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Bertoldi G.,Institute for Alpine Environment | Della Chiesa S.,Institute for Alpine Environment | Della Chiesa S.,University of Innsbruck | Notarnicola C.,Institute for Applied Remote Sensing | And 4 more authors.
Journal of Hydrology | Year: 2014

This paper analyzes the spatial patterns of surface soil moisture of alpine meadows and pastures in the Matsch/Mazia Valley in the Italian Alps by comparing estimations from three different sources of information: (I) RADARSAT 2 synthetic aperture radar (SAR) images; (II) simulations by using the GEOtop hydrological model and (III) ground observations, derived from a network of fixed stations and field campaigns with mobile devices. The aim of this paper is to assess the added value of RADARSAT 2 products with respect to a distributed hydrological model in capturing soil moisture patterns in mountain areas, which is a challenging environment with a high degree of spatial variability. Moreover, the physical controls of the observed soil moisture patterns are analyzed by using the hydrological model. Results show that the model, once calibrated for soil and vegetation parameters, predicts the plot-scale temporal dynamic in station locations and the spatial averages with sufficient accuracy. However, the model output shows lower spatial variability with respect to the ground surveys, with a limited capability of reproducing moist areas in irrigated meadows. Differences arise due to difficulties in knowing soil model parameters and irrigation amounts with accurate spatial detail. RADARSAT 2 soil moisture maps well reproduce the spatial ground surveys, as well as over-irrigated meadows. However, SAR products are limited to slopes with a favorable viewing angle, to bare soil or to grassland areas. Moreover, the signal penetration depth is restricted to the soil surface layer. The major control on RADARSAT 2 patterns is land use. Irrigated meadows in the bottom of the valley have moister conditions, with respect to pastures along the upper hillslopes. In this case, model simulations suggest that differences in soil type could have a relevant impact on soil moisture estimation. A secondary control is topography, with increased moisture in convergent locations with a high topographic wetness index. Results suggest that the capability of RADARSAT 2 products to reproduce small-scale (20. m pixels size) surface soil moisture patterns in mountain grassland areas could complement the ability of the hydrological model to predict variations of soil moisture continuously in space and time. Therefore, RADARSAT 2 products can give useful information to improve spatial parameterization and validation of distributed hydrological models in mountain grassland areas, also in the perspective of implementing data integration procedures for operational soil moisture monitoring. © 2014 Elsevier B.V.

Bell R.,University of Vienna | Petschko H.,University of Vienna | Rohrs M.,Institute for Applied Remote Sensing | Dix A.,University of Bamberg
Geografiska Annaler, Series A: Physical Geography | Year: 2012

Landslides occur worldwide and contribute significantly to sediment budgets as well as to landform evolution. Furthermore, they pose hazards and risks to people and their goods. To assess the role of landslides, information on their age or persistence (i.e. the length of time the morphological characteristics of a landslide remain recognizable in the terrain) is essential. In this study, the potential of airborne laser scanning digital terrain models (ALS DTMs) is analysed for estimating landslide age, landslide persistence and human impact. Therefore, landslides in two study areas, Swabian Alb in Germany and Lower Austria in Austria, are mapped from hillshades of ALS DTMs and combined with historical information on landslide occurrence. It is tested whether the modification of the geomorphological features of landslides can be used to assess landslide age. In the Swabian Alb older landslides might show fresher features than younger ones because of different degrees of human impact, natural erosion and different histories of landslide reactivation. Estimated persistence times range between 27 and 320 years but are minimum values only. In Lower Austria four landslides show estimated minimum persistence times between 4 and 28 years. In Lower Austria 27 landslides disappeared in less than 7 years after occurrence mainly because of planation by farmers. The results show no clear trend in landslide persistence, neither regarding landslide magnitude, nor regarding land use. However, it is evident that human impact plays a major role in landslide persistence. © 2012 The authors. Geografiska Annaler: Series A, Physical Geography © 2012 Swedish Society for Anthropology and Geography.

