A multi-year geographic database of fire affected areas derived from satellite images in the National Parks of Italy

Brivio, Pietro Alessandro; Petrucci, Bruno; Boschetti, Mirco; Carrara, Paola; Pepe, Monica; Rampini, A.; Stroppiana, Daniela; Zaffaroni, Paolo

doi:10.5721/itjrs20094125

Cited by 5 publications

(8 citation statements)

References 25 publications

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“…For calibration and validation, ASTER images (AST07 product) were selected over the Italian national parks most affected by fire ( Figure 1) to cover different Mediterranean natural environments (Brivio et al 2009). One scene, selected over the Gallura (GAL) region (Sardinia), was added to assess the performance of the methodology in a different location dominated by the typical ''macchia Meditarranea.''…”

Section: Study Area and Imagery Datasetmentioning

confidence: 99%

“…The advantage offered by fuzzy sets is to convert spectral indices into a common domain [0, 1] through fuzzy membership function and to use soft rather than crisp constraints for the indices. The methodology proposed in this paper relies on previous experiments done on the same study area with ASTER data that showed the complementarities of different SI, and it constitutes a methodological development of these experiences (Zaffaroni et al 2007;Brivio et al 2009;Stroppiana et al 2009b). We computed from the ASTER bands indices commonly used for burned area mapping in the Mediterranean environment and specifically tested for Southern Italy (Lasaponara 2006; Table 2).…”

Section: Concept Of the Approachmentioning

confidence: 99%

“…The Regional Burned Forest Mapping in Italy (ITALSCAR) project (Paganini et al 2003), for example, produced burn scar maps for the period 1997-2000 using Landsat TM data. The Italian Ministry of the Environment used National Aeronautics and Space Administration (NASA) Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and SPOT 4-5 High-Resolution Visible Infrared (HRVIR) images to provide detailed information on burned area perimeters in the Italian national parks for the period 2001-05 (Brivio et al 2009;Petrucci et al 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Several methods have been developed for mapping fire-affected areas from multitemporal or single postfire satellite images: supervised classifications (i.e., maximum likelihood, decision tree, and neural network; Silva et al 2005;Kontoes et al 2009;Brivio et al 2003), linear transformations (i.e., tasselled cap and principal component analysis; Patterson and Yool 1998;Hudak and Brockett 2004), spectral unmixing techniques (Román-Cuesta et al 2005;Smith et al 2007), and logistic regression models (Koutsias and Karteris 2000). Techniques based on thresholding the spectral indices (SIs) are widely applied, because they are computationally fast and efficient to detect fire-affected areas with both MR and HR data (Chongo et al 2007;Smith et al 2007).…”

Section: Introductionmentioning

confidence: 99%

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Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High-Resolution Satellite Images

2010

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This article presents a new method for burned area mapping using high-resolution satellite images in the Mediterranean ecosystem. In such a complex environment, high-resolution satellite images represent an appropriate data source for identifying fire-affected areas, and single postfire data are often the only available source of information. The method proposed here integrates several spectral indices into a fuzzy synthetic indicator of likelihood of burn. The indices are interpreted through fuzzy membership functions that have been derived with a partially data-driven approach exploiting training data and expert knowledge. The final map of fire-affected areas is produced by applying a region growing algorithm on the basis of seed pixels selected on a conservative threshold of the synthetic fuzzy score. The algorithm has been developed and tested on a set of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes acquired over Southern Italy. Validation showed that the accuracy of the burned area maps is comparable or even better [overall accuracy (OA) . 90%, K . 0.76] than that obtained with approaches based on single index thresholds adapted to each image. The method described here provides an automatic approach for mapping fire-affected areas with very few false alarms (low commission error), whereas omission errors are mainly related to undetected small burned areas and are located in heterogeneous sparse vegetation cover.

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Section: Study Area and Imagery Datasetmentioning

confidence: 99%

Section: Concept Of the Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High-Resolution Satellite Images

2010

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“…• Supporto tecnico scientifico alla Pianificazione Anti Incendi Boschivi (PAIB) nei parchi nazionali Italiani (SBI e CRI BFEP, AA.VV 2006); • Un sistema per il monitoraggio e la mappatura per le aree percorse da incendio nei parchi nazionali attraverso l'utilizzo dei dati da satellite (Brivio et al 2007, Brivio et al 2009 , IREA-CNR). I tematismi AIB sono sovrapposti a carte tematiche e di base utili alla lettura e comprensione della pro blematica incendi nei parchi nazionali.…”

