Abstract
The sustainable conservation of mountain semi-natural meadows depends on the knowledge of their vegetation dynamics and management practices. Time series of vegetation indices (VI) derived from high temporal resolution satellite images can be a useful tool to the sustainable management of semi-natural meadows ecosystem and grazing activities. In this study satellite VI from the Moderate Resolution Imaging Spectroradiometer (MODIS) are evaluated against in situ measurements of VIs and plant height in the semi-natural mountain meadows of Northeast Portugal. In two testes sites, we evaluated the performance of Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from MODIS and field spectroradiometer sensor in characterizing semi-natural meadows phenology and plant height. The Savitzky-Golay filter was used for smoothing each VI time series, as well as to extract a number of NDVI and EVI metrics by computing derivatives. There was weak to reasonable agreement between VIs-metrics from MODIS and ground based derived phenology. The NDVI had a great sensitivity to crop growth changes during start of growth season, whereas the EVI exhibited more sensitivity at the pick of the maximum green biomass. The relationship between vegetation height and both VI from MODIS or field spectroradiometer, fit a non-linear model with similar pattern function for each test site. Regression analysis revealed that 67% of the in-season plant height variability could be explained by MODIS(EVI). These results suggest a great sensibility of MODIS(EVI) to detect the phenology and plant height of semi-natural meadows, even in situations of high plant height.

Abstract
The segmentation stage is a key aspect of an object-based image analysis system. However, the segmentation quality is usually difficult to evaluate for satellite images. The Synthetic Image TEsting Framework (SITEF) is a tool to evaluate and compare image segmentation results. This paper presents an example of the use of SITEF for the evaluation of a segmentation algorithm, using a SPOT HRG satellite image with 6 vegetation land cover classes identified in an agricultural area. The segmentation results were evaluated under various perspectives, including the parcel size and shape, the land cover types, and the parameters used in the segmentation algorithm.

Abstract
The segmentation stage is a key aspect of an object-based image analysis system. However, the segmentation quality is usually difficult to evaluate for satellite images The Synthetic Image TEsting Framework (SITEF) is a tool to evaluate and compare image segmentation results. This paper presents the SITEF with an extension to model adjacency effects between neighboring parcels, using the sensor's point spread function and a grid offset A practical application of SITEF is presented using a SPOT HRG satellite image, with 6 vegetation land cover classes identified on a mountainous area The segmentation results were evaluated under various perspectives, including the parcel size and shape, the land cover types, the sensor grid offset and one parameter used in the segmentation algorithm.

Abstract
The hierarchical structuring of a classified image with N classes provides a set of solutions for a classification problem, with N, N-1,..., 2 classes. The visual analysis of this set of images requires that a consistent color indexing is available for the whole structure. This paper addresses this issue, proposing a number of methods for the automatic assignment of lookup tables for the classified images at the various levels of a hierarchical structure. The various strategies are compared and some methods illustrated with a section of a Landsat TM image classified in 15 classes.

Abstract
Counting the number of Collembola in digital images is a routine task in laboratories of soil ecotoxicology. This process is based on a direct visual identification of Collembola, and is consequently a time consuming task. This paper present a fully automatic system for counting the number of Collembola in digital images. The system selects the interest area of the image, detects and removes the specular reflection of the incident light, as well as the foam developed during laboratory experiment and finally identifies and counts the number of Collembola. The system performance was tested using 5 treatments with 9 or 10 replicates and 13 treatments with 4 or 5 replicates. A total of 111 images were tested and the results were compared with those obtained by manual identification. The average relative error between automatic and manual counts from multiple observations of the same treatment was 2:1%, which can be considered a good result, given that this value is below the standard deviation between multiple replicate counts. © 2011 IEEE.

Abstract
A forecast model for estimating the annual variation in regional wine yield based on remote sensing was developed for the main wine regions of Portugal. Normalized Difference Vegetation Index (NDVI) time-series obtained by the VEGETATION sensor, on board the most recent Satellite Pour l'Observation de la Terre (SPOT) satellite, over the period 1998-2008 were used for four test sites located in the main wine regions of Portugal: Douro (two sites), Vinhos Verdes and Alentejo. The CORINE (Coordination of Information on the Environment) Land Cover maps from 2000 were initially used to select the suitable regional test sites. The NDVI values of the second decade of April of the previous season to harvest were significantly correlated to the wine yield for all studied regions. The relation between the NDVI and grapevine induction and differentiation of the inflorescence primordial or bud fruitfulness during the previous season is discussed. This NDVI measurement can be made about 17 months before harvest and allows us to obtain very early forecasts of potential regional wine yield. Appropriate statistical tests indicated that the wine yield forecast model explains 77-88% of the inter-annual variability in wine yield. The comparison of official wine yield and the adjusted prediction models, based on 36 annual data records for all regions, shows an average spread deviation between 2.9% and 7.1% for the different regions. The dataset provided by the VEGETATION sensor proved to be a valuable tool for vineyard monitoring, mainly for inter-annual comparisons on a regional scale due to their high data acquisition rates and wide availability. The accuracy, very early indication and low-cost of the developed forecast model justify its use by the winery and viticulture industry.