Romieu E.,Bureau de Recherches Géologiques et Minières | Welle T.,University of Bonn | Schneiderbauer S.,Institute for Applied Remote Sensing | Pelling M.,King's College London | Vinchon C.,Bureau de Recherches Géologiques et Minières
Sustainability Science | Year: 2010

The climate change and natural hazard communities have developed the notion of vulnerability and associated methods for its assessment in parallel, with only limited interaction. What are the underlying reasons for this diversity; is there advantage in greater synergy? If yes, what are the pathways through which greater integration could be fostered? This paper discusses these issues using vulnerability studies in coastal areas to describe gaps between climate change and natural hazard approaches, and investigates scope for mutual learning and collaboration in the development of methodologies for vulnerability assessment. An overview of methods highlights the separation between climate change and natural hazard approaches. The main differences identified, beyond formal divergences in terminology, are linked to: process (stress vs shock), scale (temporal, functional and spatial), assessment approach (statistical vs prospective) and levels of uncertainty. We argue that the underlying source of divergence is the initial difference of purpose, one being identification of climate change adaptation pathways, the other being disaster risk reduction. In this context, the notion of vulnerability and its expression through assessment studies is the focal point connecting both domains. Indeed, the ongoing and active development of vulnerability concepts and methods have already produced some tools to help overcome common issues, such as acting in a context of high uncertainties, taking into account the dynamics and spatial scale of a social-ecological system, or gathering viewpoints from different sciences to combine human and impact-based approaches. Based on this assessment, this paper proposes concrete perspectives and possibilities to benefit from existing commonalities in the construction and application of assessment tools. © 2010 Integrated Research System for Sustainability Science, United Nations University, and Springer.

Zscheischler J.,Max Planck Institute for Biogeochemistry | Zscheischler J.,Max Planck Institute for Intelligent Systems (Tübingen) | Zscheischler J.,ETH Zurich | Reichstein M.,Max Planck Institute for Biogeochemistry | And 5 more authors.
Biogeosciences | Year: 2014

Climate extremes can affect the functioning of terrestrial ecosystems, for instance via a reduction of the photosynthetic capacity or alterations of respiratory processes. Yet the dominant regional and seasonal effects of hydrometeorological extremes are still not well documented and in the focus of this paper. Specifically, we quantify and characterize the role of large spatiotemporal extreme events in gross primary production (GPP) as triggers of continental anomalies. We also investigate seasonal dynamics of extreme impacts on continental GPP anomalies. We find that the 50 largest positive extremes (i.e., statistically unusual increases in carbon uptake rates) and negative extremes (i.e., statistically unusual decreases in carbon uptake rates) on each continent can explain most of the continental variation in GPP, which is in line with previous results obtained at the global scale. We show that negative extremes are larger than positive ones and demonstrate that this asymmetry is particularly strong in South America and Europe. Our analysis indicates that the overall impacts and the spatial extents of GPP extremes are power-law distributed with exponents that vary little across continents. Moreover, we show that on all continents and for all data sets the spatial extents play a more important role for the overall impact of GPP extremes compared to the durations or maximal GPP. An analysis of possible causes across continents indicates that most negative extremes in GPP can be attributed clearly to water scarcity, whereas extreme temperatures play a secondary role. However, for Europe, South America and Oceania we also identify fire as an important driver. Our findings are consistent with remote sensing products. An independent validation against a literature survey on specific extreme events supports our results to a large extent. © Author(s) 2013.

