Alessandro Mei

PET and PVC Separation with Hyperspectral Imagery

Traditional plants for plastic separation in homogeneous products employ material physical properties (for instance density). Due to the small intervals of variability of different polymer properties, the output quality may not be adequate. Sensing technologies based on hyperspectral imaging have been introduced in order to classify materials and to increase the quality of recycled products, which have to comply with specific standards determined by industrial applications. This paper presents the results of the characterization of two different plastic polymers—polyethylene terephthalate (PET) and polyvinyl chloride (PVC)—in different phases of their life cycle (primary raw materials, urban and urban-assimilated waste and secondary raw materials) to show the contribution of hyperspectral sensors in the field of material recycling. This is accomplished via near-infrared (900–1700 nm) reflectance spectra extracted from hyperspectral images acquired with a two-linear-spectrometer apparatus. Results have shown that a rapid and reliable identification of PET and PVC can be achieved by using a simple two near-infrared wavelength operator coupled to an analysis of reflectance spectra. This resulted in 100% classification accuracy. A sensor based on this identification method appears suitable and inexpensive to build and provides the necessary speed and performance required by the recycling industry.

Integration of Field and Laboratory Spectral Data with Multi-Resolution Remote Sensed Imagery for Asphalt Surface Differentiation

The ability to classify asphalt surfaces is an important goal for the selection of suitable non-variant targets as pseudo-invariant targets during the calibration/validation of remotely-sensed images. In addition, the possibility to recognize different types of asphalt surfaces on the images can help optimize road network management. This paper presents a multi-resolution study to improve asphalt surface differentiation using field spectroradiometric data, laboratory analysis and remote sensing imagery. Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) airborne data and multispectral images, such as Quickbird and Ikonos, were used. From scatter plots obtained by field data using λ = 460 and 740 nm, referring to MIVIS Bands 2 and 16 and Quickbird and Ikonos Bands 1 and 4, pixels corresponding to asphalt covering were identified, and the slope of their interpolation lines, assumed as asphalt lines, was calculated. These slopes, used as threshold values in the Spectral Angle Mapper (SAM) classifier, obtained an overall accuracy of 95% for Ikonos, 98% for Quickbird and 93% for MIVIS. Laboratory investigations confirm the existence of the asphalt line also for new asphalts, too.

Top-down approach from satellite to terrestrial rover application for environmental monitoring of landfills

This paper describes a methodology to perform chemical analyses in landfill areas by integrating multisource geomatic data. We used a top-down approach to identify Environmental Point of Interest (EPI) based on very high-resolution satellite data (Pleiades and WorldView 2) and on in situ thermal and photogrammetric surveys. Change detection techniques and geostatistical analysis supported the chemical survey, undertaken using an accumulation chamber and an RIIA, an unmanned ground vehicle developed by CNR IIA, equipped with a multiparameter sensor platform for environmental monitoring. Such an approach improves site characterization, identifying the key environmental points of interest where it is necessary to perform detailed chemical analyses.

Anthropogenic activities monitoring by a multi-sensor approach: a case study of Rossano (CS) landfill

In this paper the landfill located to Rossano (CS) is analyzed through an integrated approach by the use of different remote sensed data from 1994 to 2010. In particular, the Multispectral Visible and Infrared Imaging Spectrometer (MIVIS) airborne sensor, orthophotos and Google Earthtm satellite image are used. The integrated multi-temporal data and downscaling analysis have identified several evidences such as the increase of land consumption. The Normalized Difference Vegetation Index (NDVI) of MIVIS image allows to observe the distribution of exposed soil and vegetated areas. At the same time, MIVIS thermal data shows some superficial thermal anomalies which highlighted these changes. Finally, this study shows how this approach can be useful to support both monitoring studies of land consumption as well as planning environmental campaigns in such areas.

Integrazione tra cartografia storica e moderna per lo studio dell'evoluzione di alcuni quartieri di Napoli tra il XVI e il XXI sec

In geographical science, data derived from historical sources can be effective for urban studies and for planning strategies. The antique maps are a fundamental thematic layer for the understanding of the urban dynamic structure of a City. In this work the results obtained from the comparative analysis of historical maps (1575, 1772 and 1800), orthophotos of 2000 and the Regional Technical Map (CTR) of 2006 in the city of Naples are presented.To achieve this goal several stages have been carefully implemented: research and acquisition of historical and recent maps; organization of a Geographical Information System project to manage raster dataset; geo-referencing of maps and analysis of thematic layers. The results allow to evaluate the urban evolution of the city through its monuments and buildings.

Biomass evaluation by the use of Landsat satellite imagery and forestry data

Satellite imagery allows to estimate vegetation parameters related to large areas and to evaluate biogeochemical cycles and radiative energy transfer processes between soil/vegetation and atmosphere.Moreover, the spectral indices derived from remote sensed data can be used for biomass estimation.This paper focuses on the evaluation of above-ground biomass in the Leonessa Municipality, Latium Region (Italy) by the use of Landsat 7 ETM+ (2001) and Landsat 8-OLI (2015) data. To achieve this goal, Rural Development Programs (PSR) and Forest Management Plans(FMP) (2001-2010) have been analyzed to retrieve the main information related to the different types of wood resources. In particular, dendrometry and prospects of different cultivation classes provide the main data such as the extension (ha), the biomass production (m3/ha), the number of plants, the cuts plan of each Forest Management Unit (FMU). This dataset was organized within a Geographical Information System (GIS) as well as Landsat images.Landsat 7 imagery was classified with two spectral indices, Normalized Difference Vegetation Index (NDVI) and Tasseled Cup, in order to find a correlation between remote sensed data and biomass production in m3/ha. Once obtained the spectral model, the analysis was extended to Landsat 8 and the 2015 biomass map was produced and exported on the web. The results, obtained by the exclusively analysis of open source optical remote sensing data, demonstrate their suitability to update FMPs with lower cost if compared to canonical field methods. Additionally, the analysis allows to extend the investigation to un-analyzed areas by forestry studies, too.

A multi-source approach for Environmental Point of Interest detection in landfills

Landfills play an important role in urban society. Consequently, an accurate management and planning of such anthropic activities are essential to improve decision making eco-strategies.The application of remote sensing techniques may improve the monitoring of different environmental matrices and the assessment of both gas emission in the atmosphere and leachate migration. This work describes a multi-source approach based on remote sensing and field data. To achieve this goal a downscaling and multi-temporal approach was adopted by using aerial photos, multispectral and hyperspectral imagery and overnight thermal and photogrammetric surveys. The integration of such different geomatic processing techniques allows to detect Environmental Point of Interest (EPI) inside and around the landfill.

Analysis of anthropic activities by optical remote sensing data at different spatial, spectral and temporal resolutions

This paper describes a remote sensing based downscaling approach for the analysis of the area affected by anthropic activities as quarrying and landfill. We selected the South-East flank of Mt. Vesuvius National Park as study area because in the last decades there have been a strong anthropic pressure with mining and municipal solid waste dumping as the main activities. The changes occurred were analysed by optical remote sensing at different spatial and spectral resolution. These activities had an environmental impact that is highlighted by integration of multi-source data.The multi- and hyper-spectral remote sensing data were adopted to study spectral indices and spatial patterns. The spectral response of targets supported the interpretation of stress conditions and other environmental anomalies in specific zones. Landsat, MIVIS, aerial photos and thematic maps were fused in a GIS for environmental analysis providing some Warning Zones defined in core and neighbouring of the anthropic area.