Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies

Abstract

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003-2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highl...

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

“…Land Surface Temperature (LST) represents the temperature of the Earth's surface, and it is one of the key parameters that affect surface energy balance, regional climates, heat fluxes, and energy exchanges [4][5][6][7][8][9][10][11][12][13][14][15]. Many researchers have investigated the importance and effects of LST on various topics, including urban climate and Surface Heat Island (SHI) studies [16][17][18][19][20], evapotranspiration [21], forest fire monitoring [22], geological, and geothermal studies [23][24][25][26][27]. Besides, LST has been approved as one of the high-priority parameters for the International Geosphere and Biosphere Program (IGBP) [7,28].…”

A Framework for Multi-Dimensional Assessment of Wildfire Disturbance Severity from Remotely Sensed Ecosystem Functioning Attributes

“…Burn severity influences the magnitude of changes in land surface albedo [21,22], which can undergo a darkening and/or a brightening that can persist in time [23,24]. Such alterations cause an increase in land surface temperature immediately following fires [25], which influences both burned area and the duration of fires [26], since vegetation is a regulator of land surface energy fluxes [27]. Although the effects of wildfires span across multiple dimensions of ecosystem functioning, most studies evaluating those effects seldom address those multiple dimensions at the same time, thus failing to fully depict spatiotemporal changes caused by these disturbances and limiting the assessment of environmental impacts.…”

A Calibration Procedure for Field and UAV-Based Uncooled Thermal Infrared Instruments

“…Land surface temperature (LST) measured from satellite platforms can be used to detect urban heat islands and improve the planning and heat mitigation strategies of megacities [9][10][11]. Additionally, LST is a key variable for monitoring environmental responses to water availability, such as forest fire risk and severity [12,13], evapotranspiration [14][15][16][17] and drought and water stress [18][19][20][21]. However, with pixel sizes in the tens of meters, satellite based LST retrievals do not always reach the fine spatial or temporal resolutions required for many applications, with precision agriculture being a prime example [22].…”