This paper presents the results we obtained in the context of the FIRE-SAT project focused on the use of satellite data for pre-operational monitoring of fire danger and fire effects in the Basilicata Region. The use of satellite data was manyfold, to obtain: (i) fuel property (type and loading) maps, mainly obtained from satellite Landsat TM data, (ii) fuel moisture estimation (mainly from MODIS), (iii) fire danger/susceptibility indices as well as (iv) post fire effects including fire severity and vegetation recovery assessment. Results obtained during the first year of project (2008) suggested that the integrated model identified the main fire danger zones by means of the integration of fuel types with daily fuel moisture and Greenness maps. MODIS multitemporal data analyses enable us to dynamically estimate fire severity as well as to map fire affected areas and evaluate the vegetation recovery capability over time. The pre-operative use of the integrated model, carried out within the framework of the FIRE-SAT project funded by the Basilicata Region, pointed out that the system enables us to timely monitor spatial and temporal variations of fire susceptibility and promptly provide useful information on both fire severity and post fire regeneration capability.

Low Cost Pre-operative Fire Monitoring from Fire Danger to Severity Estimation Based on Satellite MODIS, Landsat and ASTER Data: The Experience of FIRE-SAT Project in the Basilicata Region (Italy)

Lanorte A;De Santis F;Aromando A;Lasaponara R
2012

Abstract

This paper presents the results we obtained in the context of the FIRE-SAT project focused on the use of satellite data for pre-operational monitoring of fire danger and fire effects in the Basilicata Region. The use of satellite data was manyfold, to obtain: (i) fuel property (type and loading) maps, mainly obtained from satellite Landsat TM data, (ii) fuel moisture estimation (mainly from MODIS), (iii) fire danger/susceptibility indices as well as (iv) post fire effects including fire severity and vegetation recovery assessment. Results obtained during the first year of project (2008) suggested that the integrated model identified the main fire danger zones by means of the integration of fuel types with daily fuel moisture and Greenness maps. MODIS multitemporal data analyses enable us to dynamically estimate fire severity as well as to map fire affected areas and evaluate the vegetation recovery capability over time. The pre-operative use of the integrated model, carried out within the framework of the FIRE-SAT project funded by the Basilicata Region, pointed out that the system enables us to timely monitor spatial and temporal variations of fire susceptibility and promptly provide useful information on both fire severity and post fire regeneration capability.
2012
Istituto di Metodologie per l'Analisi Ambientale - IMAA
Inglese
Beniamino Murgante, Osvaldo Gervasi, Sanjay Misra, Nadia Nedjah, Ana Maria A. C. Rocha, David Taniar, Bernady O. Apduhan
Computational Science and Its Applications - ICCSA 2012
12th International Conference on Computational Science and Its Applications (ICCSA)
481
496
16
978-3-642-31136-9
Springer-Verlag
Berlin Heidelberg
GERMANIA
Sì, ma tipo non specificato
JUN 18-21, 2012
Salvador de Bahia, BRAZIL
Satellite fire monitoring
NDVI
NDWI
greenness moisture burned areas mapping
fire resilience
4
none
Lanorte, A; De Santis, F; Aromando, A; Lasaponara, R
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/179867
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