Characterization and Mapping of Fuel Types for the Mediterranean Ecosystems of Pollino National Park in Southern Italy by Using Hyperspectral MIVIS Data

Abstract The characterization and mapping of fuel types is one of the most important factors that should be taken into consideration for wildland fire prevention and prefire planning. This research aims to investigate the usefulness of hyperspectral data to recognize and map fuel types in order to ascertain how well remote sensing data can provide an exhaustive classification of fuel properties. For this purpose airborne hyperspectral Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) data acquired in November 1998 have been analyzed for a test area of 60 km2 selected inside Pollino National Park in the south of Italy. Fieldwork fuel-type recognitions, performed at the same time as remote sensing data acquisition, were used as a ground-truth dataset to assess the results obtained for the considered test area. The method comprised the following three steps: 1) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; 2) model construction for the spectral characterization and mapping of fuel types based on a maximum likelihood (ML) classification algorithm; and 3) accuracy assessment for the performance evaluation based on the comparison of MIVIS-based results with ground truth. Results from our analysis showed that the use of remotely sensed data at high spatial and spectral resolution provided a valuable characterization and mapping of fuel types being that the achieved classification accuracy was higher than 90%

Dynamic Fire Danger Mapping from Satellite Imagery and Meteorological Forecast Data

Abstract This study aims at ascertaining if and how remote sensing data can improve fire danger estimation based on meteorological variables. With this goal in mind, a dynamic estimation of fire danger was performed using an approach based on the integration of satellite information within a comprehensive fire danger rating system. The performances obtained with and without using satellite data were carried out for fires that occurred during the fire season in the year 2003 in the Calabria region (southern Italy). This study area was selected, first, because it is highly representative of Mediterranean ecosystems and, second, because it is an interesting test case for wildfire occurrences within the Mediterranean basin. The results obtained have shown that the use of satellite data reduced efficiently the overestimated danger areas, thus improving at least by 10% the fire forecasting rate obtained without using satellite-based maps. Such findings can be directly extended to other similar Mediterranean ecosystems

Assessing Fire Severity in Semiarid Environments with the DNBR and RDNBR Indices

Available remote sensing historical Landsat TM images allow identifying of first order effects of wildfires also in huge and inaccessible regions. In this paper the usefulness of the best known satellitederived severity indices was tested on a large wildfire occurred in January 1999 in a steppe of Northwestern Patagonia. The main objective of the work was to analyze and compare the behavior of dNBR and RdNBR in their ability to discriminate the degrees of fire severity in semiarid ecosystems principally dominated by herbaceous vegetation. For this purpose the values of the two indexes were compared in all vegetation communities (shrubl and, meadow, grassland and forestation). To interpret the results, we considered the variability of the principal factors that influence the fire severity, as fire intensity, fire duration and vegetation susceptibility to fire. The analysis showed that the interaction between fire and vegetation changes the fire effects because the vegetation parameter as fuel load, moisture content, species composition, horizontal continuity and the topography affect the fire behavior and then the fire severity. Furthermore the results suggest that dNBR and RdNBR provide substantially different information respectively related to the effects on soil and vegetation. This work is an important contribution to the utilization of fire severity indexes in ecosystems dominated by herbaceous species that change more subtly the post-fire biomass than ecosystems dominated by woody species.Fil: Ghermandi, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. Laboratorio de Ecotono; ArgentinaFil: Lanorte, Antonio. Consiglio Nazionale delle Ricerche; Italia. Istituto di Metodologie per l Analisi Ambientale; ItaliaFil: Oddi, Facundo José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural. - Universidad Nacional de Rio Negro. Instituto de Investigaciones en Recursos Naturales, Agroecología y Desarrollo Rural; ArgentinaFil: Lasaponara, Rosa. Consiglio Nazionale delle Ricerche; Italia. Istituto di Metodologie per l Analisi Ambientale; Itali

Remote Sensing and Spatial Analysis for Land-Take Assessment in Basilicata Region (Southern Italy)

Land use is one of the drivers of land-cover change (LCC) and represents the conversion of natural to artificial land cover. This work aims to describe the land-take-monitoring activities and analyze the development trend in test areas of the Basilicata region. Remote sensing is the primary technique for extracting land-use/land-cover (LULC) data. In this study, a new methodology of classification of Landsat data (TM-OLI) is proposed to detect land-cover information automatically and identify land take to perform a multi-temporal analysis. Moreover, within the defined model, it is crucial to use the territorial information layers of geotopographic database (GTDB) for the detailed definition of the land take. All stages of the classification process were developed using the supervised classification algorithm support vector machine (SVM) change-detection analysis, thus integrating the geographic information system (GIS) remote sensing data and adopting free and open-source software and data. The application of the proposed method allowed us to quickly extract detailed land-take maps with an overall accuracy greater than 90%, reducing the cost and processing time

