52 research outputs found
Assessment 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
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
No full textThe 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
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
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
Gli impatti del cambiamento climatico in Italia
No full textIl capitolo descrive l’evoluzione spaziale e temporale delle condizioni climatiche attese sull’area italiana utilizzando diversi scenari di cambiamento climatico. In particolare, a valle di un’introduzione sulla metodologia utilizzata, vengono valutati alcuni indicatori che descrivono specifiche caratteristiche del clima ritenute rilevanti per gli impatti che possono determinare danni e rischi sulle infrastrutture di interesse.
Oltre agli impatti fisici dei cambiamenti climatici, il capitolo contiene anche una valutazione economica dei rischi associati a tali variazioni climatiche e degli impatti sui settori economici chiave per l’Italia
Global warming and drought impacts in the EU. PESETA IV Task 7 – Droughts: Final Sector Report
No full textDroughts induce a complex web of impacts that span many sectors of the economy, as exemplified by extensive crop failure, reduced power supply, and shipping interruptions in the EU during 2018 and 2019. With global warming droughts will happen more frequent, last longer and become more intense in southern and western parts of Europe, while drought conditions will become less extreme in northern and north-eastern Europe. With 3°C of global warming in 2100 drought losses could be 5 times higher compared to today, with the strongest increase in drought losses projected in the Mediterranean and Atlantic regions of Europe. When expressed with respect to the total size of the economy the effects are dampened relatively, because drought-sensitive sectors like agriculture are projected to become relatively less economically prevalent in future EU economies than they are nowadays. The consequences on ecosystems are typically not monetized and hence are not reflected in the loss estimates
Riparian vegetation mapping for hydraulic roughness estimation using very high resolution remote sensing data fusion
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