Section: Il "Progetto Incendi" -Contenuti E Versatilititàunclassified

The “Forest Fire Project”, National cartographic portal of the Italian Environmental Department: an example of management of cartographic data to support forest fires fighting plans in national parks

Petrucci¹,

Capitoni²,

Borelli³

et al. 2010

Forest@

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IntroduzioneDall'anno 2000 la Direzione Protezione Natura (DPN) del Ministero dell'Ambiente e della Tutela del Territorio e del Mare (MATTM) è competente nel settore antincendi boschivi (AIB) specificatamente per la previsione e prevenzione nelle aree protette statali -Parchi Nazionali (PN) e Riserve Naturali Sta tali (RNS) -in attuazione dell'art. 8 comma 2 della L. 353/2000 (Legge quadro in materia di incendi boschi vi). In particolare cura il supporto ed il coordina mento della pianificazione AIB in dette aree, verifica i piani degli enti gestori e quindi, previo il parere d'obbligo del Corpo Forestale dello Stato (CFS), chie de l'intesa alle Regioni territorialmente interessate per l'inserimento di questi piani nei corrispondenti piani AIB regionali (Petrucci 2007).La legge ha introdotto importanti novità in materia di lotta agli incendi boschivi, tra cui quella di favori re la prevenzione rispetto alla lotta attiva e, per far questo, obbliga le regioni e gli enti gestori delle aree protette statali ad elaborare un "Piano Anti Incendi Boschivi" (o Piano AIB). Il Piano elaborato da queste ultime, secondo criteri stabiliti in appositi Schemi di Piano AIB del MATTM, deve avere una serie di con Forest@ 7: 13-21 (2010) 13 13 353 "Fra mework law on forest fires". The project brings out the results of previous projects carried out in collabora tion with several research institutes. Cartographic information is made available as free and reliable know ledge base in order to facilitate the draw up and implementation of the "Forest Fire Plans", including the ac tual activity of forest fire extinction. Map information can be further implemented by various subjects such as researchers, land planning programmers or managers. The National cartographic portal gives the oppor tunity of overlaying various cartographic information and base maps supporting the "Forest Fire Project"; moreover it is possible to add other layers from other sources, through URL. Adequate "personalised" over laps -which can be saved on one's own GIS -allow in depth analysis and deductions aimed at specific objec tives of territorial planning and management and in particular of Forest Fire Fighting Plans.Keywords: Forest fires, National Parks, Fires risk, GIS, Data forest fires by satellite Received: Nov 26, 2009; Accepted: Jan 12, 2010 Citation: Petrucci B, Capitoni B, Borelli R, Popolizio F, 2010. "Progetto Incendi", portale cartografico nazionale del Ministero dell'Ambiente: un esempio di gestione di dati cartografici a supporto dei piani antincendio boschivi nei parchi nazionali. Forest@ 7: 13-21 [online: 2010-02-11] URL: http://www.sisef.it/forest@/.

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Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery

Chirici¹,

Scotti²,

Montaghi³

et al. 2013

International Journal of Applied Earth Observation and Geoinfor

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a b s t r a c tThis paper presents an application of Airborne Laser Scanning (ALS) data in conjunction with an IRS LISS-III image for mapping forest fuel types. For two study areas of 165 km 2 and 487 km 2 in Sicily (Italy), 16,761 plots of size 30-m × 30-m were distributed using a tessellation-based stratified sampling scheme. ALS metrics and spectral signatures from IRS extracted for each plot were used as predictors to classify forest fuel types observed and identified by photointerpretation and fieldwork. Following use of traditional parametric methods that produced unsatisfactory results, three non-parametric classification approaches were tested: (i) classification and regression tree (CART), (ii) the CART bagging method called Random Forests, and (iii) the CART bagging/boosting stochastic gradient boosting (SGB) approach. This contribution summarizes previous experiences using ALS data for estimating forest variables useful for fire management in general and for fuel type mapping, in particular. It summarizes characteristics of classification and regression trees, presents the pre-processing operation, the classification algorithms, and the achieved results. The results demonstrated superiority of the SGB method with overall accuracy of 84%. The most relevant ALS metric was canopy cover, defined as the percent of non-ground returns. Other relevant metrics included the spectral information from IRS and several other ALS metrics such as percentiles of the height distribution, the mean height of all returns, and the number of returns.

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A multi-year geographic database of fire affected areas derived from satellite images in the National Parks of Italy

Cited by 5 publications

References 25 publications

Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High-Resolution Satellite Images

Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High-Resolution Satellite Images

The “Forest Fire Project”, National cartographic portal of the Italian Environmental Department: an example of management of cartographic data to support forest fires fighting plans in national parks

Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery

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