Abstract
PhenoSat is an experimental software tool that produces phenological information from satellite vegetation index time series. The main characteristics and functionalities of the PhenoSat tool are presented, and its performance is compared against observed measures and other available software applications. A multiyear experiment was carried out for different vegetation types: vineyard, low shrublands, and seminatural meadows. Temporal satellite normalized difference vegetation index (NDVI) data provided by MODerate resolution Imaging Spectroradiometer and Satellite Pour l'Observation de la Terre VEGETATION were used to test the ability of the software in extracting vegetation dynamics information. Three important PhenoSat features were analyzed: extraction of the main growing season information, estimation of double growth season parameters, and the advantage of selecting a temporal region of interest. Seven noise reduction filters were applied: cubic smoothing splines, polynomial curve fitting, Fourier series, Gaussian models, piecewise logistic, Savitzky-Golay (SG), and a combination of the last two. The results showed that PhenoSat is a useful tool to extract NDVI metrics related to vegetation dynamics, obtaining high significant correlations between observed and estimated parameters for most of the phenological stages and vegetation types studied. Using the combination of SG and piecewise logistic to fit the NDVI time series, PhenoSat obtained correlations higher than 0.71, except for the seminatural meadow start of season. The selection of a temporal region of interest improved the fitting process, consequently providing more reliable phenological information.

Abstract
PhenoSat is a software tool that extracts phenological information from satellite based vegetation index time-series. This chapter presents PhenoSat and tests its main characteristics and functionalities using a multi-year experiment and different vegetation types – vineyard and semi-natural meadows. Three important features were analyzed: (1) the extraction of phenological information for the main growing season, (2) detection and estimation of double growth season parameters, and (3) the advantages of selecting a sub-temporal region of interest. Temporal NDVI satellite data from SPOT VEGETATION and NOAA AVHRR were used. Six fitting methods were applied to filter the satellite noise data: cubic splines, piecewise-logistic, Gaussian models, Fourier series, polynomial curve-fitting and Savitzky-Golay. PhenoSat showed to be capable to extract phenological information consistent with reference measurements, presenting in some cases correlations above 70% (n=10; p=0.012). The start of in-season regrowth in semi-natural meadows was detected with a precision lower than 10-days. The selection of a temporal region of interest, improve the fitting process (R-square increased from 0.596 to 0.997). This improvement detected more accurately the maximum vegetation development and provided more reliable results. PhenoSat showed to be capable to adapt to different vegetation types, and different satellite data sources, proving to be a useful tool to extract metrics related with vegetation dynamics. © Springer International Publishing AG 2016.

Abstract
Earth Observation Satellite (EOS) data have a great potential for land cover mapping, which is mostly based on high resolution images. However, in tropical areas the use of these images is seriously limited due to the presence of clouds. This paper evaluates the ability of temporal-based image classification methods to produce land cover maps in tropical regions. A new approach is proposed for land cover classification and updating based exclusively on temporal series data, illustrated with a practical test using SPOT VEGETATION satellite images from 1999 to 2011 for Rondonia (Amazon), Brazil. Using the GLC2000 as reference, a Normalized Difference Vegetation Index (NDVI) time series of 15 distinct land cover classes (LCC) were created. Two classifiers were used (Euclidean Distance and Dynamic Time Warping) to produce maps of land cover changes for 1999-2011. Due to the difficulties in discriminating 15 LCC in the Amazon region, a hierarchical aggregation was performed by joining the initial classes gradually up to four broad classes. The land cover changes in the 1999-2011 period were evaluated using criteria based on the classification results for the individual years. The comparison with reference data showed consistent results, proving that this approach is able to produce accurate land cover maps using exclusively temporal series EOS data.

Abstract
The identification of land cover changes on a continental scale is a laborious and time-consuming process. A new methodology is proposed based exclusively on SPOT VGT data, illustrated for the African Continent using GLC2000 as reference to select 26 distinct land cover types (classes). For each class, the normalized difference vegetation index (NDVI) time-series are extracted from SPOT VGT images and a hierarchical aggregation is done using two different methods: one that preserves the initial signatures throughout the hierarchical process, and another that recalculates the signatures for each aggregation level. The average classification agreement was above 89% using 26 classes. Reducing the number of classes improves classification agreement. In order to study the influence of temporal variability in the classification results, the methodology was applied on data from 1999, 2001, 2008, and 2010. With 26 classes, the best average classification agreement obtained was 94.5% with annual data, against 74.1% with interannual data.

Abstract
Processes shaping urban ecosystems reflect and influence the cultural context in which they emerge, bearing implications for ecosystem services (ES) planning and management. Investigating the perception of benefits and losses / costs delivered by a specific service providing unit (SPU) can generate objective orientations suitable for urban planning and management deeply embedded in the social-ecological systems where they occur, because the realization of ES into benefits and losses / costs is mediated by specific beneficiaries and reflects their characteristics, information and use of ecosystems. Street trees are a particularly relevant SPU in many densely built Southern-European cities due to the difficulty in implementing new sizeable green areas. In this study, a questionnaire was developed and applied in Porto to investigate how benefits (cultural, regulating and economic) and losses / costs caused by street trees are perceived by citizens and influenced by a set of socioeconomic variables (N = 819 people aged 18 years or older), and parametric statistical tests were used to analyze the effect of gender, age and school level. Results evidenced that people in Porto valued more environmental benefits (particularly air quality improvement) than cultural ones. School level was the variable accounting for more differences, underlining a tendency in people with lower level of academic education to value less the benefits provided by street trees in Porto and attribute more importance to losses and damages, compared to people who attended university or had higher academic degree. Age also held considerable differences in mean responses, with older people showing more concern towards losses and costs, while gender influenced perception of cultural benefits, which were more important for women than for men. The findings of the research are discussed concerning implications for environmental justice, planning and management of urban ecosystems.

Abstract
Significant advances have been made in identifying, quantifying and valuing multiple urban ecosystem services (UES), yet this knowledge remains poorly implemented in urban planning and management. One of the reasons for this low implementation is the insufficient thematic and spatial detail in UES research to provide guidance for urban planners and managers. Acknowledging how patterns of UES delivery are related with vegetation structure and composition in urban green areas could help these stakeholders to target structural variables that increase UES provision. This investigation explored how different types of urban green spaces influence UES delivery in Porto, a Portuguese city, and how this variation is affected by a socioeconomic gradient. A stepwise approach was developed using two stratification schemes and a modelling tool to estimate urban forest structure and UES provision. This approach mapped explicit cold and hotspots of UES provision and discriminated the urban forest structural variables that influence UES at the local scale. Results revealed that different types of green spaces affect UES delivery as a direct result of the influence of structural variables of the urban forest. Furthermore, the uneven distribution of green spaces types across socioeconomic strata alters UES delivery across the city. This case study illustrates how a methodology adaptable to other geographic contexts can be used to map and analyze coupled social and ecological patterns, offering novel insights that are simple to understand and apply by urban planners and managers.