A. Calle

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%.

A navigation algorithm for satellite images

In this work a method is presented for the navigation of National Oceanographic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) images based on an orbital model and on the use of control points. The orbital model assumes a circular orbit whose parameters are derived using the TBUS messages and one or more ground control points. The errors caused by these simplifications are corrected by means of linear functions which relate the coordinates measured in the image and obtained by applying the orbital model to a series of control points of known geographical coordinates. This procedure offers results which have an error of around one pixel.

Validation of active forest fires detected by MSG-SEVIRI by means of MODIS hot spots and AWiFS images

The detection of forest fires and the determination of their parameters have been usually carried out by polar‐orbit sensors: AVHRR, (A)ATSR, BIRD, and MODIS mainly. However, their time resolution prevents them from operating in real time. In contrast, the new geostationary sensors have very appropriate capacities for the observation of the Earth and monitoring of forest fires, as is being proved. GOES, MSG, and MTSAT are already operative, and they have led the international community to think that the global observation network in real time may become a reality. The implementation of this network is the aim of the Global Observations of Forest Cover and Land Cover Dynamics (GOFC/GOLD) FIRE Mapping and Monitoring programme, focused internationally on taking decisions concerning the research of the Global Change. In this Letter, the operation in real time by the MSG‐SEVIRI sensor over the Iberian Peninsula is studied. On the other hand, the reliability of validation results by means of polar sensors, with a finer spatial resolution, is difficult to analyse due to errors caused by confused location of fires. This Letter shows that fires detected by means of MSG‐SEVIRI can be an useful option in order to estimate burnt areas at global scale, considering a spatial resolution of 40 km.

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.

Using AHS hyper-spectral images to study forest vegetation recovery after a fire

Recent advances in sensor technology have led to the development of new hyper-spectral instruments capable of measuring reflected radiation over a wide range of wavelengths. These instruments can be used to assess the diverse characteristics of vegetation recovery that are only noticeable in certain parts of the electromagnetic spectrum. In this research, such instruments were used to study vegetation recovery following a forest fire in a Mediterranean ecosystem. The specific event occurred in an area called El Rodenal of Guadalajara (in Central Spain) between 16 and 21 July 2005. Remotely sensed hyper-spectral multitemporal data were used to assess the forest vegetation response following the fire. These data were also combined with remotely sensed fire severity data and satellite high temporal resolution data. Four Airborne Hyperspectral Scanner (AHS) hyper-spectral images, 361 Moderate Resolution Imaging Spectroradiometer (MODIS) images, field data, and ancillary information were used in the analysis. The total burned area was estimated to be 129.4 km2. AHS-derived fire severity level-of-damage assessments were estimated using the normalized burn ratio (NBR). Post-fire vegetation recovery was assessed according to a spectral unmixing analysis of the AHS hyper-spectral images and the normalized difference vegetation index (NDVI), as calculated from the MODIS time series. Combining AHS hyper-spectral images with field data provides reliable estimates of burned areas and fire severity levels-of-damage. This combination can also be used to monitor post-fire vegetation recovery trends. MODIS time series were used to determine the types and rates of vegetation recovery after the fire and to support the AHS-based estimates. Data and maps derived using this method may be useful for locating priority intervention areas and planning forest restoration projects.

Columnar and surface aerosol load over the Iberian Peninsula establishing annual cycles, trends, and relationships in five geographical sectors

The study of atmospheric aerosol load over the Iberian Peninsula (IP) under a climatological perspective is accomplished by means of PM10 and AOD440 nm measurements from EMEP and AERONET networks, respectively, in the period 2000-2013. The PM10 annual cycles in five Iberian sectors show a main maximum in summer and a secondary maximum in spring, which is only observed in the southern area for the AOD climatology. The characteristics of PM10-AOD annual cycles of each geographical sector are explained by the different climatology of the air mass origins and their apportioning. The two magnitudes are correlated with a factor ranging between 20 and 90 depending on the sector. The temporal evolution of the aerosol load has shown a notable decrease in the IP since the 1980s. Statistically significant trends are obtained in the Northeastern sector with a reduction of 26% (period 1985-2000) for the total suspended particles, which continues for the PM10 data with a value of 35% per decade (2001-2013), and also in the whole column, 61% per decade in the AOD440 nm (2004-2013).

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.

Forest Fires And Remote Sensing

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. Three main topics 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 thermal parameters and, finally, cartography of affected areas. In the last years, other two important topics are getting increasing interest; the first one is the estimation of severity, related to the post-fire phase, and the other one is the atmospheric impact of fire emissions.