1,721,050 research outputs found
A novel Multilevel Biodiversity Index (MBI) for combined field and satellite imagery surveys
Full text linkIn an epoch of fast and dramatic changes in ecosystems, a complete survey of biodiversity and an analysis of its spatial patterns from the field shall be associated with satellite imagery, which can provide a synoptic observation in space and time of the territory. This work presents the preliminary results of a new approach to monitor the biodiversity of a well-defined area, which takes into account species diversity and the landscape characteristics in terms of ecosystems diversity (land cover classes). We developed a Multilevel Biodiversity Index (MBI) and we tested it in a study site of 400 km2, belonging to the Central Mediterranean Ecoregion and consisting of forested areas, scrublands and steppes (Murge). With the aim to reach a global land cover classification system and a standardisation of the biodiversity surveys, the MBI can be easily adapted to study areas of different ecoregions on Earth. This index could certainly be useful for future studies and to address environmental policies in order to protect vulnerable ecosystems and assign a conservation priority rank that takes into account the presence of diversity both in terms of species and ecosystems
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Satellite and airborne remote sensing data for monitoring degraded areas
No full textThis study introduces two applications of remotely sensed data to detect degraded areas and to evaluate the relative pollution produced in the neighborhood areas. The first one regards the possible methodology to extract spatial information for dumps monitoring based on the discrimination ability of texture analysis. The second one analyses the spectral behaviour of an area stressed by an industrial settlement in Southern Italy. Both cases have been studied with the aid of high and very high resolution images. The general purpose is the development of models and automated procedures to identify environmental parameters associated to pollution and degradation by using satellite images
Estimation of vegetation parameters by means of hyperspectral data and neural networks
No full textAccurate knowledge of the distribution of vegetation can form a critical component for managing ecosystems and preserving biological diversity. Satellite multi-spectral remote sensing images have already been involved in vegetation types
classification. The multi-spectral sensors include the Advanced Very High Resolution Radiometer (AVHRR), the Landsat
Thematic Mapper (TM), and SPOT HRV. However, multi-spectral images have poor capability of discriminating forest species
precisely. With the development of imaging spectroscopy, it has been found that the vegetation communities can be better
differentiated using their hyperspectral reflectance in the visible to shortwave infrared spectral range.
On the other hand, application of hyperspectral images also brings some problems: high-dimensional datasets have
extremely huge volume and the narrow band tends to be strongly related with the adjacent ones, hence even useful
information is immersed in useless signals. Therefore feature extraction is necessary for hyperspectral classification.
Conventional Principal Component Analysis (PCA) is one of the most commonly used feature extraction techniques, which is
based on extracting the axes where the data shows the highest variability, but the results of PCA maybe not related strongly
with the characteristics of land cover class.
In this work we present a methodology based on neural networks for extracting nonlinear principal components (NLPC) from
hyperspectral data in vegetation analysis (forest and grassland). In the first stage of the study, the NLPC of the vegetation
types were analysed looking for differences between type classes. In a second stage an inversion scheme, always based on
neural networks, has been designed for the production of a classification map.
The area located in alpine region (South Tyrol-Northern Italy) was the selected test site for the study. Indeed, hyperspectral
data provided by the Hyperion and CHRIS platforms and concurrent extended ground-truth were available for this area.
Further investigations also focused on possible synergies between hyperspectral data and multi-spectral and/or SAR data
Southern Italy illegal dumps detection based on spectral analysis of remotely sensed data and land cover maps
No full textThis work intends to test the use of remotely sensed data, as a mean to identify degraded lands with a high environmental hazard. The approach uses data from the sensor Thematic Mapper on Landsat 5 in synergy with digital ortho-photos (1:10000) and land cover map Corine 1990 to create a methodology useful to identify areas with dumps. The analysed scene is relative to an area located in the Apulia Region in Southern Italy, where it is known the presence of a dump nearthe Margherita di Savoia "saline" (salt evaporation pool). As this dump is in its early phase, it is impossible to use thermal anomaly as a characteristic sign of its presence. So its identification proceeds through the extraction of the spectral signatures of the dump area and of the neighbourhood zones. The analysis is developed in three steps: 1. Monitoring the change in the zone nearby the pools, especially if abandoned; 2. Pointing out the dump presence by the spectral signature specificity; 3. Individuating areas characterized by the same spectral properties. A pre-processing analysis is carried out by the Principal Component Transformation in order to minimize spectral noise and redundancy. Subsequently, the images are classified by the unsupervised algorithm ISODATA aiming at automatically individuating radiometric classes. The regions of interest are identified by help of the land cover map and then characterised by their spectral signatures. The identification of the dump is a feasible objective because of the temporal stability of its spectral signature, with respect to those of the other areas
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Evaluating snow in EURO-CORDEX regional climate models with observations for the european alps: Biases and their relationship to orography, temperature, and precipitation mismatches
Full text linkClimate models are important tools to assess current and future climate. While they have been extensively used for studying temperature and precipitation, only recently regional climate models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain terrain. Here, we present an evaluation of the snow variables in theWorld Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well as with point (station) observations of snow depth and temperature for the European Alps. Large scale snow cover dynamics were reproduced well with some over-and under-estimations depending on month and RCM. The orography, temperature, and precipitation mismatches could on average explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the winter period November-April. Biases in average monthly snow depth were remarkably low for reanalysis driven RCMs
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Forest/Vegetation Types Discrimination in an Alpine Area Using Radarsat2 and ALOS PALSAR Polarimetric Data and Neural Networks
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