Mastrogiuseppe M.,University of Rome La Sapienza | Poggiali V.,University of Rome La Sapienza | Hayes A.,Cornell University | Lorenz R.,Johns Hopkins University | And 8 more authors.
Geophysical Research Letters | Year: 2014

We construct the depth profile - the bathymetry - of Titan's large sea Ligeia Mare from Cassini RADAR data collected during the 23 May 2013 (T91) nadir-looking altimetry flyby. We find the greatest depth to be about 160 m and a seabed slope that is gentler toward the northern shore, consistent with previously imaged shoreline morphologies. Low radio signal attenuation through the sea demonstrates that the liquid, for which we determine a loss tangent of 3 ± 1·10-5, is remarkably transparent, requiring a nearly pure methane-ethane composition, and further that microwave absorbing hydrocarbons, nitriles, and suspended particles be limited to less than the order of 0.1% of the liquid volume. Presence of nitrogen in the ethane-methane sea, expected based on its solubility and dominance in the atmosphere, is consistent with the low attenuation, but that of substantial dissolved polar species or suspended scatterers is not. Key Points First direct measurement of the depth of a Titan sea First determination of the nearly pure methane-ethane Ligeia Mare composition Determination of the total volume of Ligeia Mare ©2014. American Geophysical Union. All Rights Reserved.

Pasolli L.,University of Trento | Pasolli L.,Institute for Applied Remote Sensing | Notarnicola C.,Institute for Applied Remote Sensing | Bruzzone L.,University of Trento
IEEE Geoscience and Remote Sensing Letters | Year: 2011

This letter presents an experimental analysis of the application of the ε-insensitive support vector regression (SVR) technique to soil moisture content estimation from remotely sensed data at field/basin scale. SVR has attractive properties, such as ease of use, good intrinsic generalization capability, and robustness to noise in the training data, which make it a valid candidate as an alternative to more traditional neural-network-based techniques usually adopted in soil moisture content estimation. Its effectiveness in this application is assessed by using field measurements and considering various combinations of the input features (i.e., different active and/or passive microwave measurements acquired using various sensor frequencies, polarizations, and acquisition geometries). The performance of the SVR method (in terms of estimation accuracy, generalization capability, computational complexity, and ease of use) is compared with that achieved using a multilayer perceptron neural network, which is considered as a benchmark in the addressed application. This analysis provides useful indications for building soil moisture estimation processors for upcoming satellites or near-real-time applications. © 2011 IEEE.

Notarnicola C.,Institute for Applied Remote Sensing
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2012

A new change detection algorithm based on a Bayesian approach is developed and tested. The main objective of this approach is to exploit the changes in backscattering signals and relate them to soil moisture variations over agricultural fields under the hypothesis of both constant and variable roughness. The proposed methodology overcomes the limitations of the some change detection methods because it takes into account also possible changes in the radar signal due to roughness variability. The method is trained and tested on two data sets considering both C and L-band backscattering coefficients in relation to soil moisture and roughness measurements. The C-band dataset was acquired over bare soils while the L-band data set was acquired on vegetated fields and was exploited to understand the impact of vegetation in such approach. The results indicate that the approach is able to detect soil moisture changes both for C-and L-band data. In case of L band data, the presence of vegetation seems to determine backscattering dynamics reduction with respect to soil moisture changes. © 2012 IEEE.

Beer C.,Max Planck Institute for Biogeochemistry | Beer C.,University of Stockholm | Weber U.,Max Planck Institute for Biogeochemistry | Tomelleri E.,Max Planck Institute for Biogeochemistry | And 4 more authors.
Journal of Climate | Year: 2014

Temporal variability of meteorological variables and extreme weather events is projected to increase in many regions of the world during the next century. Artificial experiments using process-oriented terrestrial ecosystem models make it possible to isolate effects of temporal variability from effects of gradual climate change on terrestrial ecosystem functions and the system state. Such factorial experiments require two longterm climate datasets: 1) a control dataset that represents observed and projected climate and 2) a dataset with the same long-term mean as the control dataset but with altered short-term variability. Using a bias correction method, various climate datasets spanning different periods are harmonized and then combined with the control dataset with consistent time series for Europe during 1901-2100. Then, parameters of a distribution transformation function are estimated for individual meteorological variables to derive the second climate dataset, which has similar long-term means but reduced temporal variability. The transformation conserves the number of rainy days within a month and the shape of the daily meteorological data distributions, which is important to ensure that, for example, drought duration does not modify the suitability of localized vegetation type to precipitation regimes. The median absolute difference between daily data of both datasets is 5% to 20%. On average, decadal extreme values are reduced by 2% to 35%. Driving a terrestrial ecosystem model with both climate datasets shows a general higher gross primary production under reduced temporal climate variability. This effect of climate variability on productivity demonstrates the potential of the climate datasets for studying various effects of temporal variability on ecosystem state and functions over large domains. © 2014 American Meteorological Society.

