Successional stage after land abandonment modulates fire severity and post-fire recovery in a Mediterranean mountain landscape

This study analyses the effect of successional stage after farmland terrace abandonment on post-fire plant recovery in a Mediterranean landscape. Specific objectives of the study were to (1) compare fuel characteristics and fire severity in three successional stages after farmland abandonment – dry grassland, dense shrubland and pine stands; (2) analyse the effect of pre-fire successional stage and fire severity on vegetation recovery and (3) analyse the relative vulnerability (i.e. potential for ecosystem shift and soil degradation) to wildfires of the successional stages. We assessed 30 abandoned terraces (15 unburned and 15 burned), with diverse successional stages, on the Xortà Range (south-east Spain). Post-fire recovery was measured 1, 4 and 7 years after fire. The successional stages varied in aboveground biomass, litter amount, vertical structure and continuity of plant cover, and flammability. Dry grassland showed the lowest fire severity, whereas no differences in severity were found between shrubland and pine stands. One year after fire, plant cover was inversely related to fire severity; this relationship attenuated with time after fire. Post-fire recovery of pine stands and shrubland led in both cases to shrublands, contributing to landscape homogenisation. The pine stands showed the largest changes in composition due to fire and the lowest post-fire plant recovery – a sign of high vulnerability to fire.

Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation

Staff detection and removal is one of the most important issues in optical music recognition (OMR) tasks since common approaches for symbol detection and classification are based on this process. Due to its complexity, staff detection and removal is often inaccurate, leading to a great number of errors in posterior stages. For this reason, a new approach that avoids this stage is proposed in this paper, which is expected to overcome these drawbacks. Our approach is put into practice in a case of study focused on scores written in white mensural notation. Symbol detection is performed by using the vertical projection of the staves. The cross-correlation operator for template matching is used at the classification stage. The goodness of our proposal is shown in an experiment in which our proposal attains an extraction rate of 96 % and a classification rate of 92 %, on average. The results found have reinforced the idea of pursuing a new research line in OMR systems without the need of the removal of staff lines.