of global burned area products from spaceborne remote sensing-A review: Analysis of user needs and recommendations for future developments. Int. J. Appl. Earth Obs. Geoinf. 26, 64–79, https://doi.org/10.1016/j.jag.2013.05.014 (2014).
  • Polychronaki, A., Gitas, I., Veraverbeke, S. & Debien, A. Evaluation of ALOS PALSAR Imagery for Burned Area Mapping in Greece Using Object-Based Classification. Remote. Sens. 5, 5680–5701, https://doi.org/10.3390/rs5115680 (2013).
  • Stroppiana, D. et al. Integration of optical and SAR data for burned area mapping in Mediterranean Regions. Remote. Sens. 7, 1320–1345, https://doi.org/10.3390/rs70201320 (2015).
  • Tanase, M. A., Pérez-Cabello, F., De La Riva, J. & Santoro, M. TerraSAR-X data for burn severity evaluation in mediterranean forests on sloped terrain. IEEE Trans. Geosci. Remote. Sens. 48, 917–929, https://doi.org/10.1109/TGRS.2009.2025943 (2010).
  • Tanase, M. A. et al. Sensitivity of X-, C-, and L-band SAR backscatter to burn severity in Mediterranean pine forests. IEEE Trans. Geosci. Remote. Sens. 48, 3663–3675, https://doi.org/10.1109/TGRS.2010.2049653 (2010).
  • Bourgeau-Chavez, L., Kasischke, E., Brunzell, S., Mudd, J. & Tukman, M. Mapping fire scars in global boreal forests using imaging radar data. Int. J. Remote. Sens. 23, 4211–4234, https://doi.org/10.1080/01431160110109589 (2002).
  • Gimeno, M., San-Miguel-Ayanz, J. & Schmuck, G. Identification of burnt areas in Mediterranean forest environments from ERS-2 SAR time series. Int. J. Remote. Sens. 25, 4873–4888, https://doi.org/10.1080/01431160412331269715 (2004).
  • Verhegghen, A. et al. The Potential of Sentinel Satellites for Burnt Area Mapping and Monitoring in the Congo Basin Forests. Remote. Sens. 8, 986, https://doi.org/10.3390/rs8120986 (2016).
  • Kurum, M. C-Band SAR Backscatter Evaluation of 2008 Gallipoli Forest Fire. IEEE Geosci. Remote. Sens. Lett. 12, 1091–1095, https://doi.org/10.1109/LGRS.2014.2382716 (2015).
  • Siegert, F. & Ruecker, G. Use of multitemporal ERS-2 SAR images for identification of burned scars in south-east Asian tropical rainforest. Int. J. Remote. Sens. 21, 831–837, https://doi.org/10.1080/014311600210632 (2000).
  • Menges, C., Bartolo, R., Bell, D. & Hill, G. E. The effect of savanna fires on sar backscatter in northern australia. Int. J. Remote. Sens. 25, 4857–4871, https://doi.org/10.1080/01431160410001712945 (2004).
  • Huang, S. & Siegert, F. Backscatter change on fire scars in Siberian boreal forests in ENVISAT ASAR wide-swath images. IEEE Geosci. Remote. Sens. Lett. 3, 154–158, https://doi.org/10.1109/LGRS.2005.860483 (2006).
  • Belenguer-Plomer, M. A., Tanase, M. A., Fernandez-Carrillo, A. & Chuvieco, E. Burned area detection and mapping using sentinel-1 backscatter coefficient and thermal anomalies. Remote. Sens. Environ. 233, 111345, https://doi.org/10.1016/j.rse.2019.111345 (2019).
  • Reiche, J., de Bruin, S., Hoekman, D., Verbesselt, J. & Herold, M. A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection. Remote. Sens. 7, 4973–4996, https://doi.org/10.3390/rs70504973 (2015).
  • Reiche, J., Verbesselt, J., Hoekman, D. & Herold, M. Fusing Landsat and SAR time series to detect deforestation in the tropics. Remote. Sens. Environ. 156, 276–293, https://doi.org/10.1016/j.rse.2014.10.001 (2015).
  • Reiche, J., Hamunyela, E., Verbesselt, J., Hoekman, D. & Herold, M. Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2. Remote. Sens. Environ. 204, 147–161, https://doi.org/10.1016/j.rse.2017.10.034 (2018).
  • Ban, Y. Multitemporal remote sensing, https://doi.org/10.1007/978-3-319-47037-5 (2016).
  • DeVries, B., Verbesselt, J., Kooistra, L. & Herold, M. Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series. Remote. Sens. Environ. 161, 107–121, https://doi.org/10.1016/j.rse.2015.02.012 (2015).
  • Hansen, M. C. et al. High-Resolution Global Maps of. Sci. 850, 2011–2014, https://doi.org/10.1126/science.1244693 (2013).
