Monica Pepe

DETECTING CHLOROPHYLL, SECCHI DISK DEPTH AND SURFACE TEMPERATURE IN A SUB-ALPINE LAKE USING LANDSAT IMAGERY

Some bio-physical parameters, such as chlorophyll a concentration, Secchi disk depth and water surface temperature were mapped in the sub-alpine Lake Iseo (Italy) using Landsat Thematic Mapper (TM) data acquired on the 7 March 1997. In order to adequately investigate the water-leaving radiance, TM data were atmospherically corrected using a partially image-based method, and the atmospheric transmittance was measured in synchrony with the satellite passage. An empirical approach of relating atmospherically corrected TM spectral reflectance values to in situ measurements, collected during the satellite data acquisition, was used. The models developed were used to map the chlorophyll concentration and Secchi disk depth throughout the lake. Both models gave high determination coefficients (R2 = 0.99 for chlorophyll and R2 = 0.85 for the Secchi disk) and the spatial distribution of chlorophyll concentration and Secchi disk depth was mapped with contour intervals of 1 mg/m3 and 1 m, respectively. A scene-independent procedure was used to derive the surface temperature of the lake from the TM data with a root mean square error of 0.3 degrees C.

BOMBER: A tool for estimating water quality and bottom properties from remote sensing images

BOMBER (Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images) is a software package for simultaneous retrieval of the optical properties of water column and bottom from remotely sensed imagery, which makes use of bio-optical models for optically deep and optically shallow waters. Several menus allow the user to choose the model type, to specify the input and output files, and to set all of the variables involved in the model parameterization and inversion. The optimization technique allows the user to retrieve the maps of chlorophyll concentration, suspended particulate matter concentration, coloured dissolved organic matter absorption and, in case of shallow waters, bottom depth and distributions of up to three different types of substrate, defined by the user according to their albedo. The software requires input image data that must be atmospherically corrected to remote sensing reflectance values. For both deep and shallow water models, a map of the relative error involved in the inversion procedure is also given. The tool was originally intended to estimate water quality in lakes; however thanks to its general design, it can be applied to any other aquatic environments (e.g., coastal zones, estuaries, lagoons) for which remote sensing reflectance values are known. BOMBER is fully programmed in IDL (Interactive Data Language) and uses IDL widgets as graphical user interface. It runs as an add-on tool for the ENVI+IDL image processing software and is available on request.

A cognitive pyramid for contextual classification of remote sensing images

Many cases of remote sensing classification present complicated patterns that cannot be identified on the basis of spectral data alone, but require contextual methods that base class discrimination on the spatial relationships between the individual pixel and local and global configurations of neighboring pixels. However, the use of contextual classification is still limited by critical issues, such as complexity and problem dependency. We propose here a contextual classification strategy for object recognition in remote sensing images in an attempt to solve recognition tasks operatively. The salient characteristics of the strategy are the definition of a multiresolution feature extraction procedure exploiting human perception and the use of soft neural classification based on the multilayer perceptron model. Three experiments were conducted to evaluate the performance of the methodology, one in an easily controlled domain using synthetic images, the other two in real domains involving builtup pattern recognition in panchromatic aerial photographs and high-resolution satellite images.

A linguistic decision making approach to assess the quality of volunteer geographic information for citizen science

The paper analyses the challenges and problems posed by the use of Volunteered Geographic Information (VGI) in citizen science and a proposal is formulated for assessing VGI quality based on a linguistic decision making approach so as to allow its feasible use for scientific purposes. VGI quality is represented by indicators at distinct levels of granularity which take into account the distinct components of the VGI items. The quality indicators represent both the extrinsic quality, depending on the characteristics and reputation of the sources of information; the intrinsic quality, depending on the distinct accuracy and precision of information; and, last but not least, the pragmatic quality, depending on the user needs and intended purposes. In order to assess the pragmatic quality of VGI items, a linguistic decision making approach is defined that allows users to rank and finally filter the VGI items based on the satisfaction of distinct criteria expressed by means of both linguistic terms, defining soft constraints on the distinct quality indicators, and linguistic aggregators, defining fuzzy operators which combine the satisfaction degrees of the soft constraints at distinct hierarchical levels to yield the final satisfaction of the VGI items. Finally, an example of quality assessment in a glaciological citizen science project is discussed.

