Abstract
Frequent cloud cover in the tropics significantly affects the observation of the surface by satellites. This has enormous implications for current approaches that estimate greenhouse gas (GHG) emissions from fires or map fire scars. These mainly employ data acquired in the visible to middle infrared bands to map fire scars or thermal data to estimate fire radiative power and consequently derive emissions. The analysis here instead explores the use of microwave data from the operational Sentinel-1A (S-1A) in dual-polarisation mode (VV and VH) acquired over Central Kalimantan during the 2015 fire season. Burnt areas were mapped in three consecutive periods between August and October 2015 using the random forests machine learning algorithm. In each mapping period, the omission and commission errors of the unburnt class were always below 3%, while the omission and commission errors of the burnt class were below 20% and 5% respectively. Summing the detections from the three periods gave a total burnt area of ∼1.6 million ha, but this dropped to ∼1.2 million ha if using only a pair of pre- and post-fire season S-1A images. Hence the ability of Sentinel-1 to make frequent observations significantly increases fire scar detection. Comparison with burnt area estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product at 5 km scale showed poor agreement, with consistently much lower estimates produced by the MODIS data-on average 14%–51% of those obtained in this study. The method presented in this study offers a way to reduce the substantial errors likely to occur in optical-based estimates of GHG emissions from fires in tropical areas affected by substantial cloud cover.
Highlights
Data acquired by orbital platforms are the only way of routinely, consistently and affordably observing and monitoring land processes at large spatial and long temporal scales (Harris et al 2012, Hansen et al 2013, Tyukavina et al 2015, Reiche et al 2016)
The relative variable importance score obtained from the fitted Random Forests (RF) model showed that the VH and VV backscatter change from pre-fire to post-fire conditions were the most important variables to discriminate burnt from unburnt areas
The analysis in this paper makes clear that Sentinel-1 provides a powerful tool for mapping fire scars, and can yield important information about fire dynamics in the landscape
Summary
Data acquired by orbital platforms are the only way of routinely, consistently and affordably observing and monitoring land processes at large spatial and long temporal scales (Harris et al 2012, Hansen et al 2013, Tyukavina et al 2015, Reiche et al 2016). Estimates of carbon released by fires rely on burnt area mapping (Giglio et al 2013), identification of hotspots (Randerson et al 2012) or estimates of fire radiative power output (Wooster et al 2012) from satellite data. The vast majority of methods to map burnt areas rely on automatic or semi-automatic processing of optical data from high or moderate spatial and temporal resolution satellite data (Gregoire et al 2003, Simon et al 2004, Silva et al 2005, Miettinen et al 2007, Giglio et al 2009, 2013, Sedano et al 2013). In these regions, frequent cloud cover and haze and smoke from the fire activity itself can hamper observation of the surface by optical sensors and limit their ability to map fire scars (Schroeder et al 2008)
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