  • Raspini, F. et al. Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites. Sci. Rep. 8, 7253, https://doi.org/10.1038/s41598-018-25369-w (2018).
  • Zhu, Z. & Woodcock, C. E. Continuous change detection and classification of land cover using all available Landsat data. Remote. Sens. Environ. 144, 152–171, https://doi.org/10.1016/j.rse.2014.01.011 (2014).
  • Yousif, O. & Ban, Y. Improving SAR-based urban change detection by combining MAP-MRF classifier and nonlocal means similarity weights. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 7, 4288–4300, https://doi.org/10.1109/JSTARS.2014.2347171 (2014).
  • Bruzzone, L. & Prieto, D. F. Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Remote. Sens. 38, 1171–1182, https://doi.org/10.1109/36.843009 (2000).
  • Bazi, Y., Bruzzone, L. & Melgani, F. An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Trans. Geosci. Remote. Sens. 43, 874–887, https://doi.org/10.1109/TGRS.2004.842441. (2005).
  • Bovolo, F. & Bruzzone, L. A detail-preserving scale-driven approach to change detection in multitemporal SAR images. IEEE Trans. Geosci. Remote. Sens. 43, 2963–2972, https://doi.org/10.1109/TGRS.2005.857987 (2005).
  • Bovolo, F. & Bruzzone, L. An adaptive thresholding approach to multiple-change detection in multispectral images. In Proc. of the IEEE Int. Geosci. and Remote Sens. Symp., 233–236, https://doi.org/10.1109/IGARSS.2011.6048935 (IEEE, 2011).
  • Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. In Adv. in Neural Inf. Process. Syst. 1097–1105, https://doi.org/10.1145/3065386 (2012).
  • He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. of the IEEE Conf. on Comput. Vision and Pattern Recognit., 770–778, https://arxiv.org/abs/1512.03385v1 (2016).
  • Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. In Proc. of the IEEE Conf. on Comput. Vision and Pattern Recognit., 779–788, https://doi.org/10.1109/CVPR.2016.91 (2016).
  • Long, J., Shelhamer, E. & Darrell, T. Fully convolutional networks for semantic segmentation. In Proc. of the IEEE Conf. on Comput. Vision and Pattern Recognit., 3431–3440, https://doi.org/10.1109/TPAMI.2016.2572683 (2015).
  • Gong, M., Zhao, J., Liu, J., Miao, Q. & Jiao, L. Change detection in synthetic aperture radar images based on deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. 27, 125–138, https://doi.org/10.1109/TNNLS.2015.2435783 (2015).
  • Gong, M., Yang, H. & Zhang, P. Feature learning and change feature classification based on deep learning for ternary change detection in SAR images. ISPRS J. Photogramm. Remote. Sens. 129, 212–225, https://doi.org/10.1016/j.isprsjprs.2017.05.001 (2017).
  • Zhu, X. X. et al. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geosci. Remote. Sens. Mag. 5, 8–36, https://doi.org/10.1109/MGRS.2017.2762307 (2017).
  • Zhang, P., Gong, M., Zhang, H., Liu, J. & Ban, Y. Unsupervised difference representation learning for detecting multiple types of changes in multitemporal remote sensing images. IEEE Trans. Geosci. Remote. Sens. 57, 2277–2289, https://doi.org/10.1109/TGRS.2018.2872509 (2018).
  • Sentinel-1 mission, https://sentinel.esa.int/web/sentinel/missions/sentinel-1 Accessed Jun 25 (2019).
  • Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote. Sens. Environ. 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031 (2017).
  • Miller, J. D. et al. Calibration and validation of the relative differenced normalized burn ratio (rdnbr) to three measures of fire severity in the sierra nevada and klamath mountains, california, usa. Remote. Sens. Environ. 113, 645–656, https://doi.org/10.1016/j.rse.2008.11.009 (2009).
  • Sorooshian, S. et al. NOAA Climate Data Record (CDR) of Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN-CDR), Version 1, Revision 1. NOAA Natl. Centers for Environ. Inf., https://doi.org/10.7289/V51V5BWQ (2014).
  • Ashouri, H. et al. PERSIANN-CDR: Daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull. Am. Meteorol. Soc. 96, 69–83, https://doi.org/10.1175/BAMS-D-13-00068.1 (2015).
  • Funk, C. et al. The climate hazards infrared precipitation with stations-a new environmental record for monitoring extremes. Sci. Data 2, 150066, https://doi.org/10.1038/sdata.2015.66 (2015).
  • Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man, Cybern. 9, 62–66, https://doi.org/10.1109/TSMC.1979.4310076 (1979).
  • Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. arXiv preprint, https://arxiv.org/abs/1412.6980 (2014).
... Less

WordPress Lightbox Plugin