Multispectral and multiscale remote sensing data for archaeological prospecting in an alpine alluvial plain

Abstract This work is part of a multidisciplinary research project, developed in collaboration with archaeologists and geophysical experts, which aims at delineating the spatio-temporal relationships between paleoenvironmental conditions of an alluvial plain in an alpine environment and the human settlements during past ages. The study area is located in the upper Lake Como region at the confluence of the Valtellina (Adda river) and Val Chiavenna (Mera River) valleys in northern Italy. The area is a deltaic zone which was affected by great adjustments due to varying sediment loads and separate parts emerged at different times, thus conditioning the human presence and distribution. Archaeological evidences dating back to the last millennium BC and relicts of Roman ages were discovered in the area, particularly during the 19th century Adda River canalisation. Remotely sensed images taken from space orbiting satellite at different wavelengths of the electromagnetic spectrum, from visible up to thermal infrared, were used to delineate landscape features not easily detected on ground. Geomorphological study of the area was improved by means of historical aerial b/w photographs taken before the Second World War by the Royal Air Force. Ground surveys and proximal sensing measurements, using portable spectral radiometers operating at the same wavelengths as the satellite sensors, were conducted at some experimental sites. Integrated analysis of remote sensing multilevel derived information, cartographic data and archaeological evidences proved to be useful for archaeological research with indications of favourable sites for future exploration in the area.

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and functionalities provided to end users. Peculiarities of the system reside in its ability to cope with the needs of different stakeholders within a common platform, and in a tight integration between EO data processing and information retrieval, crop modeling, in situ data collection, and information dissemination. The ERMES system has been operationally tested in three European rice-producing countries (Italy, Spain, and Greece) during growing seasons 2015 and 2016, providing a great amount of near-real-time information concerning rice crops. Highlights of significant results are provided, with particular focus on real-world applications of ERMES products and services. Although developed with focus on European rice cultivations, solutions implemented in the ERMES system can be, and are already being, adapted to other crops and/or areas of the world, thus making it a valuable testing bed for the development of advanced, integrated agricultural monitoring systems.

Comparing the performance of fuzzy and crisp classifiers on remotely sensed images: a case of snow classification

This study deals with the evaluation of accuracy benefits offered by a fuzzy classifier as compared to hard classifiers using satellite imagery for thematic mapping applications. When a crisp classifier approach is adopted to classify moderate resolution data, the presence of mixed coverage pixels implies that the final product will have errors, either of omission or commission, which are not avoidable and are solely due to the spatial resolution of the data. Theoretically, a soft classifier is not affected by such errors, and in principle can produce a classification that is more accurate than any hard classifier. In this study we use the Pareto boundary of optimal solutions as a quantitative method to compare the performance of a fuzzy statistical classifier to the one of two hard classifiers, and to determine the highest accuracy which could be achieved by hard classifiers. As an application, the method is applied to a case of snow mapping from Moderate-Resolution Imaging Spectroradiometer (MODIS) data on two alpine sites, validated with contemporaneous fine-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. The results for this case study showed that the soft classifier not only outperformed the two crisp classifiers, but also yielded higher accuracy than the maximum theoretical accuracy of any crisp classifier on the study areas. While providing a general assessment framework for the performance of soft classifiers, the results obtained by this inter-comparison exercise showed that soft classifiers can be an effective solution to overcome errors which are intrinsic in the classification of coarse and moderate resolution data.

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.

A neural adaptive model for feature extraction and recognition in high resolution remote sensing imagery

Contextual classification methods, which require the extraction of complex spatial information over a range of scales, from fine details in local areas to large features that extend across the image, are necessary in many remote sensing image classification studies. This work presents a supervised adaptive object recognition model which integrates scale-space filtering techniques for feature extraction within a Multilayer Perceptron neural network and the back-propagation learning task of the search of the most adequate filter parameters. The experimental evaluation of the method has been conducted in an easily controlled domain using synthetic imagery, and in the real domain coping with object recognition in high-resolution remote sensing imagery. To investigate whether the strategy can be considered an alternative to conventional procedures the results were compared with those obtained by a well known contextual classification scheme.

RITMARE: Semantics-aware Harmonisation of Data in Italian Marine Research

Abstract RITMARE is a Flagship Project by the Italian Ministero dell’Istruzione, dell’Universita e della Ricerca (MIUR) and coordinated by the National Research Council (CNR). It aims at the interdisciplinary integration of national marine research. In pursuing a Linked Open Data (LOD) vocation, the RITMARE sub-project 7 is building the necessary domain-related data structures by leveraging existing RDF-based schemata and sources. These data structures are grounding semantics-aware profiling of end users, data providers, and resources. The goal is designing a flexible infrastructure that adapts to the audiences specificities.