Fire danger estimation from MODIS Enhanced Vegetation Index data: application to Galicia region (north-west Spain)
Galicia, in north-west Spain, is a region especially affected by devastating forest fires. The development of a fire danger prediction model adapted to this particular region is required. In this paper, we focus on changes in the condition of vegetation as an indicator of fire danger. The potential of the Enhanced Vegetation Index (EVI) together with period-of-year to monitor vegetation changes in Galicia is shown. The Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the Terra satellite, was chosen for this study. A 6-year dataset of EVI images, from the product MOD13Q1 (16-day composites), together with fire data in a 10 × 10-km grid basis, were used. Logistic regression was…
Impact of Land Cover Change Induced by a Fire Event on the Surface Energy Fluxes Derived from Remote Sensing
Forest fires affect the natural cycle of the vegetation, and the structure and functioning of ecosystems. As a consequence of defoliation and vegetation mortality, surface energy flux patterns can suffer variations. Remote sensing techniques together with surface energy balance modeling offer the opportunity to explore these changes. In this paper we focus on a Mediterranean forest ecosystem. A fire event occurred in 2001 in Almodovar del Pinar (Spain) affecting a pine and shrub area. A two-source energy balance approach was applied to a set of Landsat 5-TM and Landsat 7-EMT+ images to estimate the surface fluxes in the area. Three post-fire periods were analyzed, six, seven, nine, and 11 y…
Thermal-Infrared Spectral and Angular Characterization of Crude Oil and Seawater Emissivities for Oil Slick Identification
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 …
Estimating high resolution evapotranspiration from disaggregated thermal images
Abstract Accurate evapotranspiration (ET) estimations based on surface energy balance from remote sensing require information in the thermal infrared (TIR) domain, normally provided with an insufficient spatial resolution. In order to estimate ET in heterogeneous agricultural areas, we inspect in this paper the use of disaggregation techniques applied to two different sensors, such as MODIS (daily revisit cycle and 1 km spatial resolution in the TIR domain) and Spot 5 (5 days revisit cycle and 10 m spatial resolution in the VNIR bands but no TIR band). Spot 5 images were used as a proxy for upcoming Sentinel-2. The Simplified Two-Source Energy Balance (STSEB) model was used for the estimati…
Long-term accuracy assessment of land surface temperatures derived from the Advanced Along-Track Scanning Radiometer
Abstract The accuracy of land surface temperatures (LSTs) derived from the Advanced Along-Track Scanning Radiometer (AATSR) was assessed in a test site in Valencia, Spain from 2002 to 2008. AATSR LSTs were directly compared with concurrent ground measurements over homogeneous, full-vegetated rice fields in the conventional temperature-based (T-based) method. We also applied the new radiance-based (R-based) method over bare soil and water surfaces, where ground LST measurements were not available. In the R-based method, ground LSTs are simulated from AATSR brightness temperatures in the 11 μm band and radiative transfer simulations using surface emissivity data and atmospheric water vapor an…
Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data
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 f…
A Simple Fusion Method for Image Time Series Based on the Estimation of Image Temporal Validity
High-spatial-resolution satellites usually have the constraint of a low temporal frequency, which leads to long periods without information in cloudy areas. Furthermore, low-spatial-resolution satellites have higher revisit cycles. Combining information from high- and low- spatial-resolution satellites is thought a key factor for studies that require dense time series of high-resolution images, e.g., crop monitoring. There are several fusion methods in the bibliography, but they are time-consuming and complicated to implement. Moreover, the local evaluation of the fused images is rarely analyzed. In this paper, we present a simple and fast fusion method based on a weighted average of two in…
Evaluation of Disaggregation Methods for Downscaling MODIS Land Surface Temperature to Landsat Spatial Resolution in Barrax Test Site
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 mod…
Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS images
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…