Rosa Lasaponara

Evaluation of a new satellite-based method for forest fire detection

Advanced Very High Resolution Radiometer (AVHRR)-based fire detection methods are considered in this work in order to assess their effective usefulness in the framework of civil programmes for fire risk and damage mitigation. The discussion is divided into the evaluation of the most commonly used methods and the description of a new fire detection procedure which is proposed in this paper. Commonly used detection methods are based on using absolute threshold values for decision tests. These values usually match only with very local, uniform (in space and time) situations, and are often inadequate when applied to heterogeneous, or simply different, geographical areas or seasons. A high number of false alarms, so high as to make the satellite-based product not operationally utilizable, is the main disadvantage of the fixed-threshold approach. The new fire-detection procedure proposed here makes use only of historical AVHRR data in order to automatically produce local (in space and time) threshold values, suitable for fire-event detection also in very critical situations. High fire discrimination capabilities with low false-alarm rates, simple unsupervised implementation and, above all, flexibility for automatic extension to completely different geographic areas and observation conditions, are the main advantages associated with this new technique. Results obtained for different Italian areas have been successfully compared with ground observations made by the Italian Forestry Service. Tests made over a long observation period show that, on cloud-free regions, more than 75% of significant forest fires are detected with less than 20% of false alarms.

A self-adaptive algorithm based on AVHRR multitemporal data analysis for small active fire detection

The present study proposes and improved self-adaptive algorithm (ISAA) for the detection of active fires using only channel 3 data of the Advanced Very High Resolution Radiometer (AVHRR). ISAA is specifically devised for the detection of small fires. The fire detection procedure is mainly based on the multitemporal approach (TN-ALT) devised by Cuomo et al . (2001a) and makes use of statistical analyses of real fires from different regions of the Italian peninsula. Such analyses allow the characterization of these fires as well as the computation of dynamic threshold values, which are variable in time and space and calibrated on local environmental conditions. ISAA was developed using an initial data sample of 1000 fires that occurred in 1996, and then in order to achieve a highly satisfactory performance in fire detection, the statistical analyses are updated yearly, so that a wider data sample can be considered for subsequent years. The evaluation tests made use of multitemporal satellite data (from 1997 to 1999) and ground observations provided by the Italian Forestry Service. The results obtained in different regions of North and South Italy demonstrated that ISAA detected about 80% of fires (with a low rate of false alarms at 15%) and showed a high fire discrimination capability both in the worst and good light conditions. The most recent contextual methods of fire detection were applied to significant test cases and compared with the results obtained from ISAA. This comparison showed that ISAA was able to find an increased number of fires as well as to reduce false alarms in all different light conditions.

Identification of archaeological buried remains based on the normalized difference vegetation index (NDVI) from Quickbird satellite data

In this study, Quickbird normalized difference vegetation index (NDVI) data were used in order to assess their capability in the field of archaeological prospection. The investigations were performed for a test case (Jure Vetere in the south of Italy) that is characterized by the presence of dense vegetation mainly composed by herbaceous plants. The results showed the high capability of QuickBird NDVI to enhance the typical surface anomalies linked to the presence of archaeological buried remains. The detected anomalies were confirmed by independent investigations based on geophysical prospections performed in 2005

On the potential of QuickBird data for archaeological prospection

In this study, the potential and feasibility of the use of panchromatic and multispectral QuickBird data for the identification and spatial characterization of archaeological sites was evaluated. The analysis focused on an assessment of the capability of QuickBird images to detect surface anomalies expected in the presence of archaeological buried remains. The investigations were performed for a test case in the south of Italy, where human activity has been logged from the Palaeolithic to the Middle Ages. The results show that the QuickBird panchromatic and data fusion products can be a flexible data source for archaeological prospection, and can be useful for extracting features of archaeological sites prior to any excavation work and for increasing the cultural value of historical sites.

Multiscale mapping of burn area and severity using multisensor satellite data and spatial autocorrelation analysis

Abstract Traditional methods of recording fire burned areas and fire severity involve expensive and time-consuming field surveys. Available remote sensing technologies may allow us to develop standardized burn-severity maps for evaluating fire effects and addressing post fire management activities. This paper focuses on multiscale characterization of fire severity using multisensor satellite data. To this aim, both MODIS (Moderate Resolution Imaging Spectroradiometer) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data have been processed using geo-statistic analyses to capture pattern features of burned areas. Even if in last decades different authors tried to integrate geo-statistics and remote sensing image processing, methods used since now are only variograms, semivariograms and kriging. In this paper, we propose an approach based on the use of spatial indicators of global and local autocorrelation. Spatial autocorrelation statistics, such as Morans I and Getis–Ord Local Gi index, were used to measure and analyze dependency degree among spectral features of burned areas. This approach enables the characterization of pattern features of a burned area and improves the estimation of fire severity.

