| Remotely sensed (RS) data can fruitfully support both research activities and operative monitoring of fire at different temporal and spatial scales with a synoptic view and cost effective technologies. “The contribution of remote sensing (RS) to forest fires may be grouped in three categories, according to the three phases of fire management: (i) risk estimation (before fire), (ii) detection (during fire) and (iii) assessment (after fire)” Chuvieco (2006).
Relating each phase, wide research activities have been conducted over the years.
(i) Risk estimation (before fire) has been mainly based on the use of RS data for (i) monitoring vegetation stress and assessing variations in vegetation moisture content, (ii) fuel type mapping, at different temporal and spatial scales from global, regional down to a local scale (using AVHRR, MODIS, TM, ASTER, Quickbird images and airborne hyperspectral and LIDAR data). Danger estimation has been mainly based on the use of AVHRR (onborad NOAA), MODIS (onboard TERRA and AQUA), VEGETATION (onboard SPOT) due to the technical characteristics (i.e. spectral, spatial and temporal resolution). Nevertheless microwave data have been also used for vegetation monitoring.
(ii) Detection: identification of active fires, estimation of fire radiative energy and fire emission. AVHRR was one of the first satellite sensors used for setting up fire detection algorithms. The availbility of MODIS allowed us to obtain global fire products free downloaded from NASA web site. Sensors onboard geostationary satellite platforms, such as GOES, SEVIRI, have been used for fire detection, to obtain a high temporal resolution (at around 15 minutes) monitoring of active fires.
(iii) Post fire damage assessment includes: burnt area mapping, fire emission, fire severity, vegetation recovery, fire resilience estimation, and, more recently, fire regime characterization.
Chuvieco E. L. Giglio, C. Justice, 2008 Global charactrerization of fire activity: toward defining fire regimes from Earth observation data Global Change Biology vo. 14. doi: 10.1111/j.1365-2486.2008.01585.x 1-15,
Chuvieco E., P. Englefield, Alexander P. Trishchenko, Yi Luo Generation of long time series of burn area maps of the boreal forest from NOAA–AVHRR composite data. Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2381-2396
Chuvieco Emilio 2006, Remote Sensing of Forest Fires: Current limitations and future prospects in Observing Land from Space: Science, Customers and Technology, Advances in Global Change Research Vol. 4 pp 47-51
De Santis A., E. Chuvieco Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models, Remote Sensing of Environment, Volume 108, Issue 4, 29 June 2007, Pages 422-435.
De Santis A., E. Chuvieco, Patrick J. Vaughan, Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models, Remote Sensing of Environment, Volume 113, Issue 1, 15 January 2009, Pages 126-136
García M., E. Chuvieco, H. Nieto, I. Aguado Combining AVHRR and meteorological data for estimating live fuel moisture content Remote Sensing of Environment, Volume 112, Issue 9, 15 September 2008, Pages 3618-3627
Ichoku C., L. Giglio, M. J. Wooster, L. A. Remer Global characterization of biomass-burning patterns using satellite measurements of fire radiative energy. Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2950-2962.
Lasaponara R. and Lanorte, On the capability of satellite VHR QuickBird data for fuel type characterization in fragmented landscape Ecological Modelling Volume 204, Issues 1-2, 24 May 2007, Pages 79-84
Lasaponara R., A. Lanorte, S. Pignatti,2006 Multiscale fuel type mapping in fragmented ecosystems: preliminary results from Hyperspectral MIVIS and Multispectral Landsat TM data, Int. J. Remote Sens., vol. 27 (3) pp. 587-593.
Lasaponara R., V. Cuomo, M. F. Macchiato, and T. Simoniello, 2003 .A self-adaptive algorithm based on AVHRR multitemporal data analysis for small active fire detection.n International Journal of Remote Sensing, vol. 24, No 8, 1723-1749.
Minchella A., F. Del Frate, F. Capogna, S. Anselmi, F. Manes Use of multitemporal SAR data for monitoring vegetation recovery of Mediterranean burned areas Remote Sensing of Environment, In Press
Næsset E., T. Gobakken Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 3079-3090
Peterson S. H, Dar A. Roberts, Philip E. Dennison Mapping live fuel moisture with MODIS data: A multiple regression approach, Remote Sensing of Environment, Volume 112, Issue 12, 15 December 2008, Pages 4272-4284.
Schroeder Wilfrid, Elaine Prins, Louis Giglio, Ivan Csiszar, Christopher Schmidt, Jeffrey Morisette, Douglas Morton Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2711-2726
Shi J., T. Jackson, J. Tao, J. Du, R. Bindlish, L. Lu, K.S. Chen Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E Remote Sensing of Environment, Volume 112, Issue 12, 15 December 2008, Pages 4285-4300
Tansey, K., Grégoire, J-M., Defourny, P., Leigh, R., Pekel, J-F., van Bogaert, E. and Bartholomé, E., 2008 A New, Global, Multi-Annual (2000-2007) Burnt Area Product at 1 km Resolution and Daily Intervals Geophysical Research Letters, VOL. 35, L01401, doi:10.1029/2007GL031567, 2008.
Telesca L. and Lasaponara R., 2006; ”Pre-and Post- fire Behaviural trends revealed in satellite NDVI time series” Geophysical Research Letters,., 33, L14401, doi:10.1029/2006GL026630
Telesca L. and Lasaponara R 2005 Discriminating Dynamical Patterns in Burned and Unburned Vegetational Covers by Using SPOT-VGT NDVI Data. Geophysical Research Letters,, 32, L21401, doi:10.1029/2005GL024391.
Telesca L. and Lasaponara R. Investigating fire-induced behavioural trends in vegetation covers , Communications in Nonlinear Science and Numerical Simulation, 13, 2018-2023, 2008
Telesca L., A. Lanorte and R. Lasaponara, 2007. Investigating dynamical trends in burned and unburned vegetation covers by using SPOT-VGT NDVI data. Journal of Geophysics and Engineering, Vol. 4, pp. 128-138, 2007
Telesca L., R. Lasaponara, and A. Lanorte, Intra-annual dynamical persistent mechanisms in Mediterranean ecosystems revealed SPOT-VEGETATION Time Series, Ecological Complexity, 5, 151-156, 2008
Verbesselt, J., Somers, B., Lhermitte, S., Jonckheere, I., van Aardt, J., and Coppin, P. (2007) Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems. Remote Sensing of Environment 108: 357-368.
Zhang X., S. Kondragunta Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2886-2897
Zhang X., Shobha Kondragunta Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2886-2897 |