Non-supervised method for early forest fire detection and rapid mapping

Abstract

Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a nonsupervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.

UAV Assisted Spatiotemporal Analysis and Management of Bushfires: A Case Study of the 2020 Victorian Bushfires

“…Other studies have tested the interpolations of active fire clusters and successfully visualized these large fire perimeters, and calibrated the fire propagation models [52,54]. Various techniques have been used for the aggregation method, ranging from buffering of fire zones [52], Kriging analysis [55], weighted mean and distance methods, or the Inverse Distance Weighted (IDW) method [56], to convex hull algorithms applied to the assessing the active fire clusters [57]. Previous studies regarding active fire monitoring are carried out using coarse-resolution sensors, usually of 1 km resolution like MODIS or Advanced Very-High-Resolution Radiometer (AVHRR) [55].…”

“…Some of the more recent available sensors like VIIRS on the Suomi National Polar-Orbiting Partnership (S-NPP) satellite provide great potential for detecting small fires that may have broader impacts. This sensor has a high spatial and temporal resolution of 375 m. These improved features of the VIIRS active fire products have reopened the possibility of directly mapping burnt areas using the active fire products [52,57]. Despite the great applicability of VIIRS active fire products, there is still a lack of studies to test bushfire monitoring.…”

Near Real-Time Automated Early Mapping of the Perimeter of Large Forest Fires from the Aggregation of VIIRS and MODIS Active Fires in Mexico

“…Current operational products of global burned area [8] are generally available one month after the fire, limiting their applicability for fire near real-time perimeter monitoring [13][14][15] which can be useful for fire managers to make early evaluations of approximate fire extent and location. Unlike current products of burned area data, observations of active fires made with spaceborne sensors are available at least with a daily latency [11,16,17].…”

“…Some studies have even tested such interpolations of conglomerates of active fires to visualize the approximate advance of the perimeter of large fires [13,14,[30][31][32][33][34][35] and to calibrate fire propagation models [36][37][38][39][40][41][42][43][44]. Several techniques are being investigated for this aggregation approach, including the direct aggregation or buffering of active fires (e.g., [30,31]), the inverse distance weighted or the weighted mean and distance methods (e.g., [13]), kriging analysis (e.g., [14]), or the use of convex hull algorithms applied to active fire clusters (e.g., [15,43]).…”

“…The more recently available Visible Infrared Imaging Radiometer (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite, launched in 2011, offers greater potential for small fire detection, having a higher temporal and spatial resolution (375 m) [11,45,46]. Such enhanced characteristics of the VIIRS 375 m active fire product reopened the possibility of direct rapid mapping of burned areas from active fire detection products [15,31].…”