On the LiDAR contribution for the archaeological and geomorphological study of a deserted medieval village in Southern Italy

Airborne laser scanning (ALS) is an optical measurement technique for obtaining high-precision information about the Earths surface including basic terrain mapping (digital terrain model, bathymetry, corridor mapping), vegetation cover (forest assessment and inventory) and coastal and urban areas. Recent studies examined the possibility of using ALS in archaeological investigations to identify earthworks, although the ability of ALS measurements in this context has not yet been studied in detail. This paper focuses on the potential of the latest generation of airborne ALS for the detection and the spatial characterization of micro-topographic relief linked to archaeological and geomorphological features. The investigations were carried out near Monteserico, an archaeological area in the Basilicata region (Southern Italy) which is characterized by complex topographical and morphological features. The study emphasizes that the DTM-LiDAR data are a powerful instrument for detecting surface discontinuities relevant for investigating geomorphological processes and cultural features. The LiDAR survey allowed us to identify the urban shape of a medieval village, by capturing the small differences in height produced by surface and shallow archaeological remains (the so-called shadow marks) which were not visible from ground or from optical dataset. In this way, surface reliefs and small elevation changes, linked to geomorphological and archaeological features, have been surveyed with great detail.

Satellite-based recognition of landscape archaeological features related to ancient human transformation

This paper deals with the use of QuickBird images for the identification of features linked to ancient transformations, the landscape induced by human activities. The methodological approach adopted for the identification of these features is mainly based on the use of data fusion and edge detection. The data fusion enabled the integration of the high spatial resolution of the panchromatic image with the spectral capability of multispectral images, thus allowing us to achieve improved capabilities that are not possible solely using the individual datasets. The use of edge detection enhanced the spatial feature, thus facilitating their identification. The investigation was performed on Metaponto, one of the most important archaeological sites in the south of Italy. The analysis focused on the identification of ancient land divisions related to the Greek colonization age. The obtained results showed that the use of QuickBird images enables the detection of the archaeological features linked to buried remains with a high level of detail.

Image Enhancement, Feature Extraction and Geospatial Analysis in an Archaeological Perspective

The goal of image processing for archaeological applications is to enhance spatial patterns and/or local anomalies linked to ancient human activities and traces of palaeo-environments still fossilized in the modern landscape. In order to make the satellite data more meaningful for archaeologists and more exploitable for investigations, reliable data processing may be carried out. Over the years a great variety of digital image enhancement techniques have been devised for specific application fields according to data availability. Nevertheless, only recently these methods have captured great attention also in the field of archaeology for an easier extraction of quantitative information using effective and reliable semiautomatic data processing. The setting up of fully-automatic methodologies is a big challenge to be strategically addressed by research communities in the next years.

Multiscale fuel type mapping in fragmented ecosystems: preliminary results from hyperspectral MIVIS and multispectral Landsat TM data

This study aims to ascertain how well remote sensing data can characterize fuel type at different spatial scales in fragmented ecosystems. For this purpose, multisensor and multiscale remote sensing data such as hyperspectral Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) and Landsat Thematic Mapper (TM) data acquired in 1998 were analysed for a test area in southern Italy characterized by mixed vegetation covers and complex topography. Fieldwork fuel type recognition, performed at the same time as remote sensing data acquisitions, was used to assess the results obtained for the considered test areas. Results from preliminary analysis showed that the use of unmixing techniques allows an increase in accuracy of around 7% compared with the accuracy level obtained by applying a widely used classification algorithm.

Multi-frequency, polarimetric SAR analysis for archaeological prospection

Abstract The aim of this study is to assess the sensitivity to buried archaeological structures of C- and L-band Synthetic Aperture Radar (SAR) in various polarisations. In particular, single and dual polarised data from the Phased Array type L-band SAR (PALSAR) sensor on-board the Advanced Land Observing Satellite (ALOS) is used, together with quadruple polarised (quad pol) data from the SAR sensor on Radarsat-2. The study region includes an isolated area of open fields in the eastern outskirts of Rome where buried structures are documented to exist. Processing of the SAR data involved multitemporal averaging, analysis of target decompositions, study of the polarimetric signatures over areas of suspected buried structures and changes of the polarimetric bases in an attempt to enhance their visibility. Various ancillary datasets were obtained for the analysis, including geological and lithological charts, meteorological data, Digital Elevation Models (DEMs), optical imagery and an archaeological chart. For the Radarsat-2 data analysis, results show that the technique of identifying the polarimetric bases that yield greatest backscatter over anomaly features, and subsequently changing the polarimetric bases of the time series, succeeded in highlighting features of interest in the study area. It appeared possible that some of the features could correspond with structures documented on the reference archaeological chart, but there was not a clear match between the chart and the results of the Radarsat-2 analysis. A similar conclusion was reached for the PALSAR data analysis. For the PALSAR data, the volcanic nature of the soil may have hindered the visibility of traces of buried features. Given the limitations of the accuracy of the archaeological chart and the spatial resolution of both the SAR datasets, further validation would be required to draw any precise conclusions on the sensitivity of the SAR data to buried structures. Such a validation could include geophysical prospection or excavation.