Abstract
AVHRR data from April 1995 to September 1995 have been processed to produce 1 km resolution NDVI Maximum Value Composites of Scotland. Temporal profiles of mean NDVI were obtained for Scottish administrative regions. Temporal NDVI profiles for individual vegetation classes, from the Land Cover of Scotland 1988 (LCS88) dataset, were obtained and both temporal and spatial variations within these classes are also discussed. A method for enhancing the existing LCS88 dataset is proposed, based on AVHRR NDVI data, by distinguishing vegetation regional variation within a single class.

Abstract
An attempt has been made to assess the efficiency of image data compression by wavelet transform encoding using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images. Raw and derived images were compressed to various levels and a number of parameters in the decompressed images compared with those obtained using raw data as a yardstick against which to measure the loss of information due to compression. Unsupervised classification, Normalized Difference Vegetation Index (NDVI) values and brightness temperatures appeared to suffer little degradation and only for fractal dimensions was there significant loss of integrity at compression rates of up to a factor of 32. The general conclusion from a visual inspection of the effect of such compressions on artificially generated geometrical imagettes confirms the effectiveness of this method of compression.

Abstract
Background: Bacterial spot-causing xanthomonads (BSX) are quarantine phytopathogenic bacteria responsible for heavy losses in tomato and pepper production. Despite the research on improved plant spraying methods and resistant cultivars, the use of healthy plant material is still considered as the most effective bacterial spot control measure. Therefore, rapid and efficient detection methods are crucial for an early detection of these phytopathogens. Methodology: In this work, we selected and validated novel DNA markers for reliable detection of the BSX Xanthomonas euvesicatoria (Xeu). Xeu-specific DNA regions were selected using two online applications, CUPID and Insignia. Furthermore, to facilitate the selection of putative DNA markers, a customized C program was designed to retrieve the regions outputted by both databases. The in silico validation was further extended in order to provide an insight on the origin of these Xeu-specific regions by assessing chromosomal location, GC content, codon usage and synteny analyses. Primer-pairs were designed for amplification of those regions and the PCR validation assays showed that most primers allowed for positive amplification with different Xeu strains. The obtained amplicons were labeled and used as probes in dot blot assays, which allowed testing the probes against a collection of 12 non-BSX Xanthomonas and 23 other phytopathogenic bacteria. These assays confirmed the specificity of the selected DNA markers. Finally, we designed and tested a duplex PCR assay and an inverted dot blot platform for culture-independent detection of Xeu in infected plants. Significance: This study details a selection strategy able to provide a large number of Xeu-specific DNA markers. As demonstrated, the selected markers can detect Xeu in infected plants both by PCR and by hybridization-based assays coupled with automatic data analysis. Furthermore, this work is a contribution to implement more efficient DNA-based methods of bacterial diagnostics.

Abstract
The availability of temporal satellite image data has increased considerably in recent years. A number of satellite sensors currently observe the Earth with high temporal frequency thus providing a tool for monitoring/understanding the Earth-surface variability more precisely, for several applications such as the analysis of vegetation dynamics. However, the extraction of vegetation phenology information from Earth Observation Satellite (EOS) data is not easy, requiring efficient processing algorithms to properly handle the large amounts of data gathered. The purpose of this work is to present a new, easy-to-use software tool that produces phenology information from EOS vegetation temporal data - PhenoSat. This paper describes PhenoSat, focusing on two new features: the determination of the beginning and maximum of a double growth season, and the selection of a temporal sub-region of interest in order to reduce and control the data evaluated. © 2011 IEEE.

Abstract
In this paper, we propose and evaluate six methods for the segmentation of skin lesions in dermoscopic images. This set includes some state of the art techniques which have been successfully used in many medical imaging problems (gradient vector flow (GVF) and the level set method of Chan et al. [(C-LS)]. It also includes a set of methods developed by the authors which were tailored to this particular application (adaptive thresholding (AT), adaptive snake (AS), EM level set [(EM-LS), and fuzzy-based split-and-merge algorithm (FBSM)]. The segmentation methods were applied to 100 dermoscopic images and evaluated with four different metrics, using the segmentation result obtained by an experienced dermatologist as the ground truth. The best results were obtained by the AS and EM-LS methods, which are semi-supervised methods. The best fully automatic method was FBSM, with results only slightly worse than AS and EM-LS.

Abstract
PhenoSat is an experimental software tool that extracts phenological information from satellite vegetation index time-series. Temporal satellite NDVI data provided by VEGETATION sensor from three different vegetation types (Vineyard, Closed Deciduous Forest and Deciduous Shrubland with Sparse Trees) and for different geographical locations were used to test the ability of the software in extracting vegetation dynamics information. Six noise reduction filters were tested: piecewise-logistic, Savitzky-Golay, cubic smoothing splines, Gaussian models, Fourier series and polynomial curve fitting. The results showed that PhenoSat is an useful tool to extract phenological NDVI metrics, providing similar results to those obtained from field measurements. The best results presented correlations of 0.89 (n=6; p<0.01) and 0.71 (n=6; p<0.06) for the green-up and maximum stages, respectively. In the fitting process, the polynomial and Gaussian algorithms over smoothed the peak related with a double-growth season, the opposite to the other methods that could detect more accurately this peak.