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Fast Atmospheric Signature Code (FASCODE), a line‐by‐line radiative transfer programme, was used to simulate Moderate Resolution Imaging Spectroradiometer (MODIS) data at wavelengths 11.03 and 12.02 µm to ascertain how accurately the land surface temperature (LST) can be inferred, by the split‐window technique (SWT), for a wide range of atmospheric and terrestrial conditions. The approach starts from the Ulivieri algorithm, originally applied to Advanced Very High Resolution Radiometer (AVHRR) channels 4 and 5. This algorithm proved to be very accurate compared to several others and takes into account the atmospheric effects, in particular the water vapour column (WVC) amount and a non‐unitary surface emissivity. Extended simulations allowed the determination of new coefficients of this algorithm appropriate to MODIS bands 31 and 32, using different atmospheric conditions. The algorithm was also improved by removing some of the hypothesis on which its original expression was based. This led to the addition of a new corrective term that took into account the interdependence between water vapour and non‐unitary emissivity values and their effects on the retrieved surface temperature. The LST products were validated within 1 K with in situ LSTs in 11 cases. [ABSTRACT FROM AUTHOR]

Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem affecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R 2 between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R 2 values and the statistically significant ( p -value of t -test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R 2 values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coefficient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears., Competing Interests: The authors declare no conflict of interest.

Vulnerability assessment is a vital component of wildfire management. This research focused on the development of a framework to measure and map vulnerability levels in several areas within Mediterranean Europe, where wildfires are a major concern. The framework followed a stepwise approach to evaluate its main components, expressed by exposure, sensitivity and coping capacity. Data on population density, fuel types, protected areas location, roads infrastructure and surveillance activities, among others, were integrated to create composite indices, representing each component and articulated in multiple dimensions. Maps were created for several test areas, in northwest Portugal, southwest Sardinia in Italy and northeast Corsica in France, with the contribution of local participants from civil protection institutions and forest services. Results showed the influence of fuel sensitivity levels, population distribution and protected areas coverage for the overall vulnerability classes. Reasonable levels of accuracy were found on the maps provided through the validation procedure, with an overall match above 72% for the several sites. The systematic and flexible approach applied allowed for adjustments to local circumstances with regards to data availability and fire management procedures, without compromising its consistency and with substantial operational capabilities. The results obtained and the positive feedback of end-users encourage its further application, as a means to improve wildfire management strategies at multiple levels with the latest scientific outputs., (Copyright © 2017 Elsevier Ltd. All rights reserved.)