Fire danger models are a very useful tool for the prevention and extinction of forest fires. Some inputs of these models, such as vegetation status and temperature, can be obtained from remote sensing images, which offer higher spatial and temporal resolution than direct ground measures. In this paper, we focus on the Galicia region (north-west of Spain), and MODIS (Moderate Resolution Imaging Spectroradiometer) images are used to monitor vegetation status and to obtain land surface temperature as essential inputs in forest fire danger models. In this work, we tested the potential of artificial neural networks and logistic regression to estimate forest fire danger from remote sensing and fire history data. Remote sensing inputs used were the land surface temperature and the Enhanced Vegetation Index. A classification into three levels of fire danger was established. Fire danger maps based on this classification will facilitate fire prevention and extinction tasks.

Thermal infrared (TIR) data are usually acquired at a coarser spatial resolution (CR) than visible and near infrared (VNIR). Several disaggregation methods have been recently developed to enhance the TIR spatial resolution using VNIR data. These approaches are based on the retrieval of a relation between TIR and VNIR data at CR, or training of a neural network, to be applied at the fine resolution afterward. In this work, different disaggregation methods are applied to the combination of two different sensors in the experimental test site of Barrax, Spain. The main objective is to test the feasibility of these techniques when applied to satellites provided with no TIR bands. Landsat and moderate imaging spectroradiometer (MODIS) images were used for this work. Land surface temperature (LST) from MODIS images was disaggregated to the Landsat spatial resolution using Landsat VNIR data. Landsat LST was used for the validation and comparison of the different techniques. Best results were obtained by the method based on a linear regression between normalized difference vegetation index (NDVI) and LST. An average RMSE = ±1.9 K was observed between disaggregated and Landsat LST from four different dates in a study area of 120 km ² .

Previous work has shown that the emissivity of crude oil is lower than that of the seawater in the thermal-infrared (TIR) spectrum. Thus, oil slicks cause an emissivity decrease relative to the seawater in that region. The aim of this paper was to carry out experimental measurements to characterize the spectral and angular variations of crude oil and seawater emissivities. The results showed that the crude oil emissivity is lower than the seawater emissivity and that it is essentially flat in the atmospheric window of 8-13 μm. The crude oil emissivity has a marked emissivity decrease with the angle (from 0.956 ± 0.005 at 15 ° to 0.873 ± 0.007 at 65 °), which is even higher than that of the seawater, and thus, the seawater-crude emissivity difference increases with the angle (from +0.030 ±0.007 at close-to-nadir angles up to +0.068 ±0.010 in average at 65 °). In addition, the experimental results were checked by using the dual-angle viewing capability of the Environmental Satellite Advanced Along-Track Scanning Radiometer (ENVISAT-AATSR) images (i.e., 0 °-22 ° and 53 °-55 ° for the nadir and forward views, respectively), with the data acquired during the BP Deepwater Horizon oil slick in 2010. The objective was to explore its applicability to satellite observations. Nadir-forward emissivity differences of +0.028 and +0.017 were obtained for the oil slick and the surrounding clean seawater, respectively. The emissivity differences between the seawater and the oil slick were +0.035 and +0.046 for the nadir and forward views, respectively, which was in agreement with the experimental data. The increase in the seawater-crude emissivity difference with the angle gives significant differences for off-nadir observation angles, showing a new chance of crude oil slick identification from satellite TIR data.

Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation and moisture indices can be used to monitor vegetation status; however, the different indices may perform differently depending on the vegetation species. Eight different spectral indices were selected to determine the most appropriate index in Galicia. This study was extended to the adjacent region of Asturias. Six years of MODIS (Moderate Resolution Imaging Spectroradiometer) images, together with ground fire data in a 10 × 10 km grid basis were used. The percentage of fire events met the variations suffered by some of the spectral indices, following a linear regression in both Galicia and Asturias. The Enhanced Vegetation Index (EVI) was the index leading to the best results. Based on these results, a simple fire danger model was established, using logistic regression, by combining the EVI variation with other variables, such as fire history in each cell and period of the year. A seventy percent overall concordance was obtained between estimated and observed fire frequency.