Stefano Pignatti

Remote sensing water observation for supporting Lake Victoria weed management.

This paper aims to assess the suitability of remote sensing for enhancing the management of water body resources and for providing an inexpensive way to gather, on a wide area, weed infestation extent and optical parameter linked to the water body status. Remotely sensed satellite images and ancillary ground true data were used to produce land cover maps, trough classification techniques, and water compounds maps, applying radiative transfer models. The study proposed within the framework of the cooperation between Italian Foreign Affair Ministry (through the University of Rome) and Kenyan Authorities has been carried out on the Kenyan part of the Lake Victoria. This lake is one of the largest freshwater bodies of the world where, over the last few years environmental challenges and human impact have perturbed the ecological balance affecting the biodiversity. The objective of this research study is to define the thematic products, retrievable from satellite images, like weed abundance maps and water compound concentrations. These products, if provided with an appropriate time frequency, are useful to identify the preconditions for the occurrence of hazard events like abnormal macrophyte proliferation and to develop an up-to-date decision support system devoted to an apprised territory, environment and resource management.

Experimental Approach to the Selection of the Components in the Minimum Noise Fraction

An experimental method to select the number of principal components in minimum noise fraction (MNF) is proposed to process images measured by imagery sensors onboard aircraft or satellites. The method is based on an experimental measurement by spectrometers in dark conditions from which noise structure can be estimated. To represent typical land conditions and atmospheric variability, a significative data set of synthetic noise-free images based on real Multispectral Infrared and Visible Imaging Spectrometer images is built. To this purpose, a subset of spectra is selected within some public libraries that well represent the simulated images. By coupling these synthetic images and estimated noise, the optimal number of components in MNF can be obtained. In order to have an objective (fully data driven) procedure, some criteria are proposed, and the results are validated to estimate the number of components without relying on ancillary data. The whole procedure is made computationally feasible by some simplifications that are introduced. A comparison with a state-of-the-art algorithm for estimating the optimal number of components is also made.

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.

Wheat lodging monitoring using polarimetric index from RADARSAT-2 data

Abstract The feasibility of monitoring lodging of wheat fields by exploiting fully polarimetric C-band radar images has been investigated in this paper. A set of backscattering intensity features and polarimetric features, derived by target decomposition techniques, was extracted from 5 consecutive Radarsat-2 images. The temporal evolutions of these features of lodging wheat fields were investigated as a function of DAS (day after sowing) during the entire growing season. The temporal behavior was compared between typical lodging fields and normal fields in different growing stages. It was found that polarimetric feature from synthetic aperture radar (SAR) data was very sensitive to wheat lodging. Then a method called polarimetric index, availing the sensitivity of polarimetry to the structure, was put forward to monitor wheat lodging. The method was validated by two sets of in situ data collected in Shangkuli Farmland area, Inner Mongolia, China, at heading and ripe stages of spring wheat. Almost all the lodging fields were successfully distinguished from normal fields. Furthermore, the result revealed that the polarimetric index can reflect the intrinsic feature of lodging wheat with good anti-inference ability such as wheat growth difference. While optical sensors relied on its spectral features to monitor crop lodging, the proposed method based on radar data utilized polarimetric features to monitor crop lodging.

Aerosol optical retrieval and surface reflectance from airborne remote sensing data over land.

Quantitative analysis of atmospheric optical properties and surface reflectance can be performed by applying radiative transfer theory in the Atmosphere-Earth coupled system, for the atmospheric correction of hyperspectral remote sensing data. This paper describes a new physically-based algorithm to retrieve the aerosol optical thickness at 550nm (τ550) and the surface reflectance (ρ) from airborne acquired data in the atmospheric window of the Visible and Near-Infrared (VNIR) range. The algorithm is realized in two modules. Module A retrieves τ550 with a minimization algorithm, then Module B retrieves the surface reflectance ρ for each pixel of the image. The method was tested on five remote sensing images acquired by an airborne sensor under different geometric conditions to evaluate the reliability of the method. The results, τ550 and ρ, retrieved from each image were validated with field data contemporaneously acquired by a sun-sky radiometer and a spectroradiometer, respectively. Good correlation index, r, and low root mean square deviations, RMSD, were obtained for the τ550 retrieved by Module A (r2 = 0.75, RMSD = 0.08) and the ρ retrieved by Module B (r2 ≤ 0.9, RMSD ≤ 0.003). Overall, the results are encouraging, indicating that the method is reliable for optical atmospheric studies and the atmospheric correction of airborne hyperspectral images. The method does not require additional at-ground measurements about at-ground reflectance of the reference pixel and aerosol optical thickness.

Assessment of the abnormal growth of floating macrophytes in Winam Gulf (Kenya) by using MODIS imagery time series

Abstract The objective of this research study is to assess the capability of time-series of MODIS imagery to provide information suitable for enhancing the understanding of the temporal cycles shown by the abnormal growth of the floating macrophytes in order to support monitoring and management action of Lake Victoria water resources. The proliferation of invasive plants and aquatic weeds is of growing concern. Starting from 1989, Lake Victoria has been interested by the high infestation of water hyacinth with significant socio-economic impact on riparian populations. In this paper, we describe an approach based on the time-series of MODIS to derive the temporal behaviour, the abundance and distribution of the floating macrophytes in the Winam Gulf (Kenyan portion of the Lake Victoria) and its possible links to the concentrations of the main water constituencies. To this end, we consider the NDVI values computed from the MODIS imagery time-series from 2000 to 2009 to identify the floating macrophytes cover and an appropriate bio-optical model to retrieve, by means of an inverse procedure, the concentrations of chlorophyll a, coloured dissolved organic matter and total suspended solid. The maps of the floating vegetation based on the NDVI values allow us to assess the spatial and temporal dynamics of the weeds with high time resolution. A floating vegetation index (FVI) has been introduced for describing the weeds pollution level. The results of the analysis show a consistent temporal relation between the water constituent concentrations within the Winam Gulf and the FVI, especially in the proximity of the greatest proliferation of floating vegetation in the last 10 years that occurred between the second half of 2006 and the first half of 2007.The adopted approach will be useful to implement an automatic system for monitoring and predicting the floating macrophytes proliferation in Lake Victoria.

Use of airborne hyperspectral images to assess the spatial distribution of oil spilled during the Trecate blow-out (Northern Italy)

On February 1994 a large area close to Trecate was affected by an oil blow-out from an AGIP rig located within the Ticino Regional Park. One month later an airborne survey has been carried out in the framework of the CNR Lara Project, by utilizing the Daedalus AA5000 MIVIS spectrometer with 102 channels from visible to thermal infrared. Different authors stress, for oil slicks discrimination, the utility of laser and microwaves based techniques, but the high spatial and spectral MIVIS resolutions can improve the detection of the relative coverage by spilled oil. This task has been performed by applying hyperspectral unmixing methods to the MIVIS calibrated data, obtaining an oil fractional image with respect to other chosen end-members. The analysis has shown a good agreement between the results of the unconstrained unmixing technique applied to MIVIS data and the ground truths, offering a tool useful to quantify in a synoptic overview the effects of oil spills over land, by relating the ppm of oil with the oil hyperspectral information gathered by MIVIS.

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.

Temporal Polarimetric Behavior of Oilseed Rape (Brassica napus L.) at C-Band for Early Season Sowing Date Monitoring

Spatial monitoring of the sowing date plays an important role in crop yield estimation at the regional scale. The feasibility of using polarimetric synthetic aperture radar (SAR) data for early season monitoring of the sowing dates of oilseed rape (Brassica napus L.) fields is explored in this paper. Polarimetric SAR responses of six parameters, relying on polarization decomposition methods, were investigated as a function of days after sowing (DAS) during the entire growing season, by means of five consecutive Radarsat-2 images. A near-continuous temporal evolution of these parameters was observed, based on 88 oilseed rape fields. It provided a solid basis for determining the suitable temporal window and the best polarimetric parameters for sowing date monitoring. A high sensitivity of all polarimetric parameters to the DAS at different growing stages was shown. Simple linear models could be calibrated to estimate sowing dates at early growth stages and were validated on an independent data set. Although Volume and Span parameters provided the highest sowing date estimation accuracy at the earlier growth stages, the other four parameters (Volume/Total, Odd/Total, Entropy and Alpha) were more accurate for a wider temporal window. These results demonstrate the capability and high potential of polarimetric SAR data for monitoring the sowing date of crops in the early season.