Simone Pascucci

Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy)

This study aims at comparing the capability of different sensors to detect land cover materials within an historical urban center. The main objective is to evaluate the added value of hyperspectral sensors in mapping a complex urban context. In this study we used: (a) the ALI and Hyperion satellite data, (b) the LANDSAT ETM+ satellite data, (c) MIVIS airborne data and (d) the high spatial resolution IKONOS imagery as reference. The Venice city center shows a complex urban land cover and therefore was chosen for testing the spectral and spatial characteristics of different sensors in mapping the urban tissue. For this purpose, an object-oriented approach and different common classification methods were used. Moreover, spectra of the main anthropogenic surfaces (i.e. roofing and paving materials) were collected during the field campaigns conducted on the study area. They were exploited for applying band-depth and sub-pixel analyses to subsets of Hyperion and MIVIS hyperspectral imagery. The results show that satellite data with a 30m spatial resolution (ALI, LANDSAT ETM+ and HYPERION) are able to identify only the main urban land cover materials.

Transport infrastructure surveillance and monitoring by electromagnetic sensing: the ISTIMES project

The ISTIMES project, funded by the European Commission in the frame of a joint Call “ICT and Security” of the Seventh Framework Programme, is presented and preliminary research results are discussed. The main objective of the ISTIMES project is to design, assess and promote an Information and Communication Technologies (ICT)-based system, exploiting distributed and local sensors, for non-destructive electromagnetic monitoring of critical transport infrastructures. The integration of electromagnetic technologies with new ICT information and telecommunications systems enables remotely controlled monitoring and surveillance and real time data imaging of the critical transport infrastructures. The project exploits different non-invasive imaging technologies based on electromagnetic sensing (optic fiber sensors, Synthetic Aperture Radar satellite platform based, hyperspectral spectroscopy, Infrared thermography, Ground Penetrating Radar-, low-frequency geophysical techniques, Ground based systems for displacement monitoring). In this paper, we show the preliminary results arising from the GPR and infrared thermographic measurements carried out on the Musmeci bridge in Potenza, located in a highly seismic area of the Apennine chain (Southern Italy) and representing one of the test beds of the project.

Optimal spectral domain selection for maximizing archaeological signatures: Italy case studies.

Different landscape elements, including archaeological remains, can be automatically classified when their spectral characteristics are different, but major difficulties occur when extracting and classifying archaeological spectral features, as archaeological remains do not have unique shape or spectral characteristics. The spectral anomaly characteristics due to buried remains depend strongly on vegetation cover and/or soil types, which can make feature extraction more complicated. For crop areas, such as the test sites selected for this study, soil and moisture changes within near-surface archaeological deposits can influence surface vegetation patterns creating spectral anomalies of various kinds. In this context, this paper analyzes the usefulness of hyperspectral imagery, in the 0.4 to 12.8 μm spectral region, to identify the optimal spectral range for archaeological prospection as a function of the dominant land cover. MIVIS airborne hyperspectral imagery acquired in five different archaeological areas located in Italy has been used. Within these archaeological areas, 97 test sites with homogenous land cover and characterized by a statistically significant number of pixels related to the buried remains have been selected. The archaeological detection potential for all MIVIS bands has been assessed by applying a Separability Index on each spectral anomaly-background system of the test sites. A scatterplot analysis of the SI values vs. the dominant land cover fractional abundances, as retrieved by spectral mixture analysis, was performed to derive the optimal spectral ranges maximizing the archaeological detection. This work demonstrates that whenever we know the dominant land cover fractional abundances in archaeological sites, we can a priori select the optimal spectral range to improve the efficiency of archaeological observations performed by remote sensing data.

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.

Reducing the Influence of Soil Moisture on the Estimation of Clay from Hyperspectral Data: A Case Study Using Simulated PRISMA Data

Soil moisture hampers the estimation of soil variables such as clay content from remote and proximal sensing data, reducing the strength of the relevant spectral absorption features. In the present study, two different strategies have been evaluated for their ability to minimize the influence of soil moisture on clay estimation by using soil spectra acquired in a laboratory and by simulating satellite hyperspectral data. Simulated satellite data were obtained according to the spectral characteristics of the forthcoming hyperspectral imager on board of the Italian PRISMA satellite mission. The soil datasets were split into four groups according to the water content. For each soil moisture level a prediction model was applied, using either spectral indices or partial least squares regression (PLSR). Prediction models were either specifically developed for the soil moisture level or calibrated using synthetically dry soil spectra, generated from wet soil data. Synthetically dry spectra were obtained using a new technique based on the effects caused by soil moisture on the optical spectrum from 400 to 2400 nm. The estimation of soil clay content, when using different prediction models according to soil moisture, was slightly more accurate as compared to the use of synthetically dry soil spectra, both employing clay indices and PLSR models. The results obtained in this study demonstrate that the a priori knowledge of the soil moisture class can reduce the error of clay estimation when using hyperspectral remote sensing data, such as those that will be provided by the PRISMA satellite mission in the near future.

