J. L. Casanova

A forest fire risk assessment using NOAA AVHRR images in the Valencia area, eastern Spain

Abstract The risk of widespread forest fire has been assessed from information supplied by the AVHRR sensor onboard NOAA satellites, for the area of the Autonomous Community of Valencia in eastern Spain, where several major forest fires occurred in the summer of 1994. The burnt surface data were obtained through unsupervised classification of the spectral information of the forest areas, first, from a date previous to the forest fire; and second, from a date following the fire. The methodology for the forest fire risk evaluation is based on the temporal evolution of the NDVI weekly maximum value. Actual forest fires appear to be statistically correlated with the deduced high risk forest fire areas.

Burned area mapping system and fire detection system, based on neural networks and NOAA-AVHRR imagery

New automatic systems for mapping burned areas and for fire detection, based on neural networks, were developed. The Supervised ART-II artificial neural network was employed. These two newly developed systems were applied for mapping burned areas and for fire detection in the eastern part of Spain, which suffered damage during July 1994. Images from the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) were used. These systems were tested using different sizes of training sets and different dynamic parameters. The data description and methodology are discussed. The full algorithm is listed.

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

The spatial resolution of sensors is a concept frequently described in an inappropriate way, usually identified by the sampling distance in the image capturing process. The shape of the modulation transfer function (MTF) has no influence on the results in applications based on homogeneous distribution of radiance. However, in the case of high-temperature events (HTEs), the spatial location of the burning area inside the pixel is a key issue to solve, in order to quantify the radiance. The point spread function (PSF) should be considered both in fire detection-oriented algorithms and in the application of bispectral processes. This paper analyses the impact of the PSF of the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) sensor on the determination of thermal fire parameters. The PSF influence on the brightness temperature (BT), in the mid-infrared (MIR) 3.9 μm spectral band, on detection algorithms is analysed. Errors in the fire temperature retrieved by the bispectral technique, due to non-coincidence in the PSF involved, are also analysed. The results obtained show a difference of around 20 K in the BT in the 3.9 μm spectral band, depending on the fire location inside the pixel. Finally, the probability detection of the minimal size of the burning area was analysed, and revealed that there is a 90% probability of detecting a fire with a burning area of 10 ha whereas an area of 4 ha is detected with a probability of 50%.

Rapid response for cloud monitoring through Meteosat VIS‐IR and NOAA–A/TOVS image fusion: civil aviation application. A first approach to MSG‐SEVIRI

The aim of this work is to show an automatic method of cloud classification for direct application in civil aviation. We start from the premise of an acceptable trade‐off between calculation speed and accuracy in the output data. For this reason, visible and infrared channels of the Meteosat satellite were used alongside data provided by the A/TOVS (Advanced/Tiros‐N Operational Vertical Sounder) probe onboard NOAA (National Oceanic and Atmospheric Administration) polar satellites. A historical database of mean temperatures at ground level was also used. The analysis of different significant synoptic and mesoscale situations highlighted the efficacy of this method in the representation of the different cloud structures that normally appear in these situations. Considering the results of the study and given its speed and accuracy, it can be concluded that the method is appropriate for monitoring cloud systems in real time.

Latest algorithms and scientific developments for forest fire detection and monitoring using MSG/SEVIRI and MODIS sensors

The detection of fires in an operative way is not a finished task in remote sensing. This work present approaches for fire detection and fire monitoring. The described rare detection algorithm exploits a physical radiative transfer model based on a sub-pixel description of the remote sensing data. This model allows refining the detection capabilities in order to perform early detection by exploiting geostationary sensors which have a low spatial resolution but high temporal resolution. Polar sensors are used to supply updated parameters to the physical model. The described fire monitoring approaches allows estimating fire parameters and defining the evolution of the fire, using different spatial resolutions, in order to complete and refine the analysis performed by the detection algorithm.

IFEMS: a new approach for monitoring wildfire evolution with NOAA-AVHRR imagery

A new approach for monitoring wildfire evolution was developed. It was achieved by integrating both the burned area mapping system and the fire detection system. Multi-spectral multi-temporal images of the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) were employed. The new system has an excellent performance in monitoring fire growth due to its ability of capturing areas that were burned completely between two consecutive images, as well as those which were burned before the monitoring time. In addition, the system has the ability to differentiate among burned areas, active fire (fire front) and the area beneath flames. The algorithm of the system is described. The false alarm due to the saturation problem of channel 3 was avoided. The integration of the system with fire simulation programs for fire monitoring is discussed.

Relation between meteorological conditions and the catching of red tuna (Thunnus thynnus) from the measurements of the TOVS and AVHRR sensors of the NOAA satellites

During the second half of the month of June 1997, a massive catch of red tuna (Thunnus thynnus) took place off the coast of Babarte (Spain), in contrast to the first half of that month when there was hardly any presence of this species. The aim of this paper was to examine the relation between the high fishing productivity and the meteorological conditions under which the oceanic events to which the tuna fisheries were attracted took place. This was carried out through the analysis of Advanced Very High Resolution Radiometer (AVHRR) sensor data and the data from the Tiros‐N Operational Vertical Sounder (TOVS) probe of the NOAA‐14 satellite from 10 to 24 June 1997. Results show that the formation of the fishing front was caused by an ocean–atmosphere energetic exchange, which was localized and described through the data transmitted from the NOAA satellites.

Operational cloud classification for the Iberian Peninsula using Meteosat Second Generation and AQUA-AIRS image fusion

The aim of this work was the adaptation and improvement of a previous cloud detection and classification algorithm that was developed for the Meteosat-7 satellite. The functions of this satellite have now been taken on by the new series of Meteosat Second Generation (MSG) satellites, which are not just replicas but new, much improved versions of their predecessor. The formerly used Advanced/Tiros-N Operational Vertical Sounder (A/TOVS) probe has also been superseded technologically by new sensors with better spatial resolution, capable of carrying out more accurate measurements at a greater number of wavelengths. This is the case of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the TERRA and AQUA satellites and of the Atmospheric Infrared Sounder (AIRS) probe. In this context, new potential improvements are analysed for this algorithm by using these new platforms and sensors and the results are compared to those obtained in the first classification.

An automatic self-learning cloud-filtering algorithm for Meteosat Second Generation–Spinning Enhanced Visible and Infrared Imager

Cloud detection is an important pre-processing step to derive operational products from meteorological satellites. This work presents a new cloud-detection algorithm with Meteosat Second Generation (MSG) images, operative at global scale. The algorithm takes advantage of the spectral and temporal resolution of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor. The algorithm is fully automatic in all its stages, including the thresholds definition by means of a self-learning methodology. These properties remove the need for ancillary data and restrictions in the area of application. This algorithm has been used in order to generate cloud masks during 2009. These cloud masks have been compared to the masks obtained with the National Aeronautics and Space Administration algorithm MOD35 with Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) images and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) algorithm for MSG–SEVIRI in Spain territory. The result shows an 88% agreement with EUMETSAT and a better than 83% agreement with the MOD35 algorithm.

Remote Sensing for Environmental Monitoring: Forest Fire Monitoring in Real Time

The use of remote sensing techniques for the study of forest fires is a subject that started already several years ago and whose possibilities have been increasing as new sensors were incorporated into earth observation international programmes and new goals were reached based on the improved techniques that have been introduced. In this way, three main lines of work can be distinguished in which remote sensing provides results that can be applied directly to the subject of forest fires: risk of fire spreading, detection of hot-spots and establishment of fire parameters.