Impact of point spread function of MSG-SEVIRI on active fire detection

Calle, A.; Casanova, J. L.; González-Alonso, F.

doi:10.1080/01431160802609726

Cited by 38 publications

(24 citation statements)

References 15 publications

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“…Despite its simplicity, this remapping technique remains unsatisfying due to the shape, orientation, and overlap of the IGFOV and point spread function (PSF) of both sensors. In reality, SEVIRI's detectors are diamond shaped, oriented at 45° with respect to the E-W and N-S directions, and the PSF of SEVIRI at the SSP extends ~5 km from the pixel centre [60]. Hence SEVIRI is theoretically sensitive to thermal anomalies located up to 5 km from the nominal pixel centre [60], and SEVIRI's active fire detection pattern may resemble a plus sign or a square ring in image space [60].…”

Section: Seviri Errors Of Omission and Commissionmentioning

confidence: 99%

“…In reality, SEVIRI's detectors are diamond shaped, oriented at 45° with respect to the E-W and N-S directions, and the PSF of SEVIRI at the SSP extends ~5 km from the pixel centre [60]. Hence SEVIRI is theoretically sensitive to thermal anomalies located up to 5 km from the nominal pixel centre [60], and SEVIRI's active fire detection pattern may resemble a plus sign or a square ring in image space [60]. Meanwhile the focal plane layout and scanning design of MODIS induces (i) a 2 km triangular PSF that overlaps in the along-scan direction; and (ii) substantial scan-to-scan overlap at scan angles greater than 24° [42].…”

Section: Seviri Errors Of Omission and Commissionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product

et al. 2014

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Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, however, can lead to improper interpretations of the spatiotemporal distribution of biomass burning, and flawed estimates of fuel consumption and trace gas and aerosol emissions. This work evaluates the performance of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Fire Thermal Anomaly (FTA) detection algorithm using seven months of active fire pixels detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) across the Central African Republic (CAR). Results indicate that the omission rate of the SEVIRI FTA detection algorithm relative to MODIS varies spatially across the CAR, ranging from 25% in the south to 74% in the east. In the absence of confounding artifacts such as sunglint, uncertainties in the background thermal characterization, and cloud cover, the regional variation in SEVIRI's omission rate can be attributed to a coupling between SEVIRI's low spatial resolution detection bias (i.e., the inability to detect fires below a OPEN ACCESSRemote Sens. 2014, 6 1891 certain size and intensity) and a strong geographic gradient in active fire characteristics across the CAR. SEVIRI's commission rate relative to MODIS increases from 9% when evaluated near MODIS nadir to 53% near the MODIS scene edges, indicating that SEVIRI errors of commission at the MODIS scene edges may not be false alarms but rather true fires that MODIS failed to detect as a result of larger pixel sizes at extreme MODIS scan angles. Results from this work are expected to facilitate (i) future improvements to the SEVIRI FTA detection algorithm; (ii) the assimilation of the SEVIRI and MODIS active fire products; and (iii) the potential inclusion of SEVIRI into a network of geostationary sensors designed to achieve global diurnal active fire monitoring.

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Section: Seviri Errors Of Omission and Commissionmentioning

confidence: 99%

Section: Seviri Errors Of Omission and Commissionmentioning

confidence: 99%

Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product

et al. 2014

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“…The results of the research show that the nonuniformity of a sensor's PSF exerts an influence on the object proportion extraction only through a change of the object distribution pattern. Calle et al also noted that the shape of the MTF(modulation transfer function) (i.e., the PSF nonuniformity) did not affect the measurement of the fire temperature for a uniform distribution of surface radiation [15], which means that the PSF nonuniformity had no effect on a homogeneous surface without an object distribution pattern. A spatial subpixel analysis was used to obtain information of a simple object distribution pattern within mixed pixels [13], from which the object proportion could also be derived.…”

Section: Discussionmentioning

confidence: 99%

“…The imaging processes were simulated on the basis of the PSF nonuniformity, and the resultant DNs of the mixed pixel varied with the above parameters, which better illustrated the role of the object distribution pattern. Calle et al devised different patterns by altering the distance between a fire source and a mixed pixel center to investigate the influence induced by the PSF on detecting fire sources using images from the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) [15]. The results showed that the mixed-pixel DN changed and the estimated error of the brightness temperature rose when the distance increased, and for a distance of 3 km, the error was as large as 20 K. The above studies demonstrate that the nonuniform responses of sensors exert an influence on the extraction of remote sensing information through a change in the object distribution pattern.…”

Section: Introductionmentioning

confidence: 99%

Simulation of the Impact of a Sensor’s PSF on Mixed Pixel Decomposition: 1. Nonuniformity Effect

Liu

Hou

2016

Remote Sensing

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Abstract:The nonuniformity of the spatial response to surface radiation is a fundamental characteristic of all airborne and spaceborne sensors that inevitably introduces uncertainty into the estimation of object proportions by the spectral unmixing of mixed pixels. Simulated data of the surface radiation distribution and a TM (thematic mapper) response matrix were developed and utilized to imitate the generation of mixed pixels and the extraction of the object proportion via a Monte Carlo simulation, and then, the nonuniformity effect of a sensor's PSF (point spread function) was explored. The following conclusions were drawn: (1) given a nonuniform spatial response of a sensor to a surface scene with a constant object proportion and various object distribution patterns, the mixed pixel DN (digital number) of a remotely-sensed image becomes a random variable, which causes a PSF nonuniform effect on the object proportion extraction; (2) for the estimated object proportion, the corresponding true object proportion appears with a random variation; its upper and lower bounds take on an asymmetrical spindle shape; and models of these bound curves at any probability level were established; (3) there exists a negative linear relationship between the bias of the spectral unmixing and the estimated proportion; the bias is zero at an estimated proportion of 50%, and when the estimated proportions are approximately 100% and 0%, the object proportion is overestimated by 0.78% and underestimated by 0.78%, respectively; (4) the relationship between the standard deviation of the spectral unmixing and the estimated proportion follows a symmetrical polynomial function opening downward; the standard deviation reaches a maximum of 4.4% at the estimated proportion of 50%, and when the estimated proportion is approximately 100% or 0%, the standard deviation is a minimum, 1.05%. The above findings contribute to a comprehensive understanding of the PSF nonuniformity effect, have the potential to compensate for the bias of proportion estimation and present its confidence interval at any probability level.

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“…In order to estimate the impact of PSF shape on detection suitability, it's interesting to analyze a sensor with low spatial resolution, as SEVIRI sensor onboard of MSG satellite (Calle et al, 2009). Pixel affected by fire appears a a typical cross shape when fire is detected, due to PSF effects and overlapping between pixels.…”

Section: Spatial Characterization Of Fire Detectionmentioning

confidence: 99%