1,720,987 research outputs found
Scale-dependent relations in land cover biophysical dynamics
No full textThe exploration of the relationships between plant biotic dynamics and scale can reveal important information on ecosystem spatial organization by addressing preservation of information integrity in upscaling/downscaling procedures of land-surface parameterization for environmental modeling applications. Scale-dependent relations of vegetation dynamics are investigated in this study by using emergent biophysical characteristics obtained through a predictive multidimensional model of vegetation anomalies derived from remote-sensing observations. In particular, the analysis is focused on the spatial organization of some phenological parameters including deterministic variations (seasonal range, interannual variability, jump discontinuities) and stochastic components (plant memory, spatial correlations). The analysis is performed using MODIS-based Normalized Difference Vegetation Index (NDVI) 16-day composites for the period from March 2000 to December 2006 over Italy at different levels of spatial aggregation (1-8. km). Scale-dependences of the statistical moments of the phenological parameters are quantified through simple power laws for five distinct vegetated land covers. Results suggest that some biophysical characteristics, especially deterministic components, show no preferential spatial scale for important coverage. In particular, broad-leaved forests and natural grasslands are characterized by deterministic and low-distance spatial components well explained by scale relationships, which are modulated by possible spatiotemporal dynamics of climatic drivers. Agricultural lands show high scale-dependent relations on short-term biophysical memory sources and low-distance spatial components of phenology likely related to hierarchical interactions of anthropogenic and ecological processes; whereas mixed patterns of croplands and natural areas generally present no consistent scaling relations. © 2011 Elsevier B.V
Forest cover influence on regional flood frequency assessment in Mediterranean catchments
Get PDFThe paper aims at evaluating to what extent the forest cover can explain the component of runoff coefficient as defined in a regional flood frequency analysis based on the application of the rational formula coupled with a regional model of the annual maximum rainfall depths. The analysis is addressed to evaluate the component of the runoff coefficient which cannot be captured by the catchment lithology alone. Data mining is performed on 75 catchments distributed from South to Central Italy. Cluster and correlation structure analyses are conducted for distinguishing forest cover effects within catchments characterized by hydro-morphological similarities. We propose to improve the prediction of the runoff coefficient by a linear regression model, exploiting the ratio of the forest cover to the catchment critical rainfall depth as dependent variable. The proposed regression enables a significant bias correction of the runoff coefficient, particularly for those small mountainous catchments, characterised by larger forest cover fraction and lower critical rainfall depth
ES4LUCC: A GIS-tool for remotely monitoring landscape dynamics
No full textGiven the potential impacts of land cover changes on surface processes, accurate mapping of landscape dynamics is a crucial task in environmental monitoring. The use of commercial software for remote sensing of landscape changes requires appropriate expertise in sensor technology and computing resources that are not always available to decision makers. This paper presents the development of an experimental prototype of a lightweight and user-friendly GIS tool – ES4LUCC – a semiautomatic software for change detection and classification of land use/cover. The tool is based on image processing techniques applied on multi-temporal remotely sensed spectral and surface model data. The GIS-based tiling approach allows to non-specialists of remote sensing to manage high-dimensional data even from low performance computing platforms. The paper synthesizes the implemented digital image processing that form the basis of ES4LUCC, including data correction, classification and change detection, map refinements. It also describes the software architecture, the main IDL modules and the integration with GIS through a tight coupling approach and.dll calling functions. The main modelling process is controlled through a powerful GUI developed as part of the ArcMap component of ESRI ArcGIS. The software is tested by using bi-temporal color-infrared ADS40 and Light detection and ranging data acquired on a 80-km transect of the Marecchia river (Italy). The outputs of ES4LUCC give an understanding of the natural- and human-induced surface processes, such as urban planning, agricultural and forest practices, fluvial dynamics and slope instability. The model provides reliable maps (90.77% overall classification accuracy) that represent useful layers for environmental landscape management.JRC.H.7 - Climate Risk Managemen
Mapping natural and urban environments using airborne multi-sensor ADS40-MIVIS-LiDAR synergies
No full textThe recent and forthcoming availability of high spatial resolution imagery from satellite and airborne sensors offers the possibility to generate an increasing number of remote sensing products and opens new promising opportunities for multi-sensor classification. Data fusion strategies, applied to modern airborne Earth observation systems, including hyperspectral MIVIS, color-infrared ADS40, and LiDAR sensors, are explored in this paper for fine-scale mapping of heterogeneous urban/rural landscapes. An over 1000-element array of supervised classification results is generated by varying the underlying classification algorithm (Maximum Likelihood/Spectral Angle Mapper/Spectral Information Divergence), the remote sensing data stack (different multi-sensor data combination), and the set of hyperspectral channels used for classification (feature selection). The analysis focuses on the identification of the best performing data fusion configuration and investigates sensor-derived marginal improvements. Numerical experiments, performed on a 20-km stretch of the Marecchia River (Italy), allow for a quantification of the synergies of multi-sensor airborne data. The use of Maximum Likelihood and of the feature space including ADS40, LiDAR derived normalized digital surface, texture layers, and 24 MIVIS bands represents the scheme that maximizes the classification accuracy on the test set. The best classification provides high accuracy (92.57% overall accuracy) and demonstrates the potential of the proposed approach to define the optimized data fusion and to capture the high spatial variability of natural and human-dominated environments. Significant inter-class differences in the identification schemes are also found by indicating possible sub-optimal solutions for landscape-driven mapping, such as mixed forest, floodplain, urban, and agricultural zones. © 2012 Elsevier B.V
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Riparian vegetation mapping for hydraulic roughness estimation using very high resolution remote sensing data fusion
No full textAssessment of hyperspectral MIVIS sensor capability for heterogeneous landscape classification
No full textThe potential and limitations of the hyperspectral remote sensing MIVIS sensor (Multispectral Infrared Visible Imaging Spectrometer) in classifying heterogeneous landscapes are explored in this study. In order to quantify the discriminant information derived from selected MIVIS subsets we classified a monitored scenario by progressively increasing the feature space dimensionality. The hyperspectral subsets are defined through the Sequential Forward Selection algorithm, while mapping processes have been performed through the Maximum Likelihood, Spectral Angle Mapper and Spectral Information Divergence classifiers. Impacts of spectral bands on the overall classification accuracies and single land cover-scale reliability, as well as possible dimensionality effects (Hughes phenomenon) are investigated. The analysis is tested on a 20-km stretch of the Marecchia River (Emilia Romagna, Italy) by using MIVIS data acquired in autumn 2009 and 2010 for a 17-class mapping including complex urban/rural areas. For the considered dataset, the MIVIS sensor showed an equipment failure: of the nominal 102-band MIVIS dataset, only the first 24 bands, spanning within the 0.441–1.319 μm spectral range, were exploitable. Nevertheless, the available information provided valuable discriminant contributions in land cover mapping (Maximum Likelihood Overall Accuracy ∼85%) with encouraging reliability on mixed forests, croplands, and no-vegetated floodplain patterns, whereas riparian vegetation and urban zones exhibited low classification accuracies. The relationship between the spectral space dimensionality and the minimum training-set size that is necessary to achieve a given inter-class separability has also been experimentally investigated by progressively under-sampling the original training set. The maximum under-sampling factor that avoided a decrease in the overall accuracy turned out to be, at maximum, 15 for the considered data set.JRC.H.7 - Climate Risk Managemen
Variations on the Author
Get PDF“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
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe 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
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