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This article explores the potential use of remote sensing to discriminate two grassweeds (Avena sterilis and Lolium rigidum) from four cultivars (cvs) of winter wheat and barley. Hyperspectral measurements, using a GER2600 spectroradiometer (350 to 2500 nm), were conducted throughout the life cycle of the plants in order to analyse spectral differences between weeds and crops at different phenological stages. Specific techniques for hyperspectral data, such as the Spectral Angle Mapper (SAM) were used to quantify the spectral separability between weeds and crops, while stepwise discriminant analysis was applied to detect those wavelengths providing the best discrimination ability. SAM results showed that spectral differences were generally insufficient to discriminate weeds and crops. Only during the first phenological stages were angular distances large enough to achieve a good classification of the different species. This behaviour was related to the different fraction cover of crops and weeds in this period. The wavebands that provide the best discrimination ability according to the discriminant analysis were pooled in eight spectral regions in order to determine their frequency of occurrence. The four most frequently selected spectral regions were the Far Short-Wave Infrared, Early Short-Wave Infrared, Blue and the Red Edge. [ABSTRACT FROM AUTHOR]

Disturbance events such as fire have major effects on forest dynamics, succession and the carbon cycle in the boreal biome. This paper focuses on establishing whether characteristic spatio-temporal patterns of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) occur in the initial two years after a fire event in Siberian boreal forests. Time-series of MODIS fAPAR were used to study post-fire dynamics during the year of the fire and the following two years. Three forest types (evergreen needle-leaf, deciduous needle-leaf and deciduous broadleaf) grouped into three latitudinal regions, ranging from 51° N to 65° N, were studied by analysing a sample of 14 burned areas. For each of the burned areas an adjacent unburned control plot was selected with the aim of separating inter-annual variations caused by climate from changes in fAPAR behaviour due to a burn. The results suggest that (i) the forest types exhibit characteristic fAPAR change trajectories shortly after the fire, (ii) the differences in the fAPAR trajectories are related to the forest type, (iii) fAPAR changes are not significantly different among the latitudinal regions, and (iv) the limited temporal variability observed among the 3 years of observations indicates that fAPAR varies very little in the initial years after a fire event. [ABSTRACT FROM AUTHOR]

An empirical estimation of live fuel moisture content (FMC) was generated from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) derived variables. This estimation was based on the ratio of Normalized Difference Vegetation Index (NDVI) and surface temperature ( T s ), and the relative greenness (RGRE). It was derived for the summer season (from June to September). The estimation was calibrated from field measurements collected in central Spain, and included consideration of both herbaceous and shrub live FMC values. The proposed estimation worked reasonably well for other periods in the same study area and also for other similar study areas in Mediterranean environments. This estimation of FMC from remote sensing could be used as an input to standard fire danger and fire behaviour programs, providing spatially comprehensive data, which is critical for regional planning of fire prevention. [ABSTRACT FROM AUTHOR]

The objective of this paper was to define indices based on reflectance measurements performed by the Landsat Thematic Mapper (TM) sensor for estimating water content of live Mediterranean fuels for fire danger estimation. Seven Landsat TM images were processed and correlated with fuel moisture content (FMC) of several live species of Mediterranean grassland and shrubland. Raw bands were converted to reflectances, and several indices potentially related to water content were calculated from them. Pearson r correlation coefficients and linear regression analysis were computed in order to estimate FMC. Those indices based on the short wave infrared bands (SWIR: 1.4-2.5 μm) and on the contrast between this band and the near-infrared band offered the best estimations. For grassland, the integral of visible and SWIR bands provided the highest correlation, but also raw reflectances and Normalized Difference Vegetation Indices (NDVIs) provide significant r values (r[sup 2] above 0.8). For shrub species, indices that include SWIR reflectances performed much better than NDVI, because the SWIR band is more sensitive to water absorption, whereas NDVI estimates FMC indirectly, only from the effects of chlorophyll changes due to water variation content and leaf area index. The most significant relations were found with the derivatives of bands 4-5 and 2-3, and again the integral of visible and SWIR bands. Multiple regression analysis provided adjusted r[sup 2] values of 0.84 for grasslands and 0.74 for shrublands. Average errors of 23.45-40% in the estimation of FMC for grasslands were found, depending on which variables were included in the multiple regression. For the FMC estimation of shrub species, errors were lower (from 7.94 to 19.40%), since the range of FMC values was also lower. [ABSTRACT FROM AUTHOR]