Integrated approach of RUSLE, GIS and ESA Sentinel-2 satellite data for post-fire soil erosion assessment in Basilicata region (Southern Italy)

Fire effects consist not only in direct damage to the vegetation but also in the modification of both chemical and physical soil properties. Fire can affect the alteration of soil properties in different ways depending on fire severity and soil type. The most important consequences concern changes in soil responsiveness to the water action and the subsequent increase in sediment transport and erosion. Post fire soil loss can increase in the first year by several orders of magnitude compared to pre-fire erosion. In this study a distributed model based on the Revised Universal Soil Loss Equation (RUSLE) is used to estimate potential post-fire soil loss for four different fire events occurred in Basilicata region in 2017. Geographic Information System techniques and remote sensing data have been adopted to build a prediction model of post-fire soil erosion risk. Results show that this model is not only able to quantify post-fire soil loss but also to identify the complexity of the relationships between fire severity and all the factors that influence soil susceptibility to erosion

FIRE-SAT un sistema satellitare per il monitoraggio sistematico, dinamico ed integrato degli incendi boschivi: la sperimentazione operativa nella regione Basilicata

Il problema della gestione del fenomeno degli incendi boschivi è molto complesso, perché comprende una serie diaspetti connessi alle caratteristiche della vegetazione, alla morfologia del territorio, ai fattori meteorologici, ai fattoriantropici, etc. Risulta pertanto fondamentale e molto utile un approccio modellistico. I modelli matematici fornisconoun supporto essenziale nella valutazione dell’efficacia di possibili strategie di previsione e controllo del fuoco. Abstract FIRE_SAT project has been funded by the Civil Protectionof the Basilicata Region in order to set up alow cost methodology for fire danger monitoringand fire effect estimation based on satellite EarthObservation techniques.To this aim, NASA Moderate Resolution ImagingSpectroradiometer (MODIS), ASTER, Landsat TMdata were used. Novel data processing techniqueshave been developed by researchers of the ARGONLaboratory of the CNR-IMAA for the operativemonitoring of fire. In this paper we only focuson the danger estimation model which has beenfruitfully used since 2008 to 2012 as an reliable operativetool to support and optimize fire fightingstrategies from the alert to the management ofresources including fire attacks.The daily updating of fire danger is carried outusing satellite MODIS images selected for theirspectral capability and availability free of chargefrom NASA web site. This makes these data setsvery suitable for an effective systematic (daily) and sustainable low-cost monitoring of large areas

FIRE-SAT un sistema satellitare per il monitoraggio sistematico, dinamico ed integrato degli incendi boschivi: la sperimentazione operativa nella regione Basilicata

Il problema della gestione del fenomeno degli incendi boschivi è molto complesso, perché comprende una serie diaspetti connessi alle caratteristiche della vegetazione, alla morfologia del territorio, ai fattori meteorologici, ai fattoriantropici, etc. Risulta pertanto fondamentale e molto utile un approccio modellistico. I modelli matematici fornisconoun supporto essenziale nella valutazione dell’efficacia di possibili strategie di previsione e controllo del fuoco. Abstract FIRE_SAT project has been funded by the Civil Protectionof the Basilicata Region in order to set up alow cost methodology for fire danger monitoringand fire effect estimation based on satellite EarthObservation techniques.To this aim, NASA Moderate Resolution ImagingSpectroradiometer (MODIS), ASTER, Landsat TMdata were used. Novel data processing techniqueshave been developed by researchers of the ARGONLaboratory of the CNR-IMAA for the operativemonitoring of fire. In this paper we only focuson the danger estimation model which has beenfruitfully used since 2008 to 2012 as an reliable operativetool to support and optimize fire fightingstrategies from the alert to the management ofresources including fire attacks.The daily updating of fire danger is carried outusing satellite MODIS images selected for theirspectral capability and availability free of chargefrom NASA web site. This makes these data setsvery suitable for an effective systematic (daily) and sustainable low-cost monitoring of large areas

Il sistema FIRE-SAT per il monitoraggio post-incendio: il caso-studio dell'incendio di Potenza del 21-23 luglio 2015

Remote sensing data can usefully support the fire management operational applications in different spatial and temporal scales with a synoptic point of view and low cost technologies. The satellite monitoring systems together with other geographic information, historical data and field measurements, can provide the fire management operators useful tools of fire danger assessment, fire prevention, fire-fighting and post-fire planning. The FIRE-SAT monitoring system was applied to a fire event which developed in a wildland-urban interface area of the Potenza town (Basilicata, Italy) on July 2015, in order to assess the fire occurrence danger, to evaluate the fire effects and to simulate the fire propagation

Quantifying urban sprawl with spatial autocorrelation techniques using multi-temporal satellite data

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas