Federico Santini

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.

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.

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.

LWIR hyperspectral change detection for target acquisition and situation awareness in urban areas

This paper studies change detection of LWIR (Long Wave Infrared) hyperspectral imagery. Goal is to improve target acquisition and situation awareness in urban areas with respect to conventional techniques. Hyperspectral and conventional broadband high-spatial-resolution data were collected during the DUCAS trials in Zeebrugge, Belgium, in June 2011. LWIR data were acquired using the ITRES Thermal Airborne Spectrographic Imager TASI-600 that operates in the spectral range of 8.0-11.5 μm (32 band configuration). Broadband data were acquired using two aeroplanemounted FLIR SC7000 MWIR cameras. Acquisition of the images was around noon. To limit the number of false alarms due to atmospheric changes, the time interval between the images is less than 2 hours. Local co-registration adjustment was applied to compensate for misregistration errors in the order of a few pixels. The targets in the data that will be analysed in this paper are different kinds of vehicles. Change detection algorithms that were applied and evaluated are Euclidean distance, Mahalanobis distance, Chronochrome (CC), Covariance Equalisation (CE), and Hyperbolic Anomalous Change Detection (HACD). Based on Receiver Operating Characteristics (ROC) we conclude that LWIR hyperspectral has an advantage over MWIR broadband change detection. The best hyperspectral detector is HACD because it is most robust to noise. MWIR high spatial-resolution broadband results show that it helps to apply a false alarm reduction strategy based on spatial processing.

Advanced Anomalous Pixel Correction Algorithms for Hyperspectral Thermal Infrared Data: The TASI-600 Case Study

Anomalous pixel responses often seriously affect remote sensing applications, especially in the thermal spectral range. In this paper, a new method to identify and correct anomalous pixel responses is presented. The method was specifically developed to handle with hyperspectral data and is based on the statistical analysis of a gray scale RX detector (RXD) image applied on the focal plane space rather than on the image space. An iterative thresholding method to correct anomalous pixels in automatic modality was tuned. Moreover, a band depth-based method to properly restore the lost information was applied. The band depth method serves to prevent the creation of new artifacts during the anomalous pixel correction that could affect applications such as anomaly or change detection and classification for thermal infrared (TIR) hyperspectral imagery. In this paper, we take into consideration hyperspectral TASI-600 data acquired during recent airborne campaigns in Europe. Evidences of the benefits on remote sensing applications such as classification and change detection algorithms in urban areas are shown.

Sinergistic use of radar and optical data for agricultural data products assimilation: A case study in Central Italy

The paper describes the preliminary results of the January-August 2015 multi-frequency EO data acquisition campaign conducted over the Maccarese (Central Italy) farm. From January to May radar Cosmo SkyMed Ping-Pong (HH-VV), RapidEye and ZY-3 multispectral VHR optical images, as well as in situ data, have been acquired to retrieve biophysical and/or bio-chemical characteristics of soil and crops. LAI trend has been analyzed and compared by using both polarimetric and optical retrieval algorithms while soil moisture measurements have been compared with the radar backscattering.

Hyperspectral tecniques for water quality monitoring: Application to the “Sacca di Goro” — Italy

The paper presents a comparison between an empirical algorithm and a physics based model for the assessment of water compound concentrations by remote sensing hyperspectral data. At the purpose a series of in situ measurements were carried out monthly, from June to October 2005, to spectrally characterize the water of the “Sacca di Goro” (Italy) at spatial (horizontal and vertical) and temporal (daily and seasonal) scales. The results obtained by the application of the two different methods to the in situ acquired data showed that an appreciable improvement is obtainable by considering the physical approach.

Optical model for the water characterization of the highly turbid water of the Winam Gulf (Victoria Lake)

The study, proposed within the framework of the cooperation with 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. Pollution and sediments loads from the tributaries rivers and antrophic sources caused a worrying increase of the turbidity level of the lake water. Secchi transparency index has declined from 5 meters in the 1930s to less than one meter in the 1990s. With the aim of providing an inexpensive way to gather information linked to the water clarity and quality, a method for remotely sensed data interpretation, devoted to produce chl (chlorophyll), CDOM (coloured dissolved organic matter) and TSS (total suspended solids) maps, has been assessed. At this purpose a bio-optical model, based on radiative transfer theory in water bodies, has been refined. The method has been applied on an image acquired on January 2004 by ENVISAT/MERIS sensor just a week after an in situ campaign took place. During the in situ campaign a data set for model refinement and products validation has been collected. This data comprise surface radiometric quantity and samples for laboratory analyses. The comparison between the obtained maps and the data provided by the laboratory analysis showed a good correspondence, demonstrating the potentiality of remote observation in supporting the management of the water resources.