Gabriele Candiani

Recent changes in macrophyte colonisation patterns: an imaging spectrometry-based evaluation of southern Lake Garda (northern Italy)

Temporal variation in the extent of submerged macrophytes along the littoral zone of Sirmione Peninsula in the southern part of Lake Garda (Northern Italy) was investigated using imaging spectrometry. Two images, with a spatial resolution of 5 m were acquired by the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) in the summers of 1997 and 2005. Image data were first geocoded and then corrected for both atmospheric and skylight reflection effects at the water surface using the 6S radiative transfer code. The two images were inverted using a bio-optical model, which was parameterised with the inherent optical properties of the lake. The inversion utilized the spectral range from 0.48-0.60 μm because it simultaneously provided the lowest environmental noise and the best atmospheric correction performances for the two scenes and produced images of bottom depth and of two substrate classes: bare sand and submerged vegetation, representing a mixture of valuable freshwater species. The MIVIS-derived bottom depth ranges and patterns were comparable to a bathymetry chart with a deviation less than 5%. In 2005, the image was consistent with contemporaneous in-situ derived knowledge on macrophyte distribution. In 1997, the substrate image map was deemed reasonable with respect to the macrophyte distribution documented in 2000. The comparison of the substrate products for the two dates showed a marked decrease in macrophyte beds, with a concomitant increase in sandy substrates. In the 8-year interval the extent of submerged macrophyte decreased from 72% to 52%. We expect that this study will contribute to increased knowledge of macrophyte colonisation patterns of the Sirmione Peninsula, where, despite their ecological significance, changes have been poorly documented.

Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques

Abstract Land-cover/land-use thematic maps are a major need in urban and country planning. This paper demonstrates the capabilities of Object Based Image Analysis in multi-scale thematic classification of a complex sub-urban landscape with simultaneous presence of agricultural, residential and industrial areas using pan-sharpened very high resolution satellite imagery. The classification process was carried out step by step through the creation of different hierarchical segmentation levels and exploiting spectral, geometric and relational features. The framework returned a detailed land-cover/land-use map with a Cohens kappa coefficient of 0.84 and an overall accuracy of 85%.

A Simple Fusion Method for Image Time Series Based on the Estimation of Image Temporal Validity

High-spatial-resolution satellites usually have the constraint of a low temporal frequency, which leads to long periods without information in cloudy areas. Furthermore, low-spatial-resolution satellites have higher revisit cycles. Combining information from high- and low- spatial-resolution satellites is thought a key factor for studies that require dense time series of high-resolution images, e.g., crop monitoring. There are several fusion methods in the bibliography, but they are time-consuming and complicated to implement. Moreover, the local evaluation of the fused images is rarely analyzed. In this paper, we present a simple and fast fusion method based on a weighted average of two input images (H and L), which are weighted by their temporal validity to the image to be fused. The method was applied to two years (2009-2010) of Landsat and MODIS (MODerate Imaging Spectroradiometer) images that were acquired over a cropped area in Brazil. The fusion

Evaporative Fraction as an Indicator of Moisture Condition and Water Stress Status in Semi-Arid Rangeland Ecosystems

Rangeland monitoring services require the capability to investigate vegetation condition and to assess biomass production, especially in areas where local livelihood depends on rangeland status. Remote sensing solutions are strongly recommended, where the systematic acquisition of field data is not feasible and does not guarantee properly describing the spatio-temporal dynamics of wide areas. Recent research on semi-arid rangelands has focused its attention on the evaporative fraction (EF), a key factor to estimate evapotranspiration (ET) in the energy balance (EB) algorithm. EF is strongly linked to the vegetation water status, and works conducted on eddy covariance towers used this parameter to increase the performances of satellite-based biomass estimation. In this work, a method to estimate EF from MODIS products, originally developed for evapotranspiration estimation, is tested and evaluated. Results show that the EF estimation from low spatial resolution over wide semi-arid area is feasible. Estimated EF resulted in being well correlated to field ET measurements, and the spatial patterns of EF maps are in agreement with the well-known climatic and landscape Sahelian features. The preliminary test on rangeland biomass production shows that satellite-retrieved EF as a water availability factor significantly increased the capacity of a remote sensing operational product to detect the variability of the field biomass measurements.

Mapping of Asbestos Cement Roofs and Their Weathering Status Using Hyperspectral Aerial Images

The aims of this study were: (i) the mapping of asbestos cement roofs in an urban area; and (ii) the development of a spectral index related to the roof weathering status. Aerial images were collected through the Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) sensor, which acquires data in 102 channels from the visible to the thermal infrared spectral range. An image based supervised classification was performed using the Spectral Angle Mapper (SAM) algorithm. The SAM was trained through a set of pixels selected on roofs of different materials. The map showed an average producer’s accuracy (PA) of 86% and a user’s accuracy (UA) of 89% for the asbestos cement class. A novel spectral index, the “Index of Surface Deterioration” (ISD), was defined based on measurements collected with a portable spectroradiometer on asbestos cement roofs that were characterized by different weathering statuses. The ISD was then calculated on the MIVIS images, allowing the distinction of two weathering classes (i.e., high and low). The asbestos cement map was handled in a Geographic Information System (GIS) in order to supply the municipalities with the cadastral references of each property having an asbestos cement roof. This tool can be purposed for municipalities as an aid to prioritize asbestos removal, based on roof weathering status.

SDI and Smart Technologies for the Dissemination of EO-Derived Information on a Rural District

The Po Plain (Italy) is a complex mixture of urban and rural landscapes. Between Lombardy and Piedmont, the rural zone includes the largest rice crop area in Europe, accounting for 40% and 90% of the European and Italian rice production, respectively. The monitoring of this crop system is important by both environmental and economic points of view, because of its impacts on ecosystems, society and markets. In particular, the near real time (NRT) provision of information about crop status at farm scale is relevant for different players (i.e. farmers, consultants, policy makers, insurance companies). In this study, a Spatial Data Infrastructure (SDI), implemented for the provision and dissemination of NRT crop status information, is presented. These information, derived from very high resolution satellite imagery, can be helpful to support loss adjusters in their workflows. The SDI architecture is designed as a seamless solution from satellite data download to presentation of added value information, which are retrieved and displayed on a mobile device, directly in the field.

Characterization of fine metal particles using hyperspectral imaging in automatic weee recycling systems

Waste from electric and electronic equipment (WEEE) represents the fastest growing waste stream in EU. The large amount and the high variability of electric and electronic products introduced every year in the market make the WEEE recycling process a complex task, especially considering that mechanical processes currently used by recycling companies are not flexible enough. In this context, hyperspectral imaging systems (HSI) can represent an enabling technology able to improve the recycling rates and the quality of the output products. This study shows the preliminary results achieved using a HSI technology in a WEEE recycling pilot plant, for the characterization of fine metal particles derived from WEEE shredding.

Hyperdimensional data exploitation through parametric reduction

Feature reduction of hyperspectral data is a big challenge, particularly because the reduced dimensions must preserve the separability properties and key information content. Nevertheless, various techniques have been developed so far and are well documented in the literature. Here we characterize a novel technique of feature reduction, with main emphasis on the ability of enhancing the informative content of the reduced dataset, for data exploitation purposes. The parametric reduction of hyperspaces using the Exponential Gaussian Optimization (EGO) approach allows the analyst to quickly explore the dataset in terms of the occurrence and properties of the diagnostic features and the local albedo, as well. As a consequence, this technique is able to provide new insights into the accomplishment of the delicate task of hyperspectral classification.