Rosamaria Salvatori

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.

Field reflectance of snow/ice covers at Terra Nova Bay, Antarctica

This paper studies the relationship between reflectance and physical characteristics of snow/ice surfaces in Antarctica, to support remote sensing monitoring of this environment. Field data were acquired on different targets selected at Terra Nova Bay, austral summer 1998. In each session of measurements the following data were collected: reflectance in the spectral range 350-2500 nm by a portable spectroradiometer; snow data including temperature, grain size and geometry, surface layer morphology, vertical profile of the snow pack, snow density and water content. The analysis of field data revealed that there are significant changes in surface reflectance properties, related to grain size and inclusions, and that a detailed characterization of snow metamorphosism is highly important for data interpretation. Meaningful reflectance variations were also observed after field data were re-sampled at Landsat 7 ETM spectral intervals.

Radiometric investigation of different snow covers in Svalbard

This paper examines the relationship between reflectance and physical characteristics of the snow cover in the Arctic. Field data were acquired for different snow and ice surfaces during a survey carried out at Ny-Ålesund, Svalbard, in spring 1998. In each measurement reflectance in the spectral range 350 - 2500 nm, snow data (including temperature, grain size and shape, density and water content), surface layer morphology, and vertical profile of the snow pack were recorded detailed analysis of reflectance based on the physical was performed. Field reflectance data were also re-sampled at the spectral intervals of Landsat TM to compare the ability of identifying different snow targets at discrete wavelength intervals. This analysis shows that reliable data on snow structure and thickness are necessary to understand albedo changes of the snow surfaces.

Snowpack characteristics of Brøggerhalvøya, Svalbard Islands

This paper is focused to the study of spatial variability of snowcover indifferent polar environmental contests. To this aim we present about 200 stratigraphic profiles of the snowpack, collected in the last years (1998–2015), in selected sites along the coast of the Brøggerhalvøya peninsula (Svalbard Islands). The different layers in the snowpack were identified and classified according to the International Standards, in terms of grain size, grain types, hardness and density. These data were used to calculate average values of hardness and density for each layer showing the same grain type characteristics. These snow stratigraphic profiles, coupled with meteorological observations, represent a valuable dataset to describe the relationship in the snowpack between pluriannial and seasonal snow and to study the characteristics of seasonal snow covers in the Svalbard region and their transformation during spring season. The stratigraphic dataset analysis seem to confirm an Arctic snow climate maritime for Brøggerhalvøya area as evidenced for a different site in Svalbard Island (Longyearbyen) by other authors.

Speedy methodology for geometric correction of MIVIS data

Abstract Since MIVIS images are generated from an airborne sensor they are affected by geometric distortion. In this paper a speedy procedure for the geometric correction of hyperspectral MIVIS images using algorithms such as the polynomial model (PM) and the model of rational functions (RFM) is presented. The aim of the work is to achieve an effective geometric correction of MIVIS images using an optimal compromise between result precision and elaboration time. This method could be also applied to large areas (thousands of square kilometers).

Detecting soil organic carbon by CASI hyperspectral images

Soil organic carbon (SOC) plays an important role in soil quality definition. In fact, soil organic matter (SOM) decline is one of the most relevant land degradation processes [1]. Therefore, an innovative methodology able to monitoring this soil property, collecting data more rapidly and economically, is needed. In this regard, remote sensing technique can open new scenarios of research. In particular, few studies have shown the capability to accurately determine SOC contents from airborne-hyperspectral sensors [2], [3], [4]. With this work we demonstrate that is possible to evaluate the Soil Organic Carbon in a test site in Apulia Region, Italy, through hyperspectral measurements by the airborne sensor CASI 1500, achieving very promising results.

Rock-type discrimination by field, TM and SPOT data, Tarn Flat, Antarctica

A lithologic investigation has been performed on the Tarn Flat area, Antarctica, based on Landsat-TM and SPOT-XS processed images and on radiometric field data acquired by an EXOTECH radiometer. Results from the field survey, performed during the austral summer 1994-95 expedition, were used to verify the ability of satellite images to discriminate between the main rock. Principal Component transformation allowed to enhance the spectral information of rocks, despite the high band correlation of TM data, while high-pass filtering and TM-SPOT merging allowed a fairly accurate detection of morphologic features. A supervised classification has been carried out on the processed satellite images as a further demonstration of the utility of remote observation to investigate extreme environments.

Geography of WWI Sites Along the Italian Front by Means of GIS Tools

In the framework of the commemorations for the First World War, a GIS project was started in order to identify, archive and disseminate the places involved by the war along the Italian front. The GIS operates through a set of raster layers represented by more than 500 raster maps and a set of vector layers dealing with places, catchment basins, front lines, deployment areas. Particular attention has been paid to all the occurrences of place names in order to univocally associate one place name to a set of geographic coordinates, thus creating a specific geodatabase. An online GIS version is available for data search and visualization.

Predicting new snow density in the Italian Alps: A variability analysis based on 10 years of measurements

DRST Centro Valanghe di Arabba, ARPA Veneto, Arabba, Italy National Research Council, Water Research Institute (IRSA‐CNR), Rome, Italy National Research Council, Water Research Institute (IRSA‐CNR), Brugherio, Italy National Research Council, Institute for Atmospheric Pollution (IIA‐CNR), Rome, Italy Dipartimento di Scienze, Università degli Studi Roma Tre, Rome, Italy Associazione Interregionale Neve e Valanghe (AINEVA), Trento, Italy Correspondence Nicolas Guyennon, National Research Council, Water Research Institute (IRSA‐CNR), Strada Provinciale 35d, km 0,7, Montelibretti, Rome, Italy. Email: guyennon@irsa.cnr.it

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.