Kass S.,Institute for Applied Remote Sensing | Notarnicola C.,Institute for Applied Remote Sensing | Zebisch M.,Institute for Applied Remote Sensing
International Journal of Geographical Information Science | Year: 2011

This article presents an object-oriented classification approach to identifying orchards, vineyards and agricultural fields. This approach uses texture-related parameters obtained from very high spatial resolution data, in particular Quickbird images and orthophotos. A multi-resolution segmentation procedure was applied to delimit individual agricultural plots as segments. Textural information of the generated segments was then used to classify orchards, agricultural fields (grassland) and two wine cultivation systems (trellis and pergola). In this article three different texture-based approaches are compared to correctly classify the given plots: (1) a 'zonal mean maximum' of texture measurements, which consider the maximum value of four directions of texture measurements related to plots; (2) a relational sub-object approach based on a thematic derived texture filter technique that reflects individual row structures; and (3) a hybrid approach combining the two previous ones. In order to identify relevant parameters for each approach, the data mining software See5 is used. The hybrid approach increased overall accuracy by 8% and 6% for Quickbird (92% accuracy) and orthophotos (88% accuracy), respectively. The application of the same methodology to the orthophotos alone results in a lower accuracy but still one that is acceptable. This offers the possibility of also considering orthophotos for this kind of detection, especially when Quickbird data are not available. In this sense, the developed methodology can be considered as a new object-based landscape analysis technique suitable for the provision of accurate maps able to fulfil the requirements of scientists, planners and other end-users. © 2011 Taylor & Francis.

Mastrogiuseppe M.,University of Rome La Sapienza | Poggiali V.,University of Rome La Sapienza | Seu R.,University of Rome La Sapienza | Martufi R.,University of Rome La Sapienza | Notarnicola C.,Institute for Applied Remote Sensing
Icarus | Year: 2014

The Cassini Radar is a Ku band multimode instrument capable of providing topographic and mapping information. During several of the 93 Titan fly-bys performed by Cassini, the radar collected a large amount of data observing many dune fields in multiple modes such as SAR, Altimeter, Scatterometer and Radiometer. Understanding dune characteristics, such as shape and height, will reveal important clues on Titan's climatic and geological history providing a better understanding of aeolian processes on Earth. Dunes are believed to be sculpted by the action of the wind, weak at the surface but still able to activate the process of sand-sized particle transport. This work aims to estimate dunes height by modeling the shape of the real Cassini Radar Altimeter echoes. Joint processing of SAR/Altimeter data has been adopted to localize the altimeter footprints overlapping dune fields excluding non-dune features. The height of the dunes was estimated by applying Maximum Likelihood Estimation along with a non-coherent electromagnetic (EM) echo model, thus comparing the real averaged waveform with the theoretical curves. Such analysis has been performed over the Fensal dune field observed during the T30 flyby (May 2007). As a result we found that the estimated dunes' peak to trough heights difference was in the order of 60-120. m. Estimation accuracy and robustness of the MLE for different complex scenarios was assessed via radar simulations and Monte-Carlo approach. We simulated dunes-interdunes different composition and roughness for a large set of values verifying that, in the range of possible Titan environment conditions, these two surface parameters have weak effects on our estimates of standard dune heights deviation.Results presented here are the first part of a study that will cover all Titan's sand seas. © 2014.

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