An advanced tool of the CNR IMAA EO facilities: Overview of the TASI-600 hyperspectral thermal spectrometer

The paper shows the TASI-600 thermal hyperspectral sensor acquired by the Italian National Research Council — Institute of Methodologies for Environmental Analysis (CNR-IMAA) and describes some of the checks carried out during the commissioning phase. Furthermore, the first data acquired during the test-flight on hot spots of the volcanic island of Ischia (Central Italy) are shown. TASI-600 sensor has 32 spectral bands in the 8.0–11.5 mm spectral range, with a swath of 300 pixels and an IFOV of 1.2 mRad. The paper gives an overview of the principal TASI-600 characteristics, the CNR IMAA performance requirements and an overview of the technical innovation. Some of the outcomes of the tests performed in our laboratory in the Final Acceptance Test were focused to verify the linearity of the sensor up to higher temperatures (i.e. up to 500 K). Preliminary analysis of the in-flight and lab functional tests demonstrated that TASI-600 meets CNR IMAA requirements and as regard the radiometric accuracy it results higher than the requested.

Sensitivity analysis of the Aquacrop and SAFYE crop models for the assessment of water limited winter wheat yield in regional scale applications

Process-based models can be usefully employed for the assessment of field and regional-scale impact of drought on crop yields. However, in many instances, especially when they are used at the regional scale, it is necessary to identify the parameters and input variables that most influence the outputs and to assess how their influence varies when climatic and environmental conditions change. In this work, two different crop models, able to represent yield response to water, Aquacrop and SAFYE, were compared, with the aim to quantify their complexity and plasticity through Global Sensitivity Analysis (GSA), using Morris and EFAST (Extended Fourier Amplitude Sensitivity Test) techniques, for moderate to strong water limited climate scenarios. Although the rankings of the sensitivity indices was influenced by the scenarios used, the correlation among the rankings, higher for SAFYE than for Aquacrop, assessed by the top-down correlation coefficient (TDCC), revealed clear patterns. Parameters and input variables related to phenology and to water stress physiological processes were found to be the most influential for Aquacrop. For SAFYE, it was found that the water stress could be inferred indirectly from the processes regulating leaf growth, described in the original SAFY model. SAFYE has a lower complexity and plasticity than Aquacrop, making it more suitable to less data demanding regional scale applications, in case the only objective is the assessment of crop yield and no detailed information is sought on the mechanisms of the stress factors affecting its limitations.

A new joint application of non-invasive remote sensing techniques for structural health monitoring

This paper aims at analysing the potentialities of a new technological approach for the dynamic monitoring of civil infrastructures. The proposed approach is based on the joint use of a high-frequency thermal camera and a microwave radar interferometer to measure the oscillations due to traffic excitations of the Sihlhochstrasse Bridge, Switzerland, which was selected as test bed site in the ISTIMES project (EU—Seventh Framework Programme). The good quality of the results encourages the use of the proposed approach for the static and dynamic investigation of structures and infrastructures. Moreover, the remote sensing character of the two applied techniques makes them particularly suitable to study structures located in areas affected by natural hazard phenomena, and also to monitor cultural heritage buildings for which some conventional techniques are considered invasive. Obviously, their reliability needs further experiments and comparisons with standard contact sensors.

Suitability of CASI and ATM airborne remote sensing data for archaeological subsurface structure detection under different land cover: the Arpi case study (Italy)

In this paper multi-sensor airborne remote sensing has been applied to the Arpi archaeological area of southern Italy to assess its suitability for detecting and locating subsurface archaeological structures and to delineate subsurface remains beyond the current limits of ground geophysical data. To this aim, the capability of CASI and ATM reflectances in the VIS–NIR spectral range and the ATM apparent thermal inertia for subsurface archaeological prospection have been assessed at different sites of the Arpi archaeological area. First, linear spectral mixture analysis has been applied to CASI and ATM images to retrieve the dominant land cover for the selected subsurface structures, and then, the spectral bands most effective for the archaeological buried structure detection as a function of the land cover characteristics have been evaluated. The results reveal that multi/hyperspectral airborne remote sensing data can represent an effective and rapid tool to detect subsurface structures within different land cover contexts. Therefore, the proposed methodology can be used to perform a preliminary analysis of those areas where large cultural heritage assets occur by prioritizing and localizing the sites where to apply archaeological prospection.

Hyperspectral remote sensing data to map hazardous materials in a rural and industrial district: The Podgorica dwellings case studies

In this paper, we present the results of a hyperspectral airborne and in situ campaign in Montenegro aimed at individuating and monitoring two hazardous materials. They are the residues of the bauxite processing, i.e. red mud, and the asbestos fibers applied in the building materials. We perform laboratory analyses of asbestoscement, red mud and soil samples collected in the study area for (a) recognizing the dominant minerals using XRay Diffraction and X-Ray Fluorescence; (b) identifying the optical characteristics of the samples using a portable field spectrometer; and (c) characterizing their spectral features and remote sensing detection requirements. A least-squares fitting procedure, on the basis of the significant red mud and asbestos-cement reflectance spectral features, was applied to airborne hyperspectral remote sensing data collected over the study area. Results show that hyperspectral remote sensing data can provide an efficient, fast and repeatable tool for mapping and monitoring the diffusion of pollutants providing the location of the hazardous areas to be checked.